Community-based comprehensive care system

Abstract

To provide a local general care system that sufficiently conducts medical care management with respect to elderly since coordination is failing between the medical side and the care side.SOLUTION: In a local general care system, an associating function 15 applies an elderly state image model 13 to a database 11 holding big data related to medical care, in order to associate the elderly state image model 13 with the big data. A data conversion function 16 extracts disease information and its stage, a risk or protection factor and a result factor for each elderly person, from the big data associated with the elderly state image model 13, and generates elderly state data of the elderly person.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] An embodiment of the present invention relates to the generation of an elderly condition model that can grasp the elderly's injury / illness state and mental / physical / need for care state in an integrated, bird's-eye view, and chronological manner, and to a community-based integrated care system that uses this elderly condition model to support the implementation of policies and verify their effectiveness. [Background technology]

[0002] Currently, the Ministry of Health, Labour and Welfare is aiming to build a community-based comprehensive care system that provides housing, medical care, nursing care, prevention, and lifestyle support in an integrated manner. To steadily advance the construction of this system, it is necessary to have a common understanding based on data about the situation of elderly people and the communities in which they live, as well as the policies of municipalities and other organizations. For this reason, through amendments to the Long-Term Care Insurance Act and other measures, the ministry is providing guidance and support to municipalities and other organizations to utilize medical care and nursing care data to promote initiatives such as those listed below.

[0003] By analyzing nursing care-related data provided by the Ministry of Health, Labour and Welfare, municipalities will exercise their insurer functions and promote initiatives such as nursing care prevention and prevention of worsening conditions. Municipalities and other local governments will implement health programs for the elderly aged 75 and over in cooperation with medical, nursing, and other professionals, utilizing data from the National Health Insurance Database (KDB) system, in conjunction with nursing care prevention under the long-term care insurance system.

[0004] The Ministry of Health, Labour and Welfare has been promoting the above measures, believing that it is necessary to build a community-based comprehensive care system in preparation for 2025, when all members of the baby boomer generation will be over 75. However, a new perspective has emerged regarding how we should prepare for the society of 2040, when more than 10 million people aged 85 and over, including those living alone, will be living in communities.

[0005] The FY2018 report of the Community-Based Comprehensive Care Study Group (Non-Patent Document 1) makes the following recommendations regarding 2040: The meaning of "age" for the elderly is not uniform. There are people who are "striving to maintain their health and actively participating in society even at age 90," and there are also "65-year-olds who are forced to live a reclusive life due to chronic illness." We live in an age of diversity and inequality that cannot be captured by the average image of the elderly. Furthermore, we live in an age where we cannot or do not expect family care due to the increase in single-person households and households consisting of only elderly people. At the same time, the diversification of housing and communities is progressing in the face of a declining population, and we live in a society where each individual has lived a variety of lives, lived in a variety of homes, and has a variety of family structures and living styles, and faces a variety of challenges. In order to create a "community-based society" in such a pluralistic society, it is important for residents / service users and service providers to "participate and collaborate" by discussing together, making repeated improvements, and considering how to use services that suit the residents of the area.

[0006] Under these circumstances, it is necessary to consider how to aggregate and integrate data on both medical and nursing care for each individual elderly person, and then consider the services that should be provided to that elderly person, or to consider policies for the elderly in that area. In other words, it is necessary to understand the actual situation as an accumulation of such individual data, rather than simply using average values ​​based on publicly available information, etc. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Patent No. 6951314 [Non-patent literature]

[0008] [Non-Patent Document 1] Community-Based Comprehensive Care Study Group, "2040: Community-Based Comprehensive Care System in a Plural Society - Creating an Inclusive Society through Participation and Collaboration" (FY2018 Ministry of Health, Labor and Welfare Elderly Health Care Promotion Subsidy, Elderly Health Care Promotion Project, Mitsubishi UFJ Research and Consulting)

[0009] However, when municipalities and other organizations try to integrate and understand the medical and nursing care data of individual elderly people and use it to implement policies, they face the following challenges:

[0010] I don't know how to use the data Recent advances in data processing technology have made it possible to collect and analyze big data related to medical care and nursing care, such as receipt data, health checkup data, and nursing care certification data, and various data are now being provided by the national government based on this infrastructure.However, due to a lack of staff with specialized knowledge in municipalities and other organizations, there is a lack of skills to analyze data, and the meaning of the data is not understood, or how to use it, evaluate policies, or plan based on that, resulting in the problem of not using the data.

[0011] Medical and nursing care data is provided and managed separately by each department in charge of the city, town, or village. In Japan, big data related to medical care and nursing care is managed by each respective department. Medical receipt data is managed as a National Database (NDB) by the department in charge of medical insurance (Insurance Bureau), nursing care-related information is managed by the department in charge of long-term care insurance (Health and Welfare Bureau for the Elderly), and data related to the medical care delivery system is managed by the department in charge of medical administration (Medical Affairs Bureau).

[0012] In response to this, with regard to the data provided by the national government to local governments, nursing care-related data is provided to the nursing care administration departments of cities, towns, and villages through the National Health Insurance Federation established in each prefecture, and medical care-related data is provided to the medical care administration departments of prefectures.As a result, the data used by the nursing care administration departments in efforts to support independence and prevent the condition from worsening is limited to nursing care-related data, and medical care-related data is not used.

[0013] In recent years, efforts to link home medical care and nursing care have been made, and in order to promote integrated efforts in health care projects and nursing care prevention, legal amendments have been made to enable municipalities and other organizations to keep track of medical, health checkup, and nursing care information all at once. However, the development of such systems has not progressed, and even in integrated efforts in health care projects and nursing care prevention, efforts such as public health nurses providing health guidance at outpatient facilities using health checkup data have been the focus, and these efforts have not yet led to efforts in nursing care prevention itself.

[0014] The community-based integrated care system aims to realize integrated health and nursing care prevention efforts, support for independence in nursing care, and prevention of worsening conditions through collaboration between multiple professions, including medical and nursing care providers. In other words, as an injury or illness progresses, a person's physical and mental condition changes, and the level of nursing care required changes accordingly. Furthermore, the way in which the level of nursing care required progresses varies depending on the injury or illness. For this reason, when understanding the condition and progression of elderly people through data, medical data alone or nursing care data alone is insufficient (incomplete). As it is, it is difficult to implement effective measures related to integrated health and nursing care prevention efforts, support for independence in nursing care, and prevention of worsening conditions, or to quantitatively verify their effectiveness.

[0015] Municipalities have financial constraints Creating an integrated database of medical and nursing care data as described above and conducting various data analyses based on it is expected to impose a large financial burden, but the reality is that most municipalities do not have the financial or human resources to shoulder such a burden.

[0016] The need for care among the elderly is due to an illness or injury, and as the illness or injury progresses, the physical and mental condition (external symptoms) of the elderly changes, which in turn changes the state of need for care. Furthermore, the need for care among the elderly can be due to a variety of illnesses or injuries, and the way in which the state of need for care progresses also differs depending on the illness or injury.

[0017] In promoting future community-based integrated care systems, it is believed that the integration and aggregation of medical and nursing care data for each individual elderly person will be the basis. For this reason, when understanding the condition and progress of elderly people through data, it is insufficient (incomplete) to rely solely on medical or nursing care data, and if this situation continues, it will be difficult to promote a community-based integrated care system that responds to future social changes. Summary of the Invention [Problem to be solved by the invention]

[0018] In order to overcome these challenges and move forward with the construction of a community-based comprehensive care system based on medical and nursing care data, it is necessary to organically combine information held in nationwide databases that hold both medical and nursing care data on the elderly, grasp the illness and injury status, physical and mental condition, and need for nursing care of the elderly over time, and based on that, present this information in a form that is easy to understand for local governments and those involved in medical and nursing care, etc., and provide analyzed and organized data so that the effectiveness of policies can be evaluated without imposing a large financial burden on local governments, etc.

[0019] Currently, the only national database capable of capturing both medical and nursing care data on the elderly is the National Health Insurance Database (KDB), which accumulates vast amounts of big data on medical care and nursing care. The KDB system utilizes information on specific health checkups and specific health guidance, medical insurance (National Health Insurance and Medical Care for the Elderly), and nursing care insurance, all of which are managed by the National Health Insurance Association through various operations commissioned by insurers. It provides statistical information and personal health information, and was established to support insurers in implementing efficient and effective health programs. It is currently being used in data health programs and integrated health and nursing care prevention initiatives. Its potential is significant, and by exploring ways to utilize it in the future, it is believed to have the potential to be utilized in promoting the entire community-based integrated care system.

[0020] In order to overcome the "challenges in data utilization" mentioned above, it is necessary to grasp the state of injury and illness, physical and mental condition, and need for care of the elderly over time, provide this information in a form that is easy to understand for municipalities and those involved in medical and nursing care, etc., so that the effectiveness of policies can be evaluated, and also to provide data analyzed and organized using this method to municipalities and other organizations without imposing a large financial burden.

[0021] Currently, the government is implementing two types of nursing care prevention projects: the "Nursing Care Prevention Project" implemented by the nursing care side, and the "Integrated Healthcare and Nursing Care Prevention Project" implemented by the health care (medical) side. The "Nursing Care Prevention Project" implemented by the nursing care side includes the "Nursing Care Prevention and Daily Life Support Comprehensive Project" and the "Creation of a Nursing Care Prevention Manual."

[0022] The "Comprehensive Nursing Care Prevention and Daily Life Support Project" provides primary prevention projects for the general elderly, secondary prevention projects for those at high risk of becoming people who require support, and nursing care prevention benefits for those who require support. In other words, the following projects are being carried out that are easy for the elderly to participate in and can be flexibly adapted. · Implement general nursing care prevention projects and nursing care prevention and daily life support service projects according to local conditions. · Home-visit care and day care services for those requiring support will be transferred from preventive care benefits to comprehensive preventive care and daily life support services (from April 2012). From April 2015, the comprehensive program will be reorganized into the nursing care prevention and life support service program and the general nursing care prevention program.

[0023] In addition, the "Amendments to the Social Welfare Act, etc. for the Realization of a Community-Based Coexistence Society" came into effect in April 2021, stipulating that when municipalities and other entities implement community support projects, they are obligated to make efforts to utilize relevant data to ensure that efforts are carried out effectively and efficiently in accordance with the PDCA cycle.

[0024] The "Creation of a Nursing Care Prevention Manual" presents specific items and standards for subject selection, pre-assessment, program, and post-assessment for each function (complex program, musculoskeletal function improvement, nutritional improvement, oral function improvement, prevention and support of isolation, prevention and support of cognitive decline, and prevention and support of depression).

[0025] The "Integrated Health and Nursing Care Prevention Project" being promoted by the health (medical) side includes the creation of "Health Care Project Guidelines Based on the Characteristics of the Elderly" and "Integrated Implementation and KDB Utilization Support Tools."

[0026] According to the "Guidelines for Health Care Activities Based on the Characteristics of the Elderly," medical professionals visit day care centers and other facilities to provide frailty prevention and health consultations. Furthermore, depending on the individual's situation, they introduce care prevention programs and other such programs, which lead to care prevention. Specifically, the following initiatives are being implemented: Municipalities and other organizations commissioned by the Association of Regional Medical Care Services for the Elderly will analyze medical, nursing care, and health checkup information from the KDB to select and narrow down the target populations for priority health guidance. By utilizing these facilities, public health nurses will provide early health guidance using questionnaires for the elderly, and if necessary, this will also help prevent the need for nursing care.

[0027] The "Integrated Implementation / KDB Utilization Support Tool" is a tool being developed that will make it easier for municipalities to extract people who should receive priority health guidance (a tool that will utilize KDB data to enable municipalities to automatically create lists of people who should receive guidance based on the 10 types of injury or illness, etc.).

[0028] However, in the "care prevention project" being promoted by the care sector, the data used and the services provided are focused on functional recovery and lifestyle support based on information from the care sector, and there is a problem in that no measures are being taken to verify the effectiveness of using medical and care big data in response to injuries and illnesses that may lead to care.

[0029] Furthermore, the "Integrated Health and Nursing Care Prevention Project" being promoted by the health (medical) side aims to implement health care projects and nursing care prevention for the elderly in an integrated manner, but the emphasis is on efforts to effectively implement health care projects for the elderly. In other words, by utilizing KDB data (medical and nursing care big data), it is possible to identify people who should receive priority health guidance, but the nursing care side (such as nursing care prevention departments of municipalities and community comprehensive support centers) does not provide specific instructions on what to do for these identified people, which creates the problem of a lack of coordination with the nursing care side.

[0030] The present invention proposes a model for the status of elderly people that integrates medical and nursing care data, etc., and can grasp the illness and injury status of elderly people, as well as their physical and mental state and need for nursing care, in an integrated, bird's-eye view, and chronological manner, with regard to the main illness and injury that lead to nursing care and the secondary illness and injury that cause them. The present invention aims to provide a community-based comprehensive care system that uses this model to generate elderly condition data from big data, and that can use the generated elderly condition data to support the implementation of various policies and verify their effectiveness. [Means for solving the problem]

[0031] The community-based integrated care system using a computer system according to an embodiment of the present invention includes a database that holds big data related to medical care and nursing care, including medical receipt data, health checkup data, nursing care prevention data, nursing care certification data, and nursing care receipt data, a major injury / illness data section that holds injury / illness data of major injuries / illnesses that lead to nursing care, a concomitant injury / illness data section that holds injury / illness data of concomitant injuries / illnesses that cause the major injuries / illnesses that lead to nursing care, a stage setting section that sets a plurality of stages that indicate the stage of the major injuries / illnesses or concomitant injuries / illnesses that lead to nursing care, including whether they have occurred, and risk factors (R) related to the major injuries / illnesses and concomitant injuries / illnesses that lead to nursing care, protective factors (D) that are inversely related to these risk factors, and result factors related to each of the injuries / illnesses. the elderly condition model is composed of an associated element part that holds associated elements (RDO elements) consisting of elements (O); a major injury / illness-specific RDO element correspondence master in which a correspondence relationship between the associated elements (RDO elements) is set for each major injury / illness that leads to nursing care; and an RDO element-specific big data correspondence master in which a correspondence relationship between the associated elements (RDO elements) and the big data is set for each of these associated elements (RDO elements); and a matching function part that matches the elderly condition model with big data using this matching function; and a data conversion function part that extracts injury / illness information, its stage, the risk or defense elements, and result elements for each elderly person from the big data that has been matched with the elderly condition model by this matching function, and generates elderly condition data for the corresponding elderly person.

[0032] According to an embodiment of the present invention, by integrating medical and nursing care data, etc., elderly condition data, etc. can be generated from big data using an elderly condition image model that can grasp the elderly's injury and illness status, mental and physical condition, and need for care, etc. in an integrated, bird's-eye view, and chronological manner, with regard to the main injury and illness that leads to nursing care and the accompanying injury and illness that causes them.The generated elderly condition data, etc. can also be used to provide data that enables support for the implementation of various policies and verification of their effectiveness, etc. [Brief explanation of the drawings]

[0033] [Figure 1] 1 is a diagram showing an overview of the KDB used in the first embodiment of the present invention in comparison with the NDB and nursing care database managed by the Ministry of Health, Labor and Welfare. [Figure 2] 1 is a schematic configuration diagram of a community-based integrated care system according to a first embodiment of the present invention. [Figure 3] This is a diagram showing a system that creates data such as elderly history and regional / business / municipal medical records from various elderly condition data generated by the system of Figure 1. [Figure 4] FIG. 2 is a diagram showing an elderly state image model used in the first embodiment. [Figure 5] This is a diagram showing the elderly state image model explained in FIG. 4 in time series over five phases Φ1 to Φ5. [Figure 6] 10 is a chart showing injury / illness data of major injuries / illnesses that may lead to nursing care in the first embodiment. [Figure 7] 10 is a table showing injury / illness data of other major injuries / illnesses that may lead to nursing care in the first embodiment. [Figure 8] 10 is a table showing injury / illness data of concomitant injuries / illnesses in the first embodiment. [Figure 9] FIG. 2 is a diagram illustrating a flail in the first embodiment. [Figure 10] 10 is a diagram showing a data table summarizing the relationship between the main injury or illness that leads to nursing care and the accompanying injury or illness in the first embodiment. [Figure 11] 10 is a diagram showing specific examples of RDO elements in the first embodiment. [Figure 12] FIG. 2 is a diagram showing an overview of a primary assessment of nursing care certification used in the first embodiment. [Figure 13] 10 is a table showing a master corresponding to each major injury or illness and RDO element used in the first embodiment of the present invention. [Figure 14] 10 is a diagram illustrating an overview of big data related to medical care and nursing care used in the first embodiment. [Figure 15] 10 is a diagram illustrating a master for RDO elements and big data used in the first embodiment. [Figure 16] FIG. 10 is a diagram for explaining the flow of preparing the injury / illness / medicine related master data used in the first embodiment. [Figure 17] FIG. 2 is a diagram illustrating an overall view of a preventive care measure support tool using an elderly person condition image model used in the first embodiment. [Figure 18] FIG. 10 is a diagram showing a master for identifying five phases for each illness or injury used in the first embodiment. [Figure 19] FIG. 18 is a diagram illustrating the KDB implementation and operation system of the nursing care prevention measure support tool described in FIG. 17. [Figure 20] FIG. 2 is a diagram showing an overall flow of creating elderly person condition data and the like in the first embodiment. [Figure 21] FIG. 10 is a diagram showing a specific example of an elderly person history in the first embodiment. [Figure 22] FIG. 10 is a diagram showing another specific example of elderly history in the first embodiment. [Figure 23] FIG. 10 is a diagram showing a specific example of a region / business / city / town / village chart in the first embodiment. [Figure 24] FIG. 10 is a diagram showing another specific example of a region / business / city / town / village chart in the first embodiment. [Figure 25] This is a diagram that shows a matrix of the community-based comprehensive care area (vertical axis) and each stage of the PDCA cycle (horizontal axis) in the first embodiment, with an outline of specific support methods for implementing policies and methods for verifying the effectiveness of those policies at each intersection. [Figure 26] FIG. 10 is a diagram showing an elderly state image model used in the second embodiment of the present invention. [Figure 27] 27 is a diagram showing the elderly state image model explained in FIG. 26 in time series over six phases Φ1 to Φ6. [Figure 28] FIG. 10 is a diagram showing an overall image of a standard transition image of the state of an elderly person in the second embodiment. [Figure 29] This is a diagram illustrating the specific content and outline of the legend by extracting a part of FIG. 28. [Figure 30] This is a diagram showing the dementia-related items extracted from Figure 28 together with labeling. [Figure 31]FIG. 29 is a diagram showing the stroke relationships in FIG. 28 extracted and labeled. [Figure 32] This is a diagram showing the frailty relationship in Figure 28 together with labeling. [Figure 33] FIG. 29 is a diagram showing the femoral fracture relationship of FIG. 28 with labeling. [Figure 34] FIG. 10 is a diagram illustrating the overall flow of creating a model master of an elderly person's state image in the second embodiment. [Figure 35] FIG. 10 is a diagram showing a list of priority targets in the second embodiment. [Figure 36] FIG. 10 is a diagram showing an individual record of a key target person in the second embodiment of the present invention. [Figure 37] FIG. 10 is a diagram showing the overall flow of data processing and the like in the process of creating information to be provided to municipalities and the like in the second embodiment. [Figure 38] 38 is a diagram for explaining various data generated in the data processing of FIG. 37. FIG. [Figure 39] FIG. 38 is a schematic diagram illustrating the analysis target data import process F11 shown in FIG. 37. [Figure 40] FIG. 38 is a functional block diagram illustrating an overall picture of the RDO element state identification process F21 shown in FIG. 37. [Figure 41] 41 is a diagram illustrating data processing of specific processing 1 for continuous values ​​shown in FIG. 40. FIG. [Figure 42] 41 is a diagram illustrating data processing of the identification process 2 for code values ​​shown in FIG. 40. FIG. [Figure 43] 41 is a diagram for explaining data processing of the identification process 3 for the care service detail code value shown in FIG. 40. FIG. [Figure 44] FIG. 38 is a schematic diagram illustrating the target injury or illness state identification process F22 shown in FIG. 37. [Figure 45] FIG. 38 is a diagram illustrating a PIM specification process which is a part of the target injury / illness state specification process F22 shown in FIG. [Figure 46] FIG. 38 is a schematic diagram illustrating the state duration calculation process F23 shown in FIG. 37. [Figure 47]FIG. 47 is a diagram illustrating a method for calculating a monthly duration in the state duration calculation process F23 shown in FIG. 46. [Figure 48] FIG. 47 is a diagram for explaining a method for calculating the annual duration in the state duration calculation process F23 shown in FIG. 46. [Figure 49] FIG. 38 is a schematic diagram illustrating the elderly state phase identification process F31 shown in FIG. 37. [Figure 50] FIG. 50 is a diagram for explaining data processing in the elderly state phase identification process F31 shown in FIG. 49. [Figure 51] FIG. 38 is a schematic diagram illustrating the elderly condition risk identification process F32 shown in FIG. 37. [Figure 52] FIG. 52 is a diagram for explaining data processing for individual risk determination in the elderly condition risk identification process F32 shown in FIG. 51. [Figure 53] FIG. 52 is a diagram for explaining data processing for the overall risk determination in the elderly condition risk identification process F32 shown in FIG. 51. [Figure 54] 26 is a diagram illustrating another embodiment of the "method for supporting the implementation of specific policies related to community-based integrated care using PDCA" described in FIG. 25. [Figure 55] FIG. 10 is a diagram showing an overall picture of the data processing flow of the policy execution support function (specific policy execution support function after output of key target individuals). [Figure 56] FIG. 10 is a diagram showing an overview of the data processing flow of the nursing care prevention effect identification support function related to the performance evaluation support function. [Figure 57] This is a diagram that explains the pattern of change and continuation of the state at the time of analysis when the RDO element is expressed as two values ​​such as bad R and good D. [Figure 58] FIG. 10 is a diagram showing an overall picture of the data processing flow of a cost-effectiveness specification support function related to a performance evaluation support function. [Figure 59] FIG. 10 is a diagram showing an overall picture of the data processing flow of the policy planning support function. [Figure 60] FIG. 10 is a diagram showing an overall picture of the data processing flow of the planning support function. DETAILED DESCRIPTION OF THE INVENTION

[0034] The following describes embodiments of the present invention. First, the following points are listed below that are essentially necessary to understand medical care and nursing care in an integrated manner based on the elderly condition image model proposed in the present invention.

[0035] As major illnesses that require nursing care occur or worsen, physical and mental conditions (external symptoms) deteriorate, which leads to the occurrence and progression of a state requiring nursing care. For this reason, in order to understand the overall condition of the elderly, illnesses (medical care) and nursing care must be treated as a single entity. The pattern of progression to a state requiring nursing care varies depending on the type of injury or illness. That is, there are injuries and illnesses that gradually progress from needing assistance (such as Alzheimer's disease), and injuries and illnesses that suddenly progress to a state requiring severe nursing care (such as stroke or a fracture (femur fracture) due to a fall). For integrated efforts in health and nursing care prevention and nursing care (support for independence and prevention of worsening of conditions), it is necessary to seamlessly track the state of injury or illness, physical and mental condition, and state of need for nursing care, from before to after the onset of injury or illness, as well as risk factors that promote deterioration and defensive factors that lead to improvement and maintenance. A "disease / injury (medical care) only" or "nursing care only" approach makes it difficult to integrate health and nursing care prevention efforts or to implement effective nursing care policies (support for independence and prevention of deterioration). While it is important to understand how elderly people progress to a state of nursing care need after each injury or illness, a "disease / medical care" approach makes it difficult to grasp this progression. For example, as mentioned above, dementia and frailty gradually worsen, gradually transitioning from a state requiring assistance to a state requiring nursing care. In contrast, stroke, falls, and fractures (femur fractures) suddenly progress to a higher level of nursing care need after an acute exacerbation. Thus, the process of progression to a state requiring nursing care differs depending on the injury or illness. Furthermore, a "nursing care only" approach makes it difficult to determine the process through which an individual's level of nursing care need arises, even if the individual has multiple injuries or illnesses simultaneously. While recent efforts have focused on injury-specific care management, verifying its effectiveness is impossible using nursing care data alone. It is also important to understand changes in the condition of individual elderly people, rather than just average values. In other words, in order to realize efficient and effective nursing care prevention initiatives, in addition to understanding average values ​​for cities, towns, villages, etc. (hereinafter referred to as local governments) and small areas (macro-level initiatives), it is essential to also take micro-level initiatives, such as understanding changes in the injury / illness status and physical and mental condition of individual elderly people, integrating medical care and nursing care, and assessing the risk of injury / illness or the need for nursing care.

[0036] The KDB (National Health Insurance Database) will be used as a database for grasping both medical and nursing care data for each elderly person. From fiscal year 2022, the KDB will be enriched with nursing care information, with nursing care certification information and basic checklist data being added, making it ideal for grasping both medical and nursing care data.

[0037] Figure 1 shows an overview of the KDB, comparing it with the NDB (a database used by the Ministry of Health, Labour and Welfare for surveys and analyses to create, implement and evaluate medical cost optimization plans) and the nursing care database, both managed by the Ministry of Health, Labour and Welfare. The KDB contains the data items shown in Figure 1 and has the following features: - A wide variety of information such as medical receipts, health checkups, frailty, nursing care certification, and nursing care receipts can be linked on an individual basis, allowing for cross-sectional aggregation and analysis (for the same person) even across systems. · Data can be compiled and analyzed by "district" which is even more detailed than by insurer. -Comparisons can be made by prefecture, by insurer of the same size, and with the national total. Analysis can be performed from various angles, such as by insurer, prefecture, or size, as well as by age, sex, etc. Individual histories can also be tracked and analyzed by comparing them over time. -In order to efficiently and accurately identify elderly people who are at high risk of developing injuries or illnesses and provide individual guidance, the actual names of elderly people can also be handled in a secure environment. By organizing this data, it will be possible to analyze and provide information that comprehensively grasps the elderly's injuries, illnesses, physical and mental conditions, and need for care, as well as various risk situations, and as a result of the analysis.

[0038] For these reasons, it is necessary to realize an "elderly condition model" that will enable an integrated and comprehensive understanding of the illness and injury status, physical and mental health status, and state of need for care of the elderly, based on the utilization of the KDB. In other words, the "elderly condition model" will be used to analyze KDB information and provide information to those involved in nursing care prevention projects and health projects in municipalities in a format that can be easily used to improve policies. The information provided can be used to present information provision methods for improving nursing care prevention projects in accordance with the PDCA process. In other words, it will also be possible to create nursing care prevention policy support tools and related manuals.

[0039] The "elderly condition model" here, which will be described in detail later, is a set of factors that make an injury or illness worse, factors that prevent the worsening of the condition, and what the results will be. In other words, it is a "model" in the sense that it shows a way of understanding the condition of the elderly as a collection of necessary factors combined chronologically and from a bird's-eye view, from the perspective of being the target of nursing care (preventive) measures.

[0040] In an embodiment of the present invention, this elderly condition model is used to reconstruct medical and nursing care big data, etc., accumulated in the KDB to generate elderly condition data, etc., and is positioned as a model intended to be utilized as the results of the effectiveness of nursing care (prevention) measures and as the criteria for their evaluation. The generated elderly condition data, etc., are then used to create elderly history records, medical records for regions, businesses, municipalities, etc., and nursing care prevention measure support tools, etc., which will be described later.

[0041] Next, an overview of the community-based integrated care system according to an embodiment of the present invention will be explained using Figures 2 and 3. This community-based integrated care system is constructed as a computer system including a computer and its peripheral devices, and the functional blocks shown in Figures 2 and 3 and the functions explained in each subsequent figure represent functions executed by this computer system.

[0042] 2 shows a system configuration for generating elderly person status image data 14 using an elderly person status image model 13 defined in an embodiment of the present invention, utilizing big data consisting of medical and nursing care-related data 11 stored in the KDB and elderly person information data 12 not currently included in the KDB. The elderly person status image model 13 is stored in the memory of a computer system, and a correspondence function unit 15 set in a computing device of the computer system first associates the big data 11, 12 with the elderly person status image model 13. A data conversion function unit 16 set in advance in the computing device performs data conversion based on the elderly person status image model 13 associated with the big data 11, 12, and generates a wide variety of elderly person status data 14.

[0043] Figure 3 shows a system that can create and provide data that contributes to supporting the implementation and effectiveness verification of community-based comprehensive care policies, such as elderly history (micro perspective) 19 and regional / business / municipal medical records (macro perspective) 20, using a policy implementation function 18 to link the various types of generated elderly condition data 14 to support the implementation and effectiveness verification of various policies for integrated health and nursing care prevention and nursing care (support for independence and prevention of worsening conditions).

[0044] The KDB system described above processes nationwide data collectively at a joint processing center, and the KDB 11 shown in Figure 2 contains medical receipt data, health checkup data, preventive care data, nursing care certification data, nursing care receipt data, and basic attribute information. Additionally, elderly information data 12 not currently included in the KDB 11 includes health guidance data, data on use of day care facilities, etc.

