Health promotion policy support system, method, and program

Abstract

To provide a technology supporting policies to maintain health of residents in a region.SOLUTION: A health promotion policies support system has: a management part which associates a plurality of diseases according to a preceding / following relation with a disease as a current disease and with a disease after the current disease has become serious as a higher-order disease, defines a disease index quantitatively measuring the number of patients and seriousness of each disease, coordinates the same with a combination of disease indices of a plurality of diseases in a preceding / following relation in a region, and stores an index relation correspondence master registering a problem about health maintenance of a resident residing in the region; a calculation part for calculating a disease index actual value to be an actual value of a disease index about the number of patients and seriousness of the disease with respect to a plurality of diseases in a plurality of regions; and an extraction part for identifying a problem in one or more regions with reference to the index relation correspondence master on the basis of the disease index actual value about the plurality of diseases in the plurality of regions.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a technology for supporting the formulation of health policies in a local area. [Background technology]

[0002] There is a technology for determining suitable measures to be taken to maintain a person's health condition from information stored in a database. For example, Patent Document 1 describes an information provision system that estimates and notifies the person of multiple treatment options for an individual's symptoms, and further notifies the person of information on the individual's future condition assuming that the treatments are implemented. This information provision system makes it possible to know suitable treatments for dealing with an individual's symptoms. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2009-76102 Summary of the Invention [Problem to be solved by the invention]

[0004] In local communities, efforts to maintain the health of local residents are becoming increasingly important. The state of health maintenance in local communities is affected by a variety of factors in complex ways, so it is not easy to accurately identify issues related to health projects in local communities and formulate policies to appropriately address those issues. Therefore, it is desirable to use an information processing system to support the identification of issues and the formulation of policies.

[0005] However, as mentioned above, the information provision system described in Patent Document 1 is intended to provide information on treatments for dealing with individual symptoms, and is not suitable for formulating measures to maintain the health of residents in local communities.

[0006] One objective of the present disclosure is to provide technology that supports measures to maintain the health of residents in a community. [Means for solving the problem]

[0007] A health measure support system according to one embodiment of the present disclosure has a management unit that associates multiple diseases with each other in a precedence-sequence relationship, with a certain disease being the current disease and a disease that is a more severe version of the current disease being the higher-order disease, defines disease indices that are indices that quantitatively measure the number of patients and severity of each disease, and pre-stores an index relationship correspondence master that corresponds to combinations of disease indices of multiple diseases in a precedence-sequence relationship in a certain area and registers issues related to maintaining the health of residents living in the area; a calculation unit that calculates disease index actual values, which are actual values ​​of disease indices related to the number of patients and severity of a disease for residents of a plurality of diseases in a plurality of areas; and an extraction unit that identifies issues in one or more areas by referring to the index relationship correspondence master based on the disease index actual values ​​for the plurality of diseases in the plurality of areas.

[0008] A health measure support method according to one embodiment of the present disclosure is a health measure support method executed by a health measure support system including at least one processor, at least one memory, and at least one database, which associates multiple diseases with each other based on a precedence relationship, with a certain disease being the current disease and a disease that is a more severe version of the current disease being the higher-order disease, defines disease indexes that are indicators that quantitatively measure the number of patients and severity of each disease, stores in the database an index relationship correspondence master that corresponds to combinations of disease indexes for multiple diseases that are in a precedence relationship in a certain area and registers issues related to maintaining the health of residents living in the area, calculates disease index actual values ​​for multiple diseases in multiple areas, which are actual values ​​of disease indexes related to the number of patients and severity of each disease for residents of the area, and identifies issues in one or more areas by referring to the index relationship correspondence master based on the disease index actual values ​​for the multiple diseases in the multiple areas.

[0009] A health measure support program according to one embodiment of the present disclosure is a program to be executed by a health measure support system including at least one processor, at least one memory, and at least one database, wherein the database associates a plurality of diseases with each other in a precedence-sequence relationship, with a certain disease being the current disease and a disease that has become more severe as the higher-order disease, defines disease indices that are indices that quantitatively measure the number of patients and severity of each disease, and pre-stores an index relationship correspondence master that corresponds to combinations of disease indices of a plurality of diseases in a precedence-sequence relationship in a certain area and registers issues related to maintaining the health of residents living in the area, and the health measure support program causes the processor to calculate disease index actual values ​​for a plurality of diseases in a plurality of areas, which are actual values ​​of disease indices related to the number of patients and severity of the disease for residents of the area, and identify issues in one or more areas by referring to the index relationship correspondence master based on the disease index actual values ​​for the plurality of diseases in the plurality of areas. [Effects of the Invention]

[0010] It will be possible to support measures to maintain the health of local residents. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram of a health measure support system according to an embodiment of the present invention. [Figure 2] 10 is a flowchart illustrating a disease index calculation process according to the present embodiment. [Figure 3] FIG. 4 is a diagram showing an example of disease index data according to the present embodiment. [Figure 4] 10 is a flowchart of a problem extraction process according to this embodiment. [Figure 5] FIG. 10 is a diagram showing an example of index assignment good point master data according to the present embodiment. [Figure 6] FIG. 2 is a diagram showing an example of disease level management master data according to the present embodiment. [Figure 7]FIG. 10 is a diagram showing an example of index relationship correspondence master data according to the present embodiment. [Figure 8] FIG. 10 is a diagram showing an example of disease-specific problem and merit score data according to this embodiment. [Figure 9] FIG. 10 is a diagram showing an example of a report output screen according to the present embodiment. [Figure 10] FIG. 4 is a diagram showing an example of personal attribute information according to the present embodiment. [Figure 11] FIG. 4 is a diagram showing an example of medical checkup information according to the present embodiment. [Figure 12] FIG. 10 is a diagram showing an example of prescription information according to this embodiment. [Figure 13] FIG. 4 is a diagram showing an example of regional attribute information according to the present embodiment. [Figure 14] FIG. 10 is a diagram showing an example of a report generation operation screen according to the present embodiment. [Figure 15] FIG. 10 is a diagram showing an example of a report output screen according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0013] FIG. 1 is a block diagram of a health measure support system according to this embodiment.

