Nursing Information Processing System

Abstract

To provide a system for predicting a symptom change of a nursed person on the basis of a nursing record with conversations between a nursing person and the nursed person and a state of the nursed person recorded therein.SOLUTION: A nursing information processing system for accumulating nursing information sent from a plurality of nursing service offices in a nursing information DB, includes: a nursed person identification information acquisition unit for acquiring nursed person identification information that identifies a nursed person; a nursed person identification information holding unit for holding the acquired nursed person identification information; a nursing information acquisition unit for acquiring nursing information including text data, which is information indicating contents of nursing performed for the nursed person, in association with the nursed person identification information on the nursing; a nursing information holding unit for holding the acquired nursing information; an inference model generating unit for generating an inference model for estimating a future state of the nursed person with the held nursing information as learning data; and an estimation unit for entering the acquired nursing information in the generated inference model to estimate the future state of the nursed person pertaining to the entered nursing information.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a nursing information processing system. [Background technology]

[0002] Japan is experiencing an aging population, and the proportion of the population aged 75 and over is expected to continue to increase. This is expected to lead to a further increase in demand for medical care and nursing care. In light of this situation, there is a shift in the way medical care is provided from the traditional "hospital-based" approach to a "community-based" approach, where the entire community supports medical care for people living in their familiar neighborhoods and at home.

[0003] In order to promote the shift to community-based medical care, the role of providers of nursing and care at home is important, and there is active development of systems to support these business activities.

[0004] For example, Patent Document 1 discloses an invention related to a business management system for home nursing services, which predicts the progression of a patient's condition based on acquired biological information. By predicting the progression of a patient's condition, this system contributes to the creation of a schedule that assigns a nurse suited to the patient's condition to provide care at the appropriate time. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2020-021422 Summary of the Invention [Problem to be solved by the invention]

[0006] In the case of a home nursing service specializing in psychiatry, it is difficult to predict changes in a patient's condition or the worsening of their condition using the invention of Patent Document 1. This is because it is difficult to make a psychiatric diagnosis based solely on biological data information that can be expressed in quantitative terms, such as the patient's pulse, blood pressure, body temperature, and brain waves. In other words, the words spoken by the patient and the patient's appearance are important factors in a psychiatric diagnosis.

[0007] Therefore, for psychiatric patients, where it is difficult to diagnose or understand changes in symptoms based on biometric data information alone, there is a need for a system that can predict changes in a patient's symptoms based on nursing records that record conversations between the nurse and the patient and the patient's condition. [Means for solving the problem]

[0008] In order to solve the above problems, one aspect of the present invention provides a nursing information processing system having a nursing recipient identification information acquisition unit that acquires nursing recipient identification information that identifies a nursing recipient, a nursing recipient identification information storage unit that stores the acquired nursing recipient identification information, a nursing information acquisition unit that acquires nursing information that indicates the content of nursing provided to the nursing recipient and includes text data, in association with the nursing recipient identification information for that nursing, a nursing information storage unit that stores the acquired nursing information, an inference model generation unit that uses the stored nursing information as learning data to generate an inference model for estimating the future condition of the nursing recipient, and an estimation unit that inputs the acquired nursing information into the generated inference model and estimates the future condition of the nursing recipient related to the input nursing information. [Effects of the Invention]

[0009] According to the present invention, the future condition of a care recipient can be estimated based on nursing information including text data showing the conversation and state of the patient. [Brief explanation of the drawings]

[0010] [Figure 1] A conceptual diagram showing an overview of the nursing information processing system of this embodiment. [Figure 2]FIG. 1 is a block diagram showing an example of the functional configuration of a nursing information processing system according to an embodiment of the present invention. [Figure 3] Example of hardware configuration for realizing the nursing information processing system of this embodiment [Figure 4] A diagram showing an example of the data structure of nursing information. [Figure 5] Diagram showing the structure of the training data [Figure 6] Flow diagram showing the process for generating training data and generating an inference model [Figure 7] Diagram showing the data structure of inference data [Figure 8] Flow diagram showing the process of estimation using an inference model [Figure 9] Diagram showing the word embedding process [Figure 10] An example of outputting hospitalization risk [Figure 11] An example of output of hospitalization risk increase / decrease [Figure 12] Example of hospitalization risk statistics output [Figure 13] An example of output of hospitalization risk statistics trends [Figure 14] An example of outputting estimation results in relation to the time of estimation. [Figure 15] An example of outputting the estimation results along with the actual progression of symptoms. [Figure 16] FIG. 10 is a diagram showing an example of outputting the actual symptom transition and the symptom transition estimated during that period. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, the present invention should not be limited to these embodiments and can be embodied in various forms without departing from the spirit and scope of the present invention. <Embodiment> <Summary>

[0012] FIG. 1 is a conceptual diagram showing an overview of the nursing information processing system of this embodiment. Nursing information sent from nursing facility A and nursing facility B is stored in a nursing information DB. An inference model for estimating the future condition of the care recipient is generated using the stored nursing information as learning data. By inputting the nursing information sent from nursing facility C into this generated inference model, the future condition (e.g., condition next month) of the care recipient related to this nursing information can be estimated. <Configuration>

[0013] 2 is a block diagram showing an example of the functional configuration of the nursing information processing system 200 of this embodiment. As shown in the figure, the system includes a care recipient identification information acquisition unit 201, a care recipient identification information storage unit 202, a nursing information acquisition unit 203, a nursing information storage unit 204, an inference model generation unit 205, and an estimation unit 206.