[0045] The generated elderly person condition data, etc. 14 includes condition data, condition continuation index data, risk assessment data, care cycle data, and medical and nursing care cost data, as shown in Figure 2 (details of each will be explained in Figure 20). In addition, there is future forecast data, cost-effectiveness data, etc., as shown in Figure 20, which will be described later.

[0046] The aforementioned elderly history (an example of a micro perspective)19 shown in Figure 3 visualizes elderly condition data14 integrating medical care and nursing care for each individual elderly person in chronological order, as shown in Figures 21 and 22, and allows for the confirmation of trends, leading to more efficient and active community care meetings and service provider meetings (promoting collaboration between medical care and nursing care, etc.). In addition, from the risk assessment data mentioned above, high-risk individuals who should be involved in integrated health and nursing care prevention efforts, and nursing care (support for independence and prevention of worsening conditions) can be efficiently and accurately extracted.

[0047] Furthermore, as shown in Figures 23 and 24, the Regional / Business / Municipality Medical Record (an example of a macro perspective) 20 supports the understanding of the current state and effectiveness verification of policies related to integrated health and nursing care prevention efforts and nursing care (support for independence and prevention of worsening conditions) by region, business, and municipality. This Medical Record 20 allows for a quantitative understanding of the results of one's own efforts (one's own region, business, and municipality), as well as comparisons with other regions, businesses, and municipalities. The original data used to create the Medical Record is compiled based on the results of comparisons within the same condition model group for each individual elderly person (for example, by phase of major injury or illness that leads to nursing care), and is expected to produce highly accurate and fair output.

[0048] The elderly condition image model 13 described above will now be explained. Fig. 4 shows the elderly condition image model 13 relating to a major injury or illness that may lead to nursing care. The elderly condition image model 13 is composed of a major injury or illness data section 131 that holds injury or illness data (such as the name of the injury or illness) of the major injury or illness that may lead to nursing care, a concomitant injury or illness data section 132 that holds injury or illness data (such as the name of the injury or illness) of the concomitant injury or illness that may cause the major injury or illness, a stage setting section 133 that sets five phases Φ1 to Φ5 that represent each stage including before and after the occurrence of these (i.e., whether or not they have occurred), and a related element section 134 that holds related elements (RDO elements) consisting of risk R (Risk) elements, defense D (Defense) elements, and result O (Output) elements of the injury or illness.

[0049] In this way, the Elderly Condition Image Model13 is designed to provide a chronological overview of the elderly's injury and illness status, physical and mental health status, and related factors (RDO elements) such as risk and protective factors related to the use of medical and nursing care services, for each major injury or illness that leads to nursing care, over the entire period before and after the onset of each injury or illness, and to lead to the realization (including effectiveness verification) of policies that truly contribute to integrated efforts in health and nursing care prevention and nursing care (support for independence and prevention of worsening of condition).This model is not limited to integrated efforts in health and nursing care prevention, but can also be widely applied to various areas of community-based care, such as nursing care (support for independence and prevention of worsening of condition) and medical and nursing care collaboration, in principle, where medical care and nursing care should be handled in an integrated manner.

[0050] The five phases Φ1 to Φ5 of the major injuries and illnesses that lead to nursing care and the associated injuries and illnesses that cause them will be described later. The related elements (RDO elements) 134 will also be described later.

[0051] The main injury / illness data section 131 shown in FIG. 4 holds data on the main injury / illness that may lead to nursing care, and the associated injury / illness data section 132 holds data on the associated injury / illness that may lead to nursing care (e.g., the name of the injury / illness), as well as the associated injury / illness data section 132. As shown in FIG. 6 and FIG. 7, the main injury / illnesses that may lead to nursing care include dementia (including mild cognitive impairment, such as Alzheimer's disease, cerebrovascular disease, and Lewy syndrome), stroke (cerebral infarction and cerebral hemorrhage), frailty (malnutrition, joint disorders, and the like), and fractures and falls (femur fractures, polypharmacy, and the like). Furthermore, the associated injury / illnesses that may lead to nursing care include diabetes, hypertension, dyslipidemia, atrial fibrillation, osteoporosis, aspiration pneumonia, sleep apnea syndrome, depression, normal pressure hydrocephalus, Parkinson's disease, and the like, as shown in FIG. 8.

[0052] As shown in Figures 6 and 7, the details of the major injury / illness data section 131 described above are made up of multiple injuries and illnesses that differ depending on the mechanism of occurrence (how they occur, the mechanism), location, etc. For example, the cerebral stroke shown in Figure 7 includes cerebral infarction and cerebral hemorrhage, and further, injuries of cerebral infarction include multiple infarctions and large infarctions, and each is made up of multiple injuries and illnesses.

[0053] Furthermore, the dementia shown in Figure 6 includes vascular dementia caused by stroke, but in this invention, this is distinguished from the stroke shown in Figure 7. That is, vascular dementia, which is a type of dementia, has the same mechanism as stroke, but refers to cases where the damaged area is a part of the body that controls mental functions. In contrast, when the damaged area is a part of the body that controls physical functions, it is considered a stroke and is distinguished from vascular dementia.

[0054] Mild cognitive impairment (MCI) in Figure 6 can be either progressive or reversible, with the possibility of improvement (normalization). Progressive MCI includes conditions such as Alzheimer's disease, dementia with Lewy bodies, and multiple cerebral infarctions resulting from the blockage of small cerebral blood vessels. In cases where organic changes have occurred over many years, but do not constitute a diagnosis, symptoms progress unilaterally, and the condition is called progressive MCI.

[0055] In contrast, reversible MCI, which has the potential for improvement (normalization), is caused by depression, alcoholism, physical / social / mental frailty, cognitive decline due to medication, normal pressure hydrocephalus, Parkinson's disease, etc., and may return to normal if the cause is removed, so it is called reversible MCI. Even if these reversible MCIs worsen and develop into dementia, there is a possibility that they can be improved with appropriate treatment, and these are collectively called "treatable dementias," meaning they are treatable.

[0056] It is necessary to consider these two types when considering models of elderly conditions. For progressive MCI, efforts to slow its progression (e.g., prevent the onset of dementia) are important. Since MCI is usually an illness that occurs before dementia develops, there are no medical prescriptions for it.

[0057] When we look at frailty (frailty), there are three types: physical frailty, social frailty, and mental frailty, as shown in Figure 9. Physical frailty progresses due to malnutrition caused by factors such as decreased oral function (oral frailty), muscle loss (sarcopenia) resulting from this, and joint disorders caused by aging such as osteoarthritis (these are traditionally considered to be locomotive syndromes). Furthermore, this physical frailty can lead to social frailty (social isolation, etc.) and mental frailty (decline in cognitive function, etc.). The typical progression of frailty is expected to be physical frailty → social frailty → mental frailty.

[0058] Furthermore, when physical frailty or social frailty occurs, it becomes difficult to use the functions that should be used in daily life (physical functions, communication functions, etc.), and as the so-called disuse syndrome progresses, there is a greater likelihood of falling into a vicious cycle in which physical and cognitive functions further deteriorate.

[0059] The accompanying illness data section 132 shown in Figure 8 holds data on accompanying illnesses that are the cause of the main illness, and specifically covers diabetes, hypertension, hyperlipidemia, atrial fibrillation, osteoporosis, aspiration pneumonia, sleep apnea syndrome, depression, etc.

[0060] In the present invention, diseases that are not covered by the elderly condition model include cancer, acute myocardial infarction, COPD (chronic obstructive pulmonary disease), CKD (chronic kidney disease), etc. These are not diseases specific to the elderly, and because they are not considered to be major diseases that lead to nursing care or secondary diseases that cause them due to the related factors assumed by this model (R: risk factors, D: protective factors), they are, in principle, excluded from the scope of the elderly condition model.

[0061] Figure 10 presents a data table summarizing the relationship between the primary injury or illness leading to caregiving and the secondary injury or illness mentioned above. The three horizontal columns on the left side of Figure 10 list the major and minor categories and names of the primary injury or illness leading to caregiving, with these names arranged vertically. Additionally, several columns of secondary injury or illness names for the primary injury or illness are arranged horizontally above these, and to the right of these, other risk factors are arranged horizontally. The relationship between the primary injury or illness listed vertically and the secondary injury or illness names and other risk factors listed horizontally is indicated by symbols in the cells at the intersections of the primary injury or illness names listed vertically and the secondary injury or illness names and other risk factors listed horizontally. In other words, circles indicate which secondary injury or illness of the latter influences the primary injury or illness of the former, and which other risk factors of the latter influence each primary injury or illness of the former. The reverse relationship is also indicated by squares. In addition, the black circle and black square symbols written in the above intersection cells indicate a strong influence, the white circle and white square symbols indicate an influence, and the star symbol indicates the origin of the occurrence of an injury or illness such as an acute exacerbation.

[0062] For example, in Figure 10, the second line from the top shows Alzheimer's disease, which is strongly influenced by diabetes, hypertension, and dyslipidemia as concomitant conditions, as well as sleep apnea. Aging also plays a role as a risk factor. Although not shown in Figure 10, diabetes, hypertension, and dyslipidemia—the main concomitant conditions of vascular dementia and stroke (e.g., paralysis)—can induce arteriosclerosis a certain period after the onset of each condition, ultimately leading to stroke. Similarly, in the fifth line from the top, large-infarction dementia is marked with black or white circles for diabetes, hypertension, dyslipidemia, and sleep apnea as contributory conditions. Additionally, a star is marked at the intersection with atrial fibrillation. This indicates that the initial cause of the exacerbation is an acute exacerbation in which arrhythmia causes a blood clot to travel from the atrium of the heart, embolizing a large artery in the brain (causing widespread damage). Furthermore, looking at the femoral fractures in the bottom row, osteoporosis has a strong influence as an accompanying injury, falls are cited as a risk factor for acute exacerbation, gender (female) has a strong influence, and aging also has an influence. Note that the above osteoporosis occurs mainly in women due to menopause, which causes an imbalance in bone metabolism (more bone is lost than created) due to a decrease in female hormone secretion.

[0063] Next, we will explain the phase Φ for each injury or illness shown in Figure 4. Phase Φ consists of five phases: Φ1: normal state, Φ2: before the occurrence of a concomitant injury or illness, Φ3: after the occurrence of a concomitant injury or illness, Φ4: before the occurrence of a major injury or illness that leads to nursing care, and Φ5: after the occurrence of a major injury or illness that leads to nursing care. For a major injury or illness, all phases Φ1 to Φ5 are relevant. For a concomitant injury or illness, the patient does not become dependent on nursing care, so the three phases Φ1 to Φ3 are relevant. We would like to emphasize again that these are relative phases defined for each injury or illness. Relative means that the duration of each phase differs for each injury or illness.

[0064] Φ1: Normal state literally refers to a normal state in which no abnormalities are observed in the elderly person (there are no major injuries or illnesses that require nursing care, nor any accompanying injuries or illnesses that cause them). Φ2: Before the occurrence of an accompanying injury or illness, the occurrence of an accompanying injury or illness is not diagnosed (there is no record on the medical receipt), but based on health checkup results, etc., there is a high possibility of the occurrence of the accompanying injury or illness. For example, in the case of diabetes, which is an accompanying injury or illness, this refers to a stage in which diabetes has not been diagnosed, but there is a high possibility of diabetes occurring based on blood test values ​​(HbA1c, etc.). Φ3: After the occurrence of an accompanying injury or illness is literally the stage after the occurrence (diagnosis) of the accompanying injury or illness, and it is recorded on the medical receipt.

[0065] Φ4: Before the occurrence of a major illness or injury that requires nursing care, no major illness or injury has occurred (no record on medical receipts), but the possibility of a major illness or injury occurring is high due to factors such as the length of time since the onset of a concomitant illness or injury. For example, if diabetes, hypertension, or dyslipidemia occurs as a concomitant illness in Φ3, and this condition persists for a long period of time, arteriosclerosis develops, increasing the likelihood of major illnesses such as stroke or vascular dementia. Thus, the stage before the occurrence of a major illness or injury, when the possibility of a major illness or injury increases due to factors such as the length of time since the onset of a concomitant illness or injury, is referred to as the Φ4 stage. Φ5: After the occurrence of a major illness or injury that requires nursing care, as the term suggests, is recorded on medical receipts after the occurrence of a major illness or injury. Φ5 is also the stage at which changes in physical and mental state (external symptoms) become apparent due to the onset of a major illness or injury, resulting in a state of need for nursing care.

[0066] Next, we will explain the related elements (RDO elements) 134 shown in Figure 4 that make up the elderly condition image model. Risk elements (hereinafter also referred to as R elements) are elements that promote the deterioration of the elderly's injury / illness state and mental / physical condition, while defense elements (hereinafter also referred to as D elements) are elements that lead to the improvement and maintenance of the elderly's injury / illness state and mental / physical condition, as well as injury / illness prevention and nursing care prevention. These R elements and D elements are inversely related to each other, so if each element is poor, it becomes an R element (leading to the deterioration of the injury / illness state, etc.), and if it is good, it becomes a D element (leading to the improvement / maintenance of the injury / illness state, etc.).

[0067] These R and D elements include nine items, as shown in Figure 4: mental health, daily living habits, social living situation, health guidance service usage, preventive care service usage, associated injury / illness status, medical service usage, care service usage, and risk medication management. As mentioned above, the R and D elements in each of these items are mutually exclusive, so for example, with regard to mental health, poor mental health is an R element, and good mental health is a D element. Furthermore, with regard to risk medication management, taking medications that have a negative effect on cognitive or motor function is an R element, and avoiding such medications is a D element.

[0068] Result elements (hereinafter also referred to as O elements) include the state of injury or illness, physical and mental state (external symptoms), and state of need for nursing care. The state of injury or illness can be determined from the injury or illness code on the medical receipt or the drug code corresponding to the injury or illness. As a representative example, the physical and mental state corresponds to the 76 certification survey items shown on the left side of Figure 12, which shows the primary judgment logic for nursing care certification (described below), and is determined from the certification survey data. The state of need for nursing care can be determined from the level of care required and reference time (including reference time by activity category) in the nursing care certification data.

[0069] As mentioned above, a specific method for associating R elements (risk elements), D elements (protective elements), and O elements (result elements) for each injury or illness is to pick out each element listed in the treatment guidelines for the injury or illness and associate them as evidence to define each RDO element.

[0070] In Figure 11(a) and (b), the RDO elements mentioned above are classified into large, medium, and small, and defined as masters. In other words, the "major classification" classifies one of the RDOs. As mentioned before, R elements and D elements are inversely related, and in the "major classification" column, R and D are arranged together vertically. The rows from Figure 11(a) to the top half of Figure 11(b) are RD elements, and the rows in the bottom half of Figure 11(b) are O element rows.

[0071] In the "Medium Category" column, RDO and the element names of each element, such as "Mental Health," "Daily Living Environment," etc., are arranged vertically and defined. In the "Minor Category," the names of the classification elements are defined in even more detail. For example, if the "Medium Category" is "Mental Health," the "Minor Category" would be "Purpose in Life Situation," "Caregiving Burden Situation," etc. Furthermore, in the "Summary of RDO Elements" column to the right, an overview of the elements corresponding to the "Minor Category" is defined. For example, for "Purpose in Life Situation," "Whether or not one has a purpose in life" is defined.

[0072] The specific details of these related factors (RDO factors) are explained below. The R: risk factor for poor mental health corresponds to stress, depression, anxiety, and social withdrawal due to widowhood or divorce, employment issues (unemployment), etc. Poor lifestyle habits correspond to abnormal values ​​for diet, exercise, sleep, alcohol consumption, smoking, etc. Health guidance and preventive care services correspond to poor nutritional guidance, oral care, and non-use or non-participation in day care centers. Non-use of medical and nursing care services corresponds to non-use of these services. Low quality medical and nursing care services correspond to poor diagnosis and treatment, and non-implementation of support for independence and care to prevent the condition from worsening. Drug risks that have a negative impact on cognitive and motor function correspond to the use of contraindicated drugs and polypharmacy. Poor social living environments correspond to poverty, the burden of nursing care, sluggish local government activity, and underdeveloped transportation systems.

[0073] D: Defensive factors are the opposite of R: Risk factors. For example, good mental health corresponds to satisfaction, self-efficacy, self-worth, and well-being (happiness). For each of the other items, the opposite of R: Risk factors corresponds to good lifestyle habits, use and participation of health guidance and preventive care services, use of medical and nursing care services (use of these services in response to worsening symptoms or certification of need for nursing care), high quality medical and nursing care services, avoidance of medications that have a negative effect on cognitive and motor function, and a good social living environment.

[0074] In this way, R: risk factors and D: defensive factors are inversely related, and can become R: risk factors or D: defensive factors depending on whether one has poor lifestyle habits, whether one is taking medication appropriately, whether one is receiving high-quality medical and nursing care services, etc. For example, if the relevant item in the big data has a code that indicates degree, if the code is below a certain level it will be judged as a risk factor, and if it is above a certain level it will be judged as a defensive factor.

[0075] O: The result element of the injury / illness condition includes medical checkup results, the presence or absence of injury / illness, the severity of injury / illness, medication / medication, the use of specific equipment, the type of medical institution, etc. The physical and mental condition includes physical function, daily living function, cognitive function, BPSD (behavioral behavioral disorder), social adaptation, etc. The state of need for care includes the level of care required, the standard time for each category of activity, etc.

[0076] Next, we will explain the changes in physical and mental state (the occurrence of external symptoms) that accompany the occurrence of a major injury or illness that leads to nursing care, and the resulting state of needing nursing care. When the occurrence of a major injury or illness causes damage to the relevant tissue, this manifests as a change in physical and mental state (external symptoms). Each injury or illness causes characteristic damage to the affected tissue, resulting in a decline or loss of the functions controlled by that tissue. For example, in the case of a stroke, the damaged cranial nerve tissue necroses, resulting in a decline or loss of the motor functions controlled by that tissue. Once this function is declined or lost, it manifests as specific symptoms (in the case of a stroke, mental and physical conditions such as paralysis). Then, as symptoms occur and become more severe, the person transitions into various types of nursing care needs (e.g., which activities require more nursing care).

[0077] In other words, when changes in physical and mental state (external symptoms) occur or become more severe, they are recorded, for example, in a nursing care certification survey, and these are used as inputs to make a primary assessment and then a secondary assessment (nursing care certification). Figure 12 shows an overview of the primary assessment for nursing care certification. In the certification survey shown on the left side of Figure 12, a certification survey is conducted based on 76 prescribed certification survey items. The results of this certification survey, along with the attending physician's opinion, are input into the primary assessment logic shown in the center of the figure. The primary assessment logic uses a computer to determine the standard time for each activity category using a tree model for multiple known types of activity category.

[0078] In other words, the standard time for each of the eight categories of activities (eating, toileting, mobility, hygiene, indirect daily living assistance, BPSD-related activities, functional training-related activities, and medical-related activities) is calculated as an index of the effort required for care. The level of care required is then determined based on the thresholds within which the total standard time falls, as shown on the right side of the diagram. In this way, the primary assessment logic can be thought of as a logic that converts the symptoms of the major injury or illness that leads to care into the level of care required (the amount of effort required by each type of care). Furthermore, because the certification survey data (mental and physical condition to external symptoms) and the level of care required are linked together by the primary assessment logic, they change and progress simultaneously.

[0079] The chronological relationship between the RDO elements described above and the aforementioned phases Φ1 to Φ5, as well as each associated injury or illness and the main injury or illness that leads to nursing care, is explained using Figure 5. Figure 5 is a diagram that chronologically expresses the elderly condition model described in Figure 4 across five phases Φ1 to Φ5. It can be seen that each RDO element occurs in its own specific phase.

[0080] The relationship between the R (risk element), D (defense element), and O (result element) and the phases they affect is shown by horizontal bar graphs. These bar graphs indicate that the further to the right in the illustration, the stronger the influence.

[0081] Looking at R: risk factors and D: protective factors, mental health, daily habits, and social environment are all relevant in all phases Φ1 to Φ5. Health guidance services, which involve participation or non-participation in services, are relevant from before the occurrence of an incidental injury or illness in phase Φ2 to before the occurrence of a major injury or illness in phase Φ4. Care prevention services involve preventive services for the state of needing care and services related to the certification of needing care, so they are relevant from before the occurrence of an incidental injury or illness in phase Φ3 to after the occurrence of a major injury or illness in phase Φ5, after the actual state of needing care has occurred. Medical services and risk drug management are both related to preventing the worsening of symptoms, so they are relevant from after the occurrence of an incidental injury or illness in phase Φ3 to after the occurrence of a major injury or illness in phase Φ5, after the actual state of needing care has occurred. Care services are provided to address the state of needing care after the occurrence of a major injury or illness, so phase Φ5 is relevant.

[0082] O: Looking at the resultant elements, the injury or illness state is directly related to the incidental injury or illness state, which is phases Φ2 and Φ3 before and after their occurrence, and continues through phases Φ4 and Φ5. The main injury or illness state that leads to nursing care is also directly related to phases Φ4 and Φ5 before and after their occurrence. The physical and mental state (external symptoms) and the state of needing nursing care are directly related to phase Φ5 after the occurrence of the main injury or illness.

[0083] For details of the RDO elements described above, please refer to the explanation in Figure 11. For details of the major injury or illness that leads to nursing care, please refer to the explanation in Figures 6 and 7. For details of the incidental injury or illness, please refer to the explanation in Figure 8. For the relationship between the incidental injury or illness and the major injury or illness, please refer to the explanation in Figure 10. Furthermore, for the O: Result element's physical and mental condition (external symptoms) and state of need for nursing care, please refer to the explanation in Figure 12.

[0084] Returning to Figure 5, we examine the major illnesses and conditions leading to nursing care and their associated conditions. Among the associated illnesses, symptoms such as depression, sleep apnea syndrome (SAS), oral disorders, malnutrition, and aspiration pneumonia begin to appear in phase Φ2. By phase Φ3, these conditions progress to include diabetes, hypertension, dyslipidemia, atrial fibrillation, sarcopenia, osteoarthritis, and osteoporosis. These conditions can be identified from medical claims. The four major illnesses leading to nursing care due to these associated illnesses—1. dementia and MCI (Alzheimer's disease, Lewy syndrome, vascular disease, treatable disease, etc.), 2. stroke (cerebral infarction, cerebral hemorrhage, etc.), 3. frailty (frailty, physical, social, mental, and disuse syndromes), and 4. fractures and falls (femur fractures, etc.)—have a higher risk of occurring in phase Φ4 and occur in Φ5. As mentioned above, it is important to note that the duration of phases Φ4 and Φ5 varies depending on the major injury or illness.

[0085] Figure 13 shows the correspondence master (RDO element correspondence master by major injury / illness) between the above-mentioned R: risk elements, D: defense elements, and O: outcome elements (RDO elements) and major injuries / illnesses that lead to nursing care. In Figure 13, the vertical axis lists nine R: risk elements, D: defense elements, and three O: outcome elements, while the horizontal axis lists four major injuries / illnesses that lead to nursing care, with the correspondence relationships displayed at the intersections of these. In addition, the right side of the figure shows the professions (doctors, nurses, pharmacists, public health nurses, care managers, etc.) and institution types that should be responsible for risk avoidance and prevention promotion for each RDO element.

[0086] For example, if the primary illness leading to nursing care is 1. Dementia / MCI (Alzheimer's / Lewy syndrome / vascular / treatable, etc.), then all of the R: Risk elements and D: Defense elements correspond to the "health guidance service usage status." Of these, diabetes, dyslipidemia, and depression are applicable to the "accompanying illness / condition." Furthermore, medications related to cognitive decline are applicable to the "risk medication administration management." Furthermore, cognitive function / BPSD in Figure 12 is applicable to the "mental and physical condition" of the O: Result element, and the "need for nursing care status" is applicable from the need for support onward. In this way, a corresponding master with RDO elements is configured for each primary illness / condition.

[0087] An overview of the big data 11, 12 related to medical care and nursing care will be explained using Figure 14. Figure 14 is a data table that summarizes the characteristics of the big data 11, 12 from various perspectives, with symbols A to I arranged vertically and each labeled with a "data name" ranging from "medical receipts" to "other data not in the KDB."

[0088] In Figure 14, the "Age Group" section to the right of these "Data Names" includes "Under 64," "Early (Elderly)," and "Late (Elderly)," respectively, and the "Care Needs Status" section to the right of that includes "Healthy," "Weak," "Needs Support," and "Needs Care." Furthermore, to the right of that is the "Data Summary," which lists the source of the data (the person who filled it in), the interval between occurrences, the destination, and other information included. The numbers in the explanation columns for "Data Name" and "Data Summary" indicate the number of data items.

[0089] The relevant parts of the intersecting cells of each column of the above-mentioned "age group" and "status of needing care" and each row of "data name" are marked with black circles, white circles, and white triangles. Black circles indicate cases where the relevant big data is comprehensive for users in each age group and status of needing care described above. White circles are used when comprehensiveness is not present, but is partially present. White triangles are used in exceptional cases. Comprehensiveness refers to when the big data is available for all users in the relevant age group or status of needing care.

[0090] For example, looking at A's "Medical Receipt Data," for "Late Stage (Elderly)" and subjects with "Long-Term Care Status" of "Healthy," "Weak," "Needing Support," or "Needing Long-Term Care," as described in the "Data Overview" column, when medical institutions receive medical treatment, they file claims for medical fees with the National Health Insurance Association, and so all of this data is held in the KDB and is comprehensive, with black circles. Note that the data for "Under 64 years old" and "Early Stage (Elderly)" is a white circle, meaning it is not comprehensive, because when the insurer is a health insurance association, etc., there are subjects for whom data is not available in the KDB.

[0091] Furthermore, the "Certification of Needed Long-Term Care" (G) is comprehensive in the sense that it applies to all those who require it. Those who qualify for "early stage (elderly)" and "late stage (elderly)" care, as well as those who require "support" and "care," are marked with a black circle. The "Certification of Needed Long-Term Care Receipt" (H) has the restriction that only those who have received a certification of needing long-term care can receive care services, but it is marked with a black circle because it is comprehensive in the sense that it applies to the same people as the "Certification of Needed Long-Term Care" (G). However, it should be noted that in reality, there are a certain number of people who do not apply for certification even if they are in a state of needing long-term care, and some who receive a certification but do not use care services for financial reasons or other reasons. Furthermore, for the "Certification of Needed Long-Term Care" (G) and the "Certification of Needed Long-Term Care Receipt" (H), a white triangle, indicating an exception, is marked in the "Under 64 years old" column because Type 2 insured persons with specific illnesses, even those under 64 years old, receive some care.

[0092] The "Data Summary" column in Figure 14 shows the information contained in each of the data items A to I, such as the source of the data (the person who filled it out), the interval between occurrences, and the destination. A, "Medical Receipt," is made up of data items related to the medical institution, illness, medication, medical procedure, and specific equipment. B, "Specific Health Checkup," contains information on the risk of concomitant illnesses (HbAlc (diabetes), albumin (malnutrition), blood pressure (stroke), triglycerides, HDL, LDL (dyslipidemia), etc.). C, "Daily Life Habit Questionnaire 23," contains information on daily habits, including smoking, exercise habits, eating habits, drinking, and sleep. In the current KDB, B, C, and D are included in the same health checkup data record.

[0093] D, "Questionnaire for the Elderly 15," E, "Basic Checklist 25," and F, "Survey on Needs in Daily Living Areas 64," all contain information related to frailty. At present, E (Basic Checklist) consists of 25 items, but D (Questionnaire for the Elderly) includes 8 items from the Basic Checklist (no depression items), while F (Survey on Needs in Daily Living Areas) includes 20 items from the Basic Checklist, excluding the 5 depression items. F, "Survey on Needs in Daily Living Areas 64," also contains information on care burden and economic status in addition to frailty. There are many items related to social isolation in particular, but the rest are the same as D and E above.

[0094] G's "Certification of Need for Nursing Care" contains information on the physical and mental condition (physical function 20, daily living function 12, cognitive function 9, behavioral and psychological disorders (BPSD) 15, social adaptation 6, special medical care (in-home medical treatment), etc.), as well as the state of need for nursing care (reference time 10, level of need for nursing care 1). H's "Nursing Care Receipt" contains information on whether nursing care services are provided, information on the nursing care facility, and the basic nursing care services of the nursing care facility (what kind of nursing care services are provided, what additional services are available, and whether the additional services contribute to supporting independence or preventing the condition from worsening).

[0095] "Other data not included in the KDB" in I includes key information related to integrated efforts in health and nursing care prevention, as well as efforts to support independence and prevent the condition of elderly people in nursing care from worsening. Here, integrated efforts in health and nursing care prevention are expected to initially target those up to the stage of needing support, but the perspective should be broadened to include all elderly people listed below. a: Non-certified persons (especially those who are subject to the comprehensive program and those who are frail) b: People who require support and receive preventive care services (benefits) c: Those requiring care levels 1 and 2 who have a high possibility of becoming independent or becoming less severe depending on the care provided. d: Subjects at risk of discontinuously transitioning to level 3 or higher of care needs due to stroke, femoral fracture, etc. In other words, in all four of the above cases, there is ample room for health and nursing care prevention (a, b, d), and even if a person is recognized as needing nursing care, there is ample room for support for independence and prevention of the condition from worsening (c).