[0014] Health measure support system 1 includes database 100, server 200, and terminal 300. Database 100, server 200, and terminal 300 are connected to each other so that they can communicate with each other. The communication line may be wired or wireless. The communication line may be an internet line, a telephone line, or the like.

[0015] Two or more devices among the database 100, the server 200, and the terminal 300 may be integrated.

[0016] The database 100 functions as a management unit that associates multiple diseases with each other in a precedence-sequence relationship, with a certain disease being the current disease and a disease that has become more severe as the higher-order disease, defines disease indices that are indicators that quantitatively measure the number of patients and severity of each disease, and pre-stores an index relationship correspondence master that corresponds to combinations of disease indices of multiple diseases that are precedence-sequence in a certain area and registers issues related to maintaining the health of residents living in that area.

[0017] The database 100 stores personal attribute information 101, medical checkup information 102, medical receipt information 103, area attribute information 104, disease-specific index data 108, and disease-specific problem and merit score data 109.

[0018] The database 100 also stores disease level management master data 105, index subject good score master data 106, and index relationship correspondence master data 107 as master data.

[0019] The database 100 may further store in advance policy implementation status information indicating what policies are being implemented in each of a plurality of regions.

[0020] The database 100 may further store in advance an index task master in which tasks related to maintaining the health of residents in a certain area are registered in association with a disease index for a single disease in that area.

[0021] The server 200 includes at least one processor and at least one memory.

[0022] The processor is configured, for example, with a CPU (Central Processing Unit), MPU (Micro Processing Unit), GPU (Graphics Processing Unit), FPGA (Field-Programmable Gate Array), etc. In this example, the server 200 is equipped with a CPU 201 as its processor. The processor reads and executes a health measure support program stored in memory, thereby functionally realizing a disease-specific index calculation unit 202, a disease-specific problem extraction unit 203, a region grouping unit 204, and an output unit 205.

[0023] The disease-specific index calculation unit 202 has a function of calculating, for a plurality of diseases in a plurality of regions, disease index actual values, which are actual values ​​of disease indexes relating to the number of patients and severity of the diseases for residents in the regions.

[0024] The disease-specific index calculation unit 202 may calculate disease index results for a plurality of diseases in a plurality of regions over a plurality of unit periods, such as a year, a month, or a week.

[0025] The disease-specific problem extraction unit 203 has an extraction function of identifying problems in one or more regions by referring to the index relationship correspondence master based on disease index performance values ​​for the plurality of diseases in the plurality of regions.

[0026] The region grouping unit 204 has a function of setting groups of regions that have similar attributes among multiple regions, and the disease-specific problem extraction unit 203 has a function of identifying problem-presumed regions, which are regions where problems are presumed to exist, by comparing disease index performance values ​​for diseases between regions that belong to the same group.

[0027] The disease-specific problem extraction unit 203 acquires, from the policy implementation status information stored in advance in the database 100, the policy implementation status indicating how policies are being implemented in areas that are not estimated problem areas but belong to the same group as the estimated problem area, for the above-mentioned problem in the estimated problem area.

[0028] The disease-specific problem extraction unit 203 may further identify problems in one or more regions by referring to the index problem master described above based on the disease index performance values ​​for diseases in multiple regions.

[0029] The disease-specific problem extraction unit 203 may identify a problem corresponding to a combination of the disease index actual value of a current disease in a certain unit period and the disease index actual value of a higher-level disease in a unit period subsequent to the unit period by referring to the index relationship correspondence master data 107 based on the disease index actual values ​​for multiple diseases in multiple regions.

[0030] The output unit 205 has a function of outputting the processing results of the disease index calculation unit 202, the disease problem extraction unit 203, and the region grouping unit 204, as well as information based on data accumulated in the database 100, etc.

[0031] The output unit 205 may output a graph showing the association of the disease that caused the area to be identified as a problem-presumed area with other diseases, with diseases as nodes and associations as edges.

[0032] A memory is a device that stores programs and data, such as a random access memory (RAM), a read-only memory (ROM), or a non-volatile semiconductor memory (Non-Volatile RAM (NVRAM)). The memory may also be a reading and writing device for recording media such as a hard disc drive (HDD), a solid state drive (SSD), a storage system, an integrated circuit (IC) card, a secure digital (SD) memory card, or an optical recording medium (e.g., a compact disc (CD), a digital versatile disc (DVD)), or a storage area of ​​a cloud server.

[0033] The server may further include a display device, an input / output interface, a network interface, etc. The display device displays information to the user in response to instructions from the processor. The input / output interface controls the input of information from input devices such as a keyboard and a mouse, and the display of information on a display device such as a monitor.

[0034] The terminal 300 includes at least one processor and at least one memory.

[0035] The processor is configured, for example, with a CPU (Central Processing Unit), MPU (Micro Processing Unit), GPU (Graphics Processing Unit), FPGA (Field-Programmable Gate Array), etc. In this example, the terminal 300 is equipped with a CPU 301 as a processor. The processor realizes various functions by reading and executing various programs and data stored in memory.

[0036] A memory is a device that stores programs and data, such as a random access memory (RAM), a read-only memory (ROM), or a non-volatile semiconductor memory (Non-Volatile RAM (NVRAM)). The memory may also be a reading and writing device for recording media such as a hard disc drive (HDD), a solid state drive (SSD), a storage system, an integrated circuit (IC) card, a secure digital (SD) memory card, or an optical recording medium (e.g., a compact disc (CD), a digital versatile disc (DVD)), or a storage area of ​​a cloud server.

[0037] The terminal 300 may further include a display unit 302 and an input unit 303. The display unit 302 displays information to the user in response to instructions from the processor. The input unit 303 controls information input from input devices such as a keyboard, mouse, or touch panel.

[0038] FIG. 2 is a flowchart illustrating the disease index calculation process according to this embodiment.