[0014] Each functional configuration described below is realized as a combination of hardware and software, and can be realized using a computer such as a personal computer (PC) or a server device.

[0015] FIG. 3 shows an example of the hardware configuration for implementing a nursing information processing system 300, which includes a processor 301, a storage device 302, an input / output interface 303, and a communication interface 304. The storage device 302 is composed of RAM and nonvolatile memory (such as a hard disk drive or flash memory) and stores driver programs for controlling the hardware, other application programs, data used in executing the programs, and data obtained as a result of the execution. The processor 301 performs arithmetic processing in accordance with the programs. The input / output interface 303 receives instructions from input devices such as a keyboard or touch panel and outputs information to an output device such as a display. The communication interface 304 is configured to transmit and receive data via a network such as the Internet. Each functional configuration of this embodiment may also be implemented by dedicated hardware.

[0016] Furthermore, the present invention can be realized not only as an apparatus but also as a method. Furthermore, a part of such an invention can be configured as software. Furthermore, a program used to cause a computer to execute such software, and a recording medium on which the program is fixed, are naturally included within the technical scope of the present invention (the same applies throughout this specification).

[0017] The "care recipient identification information acquisition unit" 201 has the function of acquiring care recipient identification information that identifies the care recipient. The care recipient identification information includes, for example, a "care recipient ID," which is unique identification information, and information about the care recipient identified by this care recipient ID, such as the "name," "gender," "date of birth," "nursing facility," and "medical care expense category." If the care recipient is employed or commutes to a facility, the information may also include information such as the "name of the employer" and "name of the facility." The functions of this unit are realized by the processor 301, storage device 302, input / output interface 303, etc. It may also be configured to acquire information output from client devices, such as PCs at each facility, as illustrated in FIG. 1, via the communication interface 304.

[0018] The 'care recipient identification information storage unit' 202 has the function of storing the acquired care recipient identification information, and is realized by storing it in a database using a storage device 302 or the like.

[0019] The "nursing information acquisition unit" 203 has the function of acquiring nursing information, including text data, indicating the content of nursing provided to a care recipient, in association with the care recipient identification information for that nursing. Nursing information indicates the content of nursing provided to a care recipient, and is information created at the time of nursing for each care recipient ID. The following description assumes that the nursing in this embodiment is home nursing. Specific examples of nursing information will be provided together with an explanation of the nursing information storage unit. The functions of this unit are realized by the processor 301, storage device 302, input / output interface 303, etc. It may also be configured to acquire data output from a client device, etc. via the communication interface 304.

[0020] Additionally, nursing information can be configured to include medication information, which is information regarding the type of medication prescribed for the care recipient, the dosage, and the administration method.

[0021] The "nursing information storage unit" 204 has the function of storing acquired nursing information, and is realized by creating a database using the storage device 302 or the like. Figure 4 is a diagram showing an example of the data structure of the stored nursing information. As shown in the figure, the nursing information is roughly classified into "basic visit information," "basic ADL (Activities of Daily Living) information," "vital signs," and "nursing content," and is stored in association with each nursing recipient identification ID that uniquely identifies the nursing recipient.

[0022] The "basic visit information" includes, for example, the name of the person being cared for, age at the time of the visit, medical expense category, place of business, start date and time of the visit, end date and time of the visit, absence flag indicating the person was absent, length of stay, special instruction visit flag indicating that the visit was a special instruction visit, 5 visits per week after discharge flag indicating that the visit was within 5 weeks of discharge, multiple visit flag indicating that multiple visits were made in one day, and multiple visit flag indicating that the visit was made by multiple people.

[0023] "Basic ADL information" includes, for example, eating, sleeping, taking medication, cleaning, environmental maintenance, money management, interpersonal relationships, eating, moving, excretion, changing clothes, bathing, and grooming, and ADL notes include eating (notes), sleeping (notes), taking medication (notes), cleaning (notes), environmental maintenance (notes), money management (notes), interpersonal relationships (notes), eating (notes), moving (notes), excretion (notes), changing clothes (notes), bathing (notes), and grooming (notes). Here, the above (notes) may include text data.

[0024] As "vital signs," for example, body temperature, pulse rate, systolic blood pressure, diastolic blood pressure, etc. are stored.

[0025] Examples of "nursing content" stored include attentive listening, coordination with the patient's doctor and related institutions, medication and injections, skin and pressure ulcer treatment, assistance with medical examinations, guidance and consultation with family members, rehabilitation, advice, nursing content notes (text), symptom changes, symptom content (text), and special notes (text). The above items marked "(text)" may include text data. Additionally, the medication information described above may be stored as part of the nursing information, along with basic visit information and nursing content.

[0026] The "inference model generation unit" 205 has the function of generating an inference model for estimating the future condition of a care recipient using the nursing information stored therein as learning data. The learning data is generated by vectorizing the text data contained in the nursing information, and is used to generate an inference model for estimating the future condition of a care recipient. The functions of this unit are realized by the processor 301, storage device 302, communication interface 304, etc.