[0096] Furthermore, individual data not included in the KDB could include various stresses, the status of health guidance (malnutrition, oral care, etc.), the use of day care facilities, the status of disease-specific care management, the status of independent living support care, the status of COVID-19 response, and the status of drug intervention or non-intervention.

[0097] Next, Figure 15 explains the correspondence master for medical and nursing care big data, etc., by RDO element (RDO element-big data correspondence master). This Figure 15 shows the correspondence master for R: risk element, D: defense element, O: result element (RDO element) with medical and nursing care big data 11, 12, etc. However, while it would be best if there were corresponding big data items for each RDO element, there are some that are not currently held in the KDB. However, this data will also be incorporated into the KDB in the future, contributing to the improvement of the social implementation of community-based integrated care. The right side of the figure also shows the professions (doctors, nurses, pharmacists, public health nurses, care managers, etc.) and institution types that should be responsible for risk avoidance and prevention promotion by RDO element.

[0098] Like Figure 13, Figure 15 plots nine risk factors (R) and protective factors (D) on the vertical axis and three outcome factors (O). The big data items A through I shown in Figure 14 are plotted on the horizontal axis, with the presence or absence of a black circle at the intersection indicating the correspondence. For example, regarding risk factors (R) and protective factors (D), the big data held in the KDB for mental health includes specific health checkup data (B), daily living habits data (C), and frailty-related data (D, E, and F). Regarding outcome factors (O), the big data held in the KDB for the major illness or disease state includes medical receipt data (A), specific health checkup data (B), specific health checkup data (D, E, and F), frailty-related data (G), and nursing care receipt data (H). In other words, identifying the major illness or disease state that leads to nursing care requires a comprehensive assessment of medical receipt data (such as illness name and medications) as well as health checkup, frailty, nursing care certification, and nursing care receipt data.

[0099] By defining each master related to the RDO elements as shown in Figures 13 and 15 in this way, it becomes possible to link three entities, such as the RDO element data shown in Figure 11, the injury and illness data shown in Figures 6, 7, and 8, and the big data explained in Figure 14, as mentioned above, and based on the elderly condition model 13 shown in Figure 4, it becomes possible to create and analyze elderly condition data 14 by major injury or illness by linking these entities.

[0100] That is, the correspondence between the RDO elements for each major illness that leads to nursing care is specified by the correspondence master shown in Figure 13. Furthermore, the correspondence between these RDO elements and medical and nursing care big data can be specified by the correspondence master shown in Figure 15. That is, the RDO elements identified for each major illness in Figure 13 can be determined to which of the big data they correspond by using the correspondence in Figure 15. The RDO elements identified in Figure 13 specifically correspond to which of the RDO element masters shown in Figure 11, and it is determined that if the code value of the corresponding data identified in Figure 15 is below a certain code value, for example, it corresponds to D: defensive element, and if it is above a certain code value, it corresponds to a risk element.

[0101] When associating these big data 11, 12 with the elderly condition model 13, the following points must be taken into consideration.

[0102] (1) No regular medical receipts before the injury or illness occurred Before the onset of an injury or illness in phases Φ2 and Φ4 in Figures 4 and 5, there is usually no medical receipt data on information (such as the name of the injury or illness) on concomitant injuries or major illnesses that lead to nursing care, so the risk of injury or illness occurring is estimated from health checkups (test results, daily living habits), frailty surveys (questionnaire for the elderly, basic checklist), and physical and mental information from nursing care certification. (2) The name of the injury or illness is determined when the medical receipt is issued. Concomitant illnesses are determined in phase Φ3, and major illnesses that lead to nursing care are determined in Φ5 (information on illnesses, medical procedures, medicines, and specific equipment is generated on medical receipts). However, it is important to note that the timing of the determination of illnesses does not necessarily coincide with the time of onset of the illness. For example, this would be the case for a high-risk case in which a person who has not undergone a medical checkup for a long time is diagnosed with multiple illnesses at once when they are hospitalized due to an acute exacerbation of an illness.

[0103] (3) Whether the target data is comprehensive or not As shown in Figure 14, please note that depending on the type of medical and nursing care big data, there is a difference between comprehensive and non-comprehensive data. Generally, medical receipts, nursing care certification, and nursing care receipts are comprehensive, but health checkups and frailty surveys are non-comprehensive, accounting for only about 20% of elderly people, for example. Even with big data that can be considered comprehensive, there are of course exceptions in cases where individuals do not voluntarily undergo medical examinations, certification, or use services due to some reason (economic status, level of health awareness, etc.). Furthermore, because nursing care services cannot be received unless a person has been certified as requiring nursing care, comprehensiveness of nursing care certification for those who need nursing care services can be largely guaranteed. (4) Precautions when tracking time series When tracking the time series before and after an incident, it is necessary to carefully consider the period when big data on medical care, nursing care, etc. was generated (e.g., before and after the start of medical checkups, etc.), the interval between occurrences (monthly or every three months, whether there are gaps due to acute hospitalization or relocation, etc.), changes in the elderly person's location (e.g., whether they move to a city, town, or village within the same prefecture or to another prefecture), and whether they cross over into different systems (e.g., from the early to the later period).

[0104] (5) Presence or absence of prescription medication is also effective in determining injury or illness Illnesses are identified not just by the name of the main illness on the medical receipt, but also by whether or not the associated medicines have actually been prescribed. Since there are many cases of prescriptions outside of hospitals, the medical institution and dispensing pharmacy involved for each illness are also linked. (6) Focusing on a single injury or illness Elderly people often have multiple illnesses and injuries, but when constructing a condition model for each illness and injury, cases are grouped into those that can be distinguished as a single illness and injury.

[0105] As explained in (5) above, it is necessary to link the injury or illness to the corresponding medicines, etc. in order to understand the injury or illness condition, and the reasons for this are listed below.

[0106] (1) To link all related information for each injury or illness of elderly people. "All of the above" refers to the specific medication, prescription, medical procedure, and specific equipment required for this illness, as well as this hospital and this dispensing pharmacy, and is necessary to link and understand these. However, with the current KDB data, for example, we can see that Person A received treatment for four illnesses in what year and month, and that he received four types of medication in what year and month, but we cannot see the connection between which medication he took for which illness. Also, while we can see which medical institution he visited, due to the separation of medical and pharmaceutical services, the institution that prescribes medication is a dispensing pharmacy, so it is difficult to link the illness and the corresponding medication from the names of these institutions.

[0107] In other words, injury and illness status information refers to the results of medical checkups for each injury or illness, the presence or absence of the injury or illness, the severity of the injury or illness, prescribed (taken) medicines, medical (dispensing) procedures, specific equipment, and the inpatient and outpatient medical institutions and dispensing pharmacies related to the injury or illness, but in medical receipt data (unlike electronic medical records), these are not linked (especially medicines dispensed by pharmacies and illnesses diagnosed by medical institutions, etc.). In KDB reconciliation CSV data, there are three types of data: "medical receipt management data," "medical injury and illness name data," and "medical summary data." Illness names are included in "medical injury and illness name data," and medicines, etc. are included in "medical summary data," but because there is no information linking each injury or illness to medicines, etc., the above injury and illness status information cannot be created.

[0108] (2) Necessary to identify the name of the injury or illness The names of illnesses and injuries in medical receipt data are often multiple (many), especially among the elderly, and simply using this data makes it difficult to accurately identify whether an illness or injury is actually ongoing (currently undergoing treatment) or has already been cured. For this reason, it is necessary to follow up on whether or not medications associated with each illness or injury have been prescribed. However, this linking information does not exist in medical receipt data or the illness and medication master data.

[0109] (3) Necessary to determine the severity of the injury or illness It is important to understand the severity of an injury or illness after it occurs, and one possible way to do this is to determine the amount and type of ingredients in the medicine that corresponds to the injury or illness, i.e., the effectiveness of the medicine. However, this information is not available in medical prescription data or pharmaceutical master data. Furthermore, because there are differences in the constitution of individuals, it is more realistic to focus on time-series changes (relative changes) in the same individual.

[0110] (4) Drugs must be analyzed by generic name, not by trade name. Although prescription data contains the trade name of the drug, in order to visualize more universal relationships when analyzing the data, it is necessary to analyze it using the generic name of drugs with the same ingredients, rather than the individual trade names of generic drugs, etc. In medical books, drugs are listed by their generic name, and the drug master data for medical prescription data is the trade name, so it is necessary to link the generic name and trade name for each drug in question.

[0111] (5) Necessary for solving the problem of polypharmacy To address the issue of polypharmacy in the elderly, it is necessary to identify medications that pose a risk of falls or cognitive decline. However, this information is not available in medical prescription data or pharmaceutical master data. A specific response policy involves identifying all medications that have a description of "falls and fractures," "decreased motor function," and "decreased cognitive function" as major side effects and reasons listed in the prescription optimization screening tool for the elderly in the "Guidelines for Safe Drug Therapy for the Elderly 2015 @ Japan Geriatrics Society," and incorporating these into the injury / drug / pharmaceutical master data described below.

[0112] (6) To achieve the above (1) to (5), drug history information and the latest information are also required. Injuries and illnesses in the elderly often progress over a period of 5 to 10 years or more, from before the onset of illness to after the onset and even to the point where they require nursing care. For this reason, tracking of this history must be synchronized with pharmaceutical information. Furthermore, as pharmaceuticals are added and deleted daily, maintaining the latest information is also necessary.

[0113] As mentioned above, the relationship between illness and medicines, etc. is important, and the "Illness / Medicine Relationship Master" that links these is explained using Figure 16. That is, the medical receipt data and related basic masters explained so far do not contain information that links illness and medicines, etc., so as shown in Figure 16, multiple types of information are collected and linked, and a new "Illness / Medicine Relationship Master" is prepared.

[0114] In Figure 16, the above multiple types of information include a master 41 of illness names, extracted data 42 of standard illness names corresponding to efficacy and effects, a medicine master 43, and a generic medicine name master 44, which are linked together by a processing unit 45, which searches and links them using "ICD-10", "product name", etc., to create a master 46 related to illnesses, medicines, etc.

[0115] The disease name master 41 links the disease names explained in Figure 10 with the disease names classified in ICD-10 (the 10th edition of the International Classification of Diseases (ICD-10) created by the World Health Organization (WHO)). The extracted data 42 of standard disease names corresponding to efficacy is data extracted using a search service (search service of standard disease names corresponding to efficacy) provided by the Japan Pharmaceutical Information Center (JAPIC). This search service performs searches using "disease name (JAPIC's unique disease name (text))," "product name," "generic name," "pharmacologic classification," and "ICD-10" as keys, and can extract a list of data on product name, generic name, pharmacological classification, standard, generic drug, and company name. In addition, it is possible to narrow down the route of administration (internal use, injection, topical use) and efficacy evaluation by classification into large, medium, and small.

[0116] The pharmaceutical master 43 is constructed by extracting information such as product name code, product name, and validity period from the basic master of medical prescription data. The generic pharmaceutical name master 44 is constructed from generic name code and generic name. The illness / drug / etc. relationship master 46 linked by the linking processing unit 45 has data items such as illness name code, illness name, ..., and company name, as shown in Figure 16. An example of its use is that related pharmaceuticals can be identified by searching by illness name. Also, suitable illnesses can be identified by searching by pharmaceutical name. Generic names can be associated (grouped) with product names, and can be linked to illness names.

[0117] Next, we will explain the nursing care prevention measure support tool using the elderly condition model using Figure 17. This nursing care prevention measure support tool is designed to identify people at risk of needing nursing care for each of the four major illnesses and injuries that lead to nursing care, before they progress to a state requiring nursing care. It also efficiently and accurately identifies priority targets who are at a phase where the elderly themselves have a sense of crisis and have initiated behavioral changes, and who are in a phase where there is ample on-site support, such as medical care and nursing care.

[0118] In Fig. 17, elderly person status data etc. 14 is created by function 1 (elderly person status data etc. creation function) using elderly person status model 13 for KDB medical care big data 11, 12 etc. shown in Fig. 14. Elderly person status model 13 includes the master corresponding to the major injury / illness and RDO element shown in Fig. 13, the master corresponding to the RDO element and medical care big data etc. shown in Fig. 15, the injury / illness status specific master and phase Φ specific master described below, and the injury / medical product etc. related master 46 explained in Fig. 16. Furthermore, the elderly person status data etc. 14 includes the status and duration etc. for each individual, major injury / illness and RDO element.

[0119] For the elderly condition data, etc. 14, a risk judgment master 51 by major injury or illness and RDO element is used to judge the risk level. Using this elderly condition data, etc. 14 and the risk judgment master 51 by major injury or illness and RDO element, function 2 (function for judging the risk of needing nursing care by major injury or illness) judges the risk of needing nursing care for each elderly person by major injury or illness, and obtains the nursing care risk judgment result 52. From this high risk of needing nursing care by major injury or illness judgment result 52 for each elderly person, function 3 (function for outputting information on priority targets for nursing care prevention measures) outputs information on priority targets 53 (list of priority targets, individual target records) for nursing care prevention measures. Each of these functions and masters is held and executed by a computer system.

[0120] In the above configuration, function 1 (elderly condition data creation function) creates elderly condition data 14 for all elderly people for each major injury or illness that leads to nursing care from KDB medical and nursing care big data 11, 12, etc. (medical receipts, specific health checkups, daily living habits, frailty, nursing care certification, nursing care receipts, etc.). Data creation is performed by linking the actual big data 11 with the constituent masters of the elderly condition model 13, namely, the "major injury or illness-specific RDO element correspondence master (FIG. 13)," the "RDO element-specific medical and nursing care big data correspondence master (FIG. 15)," the "injury or illness condition specific master," the "phase specific master," and the "injury or illness-medical product related master (FIG. 16)." Here, examples of the injury or illness condition specific master and the phase specific master will be described in detail.

[0121] The Illness Condition Identification Master identifies the condition of each illness based on the correspondence with big data of major illness-specific and RDO elements in the Elderly Condition Image Model 13. This master combines medical receipt data (injury / illness name code, ICD10 code, illness name, corresponding drug code and drug name (brand name, generic name), corresponding medical procedure code and medical procedure name, and corresponding specific equipment code and specific equipment name), specific health checkup data (blood test item values ​​for assessing the risk of the illness), frailty data (motor function, cognitive function, depression, etc.), and nursing care certification data (ADL, IADL, cognitive function, BPSD, social application, etc.) to identify the presence or absence of the illness itself, the risk level of the condition, and the severity of the illness. Note that the corresponding drug names for each illness are associated with the Illness / Drug Relationship Master (Figure 16) described above.

[0122] First, we will explain the difficulties and challenges involved in rigorously identifying injuries and illnesses. The main points are as follows: (1) It is reckless to identify patients solely by disease name (as many diseases are arbitrarily named for the purpose of medical fee claims); it is essential to identify patients based on pharmaceuticals, medical procedures, and specific equipment. For example, in the case of diabetes, the actual number is said to be around 8 million, but it has been reported that the number exceeds 20 million if only the disease name is used. This is due to a systemic issue in which even those who do not have diabetes must file a claim for a suspected case of diabetes in order to measure HbAlc. However, the discrepancy in the above patient numbers (the difference between the number of patients identified by disease name alone and the number identified by including pharmaceuticals, etc., that correspond to the disease) may vary significantly depending on the disease. (2) Regarding pharmaceuticals, there are a huge number of them, and the master data is frequently updated. In addition, one pharmaceutical does not necessarily have a specific relationship with one disease. Therefore, the workload of maintaining the master data for pharmaceuticals corresponding to each disease is large, and it is necessary to seek the opinion of an expert for each disease. (3) The correspondence between medical treatment procedures and specific equipment specific to each injury or illness must be investigated and maintained. (4) Furthermore, in order to track the past history of elderly people, it is essential to link together master data of pharmaceuticals, etc. with the same efficacy as those in (2) and (3) above. However, this requires history management that links the efficacy of each master data, which further increases the workload. (5) The amount of work required to maintain the system and carry out the above (1) to (4) on a continuous and regular basis is enormous, meaning that the costs associated with building and operating such a system are enormous.

[0123] In light of the above issues, it is necessary to devise a more realistic method for identifying injuries and illnesses. Below, five methods for identifying injuries and illnesses are described. Method 1: Strict method (perform steps (1) to (5) above for all relevant primary and secondary injuries and illnesses) Method 2: Simple identification method (only the name of the illness, representative medicines, medical and dental treatment procedures, and specific equipment are used for identification. Representative means selecting the main items listed in the treatment guidelines for each illness) Method 3: Judgment is made by combining only the name of the injury or illness from the medical receipt with other data (such as health checkups and nursing care certification). Method 4: Manually inputting the name of the injury or illness, its condition level, duration, etc. into the KDB Method 5: No injury or illness information is used, and only medical checkups, frailty, nursing care certification, and nursing care receipts are used.

[0124] As mentioned above, Method 1 cannot be said to be realistic because it is extremely difficult to overcome the five challenges mentioned above. Method 2 can be said to be a realistic approach from the perspective of the five issues mentioned above, but because it does not identify illnesses or injuries from drugs or medical procedures that have not been selected as representative drugs or medical procedures, the accuracy of injury and illness identification will vary depending on whether or not such drugs or medical procedures are present. Method 3 uses only the name of the injury or illness, so the problem of issue (1) above remains, but it is possible to expect improved accuracy in identification by making judgments based on other data (for example, whether or not the decline in cognitive function in the case of dementia requires the effort of care). Method 4 involves giving up on identifying the data from existing data, and there are also anticipated issues such as the effort required to develop a separate input function and whether comprehensive input will be performed. Method 5 is considered difficult to accurately identify, as there are many cases in which external symptoms and services used are related to multiple injuries and illnesses. From the above, it can be said that a hybrid method of methods 2 and 3 above is likely to be the most realistic method.

[0125] As described above, the phase identification master (FIG. 18) is a master that identifies the following five phases for each injury or illness. Φ1: Normal state Φ2: Increased risk state before the occurrence of incidental injuries or illnesses Φ3: State after the occurrence of incidental injuries Φ4: Increased risk state before major injury or illness occurs Φ5: State after occurrence of major injury or illness (external symptoms appear, nursing care becomes necessary) The specific logic may be, for example, the following conditions:

[0126] Φ1 can be identified under the condition that neither the primary injury or illness nor its underlying secondary injury or illness has occurred in the injury or illness status data (there is no corresponding data in medical receipts, etc.), and the risk of occurrence of these injuries or illnesses has not increased. Φ2 can be identified when the primary injury or illness and its underlying secondary injury or illness are not present in the injury or illness status data (no corresponding data in medical receipts, etc.), but the risk of related secondary injuries or illnesses occurring is elevated (no data for the primary injury or illness). Note that some primary injuries or illnesses have multiple secondary injuries or illnesses, so in such cases, whether or not the secondary injury or illness is Φ2 is determined for each secondary injury or illness. Φ3 can be identified when the primary injury or illness has not occurred and there is no risk of it occurring according to the injury or illness status data, but the secondary injury or illness has occurred according to the injury or illness status data. If there are multiple related secondary injuries or illnesses, each is judged separately. Φ4 can be identified under the condition that the injury or illness has not occurred in the injury or illness status data for the major injury or illness, but the risk of occurrence is increasing. Φ5 can be identified on the condition that the injury or illness has occurred as injury or illness condition data for the relevant major injury or illness.

[0127] The created elderly condition data, etc.14 is calculated as "condition by individual, by major illness, by RDO element, and the duration of that condition, etc." In other words, the degree of the condition and how long it has continued from the past are calculated for each RDO element related to the illness in question.

[0128] In general, the higher the risk level of a condition and the longer the condition has continued, the higher the possibility of risk manifestation. In the example of Figure 17, for elderly person A, "stroke" is listed as an example of a major illness that leads to nursing care. The chart also shows that the condition of "daily living habits," an RDO element, is "good," and that this condition has continued for "3 years." However, with regard to "accompanying illnesses," "diabetes" has continued for at least 1.5 years since its onset, and "high blood pressure" has also continued for 2 years.

[0129] Here, we will explain in detail how to calculate the duration of each RDO element's condition. The target medical and nursing care big data can be broadly divided into two types based on the intervals at which the data is generated. One type (hereafter referred to as Type M, where M indicates monthly data) is data that is typically generated monthly, such as medical receipt data (including data on elderly conditions such as injury and illness status and medical service usage status) and nursing care receipt data (such as nursing care service usage status). The other type (hereafter referred to as Type Y, where Y indicates annual data) is data that is typically generated annually (every six months to three to four years), such as specific health checkup data (including data on daily living habits and elderly questionnaire data) and nursing care certification data (including basic checklists).

[0130] For Type M data, the period during which the state of each piece of data (such as injury or illness status) in the month of interest (the most recent month, etc.) continues is calculated by going back through the monthly occurrence records. If the state of the data changes when going back, the state is considered to have continued from the month of change to the month of interest. However, if the state returns to the same state as in the month of interest around the time of the change, the same state is considered to have continued, depending on the type of change. On the other hand, if there is a month with missing data in between, the missing period is taken into account and a determination is made as to whether the state of the data before and after that is whether it is the same.

[0131] For Type Y data, the state of the data (for example, the dementia independence level) held in the past record closest to the target date and month (such as the most recent date and month) is considered to be the state at the time of the target date and month, meaning that the same state is considered to have continued at least from the most recent past record to the target date and month. Furthermore, by tracing back past records from that point, it is determined that as long as each piece of historical data is equal to the state of the data, that state has continued from the date of the historical record's creation to the target date and month.

[0132] What Type M and Type Y have in common is that the oldest record is merely the oldest data stored in the database. This means that the elderly person's condition prior to that point cannot be grasped as data, so it is important to note that the duration of the condition based on the above historical data retrospective is merely the longest period for which data on the same condition continues, and does not represent the actual period for which the condition has continued. In other words, it only indicates that the condition has continued for a minimum period. Therefore, when assessing risk by injury / illness and RDO element, focusing on the duration of the condition, it is a good idea to also add flag information related to the above situation.

[0133] Function 2 (function for assessing the risk of needing nursing care by major injury or illness) calculates the risk of needing nursing care by major injury or illness for each elderly person from the elderly condition data, etc. 14 created in Function 1, using the risk assessment master 51 by major injury or illness and RDO element. The method for calculating this risk is to assess the risk for each phase of each injury or illness identified in Function 1, based on the assessment criteria of the risk assessment master 51. The higher the risk level of the condition and the longer the condition continues, the higher the possibility of risk manifestation. The logic for identifying the phase is as described above.

[0134] In the example of FIG. 17, the risk judgment threshold for "daily life habits," which is an RDO element, is "good" when "good" has been present for one year or more in the elderly condition data, etc. 14 ("good for one year or more"). In contrast, when "bad" has been present for one year or more in the elderly condition data, etc. 14 ("bad for one year or more"), the risk judgment is "bad." Regarding "accompanying illness / injury status," when neither is present, the risk judgment is "good," and when both "diabetes" and "high blood pressure" are present for three years or more, the risk judgment is "bad."

[0135] Then, the risk is assessed based on the calculation results for each RDO element corresponding to the multiple injuries and illnesses (such as the sum of each risk assessment value), and a judgment such as "◎ Excellent, ○ Good, △ Poor, × Poor" is made.

[0136] In the example of Figure 17, elderly person A's major illness, "stroke," is in phase Φ3 because although the stroke itself has not occurred, a secondary illness has occurred. Furthermore, his daily living habits have been "good" for three years, meeting the risk assessment threshold of "good for one year or more," and his social life situation has been "bad" for six months, but does not meet the risk assessment threshold of "bad for one year or more." Furthermore, with regard to secondary illnesses, his diabetes has persisted for at least one and a half years since onset, and his high blood pressure has also persisted for two years, but neither of these meets the risk assessment threshold of three years or more. Therefore, taking the above into consideration, the overall risk assessment is "good." Although not shown in the figure, a similar method is used to assess Mr. A's other major illnesses.

[0137] The above results show that when the overall risk assessment for each major injury or illness is at phase Φ4 or Φ5 (Φ5 is limited to dementia and frailty), and the worse the overall risk assessment is, the more important it is to prioritize outreach with preventive care measures. Furthermore, for stroke and femoral fractures, once they reach Φ5 it is too late (they often require severe care and have a poor prognosis), so measures must be implemented by Φ4. On the other hand, for dementia (excluding vascular dementia) and frailty (physical frailty, etc.), even if they reach Φ5, there is still room for effective preventive care measures if they remain in the support-requiring stage.

[0138] Function 3 (function to output information on priority targets for nursing care prevention measures) outputs a list of priority targets and individual target records as information on priority targets for nursing care prevention measures 53, based on the results of the nursing care risk assessment by major injury or illness performed by Function 2. In other words, if there is at least one injury or illness that poses the greatest risk among the major injuries or illnesses, it will be output in the target list. In addition, the individual target record will output the basis for the risk assessment (such as a breakdown of the assessment results by RDO element for the injury or illness), the nursing care prevention measures that should be taken (D elements, etc.), and the type of occupation that should be followed up (such as the name of a medical institution, community comprehensive support center, etc.).

[0139] Once a list of priority target candidates is output in this way, it can be used to improve the results of outreach to the elderly in question by public health nurses and care managers at municipal health promotion departments and community comprehensive support centers. In other words, by increasing the efficiency and accuracy of outreach (the likelihood of identifying elderly people who need follow-up with a high degree of certainty), it is possible that, within the limited staffing available at medical and nursing care sites, the number of cases in which the transition to a state requiring nursing care for each injury or illness can be avoided will increase significantly (maximizing results).

[0140] Furthermore, the breakdown of the risk of needing nursing care by major injury or illness will be quantitative and clear, and the occupations (including the names of the people in charge) and types of institutions (including the names of the institutions) that should be responsible for risk avoidance and prevention promotion will be clearly indicated for each RDO element of each major injury or illness, so this data can be put into a common language, which may stimulate cooperation between various occupations in medical care, nursing, etc. In particular, for those on the nursing side, the above data will make it easier to obtain and refer to related information from the medical and health sides (such as information on the elderly's injuries and illnesses, medication information, and the risks associated with them), which has been very difficult to obtain until now.

[0141] Furthermore, as a result, it is possible that this will lead to a significant reduction in per capita medical and nursing care costs (which will accumulate over a lifetime). Specifically, for each of the four major illnesses, medical costs will be incurred at the time of onset of the illness and at the time of recurrence, while nursing care costs will be incurred as an average monthly nursing care benefit according to the level of care required, and these will basically continue for a lifetime.

[0142] As an example, if a person suffers a stroke and is initially placed in nursing care level 3 (e.g., left-side hemiplegia), and the average monthly nursing care benefit payment is approximately 150,000 yen, half the payment limit, even if the condition does not worsen and the person remains in nursing care level 3 for the rest of their life, the cumulative nursing care benefit payment per recipient will be 1.8 million yen per year (18 million yen over 10 years). However, the above is based on the assumption that the primary cause of the need for nursing care is stroke. As mentioned above, if the nursing care prevention measure support tool shown in Figure 17 can, for example, prevent the occurrence of stroke in advance, a very significant effect (reduction in benefit costs) can be expected, depending on the number of people involved.

[0143] Next, the KDB implementation and operation system for the above-mentioned nursing care prevention measure support tool is described with reference to FIG. 19. In this system, in processing step 1, data from the KDB main database (hereinafter referred to as DB) is copied monthly and loaded into a new analysis DB. In processing step 2, this KDB main DB copy data is used to create elderly condition data (condition duration, etc.) 14 using function 1 (elderly condition data creation function) described in FIG. 17. In processing step 3, the nursing care need risk by major injury or illness is determined using function 2 (major injury or illness care need risk determination function) described in FIG. 17, and the nursing care need risk determination result is obtained. In processing step 4, function 3 (major injury or illness care need risk output function) described in FIG. 17 creates information on priority targets for nursing care prevention measures. In processing step 5, the created priority target information, etc. is provided to municipalities nationwide as the output of the nursing care prevention measure support tool. The functions of the nursing care prevention measure support tool, consisting of functions 1, 2, and 3 described above, are executed monthly. In addition, the KDB main database provides KDB basic reports to municipalities across the country.

[0144] Next, we will explain the definition of the elderly condition data, etc. 14 shown in Fig. 2. First, we will explain the necessity of the definition of elderly condition data, etc. and the overall picture.

[0145] In order for medical and nursing care big data etc. associated with the elderly condition model to be useful for supporting the implementation of policies and verifying their effectiveness, it is necessary to process (convert) the original data into elderly condition data etc. (individual-level data) 14. Each piece of medical and nursing care big data etc., which is the original data, will be processed (converted) into the following eight elderly condition data etc., as shown in Figure 20.

[0146] (1) Status data (mental health status, lifestyle status (including medical examination status), frailty status, injury / illness (accompanying and primary) status (including medication status), medical service usage status, mental and physical status / need for nursing care, nursing care service usage status, social life status, etc.) (2) Condition continuation index data (3) Risk assessment data (4) Care cycle data (5) Medical and nursing care costs data (6) Future prediction model for elderly condition (7) Future prediction data for the condition of elderly people (8) Cost-effectiveness data (individuals, regions, businesses, municipalities, etc.)