[0039] The disease-specific index calculation unit 202 acquires various data required for calculating disease-specific indexes (S101). The acquired data includes personal attribute information 100, health check information 102, and medical receipt information 103. The disease-specific index calculation unit 202 calculates medical expenses by disease classification by region (S102). The disease-specific index calculation unit 202 calculates related indexes by region, such as medical expenses by disease, the number of patients, and the number of patients with abnormal findings in health checkups (S103). The disease-specific index calculation unit 202 saves the various calculated values ​​in disease-specific index data 108 (S104). Since medical expenses, which are the basis of disease indexes, are calculated based on the types and amounts of procedures performed as tests and treatments for patients, indexes calculated based on medical expenses by disease and the number of patients in a region are indexes that reflect the number of patients and severity of each disease in the region.

[0040] The disease-specific index calculation unit 202 determines whether or not it is time to group the regions (S105). This determination may be made, for example, by reading out flag information. For example, the user operates the terminal 300 to specify whether or not to group the regions, and the result of this specification is stored in the memory of the server 200 as flag information. The disease-specific index calculation unit 202 reads out the flag information from the memory and determines whether or not it is time to group the regions. If it is time to group the regions (S105: Yes), the process proceeds to step S106. If it is time to not group the regions (S105: No), the process proceeds to step S108.

[0041] In step S106, the disease-specific index calculation unit 202 acquires regional attribute information. The regional attributes correspond to the regional attribute information 104, and include, for example, attributes related to the population, proportion of elderly people, and economic strength of each region, as well as indices related to regional medical resources such as the number of medical institutions, number of hospital beds, and number of doctors. The disease-specific index calculation unit 202 groups regions using indices based on the regional attributes (S107). For example, the regions are divided into several groups based on indices such as the size of the population, the aging rate, and the size of medical resources per population or area.

[0042] In step S108, the disease-specific index calculation unit 202 ranks the regions using the disease-specific index for each region. If the regions have been grouped, the ranking of the regions within each group is determined.

[0043] 3 is a diagram showing an example of disease-specific index data according to this embodiment. The figure shows data summarizing indexes for the disease "high blood pressure." The data shown in FIG. 3 is stored in memory separately for each disease.

[0044] Records for each region may be stored in the disease-specific index data 108. In Fig. 3, the indexes for the region A are summarized.

[0045] If the disease is "high blood pressure" and the region is "City A," the data items are total medical expenses (unit: yen), number of patients (unit: person), medical expenses per insured person (unit: yen), medical expenses per patient (unit: yen), and consultation rate (unit: %). Also, the data items for the three-component breakdown are number of cases per person (unit: cases), number of days per person (unit: days), and medical expenses per day (unit: yen).

[0046] Three-component decomposition is the decomposition of per capita medical expenses into three components. The equation per capita medical expenses = number of cases per capita × number of days per capita × medical expenses per day holds. Understanding the values ​​of these three components makes it possible to consider factors affecting medical expenses and identify countermeasures. The ranking column records the ranking of regions compared. Note that a high number of cases per capita may indicate a high number of patients with the disease, while a high number of days per capita or medical expenses per day may indicate a more severe disease. By comparing these values ​​with other regions as indicators, it is possible to assess whether the number of patients or severity of illness is higher in a region than in other regions. It is also known that disease indicators are influenced by regional and medical resource characteristics, such as a higher patient population in an aging region and a higher number of medical resources in a region, leading to more medical consultations. In this study, regional indicators are used to compare between groups with similar regional and medical resource characteristics, allowing for comparisons that take into account differences in regional characteristics.

[0047] The data included in the disease-specific index data 108 may be aggregated by category. In Fig. 3, three categories are shown as examples: "National Health Insurance," "early elderly" indicating data on early elderly people, and "late elderly" indicating data on late elderly people. Note that the health measure support system 1 may dynamically aggregate data by category later.

[0048] FIG. 4 is a flowchart of the problem extraction process according to this embodiment.

[0049] The disease-specific problem extraction unit 203 specifies the highest level at which there is an unconfirmed disease / indicator (S201). The disease-specific problem extraction unit 203 selects a disease (S202) and an indicator (S203).

[0050] The disease-specific problem extraction unit 203 compares the selected index with the same index in other regions (S204). If the selected index is better than the other regions, the process proceeds to step S206. If the selected index is worse than the other regions, the process proceeds to step S205. Whether an index is good or bad is defined for each index, as will be described later.

[0051] In step S205, the disease-specific problem extraction unit 203 records the indicator of the relevant disease as an indicator requiring confirmation.

[0052] In step S206, the disease-specific problem extraction unit 203 refers to the data of the disease level management master data 105 and determines whether or not the index selected in step S203 has a higher-level disease. If there is a higher-level disease (S206: Yes), the process proceeds to step S207. If there is no higher-level disease (S206: No), the process proceeds to step S210.

[0053] In step S207, the disease-specific problem extraction unit 203 acquires the indexes of the top diseases for the selected disease stored in the database 100.

[0054] In step S208, the disease-specific problem extraction unit 203 refers to the data of the index relationship correspondence master data 107 in the database 100 and determines whether there is a description in the index relationship correspondence master data. If there is a description (S208: Yes), the process proceeds to step S209. If there is no description (S208: No), the process proceeds to step S210.

[0055] In step S209, the disease-specific problem extraction unit 203 records the contents described in the index relation correspondence master data 107 as disease-specific problem / good point data 109 of the index.

[0056] The disease-specific problem extraction unit 203 determines whether or not all indicators for the disease have been confirmed (S210). If confirmation has been completed (S210: Yes), the process proceeds to step S211. If confirmation has not been completed (S210: No), the process returns to step S203, where the next indicator for the disease is selected.

[0057] The disease-specific problem extraction unit 203 determines whether or not all diseases at the same level have been confirmed (S211). If confirmation has been completed (S211: Yes), the process proceeds to step S212. If confirmation has not been completed (S211: No), the process returns to step S202, where the next disease at the level is selected.

[0058] The disease-specific problem extraction unit 203 determines whether confirmation of all levels has been completed (S212). If confirmation has been completed (S212: Yes), the process proceeds to step S213. If confirmation has not been completed (S212: No), the process returns to step S201 and specifies the highest level with unconfirmed diseases and indicators, that is, a level below the level being processed at the time of step S212.

