JP7618616B2 - Nursing information processing system - Google Patents

Description

The present invention relates to a nursing information processing system.

Japan is experiencing an aging population, and the proportion of the population aged 75 and over is expected to continue to increase. As a result, the demand for medical care and nursing care is expected to increase further. In light of this situation, the way medical care is provided is shifting from the traditional "hospital-based" model to a "community-based" model in which the entire community supports medical care for people's lives in their familiar neighborhoods and at home.

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

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 nurses suited to the patient's condition to provide nursing care at the appropriate time.

JP 2020-021422 A

In a visiting nursing service specialized in psychiatry, the invention of Patent Document 1 has difficulty predicting changes in a patient's condition or the worsening of their condition. This is because it is difficult to make a psychiatric diagnosis based solely on biological data information that can be expressed in quantities, such as the patient's pulse, blood pressure, body temperature, and brain waves. In other words, the words the patient speaks and the patient's appearance are important in a psychiatric diagnosis.

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

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.

According to the present invention, it is possible to estimate the future condition of a care recipient based on nursing information that includes text data showing the patient's conversation, condition, etc.

A conceptual diagram showing an overview of the nursing information processing system according to the present embodiment. FIG. 1 is a block diagram showing an example of a functional configuration of a nursing information processing system according to an embodiment of the present invention. Example of hardware configuration for implementing the nursing information processing system of this embodiment FIG. 1 is a diagram showing an example of the data structure of nursing information. Diagram showing the structure of the learning data Flow diagram showing the process for generating training data and generating an inference model A diagram showing the data structure of inference data Flow diagram showing the process of estimation using an inference model Diagram showing the word embedding process An example of outputting the risk of hospitalization An example of output of increase or decrease in hospitalization risk Example of hospitalization risk statistics output Example of output of hospitalization risk statistics A diagram showing an example of outputting the estimation result in relation to the time of estimation. An example of outputting the estimation results together with the actual progression of symptoms. FIG. 13 is a diagram showing an example of outputting the actual symptom transition and the estimated symptom transition during that period.

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

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 predicting the future condition of a care recipient is generated using the stored nursing information as learning data. By inputting 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 predicted.
<Configuration>

Figure 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, it has a nursing recipient identification information acquisition unit 201, a nursing 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.

Each of the functional configurations 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.

Figure 3 shows an example of the hardware configuration for implementing the nursing information processing system 300, which is composed of 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 non-volatile memory (HDD, flash memory, etc.), 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 according to the programs. The input/output interface 303 obtains 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 be realized by dedicated hardware.

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

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 unique identification information "care recipient ID" 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 has a job or commutes to a facility, the information may further include information such as the "name of place of employment" and "name of facility to which the care recipient commutes." 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 exemplified in FIG. 1, via the communication interface 304.

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, etc.

The "nursing information acquisition unit" 203 has a function of acquiring nursing information, which is information indicating the content of nursing provided to a care recipient and includes text data, in association with the care recipient identification information for that nursing. Nursing information is information indicating the content of nursing provided to a care recipient, and is information created at the time of nursing for each care recipient ID. In the following, the nursing in this embodiment will be described as visiting nursing. Specific examples of nursing information will be shown 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.

Nursing information can also be configured to include medication information. Medication information is information about the type of medication prescribed for the care recipient, the dosage, and the method of administration.

The "nursing information storage unit" 204 has the function of storing acquired nursing information, and is realized by storing it as a database using the storage device 302 or the like. FIG. 4 is a diagram showing an example of the data configuration of stored nursing information. As shown in the figure, nursing information is stored in association with "basic visit information," "basic ADL (Activities of Daily Living) information," "vital signs," and "nursing content" broadly categorized for each care recipient identification ID that uniquely identifies the care recipient.

