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 aging, 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, 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.

[0003] 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.

[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 creating a schedule for allocating nurses suited to the patient's condition to provide care at the appropriate time. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2020-021422 A Summary of the Invention [Problem to be solved by the invention]

[0006] In a visiting nursing service specialized in psychiatry, it is difficult for the invention of Patent Document 1 to predict changes in condition or aggravation. This is because it is difficult to make a psychiatric diagnosis based only 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 for 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 demand for a system that can predict changes in a patient's symptoms based on nursing records that record conversations between the patient and caregiver 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 holding unit that holds the acquired nursing recipient identification information, a nursing information acquisition unit that acquires nursing information indicating the content of nursing provided to the nursing recipient, the nursing information including text data, in association with the nursing recipient identification information for that nursing, a nursing information holding unit that holds the acquired nursing information, an inference model generation unit that uses the held 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. Effect 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 indicating the conversation, state, and so forth of the patient. [Brief description of the drawings]

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

[0011] 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> <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 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>

[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 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.

[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] 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 a RAM and a non-volatile memory (HDD, flash memory, etc.), and stores a driver program 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 an input device such as a keyboard or a 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.

[0016] 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, a program used to cause a computer to execute such software, and a recording medium on which the program is fixed, are naturally included in the technical scope of the present invention (the same applies throughout this specification).

[0017] The "care recipient identification information acquisition unit" 201 has a function of acquiring care recipient identification information for identifying a 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 the 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 the place of employment" and the "name of the facility." The functions of this unit are realized by a processor 301, a storage device 302, an input / output interface 303, and the like. The unit may also be configured to acquire information output from a client device, such as a PC at each facility shown in FIG. 1, via a communication interface 304.

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

[0019] The "nursing information acquisition unit" 203 has a 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 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 a processor 301, a storage device 302, an input / output interface 303, etc. It may also be configured to acquire data output from a client device, etc. via a communication interface 304.

[0020] The nursing information may also 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 a function of storing acquired nursing information, and is realized by forming a database using the storage device 302 or the like. Fig. 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 stored in association with the following broad categories for each care recipient identification ID that uniquely identifies the care recipient: "basic visit information," "basic ADL (Activities of Daily Living) information," "vital signs," and "nursing content."

[0022] As "basic visit information", for example, the name of the cared for, age at the time of the visit, medical expenses 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, 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 occurred within 5 weeks of discharge, multiple visit flag indicating that multiple visits occurred in one day, and multiple visit flag indicating that the visit was made by multiple people are stored.

[0023] The "basic ADL information" includes, for example, eating, sleeping, taking medicine, cleaning, environmental improvement, money management, interpersonal relations, eating, moving, excretion, changing clothes, bathing, and grooming, and the 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), etc. Here, the above (notes) may include text data.

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

[0025] Examples of "nursing content" stored include attentive listening, coordination with the patient's doctor and related institutions, administration of medication and injections, skin and bedsore treatment, assistance with medical examinations, guidance and consultation for family members, rehabilitation, advice, nursing content notes (text), changes in symptoms, content of symptoms (text), and special notes (text). Here, the above items marked as (text) may include text data. In addition, the above-mentioned medication information may be stored as information constituting nursing information together with basic visit information and nursing content.

[0026] The "inference model generation unit" 205 has a function of generating an inference model for predicting 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 predicting 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 learning data. As shown in Fig. 5, the learning 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. In addition, if the nursing information includes medication information, the learning data also includes 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 visits flag.

[0029] ADL information includes, for example, 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 by others), money management (wasting), cleanliness, environmental improvement, 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 examinations, instructing and consulting with family members, rehabilitation, and giving advice.

[0032] The visiting month may be, for example, visiting month flag (January) to visiting 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 care content, such as ADL notes, symptom content, and nursing care content notes and special instructions.

[0034] 6 is a flow diagram showing the process for generating learning data and an inference model. Note that 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 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 configuration of 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 any one of the following items: length of stay, number of days since previous visit, and number of visits in which the patient was absent (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 uses an encoder to encode the items of eating, sleeping, taking medicine, and money management, which are categorical variables, of the ADL basic information.