[0147] Figure 20 shows the overall flow of creating elderly condition data, etc. Parts corresponding to those in Figures 2 and 3 are assigned the same reference numerals. The original data, medical and nursing care big data, etc. 11 and 12, are converted in a data conversion function unit 16 using an elderly condition model 13 and a matching function unit 15 to create elderly condition data, etc. 14. In the elderly condition data, etc. 14, the condition data 141 (1) above is the most basic data, and the content of the other data is determined based on the condition data 141 (1). Subsequently, the content of the condition data conversion data 142 (2) to (5) above is determined from the perspective of supporting the implementation of policies and verifying their effectiveness. Future prediction-related data 143 and 144 (6) to (8) above are further created based on the created data up to (5) above.

[0148] In the future, by using each of these 14 pieces of elderly condition data as input data for AI analysis (normalized data that makes it easier to interpret the analysis results), it will be possible to predict the future of each condition (changes in physical and mental conditions and their speed, increases or decreases in risks, extension of the care cycle, and cost-effectiveness).

[0149] An overview of the elderly condition data, etc. 14 will be explained below. (1) Condition data 141 consists of data on mental health condition, lifestyle condition, frailty condition, injury / illness condition (minor and major), medical service utilization condition, physical and mental condition, need for care condition, nursing care service utilization condition, and social life condition. An overview of each of these data is as follows:

[0150] It focuses on specific items (physical and physical condition, injury or illness, medicines, nursing care services, etc.) necessary for analysis in various types of big data with different data formats and attributes (numeric, code, etc.) and converts them into status data (codes corresponding to physical and mental condition, etc.) consisting of multiple types of items similar to certified data. For example, in health checkup data, numerical blood pressure data can be classified as "low, medium, high," or data on nursing care service usage can be classified as "yes, no use" of day care services. This makes it possible to unify (standardize) definitions from the perspective of "status (physical and mental condition, service usage status, etc.)" regardless of the type of big data, leading smoothly to the creation of the next condition continuity indicator data, which can be said to be the most important key indicator for new community-based integrated care.

[0151] For each injury or illness, the condition is linked to the corresponding medicines, medical procedures, specific equipment, medical institutions, and dispensing pharmacies. A separate master database of injury, illness, and medicines is utilized. Furthermore, to determine the state of need for nursing care, the primary judgment logic must be applied, using the physical and mental condition (certification survey information) as input. Furthermore, it is necessary to convert continuous values, such as the number of medical receipt units, the number of nursing care receipt units, blood test values ​​from health checkups, raw scores and intermediate evaluation item scores for each survey item in the nursing care certification, and the standard time for each category of nursing care status into status data (discrete data).

[0152] The condition duration index data (2) that constitutes the condition data conversion data 142 defines, for each condition, when a certain condition (a certain code value (e.g., partial assistance) of a condition data item (e.g., transfer of nursing care certification)) begins (start month and year), when it ends (end month and year; no end month if ongoing), what the previous condition was, what state it transitions to after that, and the period and amount of change until improvement, deterioration, increase, decrease, etc. Furthermore, it is easy to calculate the degree to which other conditions (attributes), such as injury or illness, dominate during the duration of the condition duration index data of interest, enabling accurate evaluation of condition duration indexes limited to the same attribute. This enables quantitative and fair evaluation of the effects of integrated efforts in health and nursing care prevention, and efforts to support independence and prevent the worsening of nursing care.

[0153] (3) Risk assessment data is used to determine the risk status based on the combination of the presence (size) of risk factors (R: worsening and promoting) and defensive factors (D: maintenance and promoting) in each phase Φ1 to Φ5 of the focused injury or illness, and also includes the duration (speed of change in status from worsening to improvement, etc.), leading to the extraction of risk subjects. For example, if R is present but D is not, the risk is highest; if R and D are present, there are both risk maintenance factors; if R and D are not, the current situation is good but the future needs to be monitored; and if R and D are present, improvement is expected in the future.

[0154] The care cycle in (4) corresponds to the period between hospitalizations, such as "hospitalization due to acute exacerbation → rehabilitation hospital → nursing facility such as rehabilitation → home → hospitalization due to recurrence of acute exacerbation → repeat the same process." The care cycle period is defined for each injury or illness, and the longer the period, the less acute exacerbations occur, the longer the stay at home, and the more effective the community-based comprehensive care that is achieved with low medical and nursing care costs. This also indicates a high quality of multidisciplinary collaboration in medical care and nursing care.

[0155] (5) Data on medical and nursing care expenses, etc., is calculated from medical and nursing care receipt data, and the cumulative costs for each individual (benefit costs for medical care, nursing care, nursing care prevention, etc.) are calculated overall or by major illness or injury that leads to nursing care.

[0156] The (6) elderly person condition image future prediction model master 143 is created by applying AI (deep learning, etc.) using the elderly person condition data, etc. 14 described above in (1) to (5) as input information.

[0157] The future prediction data for the elderly condition (7) uses the master data (6) above to predict the future condition of each elderly person based on their current and past history, according to the implementation status of R: risk factors and D: defense factors.

[0158] The cost-effectiveness in the individual cost-effectiveness data in (8) refers to the amount of benefit divided by the cost, which indicates how much money was invested and how much benefit was achieved in monetary terms. The higher this ratio (usually a ratio of 3 is a benchmark for success), the more cost-effective the policy. A concrete way to think about the amount of benefit is to calculate the average benefit amount (future forecast data in (7) above) for each major injury or illness that leads to nursing care, based on whether medical costs have been reduced by delaying or avoiding the onset of illness or injury, and whether nursing care costs have been reduced by preventing the severity of the need for nursing care.

[0159] Figure 3 explains that visualized data such as elderly history (micro perspective) 19 and regional / business / municipal / etc. medical records (macro perspective) 20 will be created from the various elderly status data etc. 14 generated in this way.

[0160] The aforementioned Elderly History (an example of a micro perspective)19 visualizes elderly condition data14 that integrates medical care and nursing care for each elderly person in chronological order, making it possible to confirm the trends. In addition, the Regional / Business / Municipality Medical Records (an example of a macro perspective)20 supports the understanding of the actual situation and verification of the effectiveness of policies related to integrated efforts for health and nursing care prevention and nursing care (support for independence and prevention of worsening conditions) by region, business, and municipality.

[0161] Specific examples of Elderly History 19 are explained in Figures 21 and 22. Figure 21's Elderly History 19 illustrates the chronological progression of an elderly person's condition: malnutrition → progression to frailty → new certification (requiring support) → nutritional guidance and outpatient care → non-certification. Specifically, the risk of malnutrition based on health checkup BMI and albumin levels increased year by year starting in 2013. The risk of physical frailty first began to increase in 2014, followed by social frailty and finally mental frailty, leading to new certification in April 2017. In parallel with this, the risk of dementia and osteoarthritis also increased. Starting in 2016, one year before new certification, medication for dementia and osteoarthritis began, and nutritional guidance and attendance at outpatient care centers were initiated and implemented. These measures reduced the risk of malnutrition and various frailty conditions, leading to a return to non-certification after 2019. Note that in the figure, the higher the risk, the darker the shading.

[0162] This Elderly History 19 data will make it much easier to understand the profiles of elderly people from past to present from an integrated perspective of medical care, nursing care, and nursing care prevention at community care meetings and service provider meetings, and is expected to improve the efficiency and quality of each meeting in order to optimally resolve issues.In addition, by using the above history data of all elderly people as input data and analyzing it (for example, with AI analysis), it will become clear how elderly people progress from frailty to the need for nursing care as dementia and osteoarthritis progress, and what can be done to prevent this from happening.

[0163] Figure 22, Elderly History 19, shows the chronological progression of an elderly person's condition: poor lifestyle habits → stroke → level of care level 3 @ special nursing home → independent living support nursing care, etc. → level of support → home. In other words, starting in fiscal year 2012, lifestyle-related risks increased every two years, and at the same time, high blood pressure and stroke risk also increased at the same pace (hypertension medication was not administered until fiscal year 2015). In April 2016, the elderly person suffered a stroke and was newly certified as level of care level 3. Six months after the new certification, the elderly person entered a special nursing home in October 2016. The nursing home's management policy of independent living support nursing care and disease-specific care management were thoroughly implemented. As a result, one year after admission, the elderly person's level of care need, motor function level (group 1), and daily living ability (group 2) improved (no lifestyle-related risks during that time). From fiscal year 2019 onward, the elderly person's condition was reduced to level of support, and the elderly person left the nursing home and returned to their home. Note that, in this figure, the higher the risk, the darker the shading.

[0164] The data from Elderly History 19 in Figure 22 will also make it extremely easy to grasp the profile of elderly people from the past to the present from an integrated perspective of medical care, nursing care, and nursing care prevention at community care meetings and service provider meetings, and is expected to improve the efficiency and quality of each meeting in order to optimally resolve issues. Furthermore, by inputting the above history data of all elderly people and analyzing it (for example, using AI analysis), it will become clear how differences in lifestyle habits, etc. lead elderly people to develop strokes, femoral fractures, etc., and as a result, how they progress to a state of needing care, and what can be done to prevent this from happening.

[0165] In the above examples of Figures 21 and 22, the transition of the elderly person's condition before and after the occurrence of a specific major illness has been explained. However, as another example, there is also an elderly person history to which the following information is added in chronological order. · The elderly person's place of residence (home, medical institution, rehabilitation ward, nursing home, etc.). -Risk of needing nursing care by major injury or illness leading to nursing care (applicable phase Φ and overall judgment, basis of the judgment and (Detailed risk assessment results for each RDO element, etc.)

[0166] A specific example of a regional / business / municipal / etc. medical record (an example of a macro perspective) 20 will be explained using Figures 23 and 24. Figure 23 is an example of a regional medical record (integrated health and nursing care prevention), and Figure 23 (a) shows a comparison of the entire municipality / by area, by physical and mental condition, and by age at end of the independence period. Figure 23 (b) shows the trends in area A by year, by physical and mental condition, and by age at end of the independence period.

[0167] In Figure 23, motor function (A) is a weighted average of the age at which independence ends for each physical and mental condition in Group 1 of the nursing care certification data. B: Daily living function (B) is a weighted average of the age at which independence ends for each category in Group 2, C: Cognitive function (C) is a weighted average of the age at which independence ends for each category in Group 3, and D: Social function (D) is a weighted average of the age at which independence ends for each category in Group 5. From the perspective of each type of frailty, each figure assumes a decline in function in the following order: A: Motor function → B: Daily living function → D: Social function → C: Cognitive function, with the period of independence for each function increasing in the order A → B → D → C. Figure 23(b) assumes that function will gradually decline until fiscal year 2014 with no measures implemented, and then a significant extension of function with the implementation of measures from fiscal year 2015.

[0168] The regional medical records in Figure 23 allow for an understanding of the actual status of the level of care required, as well as the period of independence in terms of motor and cognitive function, for the entire municipality and each of the areas A, B, C, D, and E. It also allows for the evaluation of the overall effectiveness of each region's integrated efforts and policies related to health and nursing care prevention, such as malnutrition guidance and drop-in centers for the elderly. By providing evidence to the elderly, municipalities can expect to see a significant increase in health checkup attendance rates and drop-in center participation rates. Furthermore, the municipal medical records allow for the replacement of areas with municipalities and cities with prefectures, making it possible to understand the actual status and evaluate the effectiveness of integrated efforts for health and nursing care prevention by municipality. These efforts are expected to extend the healthy lifespan of the elderly and optimize lifetime medical and nursing care benefits per person in the medium to long term.

[0169] Figure 24 is an example of a business chart (nursing care (support for independence and prevention of worsening)), where (a) compares the speed of improvement and deterioration of the degree of independence for mild dementia patients by service type and business. Also, (b) shows the annual changes in the speed of improvement and deterioration of the degree of independence for mild dementia patients at business A.

[0170] Figure 24(a) shows the speed (speed) of improvement or deterioration of the level of independence of people with mild dementia (dementia independence level II or below, no need for care) for facilities A, B, C, D, and E of a certain service type, categorized by whether it is faster or slower than the average for all facilities of the same service type. For example, "Facility A" has zero people whose condition deteriorated rapidly (E), and is assumed to provide extremely good services. (b) shows that Facility A changed its management in 2015, and has achieved remarkable results by promoting independent living support care and disease-specific care management.

[0171] The facility chart in Figure 24 makes it possible to grasp the actual status of the level of care required for each service and facility (A, B, C, D, E), as well as the speed of improvement and deterioration of various physical and mental conditions. It also allows for the effectiveness of each facility's efforts to support independence and prevent the progression of conditions, such as independent living support care for the elderly and disease-specific care management. With the consent of the relevant parties, this can be presented to the care facility to promote independent living support care and disease-specific care management efforts. Furthermore, if this is used as a municipality chart, by replacing the facility in Figure 24 with the municipality, it becomes possible to grasp the actual status and verify the effectiveness of independent living support and prevention of the progression of conditions by service type for each municipality. These results are expected to extend the healthy lifespan of the elderly and optimize lifetime medical and nursing care benefit costs per person in the medium to long term.

[0172] In other words, the medical record 20 shown in Figures 23 and 24 allows one to quantitatively grasp the results of one's own efforts (one's own district, one's own establishment, one's own city, town, village, etc.), and also makes it possible to compare with other districts, other establishments, and other cities, towns, and villages. The original data used to create the medical record is a compilation based on the results of comparisons within the same condition group for each individual elderly person, and is a highly accurate and fair output.

[0173] As another example of the medical record 20, there is a method of displaying, as an index, the duration of the condition of the risk of needing nursing care (the overall assessment and the detailed risk assessment results that serve as the basis for it) for each major injury or illness that may lead to nursing care.

[0174] Figure 25 shows an overall image of the social implementation of the KDB for community-based integrated care, listing an outline of the specific support methods for implementing policies and methods for verifying the effectiveness of those policies at each intersection as a matrix of the community-based integrated care area (vertical axis) and each stage of the PDCA cycle (horizontal axis). First, the area of ​​community-based comprehensive care on the vertical axis is broadly divided into three areas: 1) health and nursing care prevention, 2) nursing care (support for independence and prevention of worsening conditions), and 3) medical and nursing care collaboration (hospital admission and discharge, home medical care). On the other hand, the PDCA cycle on the horizontal axis is made up of four areas: P) business plan formulation, D) policy implementation, C) performance evaluation, and A) policy planning.

[0175] The nursing care prevention policy support tool mentioned above corresponds to (1) of D Policy Implementation of 1 Healthcare and Nursing Care Prevention. Similarly, the elderly history corresponds to (2) of D Policy Implementation of 1 Healthcare and Nursing Care Prevention if it is applicable to healthcare and nursing care prevention, and corresponds to (2) of D Policy Implementation of 2 Nursing Care (Support for Independence and Prevention of Severity) if it is applicable to nursing care. Furthermore, the various medical records correspond to (1) and (2) of C Performance Evaluation (Effectiveness Verification, etc.) of 1 Healthcare and Nursing Care Prevention if it is applicable to healthcare and nursing care prevention, and corresponds to (1) and (2) of C Performance Evaluation (Effectiveness Verification, etc.) of 2 Nursing Care (Support for Independence and Prevention of Severity) if it is applicable to nursing care.

[0176] As can be seen from this matrix, it is expected that this invention can be easily expanded to other areas of community-based integrated care and other PDCA stages.

[0177] The injury / illness-specific RDO elements, which are one of the main parts of the present invention, correspond to the job type (including the name of the person in charge) and the type of institution (including the name of the institution) that are key to risk avoidance and prevention promotion. Specifically, they are as follows (also shown on the right side of Figures 13 and 15):

[0178] First, regarding the RD elements, (1) mental health, (2) daily living habits, and (3) social living conditions are handled by the health promotion departments of municipalities, local public health nurses, and care managers, as well as by those in charge of day care centers, welfare officers, and family members. (4) Health guidance services are handled by the health promotion departments of municipalities and local public health nurses. (5) Care prevention services are handled by public health nurses and care managers at community comprehensive support centers and day care centers. (6) Concomitant injuries and illnesses and (7) medical services are handled by medical institutions (doctors, nurses, etc., including dentists). (8) Care services are handled by care managers and care service providers. (9) High-risk drugs are handled by doctors and pharmacists.

[0179] Regarding element O, (1) major injuries and illnesses will be identified by medical institutions, (2) physical and mental conditions will be identified by public health nurses and care managers, and (3) the need for nursing care will be identified by care managers and nursing care facilities, etc.

[0180] Based on the above correspondence, by clearly indicating the occupations (and even the names of the people in charge) and the types of institutions (and even the names of the institutions) that should respond to the list of priority targets for care prevention measures and the notable risks on the individual target records shown in Figure 17 above, it may be possible to contribute to the promotion of true collaboration between medical care, nursing care, etc., in which all occupations and institutions involved in community-based integrated care participate.

[0181] Needless to say, these correspondences can also be applied to other areas of community-based integrated care in Figure 25 (nursing care (support for independence and prevention of worsening of condition) and medical and nursing care collaboration (hospital admission and discharge and home medical care)).

[0182] The above explanation has been given of the basic contents of the present invention, and will be referred to as the first embodiment. An embodiment in which each function of the first embodiment has been improved and developed will be described below as the second embodiment.

[0183] In promoting preventive care measures for the elderly, it is important to comprehensively understand the condition of the elderly based on medical and nursing care data, etc., and for this purpose, an "elderly condition image model" has been proposed, which has been explained in detail in the first embodiment using Figures 4, 5, etc. This "elderly condition image model" is not limited to the configuration shown in Figure 4, but may also have the configuration shown in Figure 26.

[0184] The elderly condition image model 13 in the second embodiment shown in Figure 26, like the elderly condition image model 13 in the first embodiment shown in Figure 4, is composed of a major injury / illness data section 131 that holds data on major injuries / illnesses that lead to care, a concomitant injury / illness data section 132 that holds data on concomitant injuries / illnesses that cause them, a stage setting section 133 that sets an elderly condition phase Φ that represents each stage including before and after the occurrence of these (i.e., whether or not they occur), and a related element section (also called an RDO element section) 134 that holds data on related elements (RDO elements) consisting of injury / illness risk R (Risk) elements, defense D (Defense) elements, and result O (Output) elements.

[0185] The major illness etc. data section 131 in Figure 26 holds data on four illnesses etc. that are major illnesses etc. that lead to nursing care: dementia (degenerative, vascular, treatable dementia etc.), stroke (cerebral infarction, cerebral hemorrhage etc.), frailty (physical, social, mental), and femoral fracture. Note that the reason for including frailty as a major illness etc. is that, of the four illnesses etc. mentioned above, frailty is a condition other than an illness that cannot be specified on a medical receipt, i.e., is treated as another condition, but because it is a condition that leads to nursing care, frailty is treated as a major illness etc. along with the other three major illnesses.

[0186] The accompanying illness data section 132 shown in Figure 26 stores data on accompanying illnesses that are the cause of the above-mentioned major illnesses, such as sleep apnea syndrome, diabetes, hypertension, dyslipidemia, arteriosclerosis, atrial fibrillation, oral frailty (oral disorders), aspiration pneumonia, sarcopenia, osteoarthritis, depressive state / depression, osteoporosis, and chronic kidney disease.

[0187] In the stage setting unit 133 shown in FIG. 26, six phases Φ1 to Φ6 are set as elderly condition phases Φ. Here, a certain process is recognized for each major injury or illness that leads to nursing care and the occurrence, worsening, and improvement of the associated injury or illness. In the elderly condition model, based on medical knowledge, the process from a normal state to a state requiring nursing care for each major injury or illness is organized into six phases Φ1 to Φ6 as described above. These are explained below.

[0188] Φ1: Normal state The results of health checkups (blood tests, etc.) are good and there is no risk of any concomitant injuries or illnesses. Φ2: High risk of incidental injuries and illnesses (multiple causes of injuries and illnesses assumed) Although the incidental injury or illness has not yet occurred, abnormalities have been found in health checkup results, etc., increasing the risk of the incidental injury or illness occurring. Φ3: Occurrence of incidental injuries (low risk of occurrence of major injuries, etc.) Although one or more types of incidental injuries or illnesses have occurred, the results of tests and diagnoses indicate that all are still minor, and the risk of developing major injuries or illnesses is low. Φ4: Concomitant injuries and illnesses become more severe (high risk of major injuries and illnesses occurring) Regarding the accompanying injury or illness, the results of tests and diagnoses have revealed that the condition has worsened or has continued for a long period of time, and there is an increased risk of developing a major injury or illness that will require nursing care in the future. Φ5: Major injury or illness occurs (mild care required) A major injury or illness that may require nursing care has occurred, but examination and diagnosis results show that the condition is still mild, so the individual remains in a state requiring support and there is a possibility of improvement through preventive nursing care measures, etc. In the case of dementia or frailty. Φ6: Major injury or illness worsens (severe need for care) This is the stage where the major injury or illness has worsened and the patient is in a state of requiring severe nursing care. In addition to the worsening of dementia or frailty, there are injuries or illnesses such as stroke or femur fractures that can suddenly lead to a state of requiring severe nursing care.

[0189] Of the above-mentioned phases Φ1 to Φ6, phases Φ1 to Φ5 are basically the same as phases Φ1 to Φ5 in the first embodiment, with phase Φ6 being considered as a new addition.

[0190] The RDO element section 134 shown in Fig. 26 holds data on items such as basic attributes (age, sex, etc.), stress, daily living habits, social living environment, health guidance services, preventive care services, medical services, care services, and PIM (an abbreviation for Potentially Inappropriate Medications) as RD elements (risk and defense elements) which are mutually inversely related. Details of these items will be described later.

[0191] In addition, data on internal physical and mental conditions (blood test items, etc.), external physical and mental conditions (physical functions, cognitive functions, etc.), and conditions requiring nursing care (occurring simultaneously with external physical and mental conditions) are stored as O elements.

[0192] Figure 27 shows an image of the elderly condition model 13 expressed in a time series for each elderly condition phase Φ1 to Φ6. The elderly condition phases Φ1 to Φ6 are progression stages leading to the need for nursing care for each major injury or illness, etc. As this elderly condition phase progresses, a concomitant injury or illness occurs as shown in the figure, and then a major injury or illness that leads to nursing care occurs.

[0193] The RD elements mentioned above are divided into items that affect all elderly condition phases Φ1 to Φ6, i.e., items of basic attributes, stress, daily living habits, and social living environment, and items that affect only specific elderly condition phases, i.e., health guidance services (affects Φ1 to Φ4), care prevention services (affects Φ3 to Φ5), medical services (affects Φ3 to Φ6), care services (affects Φ5 to Φ6), and PIM (affects Φ3 to Φ5).

[0194] In the O element, internal mental and physical states are related to all elderly state phases Φ1 to Φ6, external mental and physical states are related to Φ4 to Φ6, and the state of needing care is related to Φ4 to Φ6. The reason for the state of needing care being included in this O element is that when a major injury or illness that leads to needing care occurs, it appears as a change in the external mental and physical states, and this change leads to the state of needing care occurring at the same time.

[0195] When a major injury or illness occurs and characteristic damage occurs in the affected area (tissue), the functions controlled by that tissue are reduced or lost, resulting in changes in the external mental and physical state. For example, in the case of a stroke, the damaged cranial nerve tissue necroses, resulting in a reduction or loss of paralysis and motor functions controlled by that area. When such external mental and physical conditions occur or become severe, they can progress to various types of nursing care needs (conditions in which various types of nursing care are required).

[0196] The mechanism by which changes in this external mental and physical condition are reflected in the state of need for care is as explained in the first embodiment with reference to the primary determination logic in Fig. 12. When an external mental or physical condition occurs or worsens, the condition is recorded in a nursing care certification survey, etc., and these are used as inputs to make a primary determination of the state of need for care, and then a secondary determination. This represents the procedure for nursing care certification under the nursing care insurance system.

[0197] The concept of the elderly condition model presents a new concept based on the concept of the nursing care certification procedure, which states that changes in external mental and physical conditions (conditions of certification survey items, etc.) lead to a state of need for nursing care, and further based on research results that show that changes in external mental and physical conditions occur with the onset and worsening of major injuries and illnesses that lead to need for nursing care.

[0198] Next, we will explain the standard transition image of the elderly person's condition. The standard transition image of the elderly person's condition (hereinafter referred to as the transition image) is a visualization of all transition images of the elderly person's condition, from a normal state (a state in which there is no risk of incidental injuries or illnesses or the need for nursing care) to a state in which nursing care is required after the occurrence of a major injury or illness. We use the term "standard" here because, while there are individual exceptions, the aim is to present a representative transition image for many elderly people. Furthermore, this transition image is important input information for creating the "Elderly Person Status Model Master," which will be described later and is a key support tool for nursing care prevention measures. We also believe that this transition image will be useful for municipalities and medical and nursing care facilities in providing an overview, essential, and basic understanding of the transition of elderly people's condition by major injury or illness.

[0199] First, the overall picture will be explained using Figure 28. This overview focuses on four major injuries and illnesses that lead to nursing care, and provides an overview of the standard progression of each injury or illness from a normal state to a state requiring nursing care. As mentioned above, a "major injury or illness" is a primary injury or illness that leads to nursing care and can be identified on a medical receipt. Major injuries and illnesses are assumed to be dementia, stroke, and femur fractures. Frailty that leads to nursing care is a different condition (a condition other than an injury or illness that cannot be identified on a medical receipt), so it is not included in the major injury or illness. However, since it is a condition that leads to nursing care, if frailty is included, it is treated as a "major injury or illness that leads to nursing care." Furthermore, a "concomitant injury or illness" is an injury or illness that can be identified on a medical receipt and that causes a major injury or illness that leads to nursing care (major injury or frailty).

[0200] In Figure 28, the transition images of dementia and stroke, the transition image of frailty, and the transition image of femoral fracture are shown separated by dashed lines.

[0201] Regarding dementia and stroke, the R factors at the origin shown on the left side of Figure 28 include high stress and poor daily habits, other conditions include obesity, and illnesses include sleep apnea syndrome. These can cause concomitant illnesses 281, including diabetes, hypertension, and dyslipidemia. If these concomitant illnesses 281 continue for a long time, they can lead to neurodegeneration and arteriosclerosis, which in turn increases the risk of dementia 282 and stroke 283.

[0202] First, let's look at dementia 282 in Figure 28. There are many causes of dementia, but we have focused on three typical classifications: degenerative, vascular, and treatable. Typical examples of degenerative dementia 2821 include "Alzheimer's disease," "dementia with Lewy bodies," and "frontotemporal lobar degeneration." Vascular dementia 2822 is caused by injuries such as stroke 283 (cerebral infarction: cerebral thrombosis, cerebral embolism, cerebral hemorrhage: intracerebral hemorrhage, subarachnoid hemorrhage, etc.) and "chronic subdural hematoma." Treatable dementia 2823 means that a fundamental treatment is possible.

[0203] As shown in the figure, degenerative dementia 2821 develops as a result of the accumulation of abnormal proteins (amyloid beta, tau, etc.) from concomitant diseases 281, which leads to neurodegeneration, and then to mild cognitive impairment (MCI), i.e., a prodromal stage of dementia. Among degenerative dementias 2821, Alzheimer's dementia is caused by the long-term accumulation of amyloid beta, tau, etc. in the brain. In dementia with Lewy bodies, alpha-synuclein accumulates. The accumulation of these abnormal proteins changes the function and structure of surrounding neurons, and as described above, progresses to mild cognitive impairment (MCI), a prodromal stage, and if further progressed, leads to degenerative dementia 2821, which interferes with daily life.

[0204] Vascular dementia 2822 is a condition in which cognitive function is lost due to the occurrence of a stroke 283, and details of this will be discussed later.

[0205] The main causes of treatable dementia282324 include "physical injuries and illnesses that cause cognitive decline," "depression and depression," and "prescription-linked medications (PIMs) that require particularly careful administration." Representative examples of "physical injuries and illnesses that cause cognitive decline" include internal diseases such as "hypothyroidism" and "vitamin deficiency," and brain diseases such as "brain tumors," "normal pressure hydrocephalus," and "epilepsy." It has also been reported that some PIMs may lead to cognitive decline.

[0206] Next, we will explain stroke 283. In this transition image, stroke that affects physical functions is considered stroke 283 in the narrow sense, and stroke that affects cognitive functions is considered vascular dementia 2822 as described above.

[0207] Stroke 283 is broadly classified into cerebral infarction 2831 and cerebral hemorrhage 2832. Cerebral infarction 2831 is a pathological condition in which a blood clot accumulates in a blood vessel and blocks the blood vessel, or multiple lacunar infarctions and widespread ischemic changes in the white matter occur due to the occurrence of multiple arteriolar sclerosis. As a result, necrosis and functional decline of brain nerve cells occur.