[0059] In step S213, the disease-specific problem extraction unit 203 outputs a report based on the data recorded in step S209, etc. An example of the report output will be described later with reference to FIG.

[0060] FIG. 5 is a diagram showing an example of index assignment good point master data according to this embodiment.

[0061] The indicator / problem / good score master data 106 includes information items such as a current disease index, a status, and problem / good scores. Here, the current disease refers to the disease being processed. The content of the current disease index is recorded as a definable indicator for the disease, such as the number of cases per person or the number of days per person. The status is stored as high, low, or the like. The high / low criteria may be set, for example, such that the top five are "high" and the bottom five are "low." Other criteria may include the top or bottom percentage, a variance, or the like. The status may be stored as a numeric value, such as 0 to 10. Information indicating the problem / good scores corresponding to the current disease index and the status is recorded as the problem / good scores. For example, when the current disease index is "number of cases per person" and the status is "low," the information indicating the problem / good scores indicates that "there is a possibility that there are few patients with [current disease]."

[0062] FIG. 6 is a diagram showing an example of disease level management master data according to this embodiment.

[0063] The disease level management master data 105 has information items such as disease, level, and higher level number. The type of disease, such as blood pressure findings, high blood pressure, diabetes, etc., is recorded as the disease content. The level indicates the severity level of the disease. The higher level number indicates an identification number that identifies a higher-level disease when the disease has progressed further. For example, the disease with identification number 1, "blood pressure findings," has higher-level diseases such as identification number 4, "blood pressure consultation recommended level" (level 2), and identification number 7, "high blood pressure" (level 3), when it becomes a higher-level disease with increased severity.

[0064] FIG. 7 is a diagram showing an example of index relationship correspondence master data according to this embodiment.

[0065] The index relation correspondence master data 107 has, as information items, a current disease index, a current disease state, a higher-level disease index, a higher-level disease state, a problem candidate, a check item, and a check item good case.

[0066] The contents of the current disease index and the higher-level disease index are the same as the current disease index in the index task good score master data 106 shown in FIG. 5. The contents of the current disease state and the higher-level disease state are the same as the states in the index task good score master data 106 shown in FIG. 5. The task candidate, confirmation item, and the contents in the case of a good confirmation item are determined in advance and stored according to the correspondence between the pair (current disease index, current disease state) and the pair (higher-level disease index, higher-level disease state). The task candidate is information indicating a candidate task predicted from the above-mentioned correspondence, such as "there are many people who have not been examined for [current disease], so it is possible that [higher-level disease] has not been prevented." The confirmation item is information indicating the item that a medical professional should check according to the above-mentioned candidate task. When the confirmation item is good, it is information indicating the effect that can be obtained when the result of a medical professional's check according to the above-mentioned candidate task is good.

[0067] Recommended measures, which are measures recommended for issues, are registered in the index relationship correspondence master data 107. The above-mentioned confirmation items correspond to recommended measures. The disease-specific issue extraction unit 203 may identify issues in one or more regions and recommended measures for the issues.

[0068] The index relationship correspondence master data 107 may register issues related to maintaining the health of residents in a region for combinations of whether the rank of a group of disease indexes for multiple diseases in a precedence-sequence relationship is equal to or greater than a predetermined first-rank threshold. The disease-specific issue extraction unit 203 identifies a region as an area with an estimated issue if the rank of the disease index performance value of any disease in the group is equal to or greater than a predetermined second-rank threshold, and extracts issues for the area with an estimated issue from the index relationship correspondence master based on the disease index performance value of the area with an estimated issue. The first-rank threshold is a threshold for determining whether the current disease state is "high" or "low." The second-rank threshold is a threshold for determining whether the state of a higher-ranking disease is "high" or "low." This combination determines whether the area is an area with an estimated issue. For example, by setting the first-rank threshold and the second-rank threshold based on the top five rankings in a region comparison, information such as potential issues can be provided for regions with the top five indicators, such as the number of cases per capita, number of days per case, and medical expenses per day, for the current disease and the higher-ranking disease. The threshold can be set as a ranking, or the average value for all regions can be used as the threshold, and values ​​higher or lower than that can be considered to indicate an issue.

[0069] The disease indexes described above may include the number of cases per person, the number of days per case, and the medical cost per day, which are the three elements of medical costs. In this case, the index relationship correspondence master data 107 registers issues related to maintaining the health of residents living in the area for combinations of any of the disease indexes of the three elements of medical costs for the current disease and whether the ranking of that disease index in the group is equal to or higher than the first ranking threshold, and any of the disease indexes of the three elements of medical costs for the higher-ranking disease and whether the ranking of that disease index in the group is equal to or higher than the first ranking threshold.

[0070] The index relationship correspondence master data 107 may register an issue in which, for a combination where the ranking of the number of cases per person of the current disease is smaller than the first-rank threshold and the ranking of the number of cases per person of the higher-ranked disease is equal to or greater than the first-rank threshold, there are many people who have not received treatment for the current disease, and the higher-ranked disease cannot be prevented.

[0071] The index relationship correspondence master data 107 may register the issue that for a combination where the ranking of the number of days per case of the current disease is smaller than the first ranking threshold and the ranking of the number of days per case of the higher-ranked disease is equal to or greater than the first ranking threshold, many people discontinue treatment for the current disease and the higher-ranked disease cannot be prevented.

[0072] The index relationship correspondence master data 107 may register an issue that the current disease is not sufficiently controlled and the higher-ranked disease has not been prevented for a combination in which the ranking of the daily medical expenses of the current disease is lower than the first ranking threshold and the ranking of the daily medical expenses of the higher-ranked disease is equal to or higher than the first ranking threshold.

[0073] FIG. 8 is a diagram showing an example of disease-specific problem and merit score data according to this embodiment.

[0074] The disease-specific problem and good score data 109 has information items such as region, level, disease, index, problem and good score, individual comment, top disease, top disease index, top disease problem and good score, problem and good score in relation to the current disease, and confirmation item. Regional problem diseases and comments determined in the previous processes are recorded.