As "basic visit information", for example, the name of the person cared for, age at the time of the visit, medical care expense category, place of business, start date and time of the visit, end date and time of the visit, absence flag indicating the absence, duration of stay, special instruction visit flag indicating that it was a special instruction visit, 5 visits per week after discharge flag indicating that the visit was made 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 are stored.

"ADL basic information" includes, for example, eating, sleeping, taking medicine, cleaning, environmental improvement, money management, interpersonal relations, eating, moving, excretion, changing clothes, bathing, and grooming, and ADL notes include eating (notes), sleeping (notes), taking medicine (notes), cleaning (notes), environmental improvement (notes), money management (notes), interpersonal relations (notes), eating (notes), moving (notes), excretion (notes), changing clothes (notes), bathing (notes), and grooming (notes). Here, the above (notes) may include text data.

For example, body temperature, pulse rate, systolic blood pressure, and diastolic blood pressure are stored as "vital signs."

Examples of "nursing content" stored include attentive listening, coordination with the patient's doctor and related institutions, administration of medication and injections, treatment of skin and bedsores, assistance with medical examinations, guidance and consultation with family members, rehabilitation, advice, notes on nursing content (text), changes in symptoms, content of symptoms (text), and special notes (text). Here, the items marked as (text) above may include text data. Additionally, the above-mentioned medication information may be stored as information constituting nursing information together with basic visit information and nursing content.

The "inference model generation unit" 205 has a function of generating an inference model for estimating the future condition of the 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 the care recipient. The functions of this unit are realized by the processor 301, storage device 302, communication interface 304, etc.

Figure 5 is a diagram showing the structure of the learning data. As shown in Figure 5, the learning data includes, for each care recipient ID, visit information, ADL information, vital signs, nursing care content, month of visit, and vector text, which is vectorized text. In addition, if the nursing information includes medication information, the learning data also includes medication information.

Examples of visit information include 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 visits flag.

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), money management (self-management), money management (management of others), money management (wasting), cleanliness, environmental improvement, interpersonal relationships, meals, movement, excretion, changing clothes, bathing, and grooming.

Vital signs include, for example, body temperature, pulse rate, systolic blood pressure, and diastolic blood pressure.

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 examinations, instructing and consulting with family members, rehabilitation, and giving advice.

Examples of visit months include visit month flag (January) to visit month flag (December) for the months January to December.

Vectorized text includes data included in nursing records that indicates the patient's condition or nursing content, such as ADL notes, symptom content, and nursing content notes and special notes.

Figure 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 that stores data in progress, and an embedding layer, all of which are stored in the storage device 302.

As shown in FIG. 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.

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

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 following items: length of stay, number of days since last visit, and number of visits in which the patient was absent (past 14 days).

Next, the processor 301 deletes nursing information that is missing information in the ADL basic information and symptom change sections from the nursing information database.

Next, the processor 301 uses an encoder to encode the categorical variable items of the ADL basic information, namely, eating, sleeping, taking medication, and financial management.

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

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

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 completes the value with "-1."

The processor 301 then supplements and corrects the text data as appropriate.

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. The processor 301 selects, for example, only nursing information whose medical care expense category is "mental." It may also select only nursing information for care recipient IDs with a predetermined number or more of nursing information items.

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.

The text data included in the nursing information that indicates the patient's condition or nursing content is selected from one or more of the following: notes on ADL, details of symptoms, notes on nursing content, and special notes on nursing content.

The processor 301 performs word embedding according to the following procedure. The processor 301 converts the sentence into a vector (positional embedding) using a tokenizer. The tokenizer can be the best publicly known open source at the time.

Then, 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 known open source at the time.

The processor 301 generates a tensor with, for example, the first level: a sentence sequence, the second level: a numeric sequence of the position embedding result, and the third level: a numeric sequence of the word embedding result. 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 obtained data in a transaction file.

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

In step 607, the processor 301 extracts features to be used for model training from the generated training data. The processor 301 extracts at least the visit information, the vectorized text, and the objective variable.

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

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 trains the model, calculates the loss, and saves the model for a predetermined number of epochs.