[0208] This cerebral infarction 2831 can be divided into cerebral thrombosis and cerebral embolism. Cerebral thrombosis occurs when a blood clot forms in a cerebral blood vessel due to high blood pressure or arteriosclerosis, causing blockage. This cerebral thrombosis is more likely to occur in relatively small cerebral blood vessels due to the progression of arteriosclerosis, and is often multi-infarct or small-vessel. Cerebral embolism occurs due to atrial fibrillation caused by arteriosclerosis. Cerebral embolism occurs when a large blood clot from atrial fibrillation breaks off and blocks a relatively large cerebral artery, causing widespread damage. Specifically, arrhythmias such as atrial fibrillation can cause blood clots to form in the heart. A blood clot attached to the wall of the heart can break off and travel through the artery to the brain, causing blockage. Furthermore, part of atherosclerotic plaque in the large cervical vessels can break off and travel to a cerebral artery, causing blockage. When an embolism blocks a relatively large cerebral artery, it can affect a wide area of ​​the brain.

[0209] Cerebral hemorrhage 2832 occurs when blood vessels in the brain rupture due to high blood pressure or other factors, causing blood to overflow and resulting in compression necrosis of the brain parenchyma. This cerebral hemorrhage 2832 can be divided into intracerebral hemorrhage and subarachnoid hemorrhage. Intracerebral hemorrhage is easily caused by the formation and rupture of an aneurysm due to the progression of arteriosclerosis in the basilar artery, which is located at the base of the brain and is important for supplying blood to the brainstem, cerebellum, temporal lobe, and occipital lobe. Subarachnoid hemorrhage occurs when bleeding occurs in an artery in the arachnoid membrane, resulting in sudden bleeding.

[0210] The main causes of intracerebral hemorrhage are thought to be arteriosclerosis and hypertension. Subarachnoid hemorrhage can also develop as an aneurysm when blood vessels become more fragile due to arteriosclerosis, hypertension, smoking, or other factors. This condition occurs when the surface of blood vessels hardens due to arteriosclerosis, and when hypertension causes high blood pressure, an action that further increases blood pressure occurs.

[0211] Normal blood vessels without arteriosclerosis are elastic and can maintain a constant pressure even when strong pressure and blood pressure are applied. On the other hand, arteriosclerotic blood vessels have reduced elasticity, so when pressure is applied, the already weakened blood vessel walls rupture and bleed. Brain compression and necrosis occur around the bleeding site, causing a significant decline in bodily functions.

[0212] Another injury related to cerebral hemorrhage is chronic subdural hematoma (2833). This condition is primarily caused by falls and cannot be classified as stroke (283), nor is it related to arteriosclerosis. However, because hemorrhage causes blood to accumulate under the dura mater and because it is related to vascular dementia (2822), as mentioned above, it is included under stroke (283). Chronic subdural hematoma (2833) is often caused by a head injury, such as a fall, but it can also occur for unknown reasons. When a person with a fall-causing injury or illness trips in a non-barrier-free environment (a fall-risk environment) inside or outside the home, they may lose their footing and fall, hitting their head on the ground, stairs, rocks, etc. When this occurs, bleeding occurs under the dura mater, just inside the skull, resulting in the accumulation of blood. This condition is called chronic subdural hematoma. This chronic subdural hematoma 2833 causes "dementia due to chronic subdural hematoma" that accompanies the above-mentioned vascular dementia 2822 as shown in the figure.

[0213] These strokes 283 and the associated chronic subdural hematomas 2833 cause the vascular dementia 2822 described above, as shown.

[0214] In this way, many strokes (283) progress from concomitant diseases (281) such as hypertension and diabetes to arteriosclerosis, which ultimately leads to stroke (2831). "Arteriosclerosis" can be said to be a risk state immediately before stroke. Below, we will explain the concomitant diseases (281) consisting of diabetes, hypertension, and dyslipidemia, which are the main causes of dementia (282) and stroke (283).

[0215] If the accompanying diseases 281, such as diabetes, hypertension, and dyslipidemia, continue for a long period of time, they can lead to neurodegeneration and arteriosclerosis, as shown in the figure, which in turn increases the risk of dementia 282 and stroke 283. As mentioned above and shown in the figure, there are four main risk factors for the development of hypertension, diabetes, and dyslipidemia: high stress situations (not only mental health issues but also elevated blood pressure), poor daily habits (diet, exercise, etc.), obesity (metabolic disorders, etc.), and sleep apnea syndrome.

[0216] Sleep apnea syndrome is an injury or illness caused by physical changes in the airways, etc., and the effects of low oxygen levels and the resulting lack of sleep place excessive stress on the body. This reduces the function of insulin, which is involved in sugar metabolism, increasing the risk of developing diabetes and high blood pressure. Furthermore, prolonged low oxygen levels place a great strain on the heart and blood vessels, making it more likely to develop myocardial infarction, cerebral infarction, high blood pressure, arrhythmia, etc.

[0217] Arteriosclerosis is a disease in which blood vessel walls harden and lose their elasticity due to the prolonged or severe course of diabetes, hypertension, etc. As this disease progresses, blood clots become more likely to form, which can lead to cerebral infarction and other conditions. Furthermore, arterial walls that have lost their flexibility cannot withstand high blood pressure and rupture, leading to cerebral hemorrhage and other conditions. This can lead to the aforementioned "multiple infarctions," "cerebral embolism," "intracerebral hemorrhage," and "subarachnoid hemorrhage." Transient ischemic attacks occur before multiple infarctions or cerebral hemorrhage. Transient ischemic attacks can also be a risk indicator for arteriosclerosis.

[0218] Next, we will explain frailty 284. As shown in Figure 9, "frailty" includes "physical frailty," "social frailty," and "mental frailty," which are all connected in a series. Figure 28 has bidirectional arrows throughout, showing the interconnections between them. Frailty 284 appears with aging and is located between being healthy and requiring nursing care. What is common to each type of frailty is that it can gradually become more severe from before the need for assistance, or it can return to being healthy (reversible). "Frailty" is not strictly a major injury or illness, as it is not an illness that can be identified on medical receipts, etc. However, because it is a very important condition when considering the condition of the elderly, it has been classified as a major injury or illness in this progression image.

[0219] This section explains the illnesses and injuries that cause physical frailty, one of the 284 types of frailty. There are two possible causes for the onset of "physical frailty": one is "sarcopenia" and the other is "osteoarthritis" of areas related to walking function. While "chronic heart failure" is also a possible cause, in this progression image, "sarcopenia" and "osteoarthritis" are considered standard and will be explained below.

[0220] "Sarcopenia" is a condition that is likely to occur primarily due to a loss of muscle mass caused by "malnutrition" and "poor exercise habits." A lack of nutrition leads to a lack of muscle-building protein, which causes muscle loss. Alternatively, even if you eat well, a lack of exercise can cause a gradual decline in muscle strength, which can easily lead to "sarcopenia." When muscle strength declines, walking function and other functions decline, making it difficult to move the body, climb stairs, or go outside. This can easily lead to "physical frailty."

[0221] One of the main causes of malnutrition is "oral frailty." "Oral frailty" can lead to a decrease in food intake, appetite, and motivation to eat, which can easily lead to malnutrition. When "oral frailty" occurs, the mouth becomes unclean and swallowing ability declines, making it easier to develop "aspiration pneumonia." In addition, a state of "depression" can also lead to a loss of appetite, which can lead to malnutrition.

[0222] Osteoarthritis is a condition that occurs when cartilage wears down with age, causing pain in the joints. If this pain develops in areas that cause walking difficulties, it can reduce motivation to move and activity, and can easily become a factor in "physical frailty."

[0223] Next, we will explain about femoral fracture 285. Femoral fracture 285 is likely to occur when a fall 2852 occurs due to factors such as fall-caused injury or illness 2851 and the "risk environment for falls inside and outside the home," and when bone density is reduced due to "osteoporosis" or the like.

[0224] The 2,851 injuries and illnesses that cause falls are divided into four categories. The first is "physical frailty," and the second is "PIM," which refers to taking medications that may lead to falls as a side effect. The third is "physical injuries and illnesses that cause falls," which are injuries and illnesses that have some kind of adverse effect on motor function or walking function, such as "normal pressure hydrocephalus," "Parkinson's disease," and "progressive supranuclear palsy." The fourth is "dementia." Cognitive decline and delirium are known to impair judgment and attention, increasing the risk of falls.

[0225] The reasons why falls 2852 occur are fall causes (injuries, illnesses, etc.) 2851 and "fall risk environments inside and outside the home." When these two conditions are met, falls 2852 are more likely to occur. "Fall risk environments inside and outside the home" refers to situations inside and outside the home that are not barrier-free, such as environments with steps, no handrails, or objects scattered on the floor.

[0226] After a fall 2852 occurs, there are two paths: one toward a chronic subdural hematoma 2833 and one toward a femur fracture 285.

[0227] Osteoporosis is thought to be a cause of femoral fractures 285. The main causes are thought to be that after menopause, elderly women experience a decrease in female hormones, which leads to bone metabolic disorders, and that the decline in female hormones can lead to worsening diabetes, hypertension, and dyslipidemia, which can worsen chronic kidney disease (CKD), which can also lead to bone metabolic disorders. When such bone metabolic disorders occur, bone density decreases, leading to osteoporosis. Osteoporosis and bone metabolic disorders can make bones brittle, which can lead to fractures when people fall, which is a factor in femoral fractures 285.

[0228] Figure 28 provides a clear image of the standard progression of elderly conditions, allowing us to understand the causes and progression of major illnesses and injuries that lead to nursing care, and serves as important input information for creating the "Elderly Condition Model Master" described below. To achieve this, it is necessary to establish correspondences between the elderly condition phases, RDO elements, and KDB data for identifying the conditions for each target illness and injury shown by each block in this progression image. To visualize these relationships, we will explain the specific content and overview of each legend.

[0229] The contents of each legend are summarized in a diagram in Figure 29. Figure 29 shows the case where each legend is labeled using "diabetes" as an example of an illness.

[0230] In Figure 29, reference numeral 291 represents the O element. As shown in legend 1, O element 291 is one of the following: "Major injury / illness," "Concomitant injury / illness," or "Other condition" that leads to nursing care. A "Major injury / illness" is a major injury / illness that can be identified on a medical prescription and leads to nursing care, and is assumed to include dementia, stroke, and femoral fracture. An "Concomitant injury / illness" is an injury / illness that can be identified on a medical prescription and that causes a major injury / illness that leads to nursing care (major injury / illness or frailty). "Other conditions" are conditions other than injury / illness that cannot be identified on a medical prescription. Frailty is treated as an other condition. As shown in Figure 27, these "Major injury / illness," "Concomitant injury / illness," and "Other conditions" can also be understood from the three conditions of "Internal physical and mental condition (blood test values, etc.)," ​​"External physical and mental condition (physical function, cognitive function, etc.)," ​​and "Need for nursing care (occurring simultaneously with an external physical and mental condition)" in each of phases Φ1 to Φ6.

[0231] In FIG. 29, an RD (risk defense) element part 292, an elderly state phase part 293, and a KDB data part 294 are arranged around the above-mentioned O element part 291, and are labeled with corresponding legends, respectively.

[0232] The RD element section 292 is labeled with the RD elements shown in legend 2. As mentioned above, if the RD element is poor (bad), it becomes an R element, and conversely, if it is good, it becomes a D element that leads to prevention, prevention of aggravation, and improvement. Such elements are shown divided into 10 categories (including detailed categories) as shown in the figure.

[0233] "R1: Basic attributes" are "basic attributes" such as gender, age, genetic information, etc. For example, in the explanation of "osteoporosis," it is written that it is a condition that is common among women. "R2: Stress" is a factor that is likely to occur in relation to life events such as one's own state of mind, the death of a family member, relationships at work, and relationships in the community. "R3: Daily Life Habits" includes eating habits, exercise habits, sleeping habits, drinking habits, smoking habits, and oral hygiene habits. "R4: Social living environment" includes isolation resulting from a lack of communication with others, the availability of consultation facilities, and the local environment, such as the availability of welfare services such as community buses. "R5: Health Guidance Services" includes nutrition, malnutrition guidance, oral care, and adult disease prevention. "R6: Care prevention services" are comprehensive programs that address areas such as exercise, nutrition, oral health, cognition, and depression, which are listed as keywords in the care manual. "R7: Cause of injury, illness, etc." is the "accompanying injury, illness" and "other conditions" shown in "Legend 1." "R8: Medical services" includes medication, medical and dental treatment, specific equipment, etc. "R9: Nursing care services" consists of basic services and additional services for each type of service, and the additional services are considered to contribute to supporting independence, etc. "R10:PIM" is a drug that requires particularly careful administration as it may lead to cognitive decline and falls.

[0234] The elderly condition phase section 293 in Fig. 29 is labeled with elderly condition phases Φ1 to Φ6 shown in legend 3. As shown in the figure, the elderly condition phases divide the elderly condition into six stages, from Φ1, which indicates a normal state, to Φ6, which indicates a state in which the elderly person requires care and has a serious injury or illness, etc., and in which there is room for prevention of the condition from worsening, but it is difficult to return to normal. Details of each state are as described above in paragraph

[0188] .

[0235] In Figure 29, the KDB data section 294 is labeled with the KDB data etc. shown in legend 4. This KDB data section 294 is labeled with KDB data etc. for identifying injuries and illnesses. As shown in the figure, the KDB data etc. is data related to medical care and nursing divided into items A to I, and is used to link target information. The alphabets added to the end of each item in legends 1 and 2 above indicate the linked data item. Note that items A to G are information currently in the KDB, and items H and I are information not currently in the KDB data. Each item A to I will be explained below.

[0236] "A: Medical receipt" is data on the name of the illness, injury, ICD10, medicines, medical and dental treatment procedures, and specific equipment. "B: Medical checkup (testing system)" is the test results from the medical checkup, and includes data such as BMI, blood pressure, albumin, HbAlc, and triglycerides. "C: Daily Life Habits" contains data on diet, exercise, sleep, drinking, and smoking habits. The "D: Questionnaire for the Elderly" is also known as a frailty checkup, and collects data on weight loss, physical function, falls, cognition, and social isolation. "E: Basic Checklist" contains data on daily life, musculoskeletal function, malnutrition, oral function, social isolation, dementia, depression, etc. "F: Nursing care certification" has around 70 to 80 items, including physical and daily living functions such as ADL and IADL, cognitive function and mental and behavioral disorders (BPSD), application to social life, special medical care in home care, and a survey of the actual situation of home care. "G: Nursing Care Receipt" covers basic and additional services for each type of nursing care service (approximately 50 types). "H: Daily Living Area Needs Survey" contains various data such as care burden and economic situation in addition to "E: Basic Checklist." "I: Other data" includes detailed examinations such as those seen in health checkups, health guidance, use of day care centers, and LIFE data.

[0237] Next, we will explain the correspondence between each RDO element shown in Figure 29 and items in KDB data, etc. In the correspondence between the O element (major injury / illness, etc.) in "Legend 1" and data items in KDB data, "internal physical and mental condition" is labeled "A," "B," and "F." "A" refers to "medical receipts," "B" refers to "health checkups (examinations)," and "F" refers to "certification of long-term care needs" in KDB data, suggesting the possibility of identifying injuries and illnesses using these data. Furthermore, for "external physical and mental condition," it is possible to understand the condition using data from "D: Questionnaire for the Elderly," "E: Basic Checklist," and "F: Certification of Long-Term Care Needs," while for "status of long-term care needs," it is possible to understand the condition using data from "F: Certification of Long-Term Care Needs."

[0238] "Concomitant illnesses" utilizes "A" "Medical prescription" and "B" "Health checkup (examination)" data, but as external physical and mental conditions do not generally occur, survey data on frailty is not included. "Other conditions" may also look at other illnesses and injuries (for example, dental treatment for oral frailty), and "A" "Medical prescription" may also be used. Furthermore, "B" "Health checkup (examination)" data is supplemented with "D: Questionnaire for the elderly" and "E: Basic checklist."

[0239] In the correspondence between RD elements and items in KDB data, etc., in [Legend 2], "R1: Basic Attributes" is not labeled because it can be obtained from various data. For "R2: Stress," "E: Basic Checklist," "H: Daily Life Area Needs Survey," and "I: Other Data" are used, as they are derived from events related to family, workplace, community, etc. in life. For "R3: Daily Life Habits," several items are available in the KDB data, but we use "B: Health Checkups (Tests)" from the KDB data and "H: Daily Life Area Needs Survey" from outside the KDB data. For "R4: Social Living Environment," "Isolation" can be addressed using "H: Daily Life Area Data Survey," but "Homeboundness" is included in "D: Questionnaire for the Elderly," and related data is also available in "E: Basic Checklist" and "F: Nursing Care Needs Certification." For "R5: Health Guidance Services," while there is some data related to "A: Medical Receipt," we primarily use "I: Other Data Not in the KDB Data." "R6: Preventive care services" also mainly uses "I: Other data not in KDB data," but there is also some related data (preventive benefit services) in "G: Care receipts." "R7: Causes of injuries, illnesses, etc." uses "A: Medical receipts" and "B: Health checkups (examinations)" data. "R8: Medical services" also uses "A: Medical receipts" and "B: Health checkups (examinations)" data. "R9: Care services" uses "G: Care receipts." "R10: PIM" looks at data from "A: Medical receipts."

[0240] In FIG. 29, "diabetes" is used as an example of an illness, and "diabetes" is displayed in the O element section 291. "Diabetes" is an "accompanying illness," and when viewed in the "Elderly Person Status Phase (legend 3)," it has already occurred, so it is in phase Φ3, and the elderly person status phase section 293 is labeled with the label "Φ3" = "Phase 3: Occurrence of Accompanying Illness." Furthermore, as the risk of developing a major illness or the like due to the worsening of the condition is also high, the label "Φ4" = "Phase 4: High risk of developing a major illness or the like" is also labeled.

[0241] In addition, in the "RD element (legend 2)", "R8: Medical service" is selected on the assumption that medical service is being received because the patient is already in an injury or illness state, and "R8" is labeled in the RD element section 292. This "R8: Medical service" is linked to "A"'s "Medical receipt" and "B"'s "Medical checkup (examination)" data in correlation with "KDB data, etc."

[0242] In the KDB data section 294, the labels of "A" "Medical Receipt" and "B" "Health Checkup (Test)" data in Legend 4 are labeled for the purpose of identifying injuries and illnesses.

[0243] Figure 28 focuses on four major illnesses and injuries that lead to nursing care, providing an overview of the typical progression from a normal state to a state requiring nursing care for each illness and injury. Below, we provide a detailed explanation of each of the four major illnesses and injuries that lead to nursing care (dementia, stroke, frailty, and femoral fracture) based on the matching rules for the target illness and elderly condition phase, RDO elements, and KDB data explained in Figure 29. In particular, for each major illness and injury, we provide an overview of the "internal physical and mental condition (blood test values, etc.)," ​​"external physical and mental condition (physical function, cognitive function, etc.)," ​​and "state requiring nursing care (occurring simultaneously with the external physical and mental condition)." This suggests the possibility of identifying the elderly condition phase and the potential for deterioration or improvement for each major illness and injury that lead to nursing care.

[0244] First, Figure 30 illustrates the progression of dementia, the main illness (degenerative dementia, vascular dementia, treatable dementia). Figure 30 extracts the upper portion of Figure 28, divided by the dashed line, and adds the related fall risk section. Therefore, the same reference numerals are used for parts corresponding to Figure 28, and redundant explanations will be omitted. Here, we will focus on "degenerative dementia," "vascular dementia," and "treatable dementia," and explain the details of the illnesses associated with each. Note that the condition is progressive (continuously worsening without improvement). Dementia diagnosis criteria include not only a medical perspective, such as cognitive decline, but also a state in which the individual is unable to care for themselves (requiring care from others). If cognitive decline persists but the individual remains independent, the condition is classified as mild cognitive impairment (MCI).

[0245] In Figure 30, the standard progression of the condition of an elderly person from the starting point on the left side of the figure to each dementia, namely, degenerative dementia 2821, vascular dementia 2822 (dementia caused by chronic subdural hematoma is treated as the same category if it affects cognitive function areas, etc.), and treatable dementia 2823, is as explained in Figure 28.

[0246] These dementias 2821, 2822, and 2823 are labeled as elderly condition phases in legend 3, with Φ5: occurrence of major injury or illness, Φ6: worsening of major injury or illness, and as RD elements in legend 2, with R8: medical services and R9: nursing care services. As KDB data, etc. in legend 4, they are labeled as A: medical prescription, B: health checkup (examination), D: questionnaire for the elderly, E: basic checklist, and F: nursing care certification.

[0247] Degenerative dementia 2821 includes Alzheimer's disease, dementia with Lewy bodies, and frontotemporal lobar degeneration. However, all of them are progressive, and different symptoms occur depending on which part of the brain is affected. Among the 70 to 80 items in "F: Nursing Care Certification" in "Legend 4 KDB Data, etc.", there are items such as BPSD, short-term memory, and visual hallucinations, so it is possible to pick these up as symptoms.

[0248] As shown in the figure, Alzheimer's disease is an internal mental and physical condition characterized by the accumulation of amyloid beta protein and tau in the cerebral cortex, which leads to the degeneration and necrosis of nerve cells in the hippocampus and cerebral cortex. The external mental and physical condition begins with impairment of short-term memory and orientation, which, as it worsens, progresses to a general decline in cognitive function. In this case, as the symptoms gradually progress, the state of need for nursing care gradually worsens and progresses from needing assistance.

[0249] In Lewy body dementia, the internal psychosomatic state is characterized by the appearance of Lewy bodies in a wide area of ​​the brain, including the cerebral cortex. The external psychosomatic state is characterized by visual cognitive impairment and a noticeable decline in visual ability, such as a decreased sense of direction, as well as visual hallucinations, REM sleep behavior disorder, and parkinsonism. It is a progressive condition that gradually progresses, leading to a need for nursing care.

[0250] Frontotemporal lobar degeneration (FTD) is an internal psychosomatic condition characterized by atrophy of the frontal and temporal lobes, with accumulation of TDP43 and tau in these areas. External psychosomatic conditions include prominent changes in personality, behavior, and language function. It is a progressive condition that gradually increases the need for nursing care.

[0251] Vascular dementia 2822 is divided into dementia caused by cerebral thrombosis / embolism and dementia caused by cerebral hemorrhage. Cerebral thrombosis and cerebral embolism are dementias caused by environmental disorders in the brain.

[0252] Cerebral embolism can be classified as "multiple infarct" or "single lesion," in which infarction occurs in an area important for cognitive function. Multiple "small vessel" lesions, known as lacunar infarctions, can also occur. In "multiple infarct" or "small vessel" dementia, as shown in the figure, cerebral infarction and lacunar infarction frequently occur as internal physical and mental conditions. Furthermore, external physical and mental conditions often include a decline in frontal lobe function, such as decreased motivation and impaired executive function, and neurological symptoms related to the site of cerebral infarction. Furthermore, the state of need for nursing care often progresses in stages from needing assistance.

[0253] In "single-lesion" dementia, the internal physical and mental state is characterized by cerebral infarction in areas important for cognitive function, such as the thalamus and hippocampus. The external physical and mental state is characterized by memory impairment and loss of motivation. The state of need for nursing care may improve over time.

[0254] Dementia caused by cerebral hemorrhage includes dementia caused by "intracranial hemorrhage" and "subarachnoid hemorrhage." The internal mental and physical state is necrosis of the brain parenchyma due to bleeding, and the external mental and physical state is a decline in cognitive function related to the area necrosed by bleeding. As symptoms and daily living activities rapidly worsen, the state of need for care rapidly progresses to severe need for care and increased care work.

[0255] "Dementia due to chronic subdural hematoma," caused by a chronic subdural hematoma 2833 due to a fall 2852, is characterized by the internal psychosomatic condition of brain parenchyma compression caused by the subdural hematoma, and the external psychosomatic condition of cognitive decline caused by the hematoma. The level of need for nursing care may gradually increase from needing assistance to a more severe state. It is also reversible, as recovery is possible if the hematoma in the dura can be removed surgically.

[0256] In treatable dementia 2823, the internal mental and physical state is a decline in neurotransmission, and the external mental and physical state is a decline in cognitive function. The state of needing nursing care gradually worsens from the state of needing assistance, but it is reversible in the sense that it can be cured if the underlying illness or injury (brain tumor, hypothyroidism, vitamin deficiency, normal pressure hydrocephalus, epilepsy, etc.) is cured.

[0257] Next, we will explain the progression of stroke (cerebral infarction, cerebral hemorrhage), a major illness, using Figure 31. Figure 31 is Figure 30, omitting degenerative dementia 2821 and related pathways. The same reference numerals are used to designate parts corresponding to Figure 30, and redundant explanations will be omitted. Note that Figure 31 also includes detailed legend labels for the O element described in Figure 29 for the accompanying illnesses 281 ("diabetes," "hypertension," and "dyslipidemia"), their underlying conditions ("high stress," "poor lifestyle habits," "obesity," and "sleep apnea syndrome"), and the precursors to stroke 283 ("arteriosclerosis" and "atrial fibrillation"), with the RD element, elderly condition phase, and KDB data sections at the center. Specifically, while "Legend 3: Elderly Condition Phase" is listed at the top, the two cases indicate mild and severe cases. Labeling is performed according to the following rule: the bottom left corresponds to "Legend 2: RD element," and the bottom right corresponds to "Legend 4: KDB data, etc." In addition, in the above-mentioned Figure 30, labeling of minor injuries and illnesses other than these main injuries and illnesses is omitted.

[0258] Figure 31 shows vascular dementia 2822 and "dementia due to chronic subdural hematoma," which are conditions that affect physical functions due to stroke (cerebral hemorrhage) and are shown as being directly related to stroke.

[0259] The term "stroke 283" here refers to cases where the brain nerve cells around the infarcted or hemorrhagic area are responsible for physical functions. Stroke 283 includes cerebral infarction 2831, cerebral hemorrhage 2832, and chronic subdural hematoma 2833.

[0260] Cerebral infarction 2831 includes cerebral thrombosis and cerebral embolism, and as an internal physical and mental state, small to medium cerebral infarction occurs frequently. As an external physical and mental state, the physical functions related to the necrotic area decline. The state of need for nursing care can rapidly worsen, but can sometimes improve with recurrence prevention and rehabilitation.

[0261] Cerebral hemorrhage 2832 includes intracerebral hemorrhage and subarachnoid hemorrhage, and the internal physical and mental conditions of these include compression and necrosis of the bleeding site and the surrounding brain parenchyma. The external physical and mental conditions include a decline in physical function related to the necrotic site. The state of need for care rapidly progresses to severe need for care, making care essential.

[0262] The internal physical and mental state of chronic subdural hematoma 2833 is compression of the brain parenchyma due to the subdural hematoma. The external physical and mental state is a decline in physical function related to the necrotic area. The state of need for nursing care gradually becomes more severe from needing assistance, but there is also the possibility of improvement (reversibility).

[0263] Next, an image of the progression of frailty, one of the major illnesses, is explained using Figure 32. Figure 32 shows the portion related to frailty 284 in the center left portion of Figure 28, separated by the dashed line, and the same reference numerals are used for portions corresponding to Figure 28. Figure 32 shows in more detail the illnesses that cause "sarcopenia" and "osteoarthritis," which are among the factors that lead to the onset of frailty 284, and labels each based on the rules for associating the target illness with the elderly condition phase, RDO elements, KDB data, etc., explained in Figure 29.

[0264] For example, one of the causes of sarcopenia, malnutrition, is associated with difficulty eating and decreased appetite, which can also be caused by oral frailty. Specific examples of oral frailty include reduced swallowing function, poor oral hygiene, decreased number of teeth, decreased speech, and decreased bite force. Furthermore, oral frailty has been shown to be associated with aging, household structure, and poor oral hygiene habits.

[0265] In Figure 32, frailty 284 includes "physical frailty," "mental frailty," and "social frailty," as mentioned above, and each of these frailties is labeled based on the rules for associating the target injury or illness with the elderly condition phase, RDO elements, KDB data, etc., as explained in Figure 29. Also, although not shown, common to each frailty type is that the internal mental and physical state manifests with "aging" and is positioned between health and the need for nursing care. The state of needing nursing care gradually worsens from before the need for assistance, but it can also be restored to health (reversible) by reducing the R elements, etc.

[0266] On the other hand, as shown in the figure, external physical and mental conditions include "physical frailty," which manifests as a decline in walking, motor function, and muscle strength, as well as malnutrition, "mental frailty," which manifests as depression, loss of motivation, and decline in cognitive function, and "social frailty," which manifests as social withdrawal and decreased social interaction. Thus, frailty 284 develops with age, occupying a position between being healthy and needing care, and common to all frailties is that it can gradually worsen from before the need for assistance, or it can return to being healthy (reversible).

[0267] Here, we will explain an example of a negative chain reaction between the above three types of frailty, starting from physical frailty. As shown in the figure, "sarcopenia" and "osteoarthritis" lead to "physical frailty," which in turn leads to a loss of motivation (mental frailty), which in turn leads to isolation and a loss of opportunities to communicate, resulting in "social frailty," which in turn leads to a decline in cognitive function and other factors, further worsening the "mental frailty," creating a negative chain reaction.