[0027] Fig. 5 is a diagram showing the structure of the training data. As shown in Fig. 5, the training data includes, for each care recipient ID, visit information, ADL information, vital signs, nursing content, month of visit, and vector text, which is vectorized text. Furthermore, if the nursing information includes medication information, the training data also includes the medication information.

[0028] Visit information includes, for example, age at time of visit, length of stay, number of days since last visit, number of absences (past 14 days), change in symptoms, special instruction visit flag, 5 visits per week after discharge flag, multiple visit flag, and multiple visit flag.

[0029] Examples of ADL information include eating (regular), eating (irregular), eating (refusal), eating (overeating), eating (other), sleep (good sleep), sleep (insomnia), sleep (difficulty falling asleep), sleep (early awakening), sleep (awakening during the night), sleep (other), medication (regular), medication (irregular), medication (refusal), medication (failure to take medication), medication (overdosing), medication (other), financial management (self-management), financial management (management by others), financial management (wasting), cleanliness, environmental maintenance, interpersonal relationships, eating, movement, excretion, changing clothes, bathing, and grooming.

[0030] Examples of vital signs include body temperature, pulse rate, systolic blood pressure, and diastolic blood pressure.

[0031] Nursing duties include, for example, listening, coordinating with the patient's doctor and related institutions, administering medication and injections, treating skin and pressure ulcers, assisting with medical appointments, providing guidance and consultation to family members, rehabilitation, and giving advice.

[0032] Examples of the visit month include visit month flag (January) to visit month flag (December) from January to December.

[0033] The vectorized text includes data included in the nursing record that indicates the patient's condition or nursing content, such as ADL notes, symptom content, and nursing content notes and special notes.

[0034] 6 is a flow diagram showing the process for generating training data and an inference model. This process also uses a tokenizer, a transaction file for storing data in progress, and an embedding layer, all of which are stored in the storage device 302.

[0035] As shown in Figure 6, in step 601, the processor 301 acquires and converts data. If the raw data is a spreadsheet, it is converted to CSV (Comma Separated Value) format. If the raw data is an electronic medical record, the data structure of the nursing information is extracted from the database in CSV format. Each piece of data generated in CSV format is stored in the nursing information database.

[0036] In step 602, the processor 301 assigns a target variable to each piece of nursing information. For example, if the patient was hospitalized within the month of the visit, the hospitalization flag is set to "1," and if not, the hospitalization flag is set to "0."

[0037] In step 603, the processor 301 creates features. Specifically, the processor 301 calculates and adds to the nursing information data one or more variables selected from the length of stay, the number of days since the previous visit, and the number of visits in the past 14 days.

[0038] Next, the processor 301 deletes nursing information that is missing information in the items of ADL basic information and symptom change from the nursing information database.

[0039] Next, the processor 301 encodes the items of eating, sleeping, taking medicine, and money management, which are categorical variable items, of the ADL basic information using an encoder.

[0040] Next, if the medical care expense category is missing, the processor 301 copies it from other nursing information of the same care recipient ID to complete it.

[0041] Next, the processor 301 converts the visit month into a visit month flag and adds it to the feature.

[0042] Next, if the vital sign value is abnormal, the processor 301 deletes the nursing information from the nursing information database. Also, if the vital sign is missing, the processor 301 complements it with a value of "-1."

[0043] Next, the processor 301 supplements or corrects the text data as appropriate.

[0044] In step 604, the processor 301 selects nursing information suitable for the learning data from the nursing information database and stores it in the transaction file. For example, the processor 301 selects only nursing information whose medical expense category is "mental." Alternatively, the processor 301 may select only nursing information for care recipient IDs whose number of nursing information items is equal to or exceeds a predetermined number.

[0045] In step 605, the processor 301 converts the text data indicating the condition of the care recipient or the content of care contained in the nursing information stored in the transaction file into a vector using word embedding, and replaces the original text data with the obtained vector and stores it.

[0046] The text data indicating the patient's condition or nursing content included in the nursing information is one or more types selected from notes on ADL, details of symptoms, notes on nursing content, and special notes on nursing content.

[0047] The processor 301 performs word embedding according to the following procedure: The processor 301 converts sentences into vectors (positional embedding) using a tokenizer. The tokenizer can use the best publicly known open source at the time.

[0048] Next, the processor 301 converts these vectors into tensors (word embeddings) by passing them through an embedding layer of a neural network. The embedding layer can use the best publicly available open source embeddings available at the time.

[0049] The processor 301 generates a tensor with, for example, a sentence sequence on the first level, a numeric sequence of position embedding results on the second level, and a numeric sequence of word embedding results on the third level. The processor 301 then calculates the average value of the third level of this tensor, pools it (average pooling), converts it into a vector, and uses it as an embedding vector. The processor 201 then stores the resulting data in a transaction file.

[0050] In step 606, the processor 301 performs data sampling and divides the data in the transaction file into training data:test data=8:2.

[0051] In step 607, the processor 301 extracts features to be used for model training from the generated training data, including at least visit information, vectorized text, and a target variable.