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

Here, the model can use a deep learning model, but if there is a better model available at that point, that model can also be used.

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 a client terminal of a 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.

Figure 7 shows the data structure of the inference data to be input into the inference model to estimate the future condition of the care recipient.

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 care content, month of visit, and vector text, which is vectorized text. It can also be configured to store medication information as well.

Examples of visit information include 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 visits flag.

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), money management (self-management), money management (management of others), money management (wasting), cleanliness, environmental improvement, interpersonal relationships, meals, movement, excretion, changing clothes, bathing, and grooming.

Vital signs include, for example, body temperature, pulse rate, systolic blood pressure, and diastolic blood pressure.

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 examinations, instructing and consulting with family members, rehabilitation, and giving advice.

Examples of visit months include visit month flag (January) to visit month flag (December) for the months January to December.

Vectorized text includes data included in nursing records that indicates the patient's condition or nursing content, such as ADL notes, symptom content, and nursing content notes and special notes.

Figure 8 is a flow diagram showing the process of estimation 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 of step 601 in Figure 6.

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

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

In step 804, the processor 301 selects features. The processing method is the same as the processing method in step 607.

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

In step 806, the processor 301 applies the obtained data to be processed to the inference model to perform inference and obtain the future possibility of 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.

The system can also be configured to obtain the degree of symptoms (indicating the severity of symptoms numerically) based on a specified symptom assessment scale as the future condition of the care recipient. Examples of symptom assessment scales include the Brief Psychiatric Rating Scale (BPRS) and the PANSS (Positive and Negative Symptoms Scale).

The future state of the care recipient estimated by the estimation unit can also be the risk of the care recipient committing violence or crime, or committing suicide or self-harming. All of these risks are risks that can occur depending on the severity and progression of the symptoms of the mental illness. Events that occur due to the type of illness, symptoms, and progression of symptoms are correlated with ADL and the content of the care recipient's statements. For example, when negative symptoms in schizophrenia appear, motivation decreases and grooming and bathing (both of which are ADL) tend to be neglected, and when positive symptoms appear, symptoms of hallucinations and delusions appear, which lead to the care recipient's attitude and statements. As these symptoms progress, situations such as suicide and violence can occur. By accumulating information about the care recipient, such as ADL information and content of statements, as nursing information and generating an inference model using the accumulated nursing information, it is possible to estimate the risks that may occur to the care recipient, such as suicide and violence.

In step 807, the processor 301 outputs the inference result of 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.

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

Next, the processor 301 performs position embedding on the obtained tokens and vectorizes the token sequence. In FIG. 9, the numbers indicated by "position embedding" are IDs, which are identifiers uniquely assigned to each token.

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.

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 FIG. 9, the numerical sequence indicated by "average pooling" is the embedding vector after reshaping by average pooling. Here, it is reshaped into 128 features.

The estimation results from the estimation unit can be output in various forms, and 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 risk of hospitalization 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).

Figure 11 shows an example of an output of an increase or decrease in the risk of hospitalization, as another output format. As shown in Figure 11, the increase or decrease in the risk of hospitalization (%) for each patient (care recipient) is displayed together with the hospitalization risk (%).

Figure 12 shows an example of output of hospitalization risk statistics as another output format. As shown in Figure 12, the degree of hospitalization risk is tallied and displayed as a pie chart.

Figure 13 shows an example of an output of the trend of hospitalization risk statistics as another output format. As shown in Figure 13, the degree of hospitalization risk is tallied and the trend is displayed as a bar graph.

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 future condition of the care recipient for each future month. The vertical axis shows the future severity of symptoms from 0 (low) to 100 (high), and the horizontal axis shows 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 the severity of symptoms predicted in May 2021, for example.

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

As shown in FIG. 16, the symptom progression shown in FIG. 15 is a diagram 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.

In this way, 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. It can also be used to verify the accuracy of an inference model.