[0268] Next, the image of the progression of the main injury or illness, femur fracture, will be explained with reference to Figure 33. Figure 33 shows an image of the progression from fall-caused injury or illness 2851 at the bottom of the figure, separated by a dashed line in Figure 28, via fall 2852 to femur fracture 285, as well as an image of progression due to other factors, and parts corresponding to Figure 28 are given the same reference numerals. Figure 33 focuses on the flow of the above-mentioned progression image of "bone metabolic abnormality," "osteoporosis," and "fall-caused injury or illness" leading to the above-mentioned femur fracture 285, and labels each block representing an injury or illness based on the association rules explained in Figure 29.

[0269] The internal physical and mental state of a femoral fracture 285 is a fracture of the femoral neck, etc., as shown in the figure. The external physical and mental state is that walking function may suddenly and significantly decline, leading to a bedridden state, and the state of need for care may rapidly shift to a severe level of need for care and an increase in the amount of care required.

[0270] As explained above in Figures 28 to 33, the transition process of an elderly person from a normal state to each state of a major injury or illness becomes clear. Then, a system is constructed on a computer in which each injury or illness in this transition process is labeled based on the rules explained in Figure 29 and associated with related data. This makes it clear which injury or illness occurs in which phase of the elderly condition image phases Φ1 to Φ6 and which RD element it corresponds to, and also makes it possible to know which KDB data, etc. should be used to identify the injury or illness.

[0271] Next, we will explain the process of creating an elderly person condition model master using the standard transition image of elderly person conditions described above. Up until now, we have proposed the "elderly person condition model" as a method for comprehensively understanding the condition of elderly people based on medical and nursing care data for the elderly. In order to apply this elderly person condition model to actual KDB data, etc. and provide the analysis results to municipalities, etc., it is necessary to link the target injury / illness and RDO elements based on the concept of the elderly person condition model to KDB data, etc. This linking process corresponds to the process of creating an "elderly person condition model master (= target injury / illness correspondence table by RDO element by KDB data, etc.)." This creation process is explained below using Figure 34.

[0272] In Figure 34, to create target injury / illness correspondence table by RDO element by KDB data etc. 340 that links the above-mentioned KDB data etc., RDO elements, and target injury / illness, a correspondence table between RDO elements and target injury / illness = target injury / illness correspondence table by RDO element 341 is created, and a correspondence table between KDB data etc. and RDO elements = RDO element correspondence table by KDB data etc. 342 is created. These two are matched using the RDO elements as common key information to create target injury / illness correspondence table by RDO element by KDB data etc. 340.

[0273] To create this target injury / illness correspondence table by RDO element 341, the target injury / illness list 345 and the RDO element list 346 are made to correspond with each other, with reference to the aforementioned standard transition image of the elderly condition (explained in Figures 28 to 33) and the treatment guidelines by target injury / illness 344, which is a compilation of medical evidence. Similarly, to create the RDO element correspondence table by KDB data, etc. 342, the KDB data, etc. information list 347 and the RDO element list 346 are made to correspond with each other.

[0274] In Figure 34, the standard transition image 343 of the elderly person's condition visualizes the transition from a normal elderly person's condition to a state requiring nursing care according to the major injury or illness that leads to nursing care, as explained using Figures 28 to 33. This standard transition image 343 of the elderly person's condition is used as reference material for creating the target injury or illness list 345, the RDO element list 346, and the target injury or illness correspondence table by RDO element 341. Note that when the target injury or illness correspondence table by RDO element 341 is revised due to changes in the medical treatment guidelines by target injury or illness 344, this image 343 is also updated in sync with the content.

[0275] The list of target injuries and illnesses 345 is a list of major injuries and illnesses, incidental injuries and illnesses, and other conditions that lead to nursing care based on the standard transition image 343 of the elderly condition described above in Figures 28 to 33, and is input information required to create the target injury and illness correspondence table 341 by RDO element shown in Figure 34. This list of target injuries and illnesses is shown below as Table 1.

[0276] Table 1 corresponds to a standard image of the progression of elderly people's conditions and first lists dementia (degenerative, vascular, treatable, etc.), stroke (cerebral infarction, cerebral hemorrhage), frailty (physical, social, mental), and femoral fractures as major illnesses that lead to nursing care. Next, it lists concomitant illnesses that may cause major illnesses, such as diabetes, hypertension, dyslipidemia, atrial fibrillation, osteoporosis, aspiration pneumonia, sleep apnea syndrome, depression, normal pressure hydrocephalus, and Parkinson's disease. Furthermore, it lists obesity, mild cognitive impairment, oral frailty, and other conditions.

[0277] The top row of Table 1 lists major illnesses, etc. that lead to nursing care horizontally, and these columns are marked with a black star mark ★ and a black circle mark ● that indicate the correspondence with the major illness, etc. A black star mark ★ indicates that the corresponding illness, etc. is a major illness, etc., and a black circle mark ● indicates that the corresponding illness, etc. is a concomitant illness or other condition that causes the major illness, etc., and the major illness, etc. [Table 1]

[0278] Injuries and illnesses that are not covered by the elderly condition model include cancer, acute myocardial infarction, chronic obstructive pulmonary disease (COPD), and chronic kidney disease (CKD). These are not injuries or illnesses specific to the elderly, and because they are not considered to be major injuries or illnesses that lead to nursing care or secondary injuries or illnesses that cause them due to the RDO elements assumed in this model, they are, in principle, excluded from the scope of the elderly condition model.

[0279] The RDO element list 346 in Figure 34 is a list of RDO element items that affect the deterioration, improvement, and maintenance of the condition of elderly people, along with their classification and overview. In other words, it is a list in which RDO elements are defined in large, medium, and small classifications. As mentioned above, RD elements are classified into D elements and R elements depending on whether the condition of the same item is good or bad. RDO element codes, etc. are also defined here. This RDO element list 346 is the input information required to create the target injury / illness correspondence table by RDO element 341 and the RDO element correspondence table by KDB data, etc. 342. The data structure of this RDO element list 346 is shown in Table 2 below. [Table 2]

[0280] Table 2 above is basically the same as that shown in Figures 11(a) and 11(b) in the first embodiment, and in the "major category" it is classified according to which of the RDO elements it corresponds to. In this case, R elements and D elements are combined into one, as they have an opposite relationship. In the "medium category" column, the element names of the RDO elements (such as "stress" and "daily life environment") are defined by arranging them vertically. In the "minor category", the RDO elements are further classified and the element names are defined. For example, if the "medium category" is "stress", the "minor category" would be "purpose in life situation" and "care burden situation", etc. Furthermore, in the "RDO element summary" column to the right, a summary of the elements corresponding to the "minor category" is defined.

[0281] Looking at the R element, for example, the minor categories of the medium category "basic attributes" include gender, age, genetic information, etc. Also, looking at the minor category of the medium category "stress" for "purpose in life situation," the summary is defined as "whether or not one's own life is worth living." Similarly, minor categories and their summaries are defined for each of the medium categories of "daily life habits," "social living environment," "health guidance services," "preventive care services," "causes of injury, illness, etc.", "medical services," "care services," and "PIM."

[0282] As mentioned above, D elements are inversely related to R elements. For example, for "stress," satisfaction, self-efficacy, self-affirmation, and well-being (happiness) correspond to D elements. For each of the other items, the opposite state to R elements corresponds to R elements. For good lifestyle habits, social living environment, health guidance and preventive care services, and medical and nursing care services, the use of each service, and for medical and nursing care services, high quality, each correspond to D elements.

[0283] In this way, R and D factors are inversely related, and can become either an R or D factor depending on whether lifestyle habits are poor, whether medications are being taken appropriately, whether high-quality medical and nursing care services are being received, etc. For example, if the relevant item in the KDB data has a code that indicates degree, if the code is below a certain level it is judged to be a risk factor, and if it is above a certain level it is judged to be a defensive factor.

[0284] Regarding the O element, there are the intermediate categories of "major injury or illness" - "internal physical and mental state," "external physical and mental state," and "state requiring nursing care," and subcategories and overviews for each of these are defined. "Internal physical and mental conditions" include health checkup results, presence or absence of injury or illness, severity of injury or illness, medication, medical treatment, use of specific equipment, medical institutions, etc. "External physical and mental conditions" include physical function, daily living function, cognitive function, BPSD (behavioral behavioral disorder), social adaptation, etc. "Need for care" includes level of need for care, standard time for each category of activity, etc.

[0285] The treatment guidelines by target injury and illness 344 in Figure 34 are treatment guidelines targeting the injuries and illnesses in the list of target injuries and illnesses 345, and are a compilation of evidence that serves as a basis for treatment obtained through clinical and epidemiological research, etc. These treatment guidelines by target injury and illness 344 are used to associate RDO elements with each target injury and illness and to create a target injury and illness correspondence table by RDO element 341.

[0286] As mentioned above, the target injury / illness correspondence table by RDO element 341 corresponds target injuries / illnesses by RDO element, and an initial draft (hypothetical version) is first created based on the standard progression image of elderly condition 343, and then the treatment guidelines by target injury / illness 344 are read in to update this correspondence table (including evidence correspondence and evidence strength). This target injury / illness correspondence table by RDO element 341 is the input information required to create the correspondence table by RDO element by KDB data, etc. 340 in Figure 34. Table 3 below shows the target injury / illness correspondence table by RDO element 341, and shows an image of the correspondence of target injuries / illnesses by RDO element. [Table 3]

[0287] Table 3 above is essentially the same as that shown in FIG. 13 in the first embodiment, with 10 RD elements and 3 O elements arranged on the vertical axis and four major illnesses, etc., that lead to nursing care arranged on the horizontal axis, with the correspondence displayed at their intersections. For example, if the major illness, etc., that lead to nursing care is "1. Dementia (degenerative dementia, vascular dementia, treatable dementia, etc.)," ​​all RD elements except "health guidance services" correspond. Of these, "cause illness, etc." corresponds to diabetes, dyslipidemia, and depression. Furthermore, "PIM" corresponds to medications related to cognitive decline. Furthermore, the O element "external mental and physical condition" corresponds to cognitive function / BPSD, and "needing nursing care state" corresponds to support required or higher. In this way, a correspondence table with RDO elements is constructed for each major illness, etc.

[0288] This correspondence table maps the RDO elements related to the elderly condition model using the medical treatment guidelines for each target injury / illness 344 in Figure 34 as evidence, but medical treatment guidelines have evidence levels and recommendation grades, and the number of levels and notation differ for each guideline. However, from the perspective of systematization that takes into account the judgment of the quality and presence of RDO elements in the nursing care prevention measure support tool, there is no particular problem in classifying them as set grades for the system. When defining something like an "overall recommendation grade (combining evidence strength and recommendation grade)" with systematization in mind, it is thought that it would be better to prioritize the strength of the recommendation grade over the strength of evidence.

[0289] The KDB data etc. information list 347 in Figure 34 is a list of information on the original data used to output the elderly person's state (RD0 element state, target illness state, etc.) using the elderly person state image master. Table 4 below shows an image of the KDB data etc. information list by data classification (A to I). [Table 4]

[0290] Table 4 is basically the same as that shown in Fig. 14 in the first embodiment. In Table 4, data A to G are all data held in the KDB system. On the other hand, data H and I are not currently held in the KDB system, but are candidates for data that, if incorporated in the future, could contribute to improving the social implementation of community-based integrated care. These are collectively referred to as big data.

[0291] The detailed explanation of Table 4 overlaps with the detailed explanation of Figure 14 in paragraphs

[0088] to

[0094] in the first embodiment, and is therefore omitted here. However, in Table 4, the data classification codes are assigned F (G in Figure 14) to "Certification of Needed Long-Term Care," G (H in Figure 14) to "Long-Term Care Receipt," and H (F in Figure 14) to "Daily Living Area Needs Survey." Therefore, when referring to paragraphs

[0088] to

[0094] for a detailed explanation of Table 4, it is necessary to read these codes differently.

[0292] Additionally, as mentioned above, the "Survey on Daily Living Area Needs (excluding the Basic Checklist)" in H and the "Other Data Not Included in the KDB Data" in I are not currently held by the KDB system, but by incorporating them in the future, they will contribute to improving the social implementation of community-based integrated care. For example, these include various stresses, utilization records of health guidance and preventive care services (day care facilities, etc.), the implementation status of care management by injury or illness, the implementation status of independent living support care, COVID-19 response status, and drug intervention / non-intervention status.

[0293] The RDO element correspondence table 342 by KDB data, etc. in Figure 34 corresponds RDO elements by KDB data, etc. item. In other words, it corresponds the list of KDB data, etc. information shown in Table 4 above by RDO element. This RDO element correspondence table 342 by KDB data, etc. is the input information required to create the target injury / illness correspondence table 340 by KDB data, etc. RDO element in Figure 34. Table 5 below shows an image of the correspondence between RDO elements and KDB data, etc. information. [Table 5]

[0294] Table 5 is basically the same as Figure 15 in the first embodiment, but there are some differences in the item names and number of items of the RDO elements. In Table 5, the vertical axis lists 10 RD elements and 3 O elements, and the horizontal axis lists KDB data items A to I shown in Table 4, and the correspondence is indicated by the presence or absence of a black circle ● at the intersection of these.

[0295] For example, when looking at the RD element, with regard to "stress," the following data are relevant as medical and nursing care data held in the KDB system: B: specific health checkup data, C: standard questionnaire data, D and E: questionnaires for the elderly and basic checklists. Furthermore, when looking at the O element, the following data are relevant as medical and nursing care data held in the KDB system: A: medical prescription data, B: specific health checkup data, D and E: questionnaires for the elderly and basic checklists, F: nursing care certification, G: nursing care receipts. Identifying major illnesses and conditions that lead to nursing care requires a comprehensive assessment of not only medical receipt data (name of illness, medication, etc.), but also health checkup, frailty, nursing care certification, and nursing care receipt data.

[0296] The target injury / illness correspondence table by RDB element by KDB data, etc. 340 in Figure 34 is a correspondence table that matches and unifies three pieces of information (target injury / illness, RDB element, KDB data, etc.) using the RDB elements of the target injury / illness correspondence table by RDO element 341 and the RDO element correspondence table by KDB data, etc. 342 as key information. As shown in Figure 34, this correspondence table is used as an elderly person condition image model master, and is indispensable for the target injury / illness identification process and the elderly person condition phase identification process by major injury / illness, etc. in the care prevention measure support tool.

[0297] Next, we will explain how the nursing care prevention measure support tool, which was constructed based on the elderly condition model explained above, can be used to provide information that can be used in nursing care prevention measures to municipalities and other organizations. This information that can be used in nursing care prevention measures aims to identify people at risk of needing nursing care efficiently and accurately, provide appropriate (potentially effective) information for implementing measures that are focused on those people, and enable measures to be implemented efficiently and effectively even with limited personnel.

[0298] To achieve this, the information must contribute to the improvement of preventive care administration by municipalities, etc., in other words, it must improve the quality of preventive care measures and be cost-effective, enable efficient identification of those at risk of needing care and be expected to have outreach effects, and improve the physical and mental condition of the elderly as a result of preventive care, etc. Furthermore, it is important that the information is easy to understand for municipal staff (not only staff with specialized medical and nursing care knowledge such as public health nurses, but also general staff without such knowledge), and be provided in a form and content that can be used in policies.

[0299] The information provided by the municipality etc. using the nursing care prevention measure support tool is a list of priority target persons 350 shown in FIG. 35 and an individual record of priority target persons 360 shown in FIG.

[0300] The list of priority targets 350 is information that can be utilized by municipalities, etc., for the purpose of selecting targets for priority implementation of nursing care prevention measures, and as shown in Figure 35, for elderly people A, B, C, D, etc., the list evaluates the "elderly state phase" of the progression stage leading to the need for nursing care for each of the four main injuries, illnesses, etc. that lead to nursing care, and the "overall risk assessment" of the risk of the elderly state phase transitioning (advancing or reverting to the progression stage leading to the need for nursing care).

[0301] As shown in Table 6 below, the "Elderly Status Phase" is set to six stages, Φ1 to Φ6, which represent the progression stages leading to the need for nursing care for each major injury or illness. [Table 6]

[0302] As shown in Table 7 below, the "Overall Risk Assessment" sets the overall risk into four levels: ◎: no risk, ○: low risk, △: medium risk, ×: high risk, and an explanation is provided for each of these levels. [Table 7]

[0303] In the priority target list 350 in Figure 35, if the overall risk assessment is marked with an X or a △, there is a risk of progressing to the next phase (worsening), regardless of the elderly condition phase. This list 350 makes it possible to efficiently and accurately determine the selection of outreach targets for all elderly people in the area, even if municipalities and on-site business departments are short-staffed.

[0304] An example of a comprehensive risk assessment of Mr. A and Mr. D, who are considered to require priority measures to be taken, among those in the list of priority targets 350 in FIG. 35, will be described below.

[0305] Case 1: Looking at Mr. A's stroke, he is in phase Φ4, his concomitant injuries and illnesses are becoming more severe, the overall risk assessment is × (high risk), and he also has problems with his daily living habits, so if he continues as he is, he is at high risk of having a stroke. Furthermore, of the four major injuries and illnesses, the overall risk assessment is × for stroke and femoral fracture, and there is a high possibility that he will move on to the next phase, so it is clear that he needs to be given guidance on improving his daily life by a public health nurse and that appropriate medical treatment should be provided by a medical institution as soon as possible.

[0306] Case 2: Mr. D's dementia has developed in phase Φ5 and he is in a state where he requires support, but the overall risk assessment is ×, and appropriate measures have not been taken, so there is a high risk of him becoming in a state where he requires nursing care. Among the major injuries and illnesses, dementia is assessed as ×, and there is a high possibility that he will move on to the next phase, so immediate action is required from the responsible department. Furthermore, the overall risk assessment for stroke, frailty, and femoral fracture is △ (medium risk), and there is a possibility that his condition will worsen and move on to the next phase, so it is clear that the responsible department needs to consider improving its measures and guidance.

[0307] The priority target individual record 360 shown in Figure 36 shows the condition and duration of each RDO element, as well as the risk assessment results, for each major injury or illness, etc., for the elderly people listed in the priority target list 350 shown in Figure 35. Figure 36 is a target individual record for Mr. A's stroke, showing the RDO elements of daily living habits and associated injury or illness conditions.

[0308] In the example of Figure 36, Mr. A's daily habits are particularly problematic, as he has had a bad drinking habit for three years. This leads to the determination that he needs to be instructed to reduce the amount and frequency of his drinking. Furthermore, with regard to "concomitant illness status," his five-year history of diabetes and hypertension, which are concomitant illnesses that can lead to stroke, indicates a high risk (high likelihood of worsening symptoms). This leads to the determination that measures are needed to prevent the worsening of the symptoms of diabetes and hypertension, so as to prevent stroke, which is a major illness. By examining the individual records of priority targets 360 in this way, it is possible to determine which RDO elements of each priority target, such as a major illness, are problematic, and the measures that should be taken for that person become clear.

[0309] By providing the above-mentioned priority target information 305, 306 based on the elderly condition image model, the following effects that contribute to the promotion of care prevention measures are expected.

[0310] The efficiency and accuracy of identifying those at risk of needing nursing care based on major illnesses and injuries will increase, improving the results of nursing care prevention. In other words, health promotion departments in municipalities and public health nurses at community comprehensive support centers will be able to identify with a high degree of accuracy those at risk of needing nursing care based on major illnesses and injuries that could lead to nursing care, and will be able to focus their efforts on them. This will ensure that, even with limited personnel, the number of cases in which people can be prevented from transitioning to a nursing care state for each illness and injury will increase.

[0311] In this way, it is possible to identify those at risk of needing nursing care based on major illnesses, etc., and encourage behavioral changes among the elderly and those in medical and nursing care settings. In other words, it is possible to identify those at risk of needing nursing care based on major illnesses, etc. that lead to nursing care, for each illness, before they progress to a state requiring nursing care, and this can encourage behavioral changes among the elderly, who will feel a sense of satisfaction from being given an explanation of illnesses and nursing care together, and the accompanying sense of crisis, and encourage changes in the way medical and nursing care settings are provided.

[0312] - Promotes collaboration between medical and nursing professionals. In other words, it is possible to clearly identify the professions and related organizations that hold the key to risk avoidance and prevention promotion for each RDO element of each major injury or illness, and by putting this information into a common language, it is possible to activate collaboration between each organization and profession.

[0313] By avoiding the transition to a state requiring nursing care, medical expenses and nursing care costs related to injuries and illnesses that lead to nursing care can be reduced. For example, for each of the four major injuries and illnesses, medical expenses incurred after the need for nursing care (medical expenses at the time of the onset of the injury or illness and when it recurs) and nursing care costs (the cumulative cost over several years of average monthly nursing care benefits according to the level of nursing care required) can be reduced.

[0314] Furthermore, by not only providing information to the health departments and nursing care prevention departments of municipalities, but also clarifying the list of priority targets for nursing care prevention measures and the relevant organizations and professions that should respond to the notable risks in the target individuals' individual records, this will contribute to promoting the participation of all organizations and professions involved in community-based comprehensive care, thereby contributing to the "promotion of true collaboration between medical care, nursing care, etc."

[0315] Table 8 below shows the correspondence between the relevant institutions (doctors, nurses, pharmacists, public health nurses, care managers, etc.) that hold the key to risk avoidance and prevention promotion for each RDO element. For example, if the RD element of health guidance services in the fifth row is poor, it is possible to work with the relevant institution, the health promotion department (public health nurse) of the city, town, or village, to share information on the elderly person's condition and take measures to address the poor condition. Furthermore, if the internal physical and mental condition of the O element is poor, it is possible to work with the relevant institution, the medical institution (doctors, dentists, nurses, etc.), to confirm the elderly person's medical treatment status and precautions for nursing care prevention efforts. [Table 8]

[0316] Next, the data processing process for creating the information on the priority target person list 350 and the priority target person individual record 360 to be provided to municipalities and the like using the care prevention measure support tool will be described with reference to FIG.

[0317] Figure 37 shows the overall flow of data processing in the process of creating information to be provided to municipalities, etc. This data processing is carried out roughly along four steps (functions), and creates a priority target person list 350 and a priority target person individual record 360 shown in the lower right of the figure from KDB data provided by the KDB system 371 on the left side of the figure.

[0318] In step 1, the analytical DB basic function F1 is executed, and the data required for analysis (analysis target data) is extracted from the KDB data provided by the KDB system by the analysis target data import process F11. This analysis target data import process F11 selects the target data to be analyzed and imports it into the analytical DB: D11.

[0319] In step 2, the elderly condition data creation function F2 is executed, and the condition of the elderly person's target injury or illness or RDO element (○ good, × bad) and the duration of that condition are calculated from the data created in step 1 using the following data processing.

[0320] In the RDO element status identification process F21, status data (○ good, × bad) is created for the data corresponding to each RDO element (lifestyle habits, blood test values, symptoms of frailty or nursing care certification, nursing care services, etc.) according to the data attributes (continuous value, code value, master value). In other words, the above status data is created from the RDO element item data in the analysis DB: D11 and imported into the RDO element status DB: D21.

[0321] In the target injury / illness condition identification process F22, the target injury / illness (main injury / illness, concomitant injury / illness, and other conditions) is identified based on the elderly condition model master M01 from data such as medical receipts, specific health checkups, and nursing care certification, in light of standards such as the guidelines shown in Figure 34. In other words, the target injury / illness is identified from the injury / illness name and medications on medical receipts in the analysis DB: D11 and the survey information for nursing care certification, and then imported into the target injury / illness condition DB: D22. As explained in Figure 34, the elderly condition model master M01 is a master that links three pieces of information, such as the target injury / illness, RDO elements, and KDB data, and serves as key information for identifying the target injury / illness, phase, and risk.

[0322] In the condition duration calculation process F23, the period during which the target condition of the RDO element continued and the time when the condition changed are determined from the elderly condition data created in the RDO element condition specification process F21 and the target condition specification process F22, and these are entered into the elderly condition basic DB: D23.

[0323] In the function F3 for identifying the elderly condition by major injury or illness in step 3, the elderly condition phase identification process F31 identifies the elderly condition phase for each elderly person from the elderly condition data created in step 2, and the elderly condition risk identification process F32 performs a risk assessment for each RDO element (whether the lifestyle is good, whether appropriate care prevention services are being received, etc.) regarding the risk of the condition worsening further, and then makes a comprehensive judgment on the risk of needing care by major injury or illness. That is, the elderly condition phase identification process F31 identifies the phase by referencing the elderly condition image model master M01 and the elderly condition phase identification condition master, and the elderly condition risk identification process F32 performs a process for determining the risk level by major injury or illness for the elderly condition identification DB: D31.

[0324] In step 4, the priority target output function F4 is executed. That is, the results of step 3 are output as the priority target list output process F41 and the priority target individual record output process F42 via the priority target output DB: D41 so that city, town, and village officials can use them as policies.

[0325] Figure 38 shows various data generated in the above-mentioned data processing, corresponding to Figure 37. For each data to be analyzed (medical / nursing care receipts, specific health checkup results, etc.), the status value of various status data (for each RDO element item, the status is identified as either ○ good or × poor) and the status duration (the duration from the earliest date for which start data for that status exists) are calculated through data processing.

[0326] In Figure 38, the analysis database: D11 stores data that has been narrowed down from the KDB data by the analysis target data import process F11 to reduce data volume and improve performance, and processed into seven categories: A: medical receipt data, B: specific health checkup data, C: standard questionnaire data, D: late-stage elderly questionnaire data, E: basic checklist data, F: nursing care certification data, and G: nursing care receipt data. In other words, the analysis database: D11 is a database that stores only data extracted from the KDB data that is actually meaningful for analysis. Data selection criteria include the need for systemic and operational analysis, comprehensiveness (data entry ratio), history retention period, and priority of the target injury or illness.

[0327] In the RDO element status DB: D21, the RDO element status identification process F21 identifies the quality of test results, symptoms, and nursing care services from the analysis target data other than A out of the seven categories of analysis data, and stores them as status values. In other words, the RDO element status DB: D21 is a database of data converted from the RDO element item data of the analysis DB: D11 into status data, and is used to create the elderly person status basic DB: D23, which will be described later. Examples of status data conversion include RD allocation based on thresholds for code values ​​such as nursing care certification, and the usage status of basic and additional services by service type for nursing care receipts, etc.

[0328] In the target injury / illness condition DB: D22, the target injury / illness condition identification process F22 references the elderly condition model master M01 and uses data A (name of injury / illness, medicines, medical treatment, etc.) and the condition data B, D, E, and F to identify whether the target injury / illness applies, and stores this as the target injury / illness condition value. In other words, this is a database related to the condition of the target injury / illness created using the medical receipts (name of injury / illness, ICD10, medicines, medical treatment, specific equipment, etc.) in the analysis DB: D11, the RDO element condition DB: D21, and the master M01 for identifying the injury / illness.

[0329] The elderly person condition basic DB: D23 stores the test values, symptoms, and nursing care service status (good, poor) calculated by the condition duration calculation process F23, as well as the duration of the target illness or injury condition. In other words, the elderly person condition basic DB: D23 is a database related to elderly person condition data that combines the RDO element condition DB: D21 and the target illness or injury condition DB: D22. In addition, condition duration data resulting from the condition duration calculation process is also stored.

[0330] The elderly condition identification DB: D31 shown in Figure 37 performs the elderly condition phase identification process F31 and the elderly condition risk identification process F32 to identify the appropriate phase for each major illness or injury of an elderly individual on the data of the elderly condition basic DB: D23, and stores the identified nursing care risk assessment results.

[0331] The priority target output DB: D41, also shown in Figure 37, stores as output data the list of priority targets for nursing care prevention measures and the individual records created by the priority target list output process F41 and the priority target individual record output process F42 from the data in the elderly condition identification DB: D31.

[0332] Next, the data processing F11 to F42 shown in Fig. 37 will be described in detail. As shown in Fig. 39, the analysis target data import processing F11 refers to the analysis target condition master M11, which will be described later, and selects data necessary for elderly person state image data from the KDB data, and stores the data in the analysis DB: D11. Because the KDB data has a large number of items, the analysis target condition master M11 manages only the data necessary for the elderly person state image model and analysis, with an eye toward improving analytical performance, etc. In other words, the analysis target condition master M11 is a master that sets an import target determination flag in the analysis DB: D11 for each data item of the KDB data, and determines the data to be imported.

[0333] 40, the RDO element status identification process F21 refers to the RDO element status identification master M21, which will be described later, from the data selected by data type in the analysis DB:D11 to identify the RDO element status (good ◯ or poor ×, etc.) and stores it in the RDO element status DB:D21. The element status identification master M21 is a master that determines the status of an elderly person as ◯ (good) or × (poor) for each data item in the analysis DB:D11, and there are three types: specific master 1:M211 for continuous values, specific master 2:M212 for code values, and specific master 3:M213 for care service detail code values.

[0334] Similarly, there are three patterns for the RDO element status specific process F21: specific process 1: F211 for continuous values ​​(blood tests for specific health checkups, etc.), specific process 2: F212 for code values ​​(frailty questionnaires and nursing care certification questionnaires, etc.), and specific process 3: F213 for nursing care service detail code values ​​(nursing care receipts), and each references a different master.