[0052] In step 608, processor 301 standardizes and scales the extracted features and outputs the resulting data to a database of training data.

[0053] In step 609, the processor 301 sequentially reads out the training data from the training data database and trains the model. Specifically, the processor 301 executes the training of the model, the calculation of the loss, and the saving of the model for a predetermined number of epochs.

[0054] In step 610, the processor 301 saves the model of the epoch with the smallest loss as the trained model (inference model).

[0055] Here, the model can be a deep learning model, but if there is a better model available at that time, that model can also be used.

[0056] The "estimation unit" 206 inputs the acquired nursing information into the generated inference model and realizes the function of estimating the future condition of the care recipient related to the input nursing information. This acquired nursing information is nursing information related to the estimation of the future condition, and is, for example, nursing information output and acquired from the client terminal of the nursing care provider using this system. The functions of this unit are mainly realized by the processor 301, storage device 302, communication interface 304, etc.

[0057] FIG. 7 is a diagram showing the data structure of inference data to be input into an inference model to estimate the future condition of a care recipient.

[0058] As shown in Fig. 7, the data structure of the inference data stores, for each care recipient ID, visit information, ADL information, vital signs, nursing content, month of visit, and vectorized text (vectorized text). It can also be configured to store medication information.

[0059] Visit information includes, for example, age at time of visit, length of stay, number of days since last visit, number of absences (past 14 days), change in symptoms, special instruction visit flag, 5 visits per week after discharge flag, multiple visit flag, and multiple visit flag.

[0060] Examples of ADL information include eating (regular), eating (irregular), eating (refusal), eating (overeating), eating (other), sleep (good sleep), sleep (insomnia), sleep (difficulty falling asleep), sleep (early awakening), sleep (awakening during the night), sleep (other), medication (regular), medication (irregular), medication (refusal), medication (failure to take medication), medication (overdosing), medication (other), financial management (self-management), financial management (management by others), financial management (wasting), cleanliness, environmental maintenance, interpersonal relationships, eating, movement, excretion, changing clothes, bathing, and grooming.

[0061] Examples of vital signs include body temperature, pulse rate, systolic blood pressure, and diastolic blood pressure.

[0062] Nursing duties include, for example, listening, coordinating with the patient's doctor and related institutions, administering medication and injections, treating skin and pressure ulcers, assisting with medical appointments, providing guidance and consultation to family members, rehabilitation, and giving advice.

[0063] Examples of the visit month include visit month flag (January) to visit month flag (December) from January to December.

[0064] The vectorized text includes data included in the nursing record that indicates the patient's condition or nursing content, such as ADL notes, symptom content, and nursing content notes and special notes.

[0065] 8 is a flow diagram showing the process of inference using the generated inference model. As shown in the figure, in step 801, data acquisition and conversion of the data to be processed are performed. The method of data acquisition and conversion is the same as the method in step 601 in FIG. 6.

[0066] In step 802, the processor 301 creates features. The processing method is the same as the processing method in step 603.

[0067] In step 803, processor 301 performs word embedding. The processing method is the same as the processing method in step 605.

[0068] In step 804, the processor 301 selects a feature amount. The processing method is the same as the processing method in step 607.

[0069] In step 805, the processor 301 performs scaling. The processing method is the same as the processing method in step 608.

[0070] In step 806, the processor 301 applies the obtained data to be processed to the inference model to perform inference and obtain the possibility of future hospitalization (hospitalization risk) as the future condition of the care recipient. The inference result, the possibility of hospitalization, can be obtained as a numerical value such as probability (%), or as a level such as high, medium, or low.

[0071] The system can also be configured to acquire the degree of symptoms (a numerical representation of the severity of symptoms) based on a predetermined symptom assessment scale as a future condition of the care recipient. Examples of symptom assessment scales include the Brief Psychiatric Rating Scale (BPRS) and the PANSS (Positive and Negative Symptom Scale).

[0072] The estimation unit can also estimate the future state of the care recipient, including the risk of the care recipient committing a crime or committing a crime, or of committing suicide or self-harm. All of these risks may arise depending on the severity and progression of the symptoms of the mental illness. Furthermore, the events that occur depending on the type of illness, symptoms, and progression of the symptoms are correlated with ADLs and the care recipient's statements. For example, the onset of negative symptoms in schizophrenia can lead to a loss of motivation and a tendency to neglect ADLs such as grooming and bathing. The onset of positive symptoms can lead to hallucinations and delusions, which can affect the care recipient's behavior and statements. As these symptoms progress, situations such as suicide or assault can occur. By accumulating information about the care recipient, such as ADL information and statements, as nursing information and generating an inference model using the accumulated nursing information, it is possible to estimate the care recipient's potential risks, such as suicide or assault.

[0073] In step 807, the processor 301 outputs the inference result, the possibility of hospitalization, to the input / output interface 303. The inference result can also be output to a client device or the like via the communication interface 304.

[0074] Figure 9 shows the flow of word embedding. As shown in Figure 9, processor 301 tokenizes (converts into a string of words, the smallest unit) the input text using a tokenizer. In Figure 9, each of the delimited words indicated by "tokenization" is a token.