[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] The processor 301 then 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. The processor 301 selects, for example, only nursing information whose medical care expense category is "mental". Also, it may select only nursing information of a care recipient ID whose number of nursing information items is a predetermined number or more.

[0045] In step 605, the processor 301 converts 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 by 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 contents included in the nursing information is one or more types selected from notes on ADL, contents of symptoms, notes on nursing contents, and special notes on nursing contents.

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

[0048] The processor 301 then converts these vectors into tensors (word embeddings) by passing them through an embedding layer of a neural network. The embedding layers can use the best currently known open source embeddings.

[0049] The processor 301 generates a tensor of, 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.

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

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

[0052] In step 608, the 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 learning data from the database of learning data and learns the model. Specifically, the processor 301 executes model training, loss calculation, and model storage 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 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.

[0057] FIG. 7 is a diagram showing the data structure of inference data to be input to an inference model in order to infer 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 care content, month of visit, and vector text, which is vectorized text. It can also be configured to store medication information as well.

[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 visits flag.

[0060] ADL information includes, for example, 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 by others), money management (wasting), cleanliness, environmental improvement, 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 examinations, instructing and consulting with family members, rehabilitation, and giving advice.

[0063] The visiting month may be, for example, visiting month flag (January) to visiting 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 care content, such as ADL notes, symptom content, and nursing care content notes and special instructions.

[0065] 8 is a flow diagram showing the flow of 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 FIG. 6.

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

[0067] In step 803, the 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 method of the selection is the same as the method of the selection in step 607.

[0069] In step 805, the processor 301 performs scaling. The method of the processing is similar to the method of the processing 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 future possibility of hospitalization (hospitalization risk) as the future condition of the care recipient. The inference result, that is, the possibility of hospitalization, can be obtained not only as a numerical value such as probability (%), but also as a level such as high, medium, or low.

[0071] In addition, the system can be configured to obtain the degree of symptoms (indicating the severity of symptoms as a number) based on a predetermined 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).

[0072] The future state of the care recipient estimated by the estimation unit may also include the risk of the care recipient committing violence or crime, or committing suicide or self-harming. All of these risks are risks that may 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 ADLs) tend to be neglected, and when positive symptoms appear, symptoms of hallucinations and delusions appear, which lead to the attitude and statements of the care recipient. As these symptoms progress, situations such as suicide and violence may 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.

[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 via the communication interface 304 to a client device or the like.

[0074] Fig. 9 is a diagram showing the flow of word embedding. As shown in Fig. 9, the processor 301 tokenizes (converts into a string of words in the smallest unit) the input text using a tokenizer. In Fig. 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 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.

[0078] The estimation result by the estimation unit can be output in various forms, and is 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).

[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 is displayed together with the hospitalization risk (%) for each patient (care recipient).

[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 trend 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 trend is displayed as a bar graph.

[0082] In addition, the estimation result by the estimation unit can be output in association with the estimated time, which is the estimated future time. FIG. 14 is a diagram showing the estimation result 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 estimation 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 of the severity of symptoms predicted in May 2021, for example.

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

[0084] In addition, FIG. 16 shows the symptom progression shown in FIG. 15, which shows 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 result with the actual result and obtain the difference between the two, it is possible to verify, for example, the effect that the nursing care provided up to that point had on the care recipient, or to verify the accuracy of an 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 vital data shown as numerical values, it is not easy to understand the condition of a care recipient and provide appropriate care, and this places a large 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. 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.

[0088] The estimated results are not limited to being output to nursing facilities, but are also preferably output to the family of the care recipient. For example, the nursing information processing system may be configured to associate and hold the identification information for identifying the family of the care recipient, and the family identified by the associated identification information may be permitted to access the nursing information processing system, allowing the family to view the estimated results within a predetermined range. As the predetermined range, for example, since the estimated results shown in Figs. 10 to 13 relate to multiple care recipients, it is not preferable to allow the family to view them, but it is preferable to allow the family of the care recipient to view the future progress of symptoms of one care recipient shown in Figs. 14 to 16 after a predetermined authentication process. It is extremely useful for the family involved in nursing at home to understand the estimated results when providing nursing care.