[0075] The region is information for identifying the region, such as City A or City B. The level indicates the current level of the disease.

[0076] For each of the current disease and the higher-ranking disease, the disease, indicators, and issues / good points are included as information items in the disease-specific issues / good points data 109. The contents of the disease, indicators, and issues / good points are the same as those described above, so detailed explanations will be omitted.

[0077] The individual comment is information indicating a comment about the current disease. For example, information indicating "there are many patients with [cerebrovascular disease]" or "the [high blood pressure consultation recommendation level] is higher than in other regions" is recorded. Note that, since different records can be registered for each region as the disease-specific problem and merit point data 109, an individual comment such as "higher than in other regions" can also be recorded.

[0078] The issues and advantages related to the current disease are information showing the issues and advantages according to the relationship between the current disease and the higher-level disease. The confirmation items are information showing the items that medical personnel should check.

[0079] FIG. 9 is a diagram showing an example of a report output screen according to this embodiment.

[0080] Figure 9 shows an example of a report output when the issues of City A are extracted as the output target. As a report, information based on the data accumulated in 109 for the disease-specific issues and good points data is output, for example, according to the flow shown in Figure 4.

[0081] The upper part of the report displays the details of the disease and the corresponding indicators according to the level of the disease. Of the displayed indicators, those that correspond to any of the patterns in the indicator relationship master data 107 may be highlighted by hatching or other methods.

[0082] The middle section of the report displays the issues and merits for each disease. More specifically, the disease, issues / merits, and comments are displayed based on the disease index value.

[0083] The bottom section of the report displays the issues and merits based on the relationships between diseases. More specifically, one or more issues and merits, potential issues / merits, and items and measures requiring consideration are displayed based on the index values ​​of multiple diseases.

[0084] FIG. 10 is a diagram showing an example of personal attribute information according to this embodiment.

[0085] The personal attribute information 101 has information items such as personal ID, year, region, sex, age, whether living with someone or not, occupation, type of occupation, and qualifications.

[0086] The personal ID is an ID that uniquely identifies the individual. The fiscal year is, for example, 2020. The region is information that uniquely identifies the region in which the individual resides, etc. For example, the region code C0001 corresponds to City A. The gender is a code that indicates the gender of the individual. The age is the age of the individual. Living with / living alone is information that indicates whether the individual lives with family, etc., or alone. Occupation / Industry is information that indicates whether the individual has an occupation and, if so, the type of occupation. Qualifications is information that indicates the type of health insurance to which the individual is enrolled, etc.

[0087] FIG. 11 is a diagram showing an example of medical checkup information according to this embodiment.

[0088] The health checkup information 102 has the following information items: a health checkup ID, an individual ID, a checkup date, an age at the time of the checkup, and various diagnostic values ​​from the health checkup. The health checkup ID is an ID for uniquely identifying one health checkup. The individual ID is the same information as the individual ID in the personal attribute information 101. The checkup date is date information indicating the date on which the individual corresponding to the individual ID underwent the health checkup corresponding to the health checkup ID. The age at the time of the checkup is the age at the time the individual corresponding to the individual ID underwent the health checkup corresponding to the health checkup ID. The various diagnostic values ​​refer to the diagnostic values ​​when the individual corresponding to the individual ID underwent the health checkup corresponding to the health checkup ID. Examples of diagnostic values ​​include abdominal circumference, diastolic blood pressure, systolic blood pressure, blood glucose level, and triglyceride level. The diagnostic values ​​are not limited to these.

[0089] FIG. 12 is a diagram showing an example of medical receipt information according to this embodiment.

[0090] The medical receipt information 103 includes information items such as a medical receipt ID, a region ID, an individual ID, the date of medical treatment, a medical institution number, the name of the illness or injury, the medical treatment, the medicine, and the billing points.

[0091] A receipt refers to the monthly medical fee statement submitted by a medical institution to the insurer. The receipt ID is an ID that uniquely identifies the receipt. The area ID is an ID that uniquely identifies the area to which the medical institution issuing the receipt belongs. The individual ID is an ID that uniquely identifies the individual who received treatment at the medical institution issuing the receipt. The treatment date is date information indicating the year and month covered by the receipt. The medical institution number is the number of the medical institution issuing the receipt. The illness name is information indicating the name of the illness or injury that was diagnosed as a result of treatment performed in the treatment date and month covered by the receipt. The medical procedure is information indicating the content of the treatment performed in the treatment date and month covered by the receipt. The medicine is information indicating the medicine provided to the individual who received treatment in the treatment date and month covered by the receipt. The billing points are points determined according to the medical procedure, and medical expenses are calculated based on these points.

[0092] FIG. 13 is a diagram showing an example of the area attribute information according to this embodiment.

[0093] The area attribute information 104 includes information items such as an area ID, a fiscal year, a name, a population, and various statistical information for the area for that fiscal year. The area ID is information that uniquely identifies the target area. For example, area code C0001 corresponds to City A. The fiscal year is the fiscal year for which the statistical information described below applies. The name is the name of the area corresponding to the area ID. Examples of statistical information include population, number of insured persons, area, habitable area, number of general clinics, number of general hospital beds, number of long-term care hospital beds, population per habitable area, number of insured persons per habitable area, number of general clinics per 100,000 population, number of long-term care hospital beds per 100,000 population, the ratio of the number of general hospital beds to the number of long-term care hospital beds, and number of diabetes specialists per 100,000 population. The statistical information that may be included in the area attribute information 104 is not limited to these. The area grouping unit 204 may set groups of multiple areas with similar attributes based on the various attributes included in the area attribute information 104.

[0094] FIG. 14 is a diagram showing an example of a report generation operation screen according to this embodiment.

[0095] The report generation operation screen is displayed on the display unit 302 or the like of the terminal 300. The user of the terminal 300 performs operations on the report generation operation screen via the input unit 303.

[0096] The report generation operation screen displays a city / town / village selection box and a disease set selection box, from which the user can select the city / town / village (region) and disease set for which the report is to be created.