[0075] Next, the processor 301 performs position embedding on the obtained tokens and vectorizes the string of tokens. In Fig. 9, the numerical values ​​indicated by "position embedding" are IDs, which are identifiers uniquely assigned to each token.

[0076] Next, the processor 301 performs word embedding for each vectorized token and converts the vector into a tensor. In Fig. 9, the string of numbers indicated by "word embedding" is a vector uniquely assigned to each token.

[0077] Next, the processor 301 performs average pooling on the third level of the obtained tensor (the numerical sequence resulting from word embedding) to reshape it and obtain an embedding vector. In Figure 9, the numerical sequence indicated by "average pooling" is the embedding vector after reshaping by average pooling. Here, it has been reshaped into 128 features.

[0078] The estimation results by the estimation unit can be output in various forms, which are realized by the processor 301 and the input / output interface 303. For example, Fig. 10 is a diagram showing an example of outputting the hospitalization risk as a future condition of a care recipient. As shown in the figure, the hospitalization risk (%) and the change from the previous month (%) are displayed for each patient (care recipient).

[0079] 11 is a diagram showing an example of an output of an increase or decrease in the risk of hospitalization, as another output mode. As shown in FIG. 11, the increase or decrease (%) in the risk of hospitalization for each patient (care recipient) is displayed together with the hospitalization risk (%).

[0080] 12 is a diagram showing an example of output of hospitalization risk statistics as another output mode. As shown in FIG. 12, the degrees of hospitalization risk are tallied and displayed as a pie chart.

[0081] 13 is a diagram showing an example of output of the transition of hospitalization risk statistics as another output mode. As shown in Fig. 13, the frequency of the degree of hospitalization risk is tallied and the transition is displayed as a bar graph.

[0082] Furthermore, the estimation results by the estimation unit can also be output in association with the estimated time, which is the estimated future time. Figure 14 is a diagram showing the estimated results of the care recipient's future condition for each future month. The vertical axis indicates the future severity of symptoms from 0 (low) to 100 (high), and the horizontal axis indicates the estimated time, and the estimated results for each month from December 2020 to September 2021 are shown in a line graph. By outputting the symptoms along with the estimated time in this way, it is possible to prepare treatments and nursing methods to reduce the peak in symptom severity predicted for May 2021, for example.

[0083] The output of the estimation results can also be displayed together with the actual progress of the care recipient's symptoms up to the present. As shown in Figure 15, the graph up to March 2021 shows the progress of the care recipient's symptoms up to that point as a solid line, and the estimated progress of the severity of symptoms from April 2021 onwards as a dotted line.

[0084] Furthermore, Figure 16 shows the symptom progression shown in Figure 15, showing both the actual symptom progression (practice) up to March 2021 and the previously estimated symptom progression (dotted line) up to March 2021. As shown in the figure, the previous estimation predicted that the symptom severity would exceed "40" in January 2021, but it can be seen that the symptom severity did not actually increase that much.

[0085] By making it possible to compare the estimated results with the actual results and obtain the difference between the two, it is possible to verify, for example, the impact that the nursing care provided up to that point has had on the care recipient, or to verify the accuracy of the inference model.

[0086] Outputting the estimated results in this way can be useful in supporting nurses and nursing facilities. Because the condition and symptom changes of a care recipient cannot be inferred from biological data shown as numerical values, understanding the condition of the care recipient and providing appropriate care is not easy, and this places a heavy burden on nurses. However, by using this nursing information processing system to view the estimated results, it can help improve understanding of the care recipient and develop appropriate nursing care plans, thereby providing support to nurses and nursing facilities.

[0087] The estimated results can also be used to educate nurses. Nursing psychiatric patients is no easy task, as it is difficult to diagnose or grasp changes in symptoms using only biological data, which can be expressed in numerical terms. In reality, the only way to improve nursing skills is through experience. However, this nursing information processing system can infer the symptoms of the care recipient from nursing information and display the results, which can be used as teaching material for nursing education, making it useful for education aimed at improving nursing skills.

[0088] The estimated results may be output not only to nursing facilities but also to the family of the care recipient. For example, the system may be configured to associate and store the care recipient's identification information with identification information identifying the care recipient's family, and allow access to the nursing information processing system by family members identified by the associated identification information, allowing them to view the estimated results within a predetermined range. For example, the estimated results shown in Figures 10 to 13 relate to multiple care recipients, so it is not preferable for the family members to view them. However, it is preferable to allow the family members of a care recipient, such as those shown in Figures 14 to 16, to view the future symptom progression of a single care recipient after a predetermined authentication process. Understanding the estimated results is extremely useful for family members involved in home care.

[0089] Furthermore, if the care recipient has a job or visits a facility, it is also preferable to configure the system to output the estimation results to the company that employs the care recipient, the facility that the care recipient visits, etc. The estimation results can be used to understand the symptoms and their progression, which helps to provide appropriate care for the care recipient and provides support to the company that employs the care recipient, the facility that the care recipient visits, etc.