[0089] In addition, if the care recipient has a job or goes to a facility, it is also preferable to configure the system to output the estimation result to the company that employs the care recipient or the facility that the care recipient goes to, etc. The estimation result makes it possible to understand symptoms and their progression, which is useful for taking appropriate measures for the care recipient and provides support to the company that employs the care recipient or the facility that the care recipient goes to.

[0090] 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>

[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 nursing information processing system described above may 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 may be configured to acquire nursing information in association with the nurse identification information for that care. The nurse identification information is information including, 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."

[0092] With the above configuration, the future condition of the care recipients that the nurse is in charge of can be estimated and output, and the nurse can easily understand the estimated result of the future condition of the care recipients that the nurse is caring for. As described above, the difference between the estimated result and the actual symptoms can be obtained, which can be useful for understanding whether the nursing care by the nurse is contributing to the improvement of the symptoms of the care recipients, and whether the nurse and the care recipient are compatible. Furthermore, by configuring the system to register and hold the nurse identification information in association with information identifying the nursing establishment to which the nurse belongs, the system can be configured to extract and output nursing information and estimated results for each nursing establishment.

[0093] Also, based on the nursing information processing system described above, it can be configured to acquire nursing information in association with a medical institution. That is, the above-mentioned nursing information processing system can be further configured to have 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, nursing information can be shared among nursing offices, 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 inference results about care recipients who are in charge 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 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 above-mentioned nursing information processing system further includes 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 holding unit that holds 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 in which the care recipients live, the facilities that the care recipients attend and the agencies that oversee them, and may also include academic societies related to nursing or medical care. Welfare service providers are not only nursing care facilities and caregiving facilities, but also businesses and associations that provide various support and assistance services such as independent living support, 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 inferences about care recipients for whom an administrative agency is involved in providing support, etc., to an 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 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, instead of the cared for, employees (employees or students) can be used, and the nursing information can be configured to include answers to questions in a stress check. 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 and includes 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 state of employees, and an estimation unit that inputs the acquired nursing information into the generated inference model and estimates the future state of employees related to the input nursing information, and the nursing information includes answers to questions in a stress check.

[0099] Here, employees include those who work for a company, 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] In addition, each component of this system is based on the nursing information processing system for the care recipient described above. The employee identification information, like the care recipient identification information, includes an "employee ID" that uniquely identifies the employee, and information about the employee identified by the employee ID, such as "name," "gender," "date of birth," "name of company," "employment type," "name of school," "name of department," "name of class," and "medical care expense category."

[0101] In addition, 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, it is configured to include in the nursing information the answers of employees, etc. to questions in 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, the answers are included in the nursing information using a questionnaire that complies with the stress check system.

[0102] Then, in the same way as the nursing information processing system for the care recipient, an inference model is generated using the nursing information as learning data. This inference model is used to estimate the future state of the employee, etc. For example, it is possible to estimate whether the employee, etc. is in a high stress state, whether the stress state will increase in the future, or diseases or symptoms that may develop. In addition, the results of this estimation can be used to improve the mental health of the employees, etc.

[0103] Also, like 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. Also, the system can be configured to acquire nursing information in association with the nursing care provider identification information, in association with the medical institution identification information, or in association with the administrative institution identification information. In this case, the nursing provider includes the employee's superior, the hygiene manager at the company employing the employee, or the school where the student, etc. attends, the industrial physician, the stress check implementer, the teacher, the school doctor, the school counselor, etc. Also, the system can be configured to output the estimation result to the medical institution identified by the medical institution identification information or the administrative institution identified by the administrative institution identification information. Also, 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. Also, 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.

[0104] The system may also be configured to further include an employer output unit that outputs the estimation result 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 result to the employer or school, it becomes possible to understand symptoms and predictions of symptoms, and to take more appropriate measures.

[0105] 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>

[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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