[0335] Figure 41 shows the data processing of the identification process 1 for continuous values: F211. This identification process 1 for continuous values: F211 is a process that targets the specific health checkup data B stored in the analysis DB: D11 shown in Figure 40, and identifies good (◯) or bad (×) from continuous values ​​such as blood test values ​​of the specific health checkup data.

[0336] In order to identify good or bad from continuous value data, specific master 1: M211 has set standard values ​​for determining whether continuous values ​​are good (o) or not (x) as shown in FIG. 41. For example, in the case of item 1, the standard values ​​for determining item 1 in specific master 1: M211 are set as good (o) for 3 or less, and x (x) for greater than 3. The data value of the specific health checkup data B to be determined is "4.2", so it is determined as x (x) not good. This determination result is stored in RDO element status DB: D21 shown in FIG. 40 as the status of item 1 of specific health checkup status data B: x (x) not good. Similar determinations are made for other items.

[0337] However, since it may be difficult to set a clear standard value for this continuous value and divide it into two values, good and bad, it is necessary to verify the standard using actual data based on the investigation and organization of clinical guidelines and health checkup standards.

[0338] Figure 42 shows the data processing of identification process 2: F212 for code values. This identification process 2: F212 is a process for identifying whether a data item is good or bad from data with selectable items, such as a frailty questionnaire. For this process, identification master 2: M212 for code values ​​is used, as shown in Figure 42. That is, to identify whether a data item is good or bad from data with selectable items (1, 2, 3, 4, 5, etc.), such as a frailty questionnaire, a judgment is made by providing a circle (O) (good) or an error (X) (bad) for each selectable item, as in identification master 2: M212. For example, if the data value of the selectable item (code) for item 1 to be judged is "3," and item 1 in identification master 2 is "1" for good and "2" or higher for X (bad), then the data value "3" is identified as X (bad).

[0339] It is thought that it would be difficult to set a clear standard value for this code value and divide it into two values, good and bad, so it is necessary to verify the standard using actual data based on the investigation and organization of clinical guidelines and various administrative documents (nursing care prevention guides, certified investigator manuals, etc.).

[0340] Figure 43 shows the data processing of Identification Process 3: F213 for nursing care service detail code values. This Identification Process 3: F213 is a process for determining whether a service with a nursing care prevention effect is being used based on the presence or absence of additional services on nursing care receipts. To determine whether a service with a nursing care prevention effect is being used, Identification Master 3 (Detail Master): M213 is used. As shown in the figure, Identification Master 3 (Detail Master): M213 has a correspondence for the presence or absence of a nursing care prevention effect for each basic service and additional service used for judgment purposes. For example, if basic service, nutritional management additional charge, and daily living function improvement additional charge are used for nursing care prevention service 1, additional charges that contribute to the necessary improvement and maintenance of physical and mental condition are recorded, so it is determined as ◯ (good).

[0341] However, the effectiveness of care prevention may depend greatly on the quality of the service provider, and it cannot be said that simply using a service means that the effect is high. Therefore, while it is possible to judge the effect of care prevention by whether or not care prevention services are used, in the future it will be necessary to understand the actual state of service use using actual data and verify the effect of care prevention, and to consider how to determine the state of service use that is effective in care prevention.

[0342] The target injury / illness condition identification process F22 shown in Figure 37 is a process that, as shown in Figure 44, refers to the related masters (target injury / illness condition identification master M22, injury / illness name / ICD10 master M23 by target injury / illness, medicine / medical procedure / specific equipment master M24, generic medicine / product name master M25, PIM master M26) for identifying the target injury / illness from the medical prescription data (injury / illness name, ICD10, medicines, etc.) in the analysis DB: D11 and RDB element status DB: D21, and health check and nursing care certification information to identify the target injury / illness, and identifies the target injury / illness, such as the main injury / illness and associated injury / illness, and the risks in the PIM, and stores them in the target injury / illness condition DB: D22.

[0343] The target illness / injury condition identification master M22 is a master that associates, for each target illness / injury, with the name of the illness / injury, medication, medical treatment, and specific equipment in medical data, abnormal test results in health checkup data, and the presence or absence of external physical and mental conditions such as nursing care certification data. The target illness / injury name / ICD10 master M23 is a master that provides information on the name of the illness / injury in medical receipt data and its ICD10 classification for each target illness / injury (main illness, concomitant illness). The medication / medical treatment / specific equipment master M24 is a master that associates, for each target illness / injury, with the medical treatment performed for treatment, the medication administered, and the specific equipment used. The generic drug name / product name master M25 is a master that associates generic drug names with their corresponding product names. The PIM master M26 is a master that associates, for each drug, the presence or absence of functions that may be impaired (e.g., cognitive function, motor function, etc.).

[0344] Table 9 below shows a data combination table that we believe will enable us to identify target injuries and illnesses, such as main injuries and illnesses and accompanying injuries and illnesses. [Table 9]

[0345] The check items for data combinations in Table 9 are the following three points. Check 1: Is there an injury or illness name and ICD10 (or ICD11) on the medical receipt that corresponds to the target injury or illness? · Check 2: Are there any medicines, medical procedures, or specific equipment on the medical prescription that were created based on the guidelines for the target injury or illness? · Check 3: Are there any abnormal blood test results during health checkups or symptoms in the nursing care certification survey that could lead to the need for nursing care?

[0346] Note that Check 3 targets health checkups, which occur at a different time than medical receipts in Checks 1 and 2. Therefore, conditions are set such as limiting the health checkups targeted by Check 3 to information from within the past year, and this information is used as reference information for identifying injuries and illnesses.

[0347] As there are three check items, there are nine possible combinations of data (cases). For example, in case 1, check 1 (presence or absence of target illness / disease name) is correct, check 2 (presence or absence of medicines, medical procedures, or specific equipment) is correct, and check 3 (presence or absence of abnormal test results or symptoms) is correct, which is considered to be a high level of accuracy in identifying the target illness / disease. In case 4, only check 1 is correct, and although only the illness / disease name is correct, it is not possible to determine whether the illness / disease has actually developed, so the target illness / disease is excluded from identification.

[0348] Furthermore, checks 1 and 2 are monthly data from medical receipts, while check 3 is data generated on an annual basis such as health checkups, frailty, and nursing care certification. Therefore, check 3 information that is not close to the year and month of interest (year and month to be analyzed) should not be used for identification purposes, and consideration must be given to the temporal relationship in this regard.

[0349] As part of the target illness / injury condition identification process F22, there is a process function for identifying risks in PIM, as mentioned above. This PIM identification process is a process for identifying medicines that require particularly careful administration (PIM: Potentially Inappropriate Medications), and uses a PIM master. This PIM master is a master that indicates whether or not there is a possibility of a decline in functions (cognitive function, motor function decline, etc.) for each medicine. The flow of the PIM identification process is as follows:

[0350] Step 1: Establishing a PIM master First, a "PIM Master" will be created from the publicly available document, "Guidelines for Safe Drug Therapy for the Elderly 2015," and generic names of PIM drugs that may lead to cognitive or motor decline and multiple corresponding trade names will be compiled along with their code information. Step 2: Check whether or not the relevant medicines are available in the medical receipt data for each elderly person. For each elderly person, we check whether or not the product name of the drug is included in the medical prescription data, limited to the drugs registered in the PIM master. Step 3: Output of PIM specific processing result information In step 2, if there are drugs registered in the PIM master, the name of the drug and the functions that may be impaired (cognitive function, motor function, etc.) are output. In other words, in addition to the conventional total number of drug prescriptions (multi-drug check), the output shows how many PIMs are included and what risks each drug poses.

[0351] As shown in FIG. 45, the PIM identification process references the PIM master M26, which indicates whether or not each drug may cause a decline in function (e.g., cognitive function or motor function), and identifies the risk status based on the medication status. For example, assume that Person A is taking six drugs: A, B, D, E, F, and H. By referencing the PIM master M26, the PIM identification process F22 (illustrated as F22 because it is part of the target disease state identification process F22) determines that the patient is taking one drug (drug F) that poses a risk of cognitive decline and one drug (drug D) that poses a risk of motor function decline. The results (PIM identification data) are then output to the target disease state DB: D22 shown in FIGS. 37 and 38.

[0352] Next, the condition duration calculation process F23 will be explained. As shown in Fig. 46, this condition duration calculation process F23 takes in monthly data and yearly data from the RDO element condition DB: D21 and the target injury / illness condition DB: D22, and calculates the condition duration of the corresponding injury / illness, etc. For this purpose, the condition duration calculation condition master M27 is referenced to calculate the duration, and the result is stored in the elderly condition basic DB: D23.

[0353] The calculation of the duration of a condition differs depending on whether the target data is monthly or annual data. First, Figure 47 explains how to calculate the duration of a condition when the target data is monthly data. In Figure 47, the square frames represent the monthly RDO element status and target injury / illness status, with ◯ meaning good, × meaning bad, and no data meaning missing data. There are roughly three patterns of consecutive conditions (the same condition continues, there is a change in the status in the middle, and there is a missing month in the middle). The calculation method for the duration of a condition for the three patterns is as follows:

[0354] - In the case of A where the same state continues Records in which the same condition of the major injury, illness, etc. or RDO element continues are traced back from the target month to the time when the condition changed. In the example shown, the period marked with an "X" in the target month is calculated. - In the case of a BC that includes a period of time during which the state changes The duration is calculated by considering the period during which the state changed and the state before and after. A threshold value for the state change (for example, 3 months or more) is set. For B, the state change period is 1 month, which does not reach the threshold, so it is determined that the x state for the target year and month continues. For C, the state change period is 3 months or more, which is the threshold, so it is determined that the state has changed, and the x period from the target year and month to the month when the state changed is calculated. - In the case of DE with missing months The duration is calculated taking into account the missing period and the state before and after. If the threshold for the missing period is set to 3 months or more, then for D, the missing period has not reached the threshold, so it is determined that the X state for the target year and month continues. For E, the missing period is 3 months or more, so it is determined that the continuation has ended, and the X period from the target year and month to the missing month is calculated.

[0355] Figure 48 shows an image of how to calculate the duration of a state when the target data is annual data. The duration of a state is calculated in the following order: The period is calculated assuming that the state of the past record a closest to the target date and month (indicated by an X in the figure) continues until the target date and month. Going back further to record b, if it is in the same state as record a, calculate the period assuming that the state (indicated by an X in the figure) continues from record b to the target year and month. Going back further to record c, if it is in a different state from record b (◯ in the diagram), the period up to record c is not calculated, and the period is calculated assuming that the state of record b (× in the diagram) continues up to the target year and month.

[0356] Next, we will explain the elderly condition phase identification process F31 shown in Fig. 37. As shown in Fig. 49, this elderly condition phase identification process F31 refers to the elderly condition phase identification condition master M31, and identifies the elderly condition phase based on the injury / illness state and RDO state in the elderly condition basic DB: D23 shown in Fig. 37, and stores the identified elderly condition phase in the elderly condition identification DB: D31.

[0357] The elderly condition phase is identified as one of phases Φ1 to Φ6, taking into consideration the presence or absence of major and secondary injuries and illnesses determined by the elderly condition image model master M01 shown in Figure 37, the presence or absence of corresponding abnormal test values ​​and symptoms, and the duration of these symptoms, etc. Figure 50 shows the data processing of the elderly condition phase identification process F31.

[0358] In Figure 50, the elderly condition phase specific condition master M31 associates the elderly condition phase with the injury / illness condition data and abnormal values ​​such as test values ​​for the main injury / illness and concomitant injury / illness for each RDO element of the main injury / illness etc. (×: no relevant data, ◯: data exists and test values ​​and symptoms are mild, ●: data exists and test values ​​and symptoms are severe).

[0359] The example in Figure 50 shows the process when the elderly state phase of Alzheimer's disease of Person A is identified as Φ5. First, it is determined which conditions are satisfied by Person A's primary illness and accompanying illness. For example, in the case of accompanying illness 1 (diabetes), the combination of presence or absence of illness state data is marked ○ and presence or absence of abnormalities in test values, etc. is marked ○, so it satisfies the condition related to Φ3. Similarly, accompanying illness 2 (hypertension) satisfies the condition related to Φ4, (dyslipidemia) satisfies the condition related to Φ1, and Alzheimer's disease satisfies the condition related to Φ5. Therefore, the elderly state phase of Alzheimer's disease is identified as Φ5.

[0360] Next, the elderly condition risk identification process F32 shown in Figure 37 will be described with reference to Figure 51. As shown in Figure 51, the elderly condition risk identification process F32 refers to the elderly condition risk identification condition master M32, and determines the risk of the condition tending to deteriorate based on the condition and duration of the RDO elements by major injury or illness in the elderly condition basic DB: D23 shown in Figure 37, and stores the result in the elderly condition identification DB: D31. The elderly condition risk identification condition master M32 is a master for creating a priority target individual record (elderly condition identification DB), and as shown in Figure 52, is a master for specifying individual risks (◎: excellent, ○: good, △: poor, ×: bad) by setting standard values ​​for the condition value and duration of the condition for each RDO element by major injury or illness (stroke in the example shown).

[0361] Figure 52 shows data processing for individual judgment in the elderly condition risk identification process F32. The elderly condition basic DB: D23 stores elderly conditions and durations for each RDO element for the major illness or injury of the elderly (in the example shown, Mr. A's stroke), so the elderly condition risk identification process F32 imports this data and performs individual risk judgment processing using the elderly condition risk identification condition master M32.

[0362] The Elderly Condition Risk Identification Condition Master M32 is a master for creating individual records of priority targets (stored in the Elderly Condition Identification DB: D31), and as shown in the figure, is used to identify individual risks (◎: Excellent, ○: Good, △: Poor, ×: Poor) by setting standard values ​​for the condition value and duration of the condition for each RDO element of each major injury or illness. The Elderly Condition Risk Identification Process F32 determines which of the four stages (◎: Excellent, ○: Good, △: Poor, ×: Poor) that the imported elderly person's condition and duration values ​​fall into, which are the risk judgment values ​​of Master M32, and uses the result as an individual risk judgment to create individual records of priority targets.

[0363] Next, data processing for the overall judgment of the elderly condition risk identification process F32 will be explained using Figure 53. In order to comprehensively judge the risk of needing nursing care for each RDO element of each major injury or illness, an individual risk judgment value master M411 and an overall risk judgment value master M412 are used.

[0364] The Individual Risk Assessment Value Master M411 is a master for creating a list of priority targets, and reference values ​​for quantifying the level of the individual risk assessment results are set for each RDO element for each major injury or illness. In the example shown in the figure, the RDO element "daily life habits" is set as ◎: Excellent = 5, ○: Good = 3, △: Poor = -3, ×: Poor = -5, and the RDO element "accompanying injury or illness status" is set as ◎: Excellent = 20, ○: Good = 10, △: Poor = -10, ×: Poor = -20.

[0365] The overall risk assessment value master M412 is a master for creating a list of priority targets, and for each major injury or illness, thresholds are set for each stage as shown in the figure to make an overall assessment (◎: excellent, ○: good, △: poor, ×: bad) from the sum of the individual risk assessment results.

[0366] The elderly condition risk identification process F32 imports the individual risk assessment results of the priority target individual records shown in the upper left of Figure 53 (the assessment results surrounded by a bold frame in the figure), and quantifies the RDO element-specific risk assessment results for each major injury or illness by referring to the individual risk assessment value master M411. In the example shown in the figure, the numerical value surrounded by a bold frame in the individual risk assessment value master M411 is the numerical value of Mr. A's individual risk assessment result for stroke. The total value of these numerical values ​​(-25 in the example shown in the figure) is then calculated, and from this total value, an overall risk assessment is made into one of four levels of priority for taking measures (◎: excellent, ○: good, △: poor, ×: poor), which is the overall assessment in the overall assessment value master 412.

[0367] In the example shown in the figure, the total value for stroke of elderly person A is -25, so the stroke is judged as "x: bad" based on the threshold of the overall judgment value master 412. This judgment result, together with the overall risk judgment results of A's other major injuries and illnesses, is stored as a list of priority targets in the elderly condition identification DB: D31 shown in FIG.

[0368] The process F41 for outputting a list of priority targets and the process F42 for outputting individual records of priority targets shown in Figure 37 will now be described. The process F41 for outputting a list of priority targets narrows down the risk assessment results for each individual's major injury or illness to those for which it is considered appropriate to take measures, and outputs a list of priority targets. The process F42 for outputting individual records of priority targets outputs, for each major injury or illness, an individual record that displays the status and duration of each RDO element, as well as the risk assessment results, for each elderly person listed on the list of priority targets. By looking at this individual record of target targets, it is possible to see which RDO element for which major injury or illness has a problem for each priority target, and it also becomes clear what measures should be taken for that person.

[0369] The list of priority targets and individual target records (created and output through the data processing process using the nursing care prevention measure support tool explained in Figure 37 and Figure 17) will be provided to municipalities across the country. Municipalities that receive this information will implement measures using specific support methods for implementing measures and methods for verifying the effectiveness of those measures at the intersections of the matrix of the community-based integrated care domain (vertical axis) and each stage of the PDCA cycle (horizontal axis) shown in Figure 25 as an overall image of the social implementation of the KDB related to community-based integrated care.

[0370] Although the expression in Figure 54 is slightly different from that in Figure 25 described above, it basically expresses the same gist. That is, the example shown in Figure 54 is based on the example of "a specific policy implementation support method for community-based integrated care using PDCA" explained in Figure 25, and adds an example of a more realistic and specific implementation method (a "new" business plan PDCA support method that takes into account the individual conditions of elderly people). First, we will define each of the PDCA steps.

[0371] (1) D: Policy implementation support function (specific policy implementation support function after output of key target individuals) (2) C: Performance evaluation support function (support function for identifying the effectiveness and cost-effectiveness of care prevention, etc.) (3) A: Policy planning support function (support function for extracting optimal measures that are expected to be effective based on the results of C) (4) P: Planning support function (support function for various future projections based on the results of DCA)

[0372] The word "new" in (1) to (4) above refers to a business plan PDCA support method that takes into account the status of each elderly person, which has been difficult to implement in the past. In other words, it calculates and creates macro information related to the infrastructure of services for the entire city, town, and village from micro information on individuals.

[0373] Here, "elderly status" refers to the "overall physical and mental status of the elderly" defined by the aforementioned "model for integrating medical care, nursing care, etc. (relationships between injury, illness, and RDO factors)." Conventional business planning involves future projections based on the historical elderly population of the entire municipality, average service usage, and nursing care benefit costs for various nursing care services. Plans do not reflect detailed realities, such as the physical and mental status of individual elderly people or their service usage. In other words, supply and demand plans for nursing care services (projected volume of nursing care services and infrastructure development plans) likely do not necessarily capture the true needs of individual elderly people. This can lead to oversupply or shortages of services in various areas (for individual elderly people) within municipalities.

[0374] In contrast, this invention implements detailed measures based on the individual conditions of elderly people, and formulates plans in a bottom-up manner based on the evaluation of the results of measures implemented for each individual and the formulation of measures. This essentially prevents waste and shortages in supply and demand, and ultimately achieves the maximum expected effect of nursing care prevention, etc. Furthermore, each function of this invention provides various masters, etc. that can manage various information that differs between individual municipalities (policy implementation policies for nursing care prevention, various service infrastructure systems, etc.), and by combining this with common information for all municipalities, etc. (national common information) analyzed using KDB data, etc., it becomes possible to support detailed business planning PDCA that reflects the actual conditions of each municipality, etc. An overview of each function is provided below.

[0375] (1) D: The policy implementation support function is a function that supports the efficient and effective selection of priority targets and the realization of an appropriate approach based on the different policies of individual municipalities (priority targets for care prevention, etc. and the concept of service infrastructure systems) and within limited systems and resources, based on the output information of priority targets for care prevention measures, etc., as explained above. This function uses the output information of priority targets as input and is composed of a process for narrowing down priority targets, a process for matching service requirements, etc., and a process for supporting the decision-making of service usage policies, etc.

[0376] (2) C: Performance evaluation support function supports quantitative identification of the effects of the above (1) D: measures from two perspectives: 1) effects of nursing care prevention (effects on improving, maintaining, or worsening physical and mental conditions) and 2) cost-effectiveness (comparison of the effects of reducing medical and nursing care costs relative to investments). Both are grouped by major illnesses, sex, age, etc. that lead to nursing care, and further divided into a group that is implementing the new measures described above in D and a group that is not, and indicators are defined based on two perspectives ("improving, maintaining, or worsening physical and mental conditions" and "reducing medical and nursing care costs relative to investments"), and the aggregated results of the indicators for each group are compared to quantify the effects of nursing care prevention and cost-effectiveness.

[0377] (3) A: Policy planning support function supports quantitative comparisons of which policies (services, etc.) are most effective for which attributes (gender, age group, etc.) according to major illnesses, etc., based on the results of identifying both the effectiveness of nursing care prevention, etc. and cost-effectiveness through C: Performance evaluation support function in (2) above. Based on the results of this comparison, it leads to the formulation of plans for the implementation volume of optimal policies (number of elderly people to be implemented) related to P: Plan formulation support function in (4) below, as well as the reduction or abolition of existing services, etc.

[0378] The (4)P: Plan Formulation Support Function supports the formulation of supply and demand plans for the next three years and the calculation of insurance premiums, taking into consideration, for example, the trends over the past three years in the supply and demand of each service, etc. (by groups with and without priority measures implemented) output by the above (1)D: Policy Execution Support Function, and the priorities of each service, etc. determined by the above (3)A: Policy Formulation Support Function. In addition, based on, for example, the trends over the past three years in the mental and physical conditions, etc., and medical and nursing care costs (by groups with and without priority measures implemented) resulting from the effects of care prevention, etc., as determined by the above (2)C: Performance Evaluation Support Function, it also predicts future trends in the mental and physical conditions, etc., of elderly people and medical and nursing care costs over the next three years due to the implementation of priority measures for which new implementation amounts by fiscal year have been determined. As mentioned above, future estimates of service demand and supply and costs (and the associated insurance premium estimates) are estimates that have been made in the past, while estimates of physical and mental conditions, etc. have traditionally been extremely difficult to make. However, in both cases, future estimates are made based on the condition of each elderly person, categorised by major injuries and illnesses that lead to nursing care, and so they can be said to be completely new methods.

[0379] As can be seen from the above explanation, the various output information (which is also information on an individual elderly person basis) from "D: Policy implementation support function," which takes as input data on the status of each elderly person, etc., is processed as input information for the subsequent "C: Performance evaluation support function," "A: Policy planning support function," and "P: Plan formulation support function," thereby realizing a "new business planning PDCA support function that takes into account the status of each elderly person."

[0380] For municipalities and other organizations to find a community-based comprehensive care policy that makes sense and is satisfactory, they must meet the following three conditions: 1. High cost-effectiveness (effectiveness in preventing nursing care, reducing medical and nursing care costs, etc.) can be expected. 2. It must be a realistic and practical (actually feasible) method within the limited financial resources, systems, and resources of the national and local governments. 3. It is a solution that can combine nationally common information analyzed and output from KDB data, etc., as well as various information that differs in individual cities, towns, and villages (status-based comprehensive care policies, characteristics by small area, service infrastructure, personnel structure, etc.).

[0381] The above three conditions are considered to be essential, especially in medium- to large-scale cities, where the number of elderly people to be targeted is enormous. The present invention proposes a solution that satisfies the above three conditions.

[0382] This invention aims to bridge the gaps that exist between medical care and nursing care, and between government and medical and nursing care facilities (particularly those related to communication between different professions), from the micro (the condition of each elderly person) to the macro (new bottom-up business planning PDCA support), and propose solutions that contribute to the realization of new societal implementation of community-based integrated care. Ideally, the results and benefits will be enjoyed by all stakeholders: the national government, municipalities, medical and nursing care facilities, health prevention facilities, private companies (consulting companies, system vendors, etc.), and, above all, the elderly themselves. In other words, the ultimate goal is for stakeholders from all levels and industries involved in community-based integrated care to work closely together and actively engage in the establishment and operation of societal implementation.

[0383] Next, we will explain the specific data processing of each DCAP function described in (1) to (4) above. Figure 55 shows an overview of the data processing flow of (1) D: Policy Execution Support Function (specific policy execution support function after outputting priority target candidates). This D: Policy Execution Support Function is a function that supports the efficient and effective selection of priority targets and the realization of appropriate approaches based on the output information of priority targets for care prevention measures, etc., based on the different policies of individual municipalities, etc., and within limited systems and resources. As shown in the figure, it consists of a priority target candidate output processing unit 551, a priority target narrowing processing unit 552, a service etc. requirement matching processing unit 553, and a service etc. usage policy decision support processing unit 554.

[0384] In Fig. 55, the priority target candidate output processing unit 551 is basically a nationwide function that uses KDB data. The subsequent priority target narrowing processing unit 552, service etc. requirement matching processing unit 553, and service etc. usage policy decision support processing unit 554 match various service infrastructure information etc. held by individual municipalities etc. with the nationwide priority target candidate information generated by the data processing unit 551, and perform the four types of data processing described below.

[0385] The priority target narrowing processing unit 552 takes as input the "priority target candidate information" output by the previous processing unit 551, and narrows down the priority targets who should actually be outreach (approached) in accordance with the prioritization policy set by the city, town, or village, etc.

[0386] The service etc. requirement matching processing unit 553 is a process that uses the ``narrowed down priority target information'' output by the previous processing unit 552 as input and matches the required service etc. requirements by major injury / illness etc., phase and RDO element.

[0387] The service usage policy decision support process 554 is a process that uses the ``service requirement matching key target information'' output by the previous processing unit 553 as input and supports the decision of service usage policies (various response policies based on whether or not required services are used).

[0388] As mentioned above, for individual municipalities to reliably implement and achieve results in policies related to health and nursing care prevention, the new method must not only be reliably expected to be effective in nursing care prevention and in reducing medical and nursing care costs, but must also be an efficient, high-quality, and realistic method that can maximize results within the limited community-based integrated care system and resources that vary from municipality to municipality. The above conditions are particularly essential for medium- to large-scale cities, where the number of elderly people who are the target of health care and nursing care prevention is enormous. In this sense, the data processing units 552 to 554 mentioned above are considered to be a solution to this problem. The functions of each of the data processing units 551 to 554 mentioned above are explained in detail below.

[0389] The priority target candidate output processing unit 551 performs the processing explained in Figures 17 and 37, and is basically a nationwide function that uses KDB data. That is, it extracts each elderly person condition data (target injury / illness condition, PIM condition, lifestyle condition, frailty condition, nursing care certification condition, etc.) from the elderly person condition basic database by major injury / illness, etc., while referencing the elderly person condition model master (major injury / illness, etc., phase, elderly person condition (RDO element, target injury / illness)), as priority target candidate information 5511, and can output a list or individual records.

[0390] Examples of items listed in the output include the analysis date, elderly person's individual ID, gender, age category, residential area category, major injury / illness, etc., phase by major injury / illness, etc., and overall risk assessment (aggregation of individual risk assessment results), making it possible to get a bird's-eye view of potential priority targets. However, no information is provided about which major injury / illness of which elderly person should be given priority.

[0391] Examples of individual output items include the analysis year and month, major illnesses that lead to nursing care, and by phase, and by referencing the associated elderly condition model master, which consists of appropriate elderly condition data items (lifestyle condition, target illness / injury condition, PIM condition, frailty condition, nursing care certification status, etc.), which can be used as information for specific actions.

[0392] The process 5521 of the priority target narrowing processing unit 552 is a process of narrowing down the priority target candidates 5511, which is the output of the previous processing unit 551, to high-priority priority targets who should actually be outreach (approached) in accordance with the prioritization policy established by the city, town, or village, etc. At that time, the priority target narrowing condition master 5522, which has been set and prepared in advance, is referenced. The master 5522 sets priorities for combinations of phases by major injury or illness, etc. and overall risk assessment.

[0393] Generally, for each major injury or illness, the higher the phase number (Φ1 to Φ6), the closer the patient is to requiring nursing care due to the occurrence or worsening of the major injury or illness, and the worse the overall risk assessment (the closer it is to × out of ◎○△×), the higher the risk of the patient worsening to the next phase. Therefore, it can be said that the higher the phase number and the worse the overall risk assessment, the higher the priority.

[0394] However, in reality, from the perspective of health and nursing care prevention, it can be said that Φ4, the phase immediately before a major injury or illness occurs, has a higher institutional and operational priority than Φ6, when severe nursing care is required. It is also possible that the approach to prioritization will change depending on the type of major injury or illness. Furthermore, the approach to prioritization may differ depending on the policies of each city, town, or village regarding the project. For this reason, it is important to repeatedly verify with actual data whether the prioritization is actually convincing, and to optimize the master.

[0395] Based on the above various prioritization concepts, the master data 5522 for narrowing down conditions for key target persons is set. Examples of management items for the master data 5522 include major injury / illness, etc., phase, overall risk assessment, and priority. For example, by setting the priority to four levels, A to D, it becomes possible to prioritize key target candidates and target major injury / illness, etc. in a detailed manner.