[0090] The invention related to the nursing information processing system can also be expressed as an invention related to a method for operating a nursing information processing system, which includes the following steps: a nursing recipient identification information acquisition step for acquiring nursing recipient identification information that identifies a nursing recipient; a nursing recipient identification information retention step for retaining the acquired nursing recipient identification information; a nursing information acquisition step for acquiring nursing information, including text data, that indicates the content of nursing provided to the nursing recipient, in association with the nursing recipient identification information for that nursing care; a nursing information retention step for retaining the acquired nursing information; an inference model generation step for generating an inference model using the retained nursing information as learning data to estimate the future condition of the nursing recipient; and an estimation step for inputting the acquired nursing information into the generated inference model and estimating the future condition of the nursing recipient related to the input nursing information. <Other embodiments>

[0091] Based on the nursing information processing system described above, it is also possible to configure the system to acquire nursing information in association with a nurse. That is, the above-described nursing information processing system can further include a nurse identification information acquisition unit that acquires nurse identification information identifying the nurse providing the care, and a nurse identification information storage unit that stores the acquired nurse identification information. The nursing information acquisition unit can be configured to acquire nursing information in association with the nurse identification information for that care. The nurse identification information includes, for example, a unique identification information "nurse ID," as well as information about the nurse identified by this nurse ID, such as the "name," "gender," "date of birth," "place of business," and "years of experience."

[0092] With the above configuration, the future condition of the care recipients that the caregiver is responsible for can be estimated and output, allowing the caregiver to easily understand the estimated results for the future condition of the care recipients that the caregiver is caring for. Furthermore, as described above, the difference between the estimated results and the actual symptoms can be obtained, which can be useful for determining whether the care provided by the nurse is contributing to the improvement of the care recipient's symptoms, or whether the nurse and the care recipient are compatible. Furthermore, by registering and storing the nurse identification information in association with information identifying the nursing facility to which the nurse belongs, the system can be configured to extract and output nursing information and estimated results for each nursing facility.

[0093] Furthermore, based on the nursing information processing system described above, it can also be configured to acquire nursing information in association with a medical institution. That is, the nursing information processing system described above can further include a medical institution identification information acquisition unit that acquires medical institution identification information (medical institution ID) that identifies a medical institution (including a doctor), and a medical institution identification information storage unit that stores the medical institution identification information, and the nursing information acquisition unit can be configured to acquire nursing information in association with the medical institution identification information of the medical institution related to the nursing.

[0094] By linking nursing information with a medical institution ID, it is possible to share nursing information between nursing facilities, care recipients (and their families), and medical institutions. In addition, for a medical institution identified by a medical institution ID linked to nursing information, it is possible to output inferred results about care recipients who are under the care of (associated with) that medical institution or a doctor belonging to that medical institution, which can be useful for verifying and reviewing nursing plans for care recipients.

[0095] Furthermore, based on the nursing information processing system described above, it can also be configured to acquire nursing information in association with an administrative agency or a welfare service provider (hereinafter referred to as administrative agency, etc.). That is, the nursing information processing system described above can further include an administrative agency, etc. identification information acquisition unit that acquires administrative agency, etc. identification information (administrative agency, etc. ID) that identifies an administrative agency or a welfare service provider (hereinafter referred to as administrative agency, etc.), and an administrative agency, etc. identification information storage unit that stores the acquired administrative agency, etc. identification information, and the nursing information acquisition unit can be configured to acquire nursing information in association with the administrative agency, etc. identification information of the administrative agency, etc. related to the nursing.

[0096] Administrative agencies include the local government offices to which nursing care facilities or medical institutions belong, the agencies that oversee nursing care facilities or medical institutions, the local government offices where the care recipients reside, the facilities that the care recipients attend school or attend, and the agencies that oversee them, and may also include academic societies related to nursing or medical care.Welfare service providers include nursing care facilities and caregiving facilities, as well as businesses and associations that provide various support and assistance services such as independent living assistance, community living support, employment transition support, and employment retention support.

[0097] By linking nursing information with an administrative agency ID, nursing information can be shared among nursing facilities, care recipients (and their families), and administrative agencies involved with care recipients. In addition, it is possible to output inferred results about care recipients for whom the administrative agency is involved in providing support to the administrative agency identified by the administrative agency ID linked to the nursing information, enabling multifaceted support to be provided to care recipients. <Mental Health Care System>

[0098] The above nursing information processing system can also be applied to a system for providing mental health care to employees in a company or students in a school. In the nursing information processing system described above, the nursing recipient can be replaced with an employee (employee or student), and the nursing information can include responses to questions in a stress check. That is, the system can be configured as a nursing information processing system including: an employee identification information acquisition unit that acquires employee identification information identifying the employee (employee or student); an employee identification information storage unit that stores the acquired employee identification information; a nursing information acquisition unit that acquires nursing information indicating the content of nursing care provided to the employee (including text data) in association with the employee identification information for the nursing care; a nursing information storage unit that stores the acquired nursing information; an inference model generation unit that uses the stored nursing information as learning data to generate an inference model for estimating the future condition of the employee; and an estimation unit that inputs the acquired nursing information into the generated inference model and estimates the future condition of the employee related to the input nursing information, where the nursing information includes responses to questions in a stress check.