[0097] The report generation operation screen displays a checkbox for selecting whether or not to perform an analysis by regional characteristic group. If this checkbox is checked and the Execute button is pressed, regions such as cities and towns will be grouped and ranked by regional characteristics, and a report will be output with the analysis performed by group. If the Execute button is pressed without checking the checkbox, a report will be output with the analysis performed without dividing into groups based on regional characteristics.

[0098] When the user presses the execute button, health measure support system 1 executes report creation. An execute button press message is sent from terminal 300 to server 200, and the server performs the process shown in Fig. 4 and sends the process results including the contents of the report to terminal 300. Upon receiving the process results, terminal 300 displays the report on display unit 302.

[0099] FIG. 15 is a diagram showing an example of a report output screen according to this embodiment.

[0100] Figure 15 shows an example of a report output when the issues of City D are extracted as the output target. As a report, information based on the data accumulated in 109 for the disease-specific issue and good point data is output, for example, according to the flow shown in Figure 4.

[0101] The upper part of the report displays the details of the disease and the corresponding indicators according to the level of the disease. Of the displayed indicators, those that correspond to any of the patterns in the indicator relationship master data 107 may be highlighted by hatching or other methods.

[0102] The bottom section of the report displays the issues and merits based on the relationships between diseases. More specifically, one or more issues and merits, potential issues / merits, and items and measures requiring consideration are displayed based on the index values ​​of multiple diseases.

[0103] The above-described embodiments of the present invention are examples for explaining the present invention, and are not intended to limit the scope of the present invention to only these embodiments. Those skilled in the art can implement the present invention in various other forms without departing from the scope of the present invention. Furthermore, the technical scope of the above-described embodiments includes the matters set forth in the following appendices. However, the present invention is not limited to the following appendices. However, the matters included in the above-described embodiments are not limited to the appendices.

[0104] In the above-described embodiment of the invention, methods for calculating disease-specific indicators, such as medical expenses and consultation rates for a certain year, and three-component decomposition, were described. By comparing the disease status of all insured persons across regions as a cross-section of a certain year, such indicators can be used to determine which diseases and conditions are currently prevalent in a region. Alternatively, data from multiple years can be used to identify individuals with low disease status in the first year as a population, and tracked over time to show an increase in disease status. This approach makes it possible to identify regional issues, such as the progression of disease severity over time, and which diseases lead to which conditions.

[0105] Furthermore, although the diseases to be analyzed in the above examples are hypertension, dyslipidemia, diabetes, etc., other diseases may also be analyzed. For example, cancers may be treated by type, or the malignancy of the detected cancer may be treated as a level.

[0106] (Appendix 1) The health policy support system includes a management unit that associates multiple diseases with each other in a precedence-sequence relationship, with a certain disease being the current disease and a disease that has worsened from the current disease being the higher-order disease, defines disease indexes that quantitatively measure the number of patients and severity of each disease, and pre-stores an index relationship correspondence master that registers issues related to maintaining the health of residents in a certain area, corresponding to combinations of disease indexes for multiple diseases in a precedence-sequence relationship in the area; a calculation unit that calculates disease index actual values ​​for multiple diseases in multiple areas, which are actual values ​​of disease indexes related to the number of patients and severity of each disease for residents in the area; and an extraction unit that identifies issues in one or more areas by referring to the index relationship correspondence master based on the disease index actual values ​​for the multiple diseases in the multiple areas. This identifies issues based on the relationship between the disease index actual values ​​for multiple diseases in a precedence-sequence relationship in the area, thereby supporting the extraction of health project issues in the area by taking into account the precedence-sequence relationship between multiple diseases.

[0107] (Appendix 2) In the health measure support system described in Supplementary Note 1, the indicator relationship correspondence master further registers recommended measures, which are measures recommended for issues, and the extraction unit identifies the issues in one or more regions along with the recommended measures for the issues. This identifies the recommended measures along with the issues, making it easier to formulate measures for maintaining health in the region.

[0108] (Appendix 3) In the health policy support system described in Supplementary Note 1, the extraction unit sets up groups of regions that have similar attributes among multiple regions, and identifies problem-predicted regions where issues are presumed to exist by comparing disease index performance values ​​for diseases between regions that belong to the same group. This allows the system to identify regions where issues are presumed to exist based on differences in disease index performance values ​​between regions with similar attributes related to medical resources, and to identify the issues in those regions, thereby making it possible to accurately extract regions where issues exist and the issues themselves.

[0109] (Appendix 4) In the health measure support system described in Supplementary Note 3, the management unit further stores in advance measure implementation status information indicating what measures are being implemented in each of the multiple regions, and the extraction unit acquires from the measure implementation status information the measure implementation status indicating how measures are being implemented in regions that are not estimated to have a problem but belong to the same group as the estimated problem region, for the problem in the estimated problem region. This identifies the measure implementation status in regions in the same group where no problem is estimated, making it easier to formulate measures by referring to it.

[0110] (Appendix 5) In the health measure support system described in Supplementary Note 3, the indicator relationship correspondence master registers issues related to maintaining the health of residents living in the area for combinations of whether the rank in a group of disease indicators for multiple diseases in a precedence-sequence relationship is equal to or greater than a predetermined first-rank threshold, and the extraction unit identifies the area as an area with an estimated issue if the rank in the group of the disease index result value of any disease is equal to or greater than a predetermined second-rank threshold, and extracts the issues of the area with an estimated issue from the indicator relationship correspondence master based on the disease index result value of the area with an estimated issue. This makes it possible to extract areas where the existence of issues is estimated from the rank within the group, and the issues of the area.

[0111] (Appendix 6) In the health policy support system described in item 5, the disease index includes the number of cases per person, the number of days per case, and the medical cost per day, which are the three medical cost elements. The index relationship correspondence master registers issues related to maintaining the health of residents living in the area for combinations of a disease index of any of the three medical cost elements for the current disease and whether the rank of that disease index in the group is equal to or higher than the first-rank threshold, and a disease index of any of the three medical cost elements for a predecessor disease and whether the rank of that disease index in the group is equal to or higher than the first-rank threshold. This makes it possible to accurately extract areas with estimated issues and their issues by evaluating the three medical cost elements as indexes and determining whether the problem is the number of patients or the worsening of patient severity. Furthermore, by evaluating the index values ​​of the three medical cost elements for multiple diseases that are in a precedent-sequence relationship with regard to the worsening of patient severity as a group, it is possible to determine whether the problem is the worsening of patient severity or the problem of patient capture.