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

The estimated results can also be useful in educating nurses. It is not easy to care for psychiatric patients, as it is difficult to diagnose or grasp changes in symptoms using only biological data information that can be expressed in numerical values. In reality, the only way to improve nursing skills is through experience. However, with this nursing information processing system, the symptoms of the person being cared for can be inferred from nursing information and the results are displayed, so that by using this as teaching material for nursing studies, it can be useful in education aimed at improving nursing skills, etc.

The estimated results are not limited to being output to nursing facilities, but are also preferably output to the family of the cared for. For example, the system can be configured to associate and hold the cared for identification information and identification information for identifying the family of the cared for, and to permit access to the nursing information processing system by family members identified by the associated identification information, so that the estimated results can be viewed within a predetermined range. As the predetermined range, for example, the estimated results shown in Figures 10 to 13 relate to multiple cared for persons, so it is not preferable to allow the family members to view them, but it is preferable to allow the family members of the cared for the future progress of symptoms of one cared for person shown in Figures 14 to 16 to view them after a predetermined authentication process. For family members involved in nursing at home, understanding the estimated results is extremely useful in providing nursing care.

In addition, if the care recipient is employed or attends a facility, it is also preferable to configure the system to output the estimation results to the company that employs the care recipient or the facility that the care recipient attends. The estimation results can be used to understand symptoms and their progression, which helps to take appropriate measures for the care recipient and provides support to the company that employs the care recipient or the facility that the care recipient attends.

The invention related to the nursing information processing system can also be expressed as an invention related to an operation method of the nursing information processing system, which includes a nursing recipient identification information acquisition step of acquiring nursing recipient identification information for identifying a nursing recipient, a nursing recipient identification information storage step of storing the acquired nursing recipient identification information, a nursing information acquisition step of acquiring nursing information including text data indicating the content of nursing care given to the nursing recipient in association with the nursing recipient identification information for the nursing care, a nursing information storage step of storing the acquired nursing information, an inference model generation step of generating an inference model for estimating the future condition of the nursing recipient using the stored nursing information as learning data, and an estimation step of 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>

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

The above configuration allows a nurse to output the estimated future condition of the care recipients under his/her care, and to easily understand the estimated results for the future condition of the care recipients he/she is caring for. As described above, the difference between the estimated result and the actual symptoms can also be obtained, which can be useful for understanding whether the care provided by the nurse is contributing to the improvement of the care recipient's symptoms, and whether the nurse and the care recipient get along well. Furthermore, by configuring the system to register and hold the nurse identification information in association with information identifying the nursing facility to which the nurse belongs, it can also be configured to extract and output nursing information and estimated results for each nursing facility.

Furthermore, based on the nursing information processing system described so far, it can 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.

By associating nursing information with a medical institution ID, nursing information can be shared between nursing facilities, the care recipient (and their family), and medical institutions. In addition, for a medical institution identified by a medical institution ID associated with the nursing information, it is also possible to output inference results about the care recipient 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 care plans for care recipients.

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 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 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.

Administrative agencies include the local government office to which the nursing facility or medical institution belongs, agencies that oversee the nursing facility or medical institution, the local government office where the care recipient lives, the facilities the care recipient attends and the agencies that oversee them, and may also include academic societies related to nursing or medicine. Welfare service providers include nursing facilities and care facilities, as well as businesses and associations that provide various support and assistance services such as assistance with independent living, support for community life, employment transition support, and employment retention support.

By associating 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 inference results about care recipients for whom the administrative agency is involved in providing support to the administrative agency identified by the administrative agency ID associated with the nursing information, thereby enabling multifaceted support to be provided to care recipients.
<Mental Health Care System>

The above nursing information processing system can 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 information processing system can be configured to include employees (employees or students) instead of the care recipients, and to include answers to questions in a stress check in the nursing information. That is, the system can be configured as a nursing information processing system having an employee identification information acquisition unit that acquires employee identification information that identifies employees who are employees or students, an employee identification information storage unit that stores the acquired employee identification information, a nursing information acquisition unit that acquires nursing information that indicates the content of nursing care provided to employees, 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 generates an inference model for estimating the future state of employees, etc., using the stored nursing information as learning data, and an estimation unit that inputs the acquired nursing information into the generated inference model and estimates the future state of employees, etc. related to the input nursing information, and the nursing information includes answers to questions in a stress check.