[0099] Here, employees, etc. refer to those who work for a company, etc., such as full-time employees, temporary employees, and part-time workers, as well as students attending or enrolled in various schools and universities, whether private or public.

[0100] Furthermore, each component of this system is based on the nursing information processing system for nursing care recipients described above. Employee identification information, like the nursing care recipient identification information, includes an "employee ID" that uniquely identifies an employee, and information about the employee identified by this employee ID, such as their "name," "gender," "date of birth," "company name," "employment status," "school name," "department name," "class name," and "medical care expense classification."

[0101] Furthermore, the nursing information in this system is based on nursing information in the nursing information processing system for the care recipient, and is arranged into appropriate information for inferring the mental health of employees, etc. For example, the system is configured to include responses from employees, etc. to questions on a questionnaire administered in a stress check system based on the Industrial Safety and Health Act, which was established for the mental health care of workers. For pupils and students, responses are included in the nursing information using a questionnaire that complies with the stress check system.

[0102] Then, just like the nursing information processing system for care recipients, an inference model is generated using the nursing information as learning data. This inference model is used to estimate the future condition of employees, etc. For example, it can estimate whether employees, etc. are in a high-stress state, whether their stress state will increase in the future, or what diseases or symptoms they may be at risk of developing. Furthermore, these estimation results can be used to improve the mental health of employees, etc.

[0103] Similarly to the nursing information processing system for the care recipient, the system can be configured to output the estimation results to the family of the employee, or to output the estimated condition of the employee in association with the estimated future time. The system can also be configured to acquire nursing information in association with caregiver identification information, nursing information in association with medical institution identification information, or nursing information in association with administrative agency identification information. In this case, the caregiver may be the employee's supervisor, a health manager at the company employing the employee, or a school where the student attends, an industrial physician, a stress check implementer, a teacher, a school doctor, or a school counselor. The system can also be configured to output the estimation results to a medical institution identified by medical institution identification information or an administrative agency identified by administrative agency identification information. The system can also be configured to acquire the difference between the employee's condition in the estimation result at the time of estimation and the actual condition of the employee. The estimated future condition of the employee can be one or a combination of two or more of hospitalization, assault, crime, and suicide.

[0104] The system may also be configured to further include an employer output unit that outputs the estimation results to the employer who employs the employee or the school where the student attends. Employers and schools are required to take into consideration the mental health of their employees and students. By outputting the estimation results to employers and schools, they can understand symptoms and predictions of symptoms, enabling more appropriate responses.

[0105] Similar to the nursing information processing system for care recipients, the system can be configured to acquire nursing information in association with information identifying medical institutions, administrative agencies, etc., and output estimation results to the medical institutions, administrative agencies, etc. identified by the identification information. By doing so, by sharing nursing information and estimation results, including answers to stress check questions, with the above-mentioned institutions, it is possible to provide multifaceted support to employees, employers, students, and schools. <Effects>

[0106] As described above, the nursing information processing system according to the present invention can estimate the future condition of a care recipient based on nursing information including text data indicating the conversation and state of the care recipient. [Explanation of symbols]

[0107] 200: Nursing Information Processing System 201: Nursing recipient identification information acquisition unit 202: Nursing recipient identification information holding unit 203: Nursing Information Acquisition Department 204: Nursing information storage unit 205: Inference model generation unit 206: Estimation Department- 300: Nursing Information Processing System 301: Processor 302: Storage device 303: Input / output interface 304: Communication interface

Claims

1. a nursing information acquisition unit that acquires nursing information including text data that indicates the content of nursing care provided to a care recipient with a mental illness; an estimation unit that inputs the acquired nursing information into an inference model and estimates future risks that may arise depending on the severity and progression of symptoms of mental illness of the care recipient related to the input nursing information; an output unit that outputs an estimation result that is a result of the estimation by the estimation unit; and The nursing information includes ADL information, and the text data includes notes about ADL. The inference model is generated using learning data based on nursing information accumulated in the past, The possible future risk of the care recipient estimated by the estimation unit is one or a combination of two or more of assault, crime, suicide, and hospitalization. Nursing information processing system.

2. An information storage unit for storing information about the family of the care recipient is further provided, 2. The nursing information processing system according to claim 1, wherein the output unit outputs the inference result to a family member of the care recipient related to the inference result based on the information stored in the information storage unit.

3. 3. The nursing information processing system according to claim 1, wherein the output unit outputs the estimated condition of the care recipient in association with an estimated future time.

4. a caregiver identification information acquisition unit that acquires caregiver identification information that identifies a caregiver providing care; a caregiver identification information storage unit that stores the acquired caregiver identification information, 4. The nursing information processing system according to claim 1, wherein the nursing information acquisition unit acquires the nursing information in association with the nurse identification information for the nursing care.

5. a medical institution identification information acquisition unit that acquires medical institution identification information that identifies a medical institution or a doctor; a medical institution identification information storage unit that stores the acquired medical institution identification information, 5. The nursing information processing system according to claim 1, wherein the nursing information acquisition unit acquires the nursing information in association with the medical institution identification information of the medical institution related to the nursing.

6. The nursing information processing system according to claim 5 , wherein the output unit outputs the estimation result to the medical institution based on the medical institution identification information stored in the medical institution identification information storage unit.