[0112] (Appendix 7) In the health measure support system described in Supplementary Note 6, the index relationship correspondence master registers the issue that there are many people who have not received medical treatment for the current disease and the higher-ranked disease has not been prevented for a combination where the ranking of the number of cases per person of the current disease is smaller than the first-ranked threshold and the ranking of the number of cases per person of the higher-ranked disease is equal to or greater than the first-ranked threshold. This makes it possible to extract the issue that there are many people who have not received medical treatment for the current disease and the higher-ranked disease has not been prevented.

[0113] (Appendix 8) In the health measure support system described in Supplementary Note 6, the index relationship correspondence master registers the issue that many people discontinue treatment for the current disease and the higher-ranked disease cannot be prevented for a combination in which the ranking of the number of days per case of the current disease is smaller than the first-ranked threshold and the ranking of the number of days per case of the higher-ranked disease is equal to or greater than the first-ranked threshold. This makes it possible to extract the issue that many people discontinue treatment for the current disease and the higher-ranked disease cannot be prevented.

[0114] (Appendix 9) In the health measure support system described in Supplementary Note 6, the index relationship correspondence master registers the issue that the current disease is not sufficiently controlled and the higher-ranked disease cannot be prevented for the combination where the ranking of the daily medical expenses for the current disease is lower than the first-ranked threshold and the ranking of the daily medical expenses for the higher-ranked disease is equal to or higher than the first-ranked threshold. This makes it possible to extract the issue that the current disease is not sufficiently controlled and the higher-ranked disease cannot be prevented.

[0115] (Appendix 10) In the health measure support system described in Supplementary Note 1, the management unit further stores in advance an index-issue master that registers issues related to maintaining the health of residents in a certain region in association with disease indexes for a single disease in that region, and the extraction unit further identifies issues in one or more regions by referring to the index-issue master based on actual disease index values ​​for diseases in multiple regions. This makes it possible to extract issues in a region even for disease indexes for a single disease.

[0116] (Appendix 11) In the health measure support system described in Supplementary Note 1, the calculation unit calculates disease index result values ​​for multiple diseases in multiple regions over multiple unit periods, and the extraction unit identifies issues corresponding to combinations of disease index result values ​​for a current disease in a certain unit period and disease index result values ​​for a higher-level disease in a unit period that follows the unit period by referring to the index relationship correspondence master based on the disease index result values ​​for the multiple diseases in the multiple regions. This makes it possible to support the extraction of issues in a region that also takes into account changes in index result values ​​over time.

[0117] (Appendix 12) The health measure support system described in Supplementary Note 5 further includes an output unit that outputs a graph showing the association of the disease that caused the region to be identified as a region with other diseases, with the diseases being nodes and the associations being edges, for the region with an estimated problem. This makes it possible to visualize the association of the disease with other diseases and show it to the user.

[0118] (Appendix 13) A health measure support method executed by a health measure support system including at least one processor, at least one memory, and at least one database includes: associating multiple diseases with each other in a precedence-sequence relationship, with a certain disease being the current disease and a disease that has become more severe as the current disease being the higher-order disease; defining disease indexes that are indices that quantitatively measure the number of patients and severity of each disease; storing in the database an index relationship correspondence master that corresponds to combinations of disease indexes for multiple diseases in a precedence-sequence relationship in a certain area and registering issues related to maintaining the health of residents living in the area; calculating disease index actual values ​​for multiple diseases in multiple areas that are actual values ​​of disease indexes related to the number of patients and severity of each disease for residents of the area; and performing an extraction process to identify issues in one or more areas by referring to the index relationship correspondence master based on the disease index actual values ​​for the multiple diseases in the multiple areas. As a result, issues are identified based on the relationship between the disease index actual values ​​of multiple diseases in a precedence-sequence relationship in the area, thereby supporting the extraction of health project issues in the area by taking the precedence-sequence relationship between multiple diseases into consideration.

[0119] (Appendix 14) A health policy support program for execution by a health policy support system including at least one processor, at least one memory, and at least one database includes a database in which multiple diseases are related to each other by a precedence-sequence relationship, with a certain disease being the current disease and a disease that has become more severe as the current disease being the higher-order disease, disease indexes that quantitatively measure the number of patients and severity of each disease are defined, and an index relationship correspondence master is pre-stored in which issues related to maintaining the health of residents in a certain area are registered in correspondence with combinations of disease indexes for multiple diseases in a precedence-sequence relationship in the area. The health policy support program causes the processor to calculate disease index actual values ​​for multiple diseases in multiple areas, which are actual values ​​of disease indexes related to the number of patients and severity of each disease for residents in the area, and identify issues in one or more areas by referring to the index relationship correspondence master based on the disease index actual values ​​for the multiple diseases in the multiple areas. This identifies issues based on the relationship between the disease index actual values ​​for multiple diseases in a precedence-sequence relationship in the area, thereby supporting the identification of health project issues in the area by taking the precedence-sequence relationship between multiple diseases into consideration. [Explanation of symbols]

[0120] 1...health policy support system, 100...database, 101...personal attribute information, 102...health check information, 103...receipt information, 104...regional attribute information, 105...disease level management master data, 106...index issue and good score master data, 107...index relationship correspondence master data, 108...disease-specific index data, 109...disease-specific issue and good score data, 200...server, 201...CPU, 202...disease-specific index calculation unit, 203...disease-specific issue extraction unit, 204...regional grouping unit, 205...output unit, 300...terminal, 301...CPU, 302...display unit, 303...input unit

Claims

1. a management unit that associates multiple diseases with each other in a precedence-sequence relationship, with a certain disease being the current disease and a disease that has become more severe as the higher-order disease, defines disease indices that are indices that quantitatively measure the number of patients and severity of each disease, and pre-stores an index relationship correspondence master that corresponds to a combination of disease indices of multiple diseases that are precedence-sequence relationships in a certain area and registers issues related to maintaining the health of residents living in that area; a calculation unit that calculates disease index actual values, which are actual values ​​of disease indexes relating to the number of patients and severity of a disease for residents in a plurality of regions, for a plurality of diseases in the regions; an extraction unit that identifies issues in one or more regions by referring to the index relationship correspondence master based on disease index actual values ​​for the plurality of diseases in the plurality of regions; A health policy support system with

2. The index relationship correspondence master further registers recommended measures that are measures recommended for the issues, the extraction unit identifies issues in the one or more regions and recommended measures for the issues; The health policy support system according to claim 1 .