Here, employees include full-time employees, temporary employees, part-time workers, and other people who work for a company, etc., as well as students attending or enrolled in various schools and universities, whether private or public.

Furthermore, each component of this system is based on the nursing information processing system for care recipients described above. Employee identification information, like 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 "name," "gender," "date of birth," "name of company," "type of employment," "name of school," "name of department," "name of class," and "medical care expense category."

The nursing information in this system is based on the nursing information in the nursing information processing system related to the care recipient, and is arranged into appropriate information for inferring the mental health of employees, etc. For example, the nursing information is configured to include the responses of 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.

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 is possible to estimate whether employees, etc. are in a high stress state, whether their stress state will increase in the future, or diseases or symptoms that they are at risk of developing. In addition, the results of this estimation can be used to help improve the mental health of employees, etc.

In addition, similar to the nursing information processing system for the care recipient, the system can be configured to output the estimation result to the family of the employee, etc., or to output the estimated state of the employee, etc. in association with the estimated future time. The system can also be configured to acquire nursing information in association with the caregiver identification information, acquire nursing information in association with the medical institution identification information, or acquire nursing information in association with the administrative agency, etc. identification information. In this case, the caregiver includes the employee's superior, the company that employs the employee, the hygiene manager at the school where the student, etc. attends, the industrial physician, the stress check implementer, the teacher, the school doctor, the school counselor, etc. In addition, the system can be configured to output the estimation result to the medical institution identified by the medical institution identification information or the administrative agency, etc. identified by the administrative agency, etc. identification information. In addition, the system can be configured to acquire the difference between the state of the employee, etc. in the estimation result at the time of estimation and the actual state of the employee, etc. In addition, the estimated future state of the employee, etc. can be configured to be one or a combination of two or more of hospitalization, assault, crime, and suicide.

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 employees and students. By outputting the estimation results to employers and schools, they can understand symptoms and predictions of symptoms, enabling them to respond more appropriately.

Similar to the nursing information processing system for the care recipient, the system can be configured to acquire nursing information in association with information identifying a medical institution, government agency, etc., and output the estimation results to the medical institution, government agency, etc. identified by the identification information. In this way, by sharing nursing information including answers to the questions in the stress check and the estimation results with each of the above-mentioned institutions, it is possible to provide multifaceted support to employees, employers, students, and schools.
＜Effects＞

As described above, the nursing information processing system of the present invention can estimate the future condition of a cared for person based on nursing information including text data showing the conversation and state of the cared for person.

200: Nursing information processing system 201: Care recipient identification information acquisition unit 202: Care recipient identification information storage unit 203: Nursing information acquisition unit 204: Nursing information storage unit 205: Inference model generation unit 206: Estimation unit
300: Nursing information processing system 301: Processor 302: Storage device 303: Input/output interface 304: Communication interface

Claims (17)