7. an administrative agency etc. identification information acquisition unit that acquires administrative agency etc. identification information that identifies an administrative agency or a welfare service provider; and an administrative agency etc. identification information storage unit that stores the acquired administrative agency etc. identification information, 7. The nursing information processing system according to claim 1, wherein the nursing information acquisition unit acquires the nursing information in association with the administrative agency or other identification information of the administrative agency or welfare service provider related to the nursing.

8. The nursing information processing system according to claim 7 , wherein the output unit outputs the estimation result to the administrative agency or a welfare service provider based on the administrative agency identification information stored in the administrative agency identification information storage unit.

9. 9. The nursing information processing system according to claim 3, further comprising a difference acquisition unit that acquires a difference between the state of the care recipient in the estimation result at the time of estimation and the actual state of the care recipient.

10. an answer information acquisition unit that acquires answer information including text data, which is information indicating answers to questions posed to employees or students who are recipients of mental health care; an estimation unit that inputs the acquired response information into an inference model and estimates future risks that may arise due to the mental health of the employee or student related to the input response information; The response information includes responses to questions in a stress check, The answer information includes ADL information, and the text data includes notes about the ADL. the inference model is generated using learning data based on previously accumulated answer information, The possible future risk of the employee or student estimated by the estimation unit is one or a combination of two or more of assault, crime, suicide, and hospitalization. Information processing system.

11. An information storage unit that stores information about an employer that employs the employee or a school that the student attends, The information processing system according to claim 10, further comprising an employer output unit that outputs the estimation result, which is the result of the estimation performed by the estimation unit based on the information stored in the information storage unit, to the employer who employs the employee or the school where the student attends.

12. a nursing information acquisition step of acquiring nursing information including text data that indicates the content of nursing provided to a care recipient with a mental illness; an estimation step of inputting the acquired nursing information into an inference model and estimating future risks that may arise depending on the severity and progression of symptoms of the mental illness of the care recipient related to the input nursing information; and The nursing information includes at least ADL information, and the text data includes at least notes about ADL, The inference model is generated using learning data based on nursing information accumulated in the past, The possible future risk of the care recipient estimated in the estimation step is one or a combination of two or more of assault, crime, suicide, and hospitalization. A method for operating a nursing information processing system.

13. an answer information acquisition step of acquiring answer information including text data, the answer information indicating answers to questions posed to employees or students who are recipients of mental health care; An estimation step of inputting the acquired response information into an inference model and estimating future risks that may arise due to the mental health of the employee or student related to the input response information; and The response information includes responses to questions in a stress check, The answer information includes ADL information, and the text data includes notes about the ADL. the inference model is generated using learning data based on previously accumulated answer information, The possible future risk of the employee or student estimated in the estimation step is one or a combination of two or more of assault, crime, suicide, and hospitalization; A method for operating an information processing system.

14. a nursing information acquisition step of acquiring nursing information including text data that indicates the content of nursing provided to a care recipient with a mental illness; an estimation step of inputting the acquired nursing information into an inference model and estimating future risks that may arise depending on the severity and progression of symptoms of the mental illness of the care recipient related to the input nursing information; A program including instructions for causing an electronic computer to execute the following: The nursing information includes at least ADL information, and the text data includes at least notes about ADL, The inference model is generated using learning data based on nursing information accumulated in the past, The possible future risk of the care recipient estimated in the estimation step is one or a combination of two or more of assault, crime, suicide, and hospitalization. program.

15. an answer information acquisition step of acquiring answer information including text data, the answer information indicating answers to questions posed to employees or students who are recipients of mental health care; An estimation step of inputting the acquired response information into an inference model and estimating future risks that may arise due to the mental health of the employee or student related to the input response information; A program including instructions for causing an electronic computer to execute the following: The response information includes responses to questions in a stress check, The answer information includes ADL information, and the text data includes notes about the ADL. the inference model is generated using learning data based on previously accumulated answer information, The possible future risk of the employee or student estimated in the estimation step is one or a combination of two or more of assault, crime, suicide, and hospitalization; program.

16. A method for generating an inference model using a computer, comprising: a nursing information acquisition step of acquiring nursing information including text data that indicates the content of nursing provided to a care recipient with a mental illness; and an inference model generation step of generating an inference model for estimating future risks that may arise depending on the severity and progression of symptoms of the mental illness of the care recipient by having a computer learn the acquired nursing information as learning data, The nursing information includes at least ADL information, and the text data includes at least notes about ADL, The possible future risk of the care recipient estimated by the inference model is one or a combination of two or more of assault, crime, suicide, and hospitalization. How to generate an inference model.

17. A method for generating an inference model using a computer, comprising: an answer information acquisition step of acquiring answer information including text data, the answer information indicating answers to questions posed to employees or students who are recipients of mental health care; and an inference model generation step of generating an inference model for estimating future risks that may arise due to the mental health of employees or students by having a computer learn the acquired response information as learning data, The response information includes responses to questions in a stress check, The answer information includes ADL information, and the text data includes notes about the ADL. The possible future risk of the employee or student estimated by the inference model is one or a combination of two or more of assault, crime, suicide, and hospitalization. How to generate an inference model.

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