3. the extraction unit sets a group of regions having similar attributes among the plurality of regions, and identifies problem-presumed regions where a problem is presumed to exist by comparing disease index performance values ​​for the disease between regions belonging to the same group; The health policy support system according to claim 1 .

4. The management unit further stores in advance policy implementation status information indicating what policies are being implemented in each of the plurality of regions; The extraction unit acquires, from the policy implementation status information, a policy implementation status indicating how policies are being implemented in areas that are not estimated problem areas but belong to the same group as the estimated problem area, with respect to the issue of the estimated problem area. The health policy support system according to claim 3.

5. The index relationship correspondence master registers issues related to maintaining the health of residents living in the area for combinations of whether the ranks of disease indexes for a plurality of diseases in a precedence-sequence relationship in the group are equal to or higher than a predetermined first rank threshold, the extraction unit identifies a region as the problem-estimated region if the ranking of the disease index result value of any disease in the group is equal to or higher than a predetermined second ranking threshold, and extracts the problem of the problem-estimated region from the index relationship correspondence master based on the disease index result value of the problem-estimated region; The health policy support system according to claim 3.

6. The disease index includes three elements of medical expenses: the number of cases per person, the number of days per case, and the medical expenses per day. The index relationship correspondence master registers issues relating to maintaining the health of residents living in the area for a combination of a disease index of any one of the three medical expense elements for a current disease and whether the ranking of the disease index in the group is equal to or higher than the first ranking threshold, and a disease index of any one of the three medical expense elements for a higher-ranking disease and whether the ranking of the disease index in the group is equal to or higher than the first ranking threshold. The health measure support system according to claim 5.

7. In the index relation correspondence master, for a combination in which the ranking of the number of cases per person of the current disease is smaller than the first ranking threshold and the ranking of the number of cases per person of the higher-ranking disease is equal to or larger than the first ranking threshold, a problem is registered that there are many people who have not received medical treatment for the current disease and the higher-ranking disease cannot be prevented. The health policy support system according to claim 6.

8. In the index relation correspondence master, for a combination in which the ranking of the number of days per case of the current disease is smaller than the first ranking threshold and the ranking of the number of days per case of the higher-ranking disease is equal to or greater than the first ranking threshold, a problem is registered in which many people discontinue treatment for the current disease and the higher-ranking disease cannot be prevented. The health policy support system according to claim 6.

9. In the index relationship correspondence master, for a combination in which the ranking of the daily medical expenses of the current disease is smaller than the first ranking threshold and the ranking of the daily medical expenses of the higher-ranking disease is equal to or greater than the first ranking threshold, a problem is registered that the control of the current disease is insufficient and the higher-ranking disease cannot be prevented. The health policy support system according to claim 6.

10. The management unit further stores in advance an index task master in which tasks related to maintaining the health of residents in a certain area are registered in association with a disease index of a single disease in the area, The extraction unit further identifies issues in one or more regions by referring to the index issue master based on disease index actual values ​​for the diseases in the plurality of regions. The health policy support system according to claim 1 .

11. the calculation unit calculates disease index result values ​​for the plurality of diseases in the plurality of regions over a plurality of unit periods; the extraction unit refers to the index relationship correspondence master based on the disease index result values ​​for the plurality of diseases in the plurality of regions, and identifies a problem corresponding to a combination of a disease index result value for a current disease in a certain unit period and a disease index result value for a higher-ranking disease in a unit period subsequent to the unit period; The health policy support system according to claim 1 .

12. The method further includes an output unit that outputs a graph showing associations between the disease that caused the area to be identified as a problem-predicted area and other diseases, with the diseases being nodes and the associations being edges. The health measure support system according to claim 5.

13. A health measure support method executed by a health measure support system including at least one processor, at least one memory, and at least one database, comprising: A plurality of diseases are related to each other by a precedence relationship, with a certain disease being the current disease and a disease that has become more severe as the higher-ranking disease, disease indices are defined that are indices that quantitatively measure the number of patients and severity of each disease, and an index relationship correspondence master that corresponds to a combination of disease indices of a plurality of diseases that are in a precedence relationship in a certain area and registers issues related to maintaining the health of residents living in that area is stored in the database, For a plurality of diseases in a plurality of regions, disease index actual values ​​are calculated, which are actual values ​​of disease indexes relating to the number of patients and severity of the diseases for residents in the respective regions; Identifying issues in one or more regions by referring to the index relationship correspondence master based on disease index actual values ​​for the plurality of diseases in the plurality of regions; How to support health measures.

14. A health measure support program to be executed by a health measure support system including at least one processor, at least one memory, and at least one database, The database prestores an index relationship correspondence master in which a plurality of diseases are related to each other by a precedence relationship, with a certain disease being the current disease and a disease that has become severe as the higher-order disease, disease indexes are defined as indexes that quantitatively measure the number of patients and severity of each disease, and issues related to maintaining the health of residents in a certain area are registered in association with combinations of disease indexes of a plurality of diseases that are in a precedence relationship in the area, The health measure support program includes: For a plurality of diseases in a plurality of regions, disease index actual values ​​are calculated, which are actual values ​​of disease indexes relating to the number of patients and severity of the diseases for residents in the respective regions; Identifying problems in one or more regions by referring to the index relationship correspondence master based on disease index performance values ​​for the plurality of diseases in the plurality of regions.

Images