a nursing information acquiring unit that acquires nursing information including text data indicating the details of nursing care provided to a care recipient;
an estimation unit that inputs the acquired nursing information into an inference model and estimates a future condition of a 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;
having
The nursing information includes ADL information, and the text data includes notes on ADL,
The inference model is generated using learning data based on nursing information accumulated in the past,
A nursing information processing system in which the future condition of the care recipient estimated by the estimation unit is one or a combination of two or more of assault, crime, and suicide . The information storage unit may further include information about the family of the care recipient.
2. The nursing information processing system according to claim 1, wherein the output unit outputs the inference result to a family member of the cared person related to the inference result based on the information stored in the information storage unit. The nursing information processing system according to claim 1 or 2, wherein the output unit outputs the estimated condition of the care recipient in association with the estimated future time. a caregiver identification information acquisition unit for acquiring caregiver identification information for identifying a caregiver providing care;
and 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. a medical institution identification information acquisition unit for acquiring medical institution identification information for identifying 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 a medical institution related to the nursing. 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 of the medical institution identification information storage unit. 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 the like identification information of an administrative agency or a welfare service provider related to the nursing. 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 of the administrative agency identification information storage unit. The nursing information processing system according to any one of claims 3 to 8, further comprising a difference acquisition unit that acquires the difference between the state of the cared person in the estimation result at the time of the estimation and the actual state of the cared person. an answer information acquisition unit that acquires answer information including text data, the answer information indicating an answer to a question posed to an employee or a student;
an estimation unit that inputs the acquired answer information into an inference model and estimates a future state of an employee or student related to the input answer information;
The answer information includes answers 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,
An information processing system in which the future status of the employee or student estimated by the estimation unit is one or a combination of two or more of assault, crime, and suicide . An information storage unit that stores information about an employer who employs the employee or a school that the student attends,
The information processing system according to claim 10, further comprising an employer etc. output unit that outputs an estimation result estimated by the estimation unit based on the information stored in the information storage unit to an employer who employs the employee or a school where the student attends. a nursing information acquisition step of acquiring nursing information including text data indicating the content of nursing care provided to a care recipient;
an estimation step of inputting the acquired nursing information into an inference model and estimating a future state of the care recipient related to the input nursing information;
having
The nursing information includes at least ADL information, and the text data includes at least notes on ADL,
The inference model is generated using learning data based on nursing information accumulated in the past,
The future state of the care recipient estimated in the estimation step is one or a combination of two or more of assault, crime, and suicide .
A method for operating a nursing information processing system. an answer information acquisition step of acquiring answer information including text data, the answer information indicating an answer to a question posed to an employee or a student;
an estimation step of inputting the acquired answer information into an inference model and estimating a future state of an employee or student related to the input answer information;
having
The answer information includes answers 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 future state of the employee or student estimated in the estimation step is one or a combination of two or more of assault, crime, and suicide .
A method for operating an information processing system. a nursing information acquisition step of acquiring nursing information including text data indicating the content of nursing care provided to a care recipient;
an estimation step of inputting the acquired nursing information into an inference model and estimating a future state 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 on ADL,
The inference model is generated using learning data based on nursing information accumulated in the past,
The future state of the care recipient estimated in the estimation step is one or a combination of two or more of assault, crime, and suicide .
program. an answer information acquisition step of acquiring answer information including text data, the answer information indicating an answer to a question posed to an employee or a student;
an estimation step of inputting the acquired answer information into an inference model and estimating a future state of an employee or student related to the input answer information;
A program including instructions for causing an electronic computer to execute the following:
The answer information includes answers 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 future state of the employee or student estimated in the estimation step is one or a combination of two or more of assault, crime, and suicide .
program. A method for generating an inference model by a computer, comprising:
a nursing information acquisition step of acquiring nursing information including text data indicating the content of nursing care provided to a care recipient;
and generating an inference model for inferring a future condition 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 on ADL,
The future state of the care recipient estimated by the inference model is one or a combination of two or more of assault, crime, and suicide .
How to generate an inference model. A method for generating an inference model by a computer, comprising:
an answer information acquisition step of acquiring answer information including text data, the answer information indicating an answer to a question posed to an employee or a student;
and generating an inference model for estimating a future state of an employee or a student by having a computer learn the acquired answer information as learning data,
The answer information includes answers to questions in a stress check,
The answer information includes ADL information, and the text data includes notes about the ADL.
The future state of the employee or student predicted by the inference model is one or a combination of two or more of assault, crime, and suicide .
How to generate an inference model.

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