[0396] The priority target narrowing process 5521 refers to the priority target narrowing condition master 5522 described above, and narrows down the priority target candidate information 5511, which is the output of the previous processing unit 551, and outputs it as narrowed priority target information 5523. Examples of components of this information 5523 include the analysis year and month, elderly person personal ID, gender, age category (as of the analysis year and month), residential area category, major injury / illness etc., phase and overall risk assessment for each major injury / illness etc., and "priority." This "priority" information corresponds to new additional information added to the priority target candidate information 5511.

[0397] The process 5531 of the service etc. requirements matching processor 553 matches the required service etc. requirements by major injury / illness etc., by phase and by RDO element with the narrowed down focused target information 5523 output from the previous processor 552. At this time, it refers to the service etc. requirements master 5532 by major injury / illness etc., by phase and by RDO element that has been set up and prepared in advance. Here, the required service etc. requirements mentioned above correspond to various services by service type such as health guidance services, nursing care preventive services, medical services and nursing care services.

[0398] As a specific example of matching, in the case of the RD element of daily living habits, if there is an increased risk of developing a causative injury or illness such as a major injury or illness (Φ2) or if it does occur but is only mild (Φ3), health guidance services provided by public health nurses will be provided. In addition, in the case of an RD element related to frailty, such as being homebound, if there is an increased risk of developing a major injury or illness (Φ4), care prevention services provided by care managers at community comprehensive support centers, etc. will be provided. Furthermore, in the case of the RD element of PIM status, medical services such as medical care (doctors) and dispensing (pharmacists) will be provided. It is anticipated that there may be multiple types of services (occupations) that correspond to the same RDO element.

[0399] In reality, approaches to matching various services may vary depending on the infrastructure development and personnel structure of each region, as well as the policies and circumstances of individual municipalities. For example, small-scale multifunctional nursing services would be ideal, but since they cannot be provided in a certain area, preventative care visiting nursing services may be provided instead (as the next best service). In this case, it is important to manage both the ideal service and the next best service.

[0400] Based on the above various viewpoints, the service etc. requirement master 5532 by major injury / illness, phase, RDO element is set. Examples of configuration items of the master 5532 include major injury / illness, phase, RDO element, service type, service, etc.

[0401] By performing this process 5531 with reference to the master data 5532 for required services, etc., by RDO element and phase by major injury / illness, etc., the narrowed-down priority target information 5523, which is the output of the previous processing section 552, is output as priority target information 5533 with service requirements, etc. associated. Examples of items constituting this information 5533 include the analysis date, elderly person's personal ID, gender, age category (as of the analysis date), residential area category, major injury / illness, etc., phase and overall risk assessment by major injury / illness, etc., "priority," "service type," and "service requirements." The above-mentioned "priority," "service type," and "service requirements" correspond to new additional information added to the priority target candidate information. If multiple services are associated with the same RDO element, the information is generated as multiple records.

[0402] The service etc. usage policy decision support processing unit 554 executes processing 5541 for supporting the decision of service etc. usage policies (various response policies based on the presence or absence of required service usage) using the service etc. requirement associated focused target information 5533 output from the previous processing unit 553. At this time, the processing is performed while referring to the residential area-specific service etc. infrastructure master 5542 and focused target service etc. usage record information (individual unit) 5543 set and prepared in advance, and outputs focused target information 5544 with service etc. usage policy decision support information.

[0403] The residential area-specific service infrastructure master 5542 comprehensively registers not only health and nursing care prevention centers, medical institutions, and nursing care facilities located in the same area as the elderly residential area, but also centers and institutions that can provide services to the elderly, even if they are located in a different, adjacent area. Therefore, the same centers and institutions will be duplicated in each area. The reason for this specification is that services are intended to be used from the elderly's perspective, and should cover all services that are actually available, regardless of whether they are located in the area where the elderly reside. Specifically, this can be expected in cases where a health center oversees two areas, or where elderly people live in facilities located in distant areas or visit the same medical institution.

[0404] From the above, it is assumed that the master 5542 will have various variations depending on the circumstances of each district of each city, town, village, etc. Examples of items that make up the master 5542 include elderly living district, service type, each service, name of provider, etc. (if there are multiple for the same service, they will be registered as separate records), location district, service user capacity, and number of service users (timely maintenance is required by each city, town, village, etc.). Note that when creating the master 5542, it may be based on service usage rules established by the city, town, village, etc., or it may be registered with content that matches the actual service usage situation.

[0405] Priority target service usage record information (individual unit) 5543 is the actual service usage record for individual elderly people and is broadly divided into two types. Health guidance services and preventive care services are not generally included in the KDB, so maintenance is required by individual municipalities. For example, monthly usage records for day care facilities are expected. Medical services and nursing care services can be accurately calculated from medical and nursing care receipts included in the KDB, respectively, and are obtained as service status data (whether or not the service was used, whether or not a specified surcharge was applied, etc.) in the elderly status data. Note that the latter information is updated in the KDB several months after the month of service use, so it should be noted that it may not be the latest information. Examples of items that make up this information 5543 include the analysis date, elderly person's individual ID, gender, age category (as of the analysis date), residential area, service type, service, service base / provider / business, etc.

[0406] The priority target information 5544 with information to support decision-making on service use policies, etc. output by this process 5541 is not only useful for supporting decision-making on service use policies, etc. for the priority target individuals, but also serves as the basis for new supply and demand adjustments and insurance premium calculations (P: business plan formulation) based on the individual conditions of elderly people, making it information with extremely high added value.

[0407] Below, we will explain specific patterns of support for determining service usage policies (various response policies based on whether or not required services are used) by this process 5541. As examples of service usage policies, six patterns are assumed as shown below. In the following explanation, the symbol "->" indicates an example of a service usage policy for each pattern.

[0408] In order to determine the policy for using services, etc., the requirements are whether or not the necessary services are used and whether or not the necessary service infrastructure exists. Combining these, there are six patterns as follows: · Pattern when using required services by RDO element. 1. RDO element: Good ⇒ Status quo continues 2. The RDO element in question: Defect ⇒ Review of service content · Patterns when required services are not used by RDO element. 3. Required service infrastructure: Available (good supply-demand balance) ⇒ Immediate service use 4. Required service infrastructure: Available (demand << supply) ⇒ Immediate service use 5. Required service infrastructure: Available (required services unavailable due to exceeding capacity, etc.) ⇒ Next-best service provided 6. Required service infrastructure: None ⇒ Next-best service provided (future infrastructure development for required services) In this way, the priority target information 5544 with the service use policy decision support information is outputted with the response policy classified into patterns.

[0409] In the case of "unused necessary services" mentioned above, a more accurate understanding of the actual situation can be obtained by analyzing the occurrence of each of the above patterns over the past three years, rather than just at a certain point in time (the month of interest), for example (C: Performance evaluation). Based on the results, A: measures can be formulated, and plans can be drawn up for the expansion, suppression, and new introduction of various service infrastructures over the next three years, leading to P: supply and demand adjustment.

[0410] Examples of items constituting the priority target information 5544 with service use policy decision support information include the analysis year and month, elderly person personal ID, gender, age category (as of the analysis year and month), residential area category, major injury / illness, etc., phase and overall risk assessment for each major injury / illness, etc., "priority," "service type," "service etc. requirements," and "service use policy decision support information (including classification of whether or not priority measures are implemented)." In the above, the information in "" corresponds to new additional information added to the priority target candidate information.

[0411] Figure 56 shows the overall data processing flow for the (2)C: performance evaluation support function's nursing care prevention effectiveness identification support function. This (C: performance evaluation support function) supports quantitative identification of the effects of the new method for implementing measures (1)D: from two perspectives: 1) nursing care prevention effectiveness identification support function (the effects of improving, maintaining, or worsening physical and mental conditions) and 2) cost-effectiveness identification support function (the effect of reducing medical and nursing care costs relative to investments). Both functions involve grouping people by major illnesses, gender, age, etc. that lead to nursing care, and further dividing them into groups implementing the new measures described above (1) and those not implementing them. Evaluation indicators based on two perspectives (physical and financial) are defined for each individual, and the aggregated results of these indicators are compared between each group to quantitatively identify the effectiveness of nursing care prevention and cost-effectiveness.

[0412] First, we will explain 1) the nursing care prevention, etc. effect identification support function. Figure 56 shows an overview of the data processing flow related to the nursing care prevention, etc. effect identification support function of C: Performance evaluation support function. Here, the "etc." in nursing care prevention, etc. means that this function can be applied equally to each of the community-based comprehensive care areas of health and nursing care prevention, nursing care (support for independence and prevention of worsening), and medical and nursing care collaboration (hospitalization and discharge and home medical care), as shown in Figures 25 and 54. Data processing for this nursing care prevention, etc. effect identification support function is performed by a major injury / illness sorting processor 561, a priority measure implementation / non-implementation sorting processor 562, a mental and physical condition improvement / maintenance / worsening index calculation processor 563, and a nursing care prevention, etc. effect aggregation processor 564.

[0413] Processing 5612 in the allocation processing unit by major injury or illness etc. 561 is to divide the analysis target groups for identifying the effect of nursing care prevention, etc., by major injury or illness etc. that lead to nursing care. That is, allocation processing by major injury or illness etc. 5612 is performed from the priority target candidate information 5611 (also output information 5541 by D: policy execution support processing), and the information is allocated to a plurality of analysis target groups, namely, Alzheimer's disease priority target candidate information 5613, cerebral infarction (physical system) priority target candidate information 5614, physical frailty priority target candidate information 5615, and femoral fracture priority target candidate information 5616.

[0414] In the past, the effect was identified using only nursing care data such as the level of care required, without considering differences in major illnesses, etc. However, since the transition patterns leading to a state of needing nursing care differ greatly depending on the major illness, etc., it is difficult to accurately quantitatively identify the effect unless these are evaluated separately, so the allocation process is carried out as described above. At the same time, allocation is also carried out by basic attributes such as gender and age group.

[0415] The process 5621 in the priority measure implementation / non-implementation allocation processor 562 is a process for allocating the analysis target group (in the example shown in the figure, Alzheimer's disease priority target candidate information 5613) into a group 5622 in which priority measures (measures effective for preventing Alzheimer's disease, etc.) have been implemented and a group 5623 in which priority measures have not been implemented. While the Alzheimer's disease priority target candidate information 5613 is used as an example of the analysis target group, other analysis target groups, such as cerebral infarction (physical system) priority target candidate information 5614, physical frailty priority target candidate information 5615, and femoral fracture priority target candidate information 5616, are also allocated to the group 5622 in which priority measures have been implemented and the group 5623 in which priority measures have not been implemented by the corresponding priority measure implementation / non-implementation allocation process 5621. Differences in outcome indicators related to care prevention, etc. between these groups lead to the identification of effectiveness. At this time, allocation is also performed by the type of service, etc., used in the priority measures.

[0416] Processing 5631 in the processing unit 563 for calculating index of improvement / maintenance / worsening of mental / physical condition, etc. calculates an index of improvement / maintenance / worsening of mental / physical condition, etc. for each individual elderly person (subjects in group 5622 for which intensive measures have been implemented and group 5623 for which intensive measures have not been implemented). That is, for all related RDO element conditions (including the causal injury / illness condition) for each major injury / illness, etc., the presence or absence of improvement / worsening at a specified analysis point in time, the duration of the condition up to that point, etc. are calculated as outcome indexes for care prevention, etc. Then, the calculated index values ​​are output separately for data 5632 for the group for which intensive measures have been implemented and data 5633 for the group for which intensive measures have not been implemented.

[0417] Processing 5641 in the nursing care prevention effect aggregation processing unit 564 aggregates the outcome indexes related to nursing care prevention for each individual calculated by the mental and physical condition improvement / maintenance / worsening index calculation processing unit 563, by implementation group and non-implementation group, by major injury / illness, etc., basic attribute, whether or not priority measures have been implemented, service type, etc., and outputs the aggregated values ​​(average values ​​for the groups) as nursing care prevention effect aggregation result information by major injury / illness, etc. 5642. Comparing each aggregated value between the implementation group and non-implementation group of priority measures (comparison of the length of duration of condition, number of improvements / worsenings, etc.) makes it possible to quantitatively identify the effects of nursing care prevention, etc.

[0418] The above-mentioned processing units 561 to 564 will be described in detail below.

[0419] Processing 5612 in the allocation processing unit 561 for allocation by major injury or illness, etc. divides the analysis target groups for identifying the effects of care prevention, etc., by major injury or illness, etc. that lead to care. That is, allocation processing is performed for Alzheimer's disease priority target candidate information 5613, cerebral infarction (physical system) priority target candidate information 5614, physical frailty priority target candidate information 5615, and femoral fracture priority target candidate information 5616. At that time, allocation is also performed by basic attributes such as gender and age group.

[0420] Below, we will provide an overview of the progression patterns from a normal state to a state requiring nursing care for the four main illnesses and injuries that lead to nursing care (Alzheimer's disease, cerebral infarction, physical frailty, and femoral fracture).

[0421] In the case of Alzheimer's disease, continued high-risk conditions such as stress, lifestyle habits, and obesity can lead to the development of diabetes, hypertension, dyslipidemia, and sleep apnea, which are the main causes of Alzheimer's disease. Furthermore, as these conditions continue to worsen, degeneration of the cranial nerves (amyloid accumulation, etc.) can occur, leading to mild cognitive impairment (MCI) and eventually to the onset of Alzheimer's disease, which requires significant nursing care. These progressions usually occur gradually, progressing from a state requiring assistance due to cognitive decline in the early stages to a state requiring severe nursing care due to the onset of BPSD in the mid-stages.

[0422] In the case of cerebral infarction, as with Alzheimer's disease, the main causes are diabetes, hypertension, and dyslipidemia, and as these conditions continue to worsen, arteriosclerosis worsens, leading to cerebral embolism caused by cerebral thrombosis or atrial fibrillation, which then causes cerebral infarction. Usually, the occurrence of a cerebral infarction causes a sudden deterioration of the external physical and mental condition (paralysis, decline in physical function, etc.), which leads to an abrupt shift to a level of severe nursing care requirement.

[0423] In the case of physical frailty, the main causes are sarcopenia, which occurs due to continued malnutrition and poor exercise habits caused by oral frailty, and osteoarthritis, which occurs mainly with age due to the loss of cartilage in the joints. These changes usually progress slowly, with the initial stage involving a need for assistance due to a decline in physical function, followed by a state requiring severe care due to a significant decline in physical function caused by disuse syndrome, etc. in the mid-stage. Furthermore, as physical frailty progresses, it can lead to social frailty, such as social isolation, and mental frailty, such as cognitive decline.

[0424] In the case of femoral fractures, a fall triggers a femoral fracture in a patient who is currently suffering from osteoporosis, which occurs due to the progression of bone metabolic disorders caused by a decline in female hormones after menopause and chronic kidney disease (CKD), as well as an increasing severity of injuries and illnesses that can lead to falls (physical frailty, PIM (drugs that require particularly careful administration), various physical injuries and illnesses that can cause falls, and dementia). Usually, a femoral fracture causes a sudden deterioration in the patient's external physical and mental condition (such as a decline in physical function), which leads to an abrupt shift to a level of severe nursing care need.

[0425] As can be seen above, the four types of major injuries and illnesses that lead to nursing care each have significantly different patterns of progression in the condition of the elderly. The measures that should be taken to prevent these injuries and illnesses from becoming more severe, and to prevent external physical and mental conditions and the need for nursing care from becoming more severe, also differ. Therefore, in order to identify the effectiveness of each nursing care prevention measure, it is necessary to classify them into groups for each major injury and illness and process them accordingly. Next, we will explain the specific logic behind the classification process for major injuries and illnesses.

[0426] Examples of configuration items of the aforementioned "D: Output information from policy implementation support processing" record include the analysis date, elderly person's personal ID, gender, age category (as of the analysis date), residential area category, major injury / illness, etc., phase and overall risk assessment by major injury / illness, etc., priority, service type, service requirements, etc., and service usage policy decision support information.

[0427] As can be seen from the example of configuration items above, even for the same person, there will be as many records as there are types of major illnesses, etc. to be analyzed at the same analysis date, so the records are sorted by narrowing down the records by "major illnesses, etc.", "gender," and "age category." In other words, records related to the same person will exist independently for each group of different major illnesses, etc.

[0428] Next, the priority measure implementation / non-implementation allocation processing unit 562 will be explained. Here, processing 5621 is a process of allocating into a group 5622 in which priority measures have been implemented and a group 5623 in which they have not. Differences in outcome indicators related to care prevention, etc. between these groups will lead to identification of effectiveness. At that time, allocation is also made by the type of service used in the priority measures. Below, the specific logic of the priority measure implementation / non-implementation allocation processing will be explained.

[0429] Examples of configuration items of the aforementioned "D: Output information from policy implementation support processing" record include the analysis date, elderly person's personal ID, gender, age category (as of the analysis date), residential area category, major injury / illness,...

Claims

1. a database that stores data related to medical care, including medical receipt data, health checkup data, nursing care prevention data, nursing care certification data, and nursing care receipt data; an elderly condition image model comprising: a major injury / illness data section that holds injury / illness data for major injuries / illnesses that lead to nursing care; a concomitant injury / illness data section that holds injury / illness data for concomitant injuries / illnesses that cause the major injuries / illnesses that lead to nursing care; a stage setting section that sets a plurality of stages that indicate the stage at which the major injuries / illnesses that lead to nursing care or concomitant injuries / illnesses are at, including whether they have occurred; and a related element section that holds related elements (RDO) consisting of risk elements (R) related to the major injuries / illnesses and concomitant injuries / illnesses that lead to nursing care, defensive elements (D) that are inversely related to these risk elements, and result elements (O) related to the major injuries / illnesses or concomitant injuries / illnesses that lead to nursing care; a correspondence function unit that associates the elderly condition image model with the data related to medical care using a major injury / illness-specific RDO correspondence master in which a correspondence relationship between the major injury / illness leading to the care and the related element (RDO) is set, and an RDO-specific data correspondence master in which a correspondence relationship between the major injury / illness leading to the care and the data related to medical care is set, and a data conversion function unit that extracts, from the medical care data associated with the elderly condition model by this association function, information on the major injury or accompanying injury or illness that leads to the care for each elderly person, the stage of the major injury or accompanying injury or illness that leads to the care, and the risk factors or defensive factors and result factors, and generates elderly condition data; A community-based comprehensive care system using a computer system equipped with the following:

2. The community-based integrated care system of claim 1, characterized in that the main illness data section has four illness classifications: dementia, stroke, frailty, and fractures / falls, and the names of illnesses for each of the four illness classifications are stored as illness data for these main illnesses, and the accompanying illness data section stores the names of accompanying illnesses that cause each of the main illnesses: dementia, stroke, frailty, and fractures / falls.

3. The community-based integrated care system of claim 1, characterized in that the stage setting unit has at least five stages set, including Φ1, which represents a normal state in which no abnormalities are observed in the elderly person; Φ2, which represents a state before the occurrence of a concomitant injury or illness when the occurrence of the concomitant injury or illness is not diagnosed but there is a high possibility of the occurrence of the concomitant injury or illness; Φ3, which represents a state after the occurrence of the concomitant injury or illness; Φ4, which represents a state before the occurrence of a major injury or illness when the major injury or illness has not occurred but there is a high possibility of the occurrence of the major injury or illness; and Φ5, which represents a state after the occurrence of a major injury or illness.

4. The community-based integrated care system described in claim 1 has a master that defines the element names representing the element contents of risk elements (R) related to the major injury or illness and accompanying injury or illness, defensive elements (D) that are the opposite of these risk elements, and result elements (O) related to the major injury or illness or accompanying injury or illness that leads to nursing care, as well as the status of the element contents for each of these element names.

5. The elderly condition data includes condition data consisting of the elderly person's mental health condition, medical examination condition, lifestyle condition, frailty condition, injury and disease condition, medical service utilization condition, physical and mental condition, state of need for care, state of use of care services, and social life condition; condition continuation index data defining the period and amount of change until improvement / worsening, increase / decrease of a certain condition of the elderly person for each condition; risk assessment data determining the risk condition based on the combination of risk factors and the presence or absence of protective factors in each phase Φ1 to Φ5 of the elderly person's notable injury and illness; care cycle data corresponding to the period between hospitalizations of the elderly person; and medical and nursing care cost data which is the cumulative cost of medical care, nursing care, and nursing care prevention for the elderly person.

6. 2. The community-based integrated care system of claim 1, further comprising: an elderly care prevention measure support tool that includes an elderly care condition data creation function that uses the elderly care condition model to create elderly care condition data including the condition and duration for each individual, each major injury or illness, and each RDO element for the data related to medical care; a risk assessment master by major injury or illness and RDO element for determining the risk level of the created elderly care condition data; a nursing care risk assessment function by major injury or illness that assesses the risk of nursing care need for each elderly person by major injury or illness from the created elderly care condition data using the risk assessment master by major injury or illness and RDO element, and obtains a nursing care risk assessment result by major injury or illness; and a nursing care prevention measure priority target information output function that outputs information on priority targets for nursing care prevention measures based on the nursing care risk assessment result by major injury or illness.

7. The community-based integrated care system described in claim 6, characterized in that the elderly condition data creation function uses the elderly condition image model to create elderly condition data by linking a master corresponding to major illnesses and RDO elements, a master corresponding to RDO elements and medical care-related data, an injury / illness condition identification master, and a phase identification master with data related to medical care.

8. The community-based integrated care system of claim 6, characterized in that the risk assessment master by major injury or illness and by RDO element has thresholds set for each RDO element for each major injury or illness, and the nursing care risk assessment function by major injury or illness compares the thresholds for each RDO element with the corresponding RDO elements of the created elderly person condition data, and based on the comparison results, obtains a comprehensive nursing care risk assessment result for each major injury or illness and the phase of the major injury or illness as a nursing care risk assessment result for each major injury or illness.

9. The community-based integrated care system of claim 6, characterized in that the function of outputting information on priority targets for nursing care prevention measures outputs a list of priority targets and individual target records as information on priority targets for nursing care prevention measures based on the results of the nursing care risk assessment by major injury or illness.

10. The community-based comprehensive care system according to claim 1, which has a function of visualizing the elderly condition data that integrates medical care and nursing care for each elderly person in chronological order and creating an elderly history.

11. The community-based integrated care system of claim 1 has a medical record creation function that visualizes data on integrated health and nursing care prevention efforts, and policies related to support for independence and nursing care to prevent the condition from worsening, by region, business establishment, and municipality, from the elderly condition data.

12. The community-based integrated care system of claim 1, characterized in that the injury / illness state, other states, and major injury / illness leading to nursing care that occur during the transition process of an elderly person from a normal state to the onset of the major injury / illness leading to nursing care are defined as result elements (O) of the elderly condition image model, and the information constituting the data related to medical care that can identify the risk elements (R), protective elements (D), elderly condition phase Φ representing the stage, and result elements (O) corresponding to each injury / illness state, other states, and major injury / illness leading to nursing care that occur during the transition process is constructed on a computer as a standard progression image of the elderly condition, which is set for each injury / illness state, other states, and major injury / illness leading to nursing care that occur during the transition process, and is used to generate the elderly condition data.

13. The community-based integrated care system of claim 12, characterized in that a care prevention measure support tool is configured that uses the standard progression image of the elderly person's condition and the medical treatment guidelines for each target injury or illness as reference data, and compares a target injury or illness correspondence table by RDO element created by matching the list of target injuries or illnesses with the list of RDO elements, and a KDB data RDO element correspondence table created by matching the RDO element list with the KDB data information list containing data on medical care, using the RDO elements as common key information to create a target injury or illness correspondence table by RDO element by KDB data, and uses this target injury or illness correspondence table by RDO element by KDB data as a model master for the elderly person's condition, to create a list of key targets and individual target records.

14. an analysis target data import process for importing analysis target data from a database that stores data related to medical care; A function to create elderly condition data that calculates the good / bad condition and duration of the target injury / illness and its RDO elements for each individual from the imported analysis data. A function for identifying the elderly condition by major injury or illness, which identifies the elderly condition phase for each elderly person from the created elderly condition data, and performs a risk assessment for each RDO element regarding the risk of the condition worsening further, and then makes a comprehensive assessment of these to determine the risk of needing nursing care by major injury or illness; A priority target output function that outputs the results of the nursing care need risk assessment by major injury or illness as a priority target list and a priority target individual record; The community-based integrated care system according to claim 13, characterized in that these functions are implemented on a computer.

15. 2. The community-based integrated care system of claim 1, further comprising: a care prevention policy support tool that utilizes the provided data to improve care prevention projects in accordance with the PDCA process, and provides local governments with information on potential priority targets generated from elderly condition data generated based on the elderly condition model by referring to an elderly condition model master for each major injury or illness.

16. a priority target narrowing processing unit that narrows down priority targets to be outreached in accordance with a predetermined prioritization policy, using, as input, priority target candidate information generated by referring to an elderly condition model master for each major injury or illness based on the elderly condition data; a service requirement matching processing unit that matches the service requirements required with the narrowed-down information on the priority target candidates; and a service use policy decision support processing unit that receives the key target information associated with the service requirements as an input and supports the decision of a service use policy based on whether or not a required service is to be used; 16. The community-based integrated care system according to claim 15, further comprising a nursing care prevention policy support tool having a policy implementation support function comprising:

17. A processing unit for sorting by major injury or illness based on the information of the priority target candidates, sorting them into groups for specific analysis of the effect of care prevention according to the major injury or illness that leads to care; a priority measure implementation / non-implementation sorting processing unit that sorts each individual in the analysis target group into a group with and without priority measure implementation; a mental and physical condition improvement / maintenance / worsening index calculation processing unit that calculates a mental and physical condition improvement / maintenance / worsening index for each group that has implemented the key measures and each group that has not implemented the key measures; a care prevention effect aggregation processing unit that aggregates the individual performance indicators related to care prevention calculated by the mental and physical condition improvement / maintenance / worsening index calculation processing unit for each group that has implemented the priority measures and each group that has not implemented the priority measures, and outputs the difference in the aggregated values ​​as information on the aggregation result of the care prevention effect by major injury or illness; 16. The community-based integrated care system according to claim 15, further comprising a nursing care prevention measure support tool having a nursing care prevention effect identification support function comprising:

18. A major illness sorting processing unit that sorts the elderly into groups to be analyzed for specific care prevention effects according to major illnesses that lead to care based on the elderly condition data; a priority measure implementation / non-implementation sorting processing unit that sorts each individual in the analysis target group into a group with or without priority measure implementation; a processing unit for calculating cumulative costs by service, which calculates cumulative costs by implementation group and non-implementation group of the priority measures by major injury or illness by referring to the various service cost master in which the costs by service are registered based on the time-series results by various services constituting the priority measures by major injury or illness; and a cost-effectiveness calculation processing unit that inputs the cumulative costs of various services related to nursing care prevention on an individual basis calculated by the cumulative cost calculation processing unit for each type of service, calculates the difference in these cumulative costs between a group that has implemented a priority measure and a group that has not implemented the measure as the effect of implementing the measure, and compares this with the cost related to implementing a new measure, thereby making it possible to identify the cost-effectiveness related to nursing care prevention; 16. The community-based integrated care system according to claim 15, further comprising a nursing care prevention measure support tool having a cost-effectiveness specific support function comprising:

19. an effectiveness level assignment processing unit that assigns an effectiveness level to each identification result by referring to an effectiveness level assignment condition master that defines threshold information for distinguishing between the magnitude of the effect and the cost-effectiveness aggregation result information by major injury or illness, which is the identification result of the effect of care prevention; an optimal measure extraction processing unit that, by inputting information on the aggregated results of the nursing care prevention effect with the effectiveness rating and information on the aggregated results of the cost-effectiveness with the effectiveness rating, grasps the effectiveness rating assigned to the specific result of the effect of each measure, and creates an optimal measure master in which measures that should be implemented with priority are prioritized as optimal measures by referring to an optimal measure extraction condition master in which priorities are defined in advance; 16. The community-based integrated care system according to claim 15, further comprising a nursing care prevention policy support tool having a policy planning support function comprising:

20. a past trend analysis processing unit that inputs the trends in service supply and demand, the trends in mental and physical conditions, and the trends in medical and nursing care costs for groups that have implemented and not implemented priority measures by major illness, analyzes the trends in how these actual values ​​have changed over a specified past period, and creates a master for each major illness by whether or not priority measures have been implemented based on the results of the trend analysis; a future estimation processing unit that includes an optimal policy master by major injury or illness that is extracted according to a predefined priority order and holds optimal policies showing how policies are to be implemented on a priority basis in the future, and a major injury or illness-specific priority policy implementation condition master that sets how each of the optimal priority policies to be implemented on a priority basis will be applied to a certain number of elderly people and in a time-series manner in the future, and that uses past transition data created by the past transition analysis processing unit to estimate future transitions in the case where the planned policies are implemented, based on each past transition, by referring to the conditions set in the major injury or illness-specific priority policy implementation condition master; 16. The community-based integrated care system according to claim 15, further comprising a nursing care prevention measure support tool having a plan formulation support function comprising:

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