Information processing system, information processing method, and program

Abstract

To provide an information processing system or the like capable of facilitating provision of patient information from a medical institution.SOLUTION: According to an aspect of the present invention, there is provided an information processing system including one or more processors, in which, in an acquisition step, the processors acquire medical institution information and patient information, the medical institution information is information regarding a medical institution where a patient receives medical care, the patient information is information regarding the patient excluding information capable of specifying the patient, and in an accumulation step, the processors accumulate the acquired medical institution information and the patient information regarding the patient who has received medical care at the medical institution indicated by the medical institution information in association with each other.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing system, an information processing method, and a program. [Background technology]

[0002] Patent Document 1 discloses a technology in which a matching function applies an elderly condition model to a database that holds big data related to medical care, matches the elderly condition model with the big data, and a data conversion function extracts injury / illness information, its stage, risk or defense factors, and result factors for each elderly person from the big data that has been matched with the elderly condition model, and generates elderly condition data for the corresponding elderly person. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2024-037184 Summary of the Invention [Problem to be solved by the invention]

[0004] For example, dementia and mild cognitive impairment come in multiple types (Alzheimer's disease, cerebrovascular disease, etc.) and conditions (mild cognitive impairment, mild dementia, etc.), and appropriate treatment according to each condition can improve symptoms or prevent deterioration. In particular, in the case of mild cognitive impairment, early and appropriate treatment can lead to a transition to a normal state. To achieve such treatment, it is believed that providing information using big data about patients, such as that used in the technology of Patent Document 1, is effective. However, medical institutions are reluctant to provide information about patients.

[0005] In view of the above circumstances, the present invention provides an information processing system and the like that can easily obtain patient information from medical institutions. [Means for solving the problem]

[0006] According to one aspect of the present invention, there is provided an information processing system having one or more processors, wherein the processor acquires medical institution information and patient information in an acquisition step, where the medical institution information is information about the medical institution where the patient receives treatment, and the patient information is information about the patient excluding information that can identify the patient, and in a storage step, the acquired medical institution information is stored in association with patient information about patients who received treatment at the medical institution indicated by the medical institution information.

[0007] According to this aspect, it is possible to make it easier for medical institutions to provide patient information. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a diagram showing an example of the overall configuration of a medical collaboration system 1. FIG. [Figure 2] FIG. 2 is a diagram illustrating an example of a hardware configuration of a link server 10. [Figure 3] FIG. 2 is a diagram illustrating an example of a hardware configuration of a user terminal 30. [Figure 4] FIG. 10 is an activity diagram illustrating an example of information provision processing. [Figure 5] FIG. 1 is a diagram illustrating an example of accumulated big data. [Figure 6] FIG. 10 is a diagram illustrating an example of a request screen. [Figure 7] FIG. 10 is a diagram illustrating an example of a response screen. [Figure 8] FIG. 10 is a diagram showing another example of a response screen. [Figure 9] FIG. 10 is a diagram showing another example of a response screen. [Figure 10] FIG. 10 is a diagram showing another example of a response screen. [Figure 11] FIG. 10 is a diagram illustrating an example of a request screen for analysis information. [Figure 12] FIG. 10 is a diagram illustrating an example of analysis information. [Figure 13] FIG. 10 is a diagram illustrating an example of a prediction result regarding population. DETAILED DESCRIPTION OF THE INVENTION

[0009] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described below with reference to the accompanying drawings. Various features shown in the following embodiments can be combined with each other.

[0010] Incidentally, the program for realizing the software appearing in one embodiment may be provided as a non-transitory computer-readable medium, or may be provided so that it can be downloaded from an external server, or may be provided so that the program is started on an external computer and its functions are realized on a client terminal (so-called cloud computing).

[0011] Furthermore, various information processing according to an embodiment may realize input and output corresponding to the input. Here, the form of information referenced in such information processing (hereinafter referred to as reference information) is not limited as long as an output is obtained as a result of the input. The reference information may be, for example, rule-based information such as a database, a lookup table, or a predetermined function (including a decision formula such as a regression formula constructed using a statistical method), a trained model that has previously trained the correlation between input and output, or a large-scale language model that can output a desired result by inputting a prompt.

[0012] In one embodiment, a "unit" may include, for example, a combination of hardware resources implemented by a circuit in the broad sense and software information processing that can be specifically realized by these hardware resources. In one embodiment, various information is handled, and this information is represented, for example, by physical values ​​of signal values ​​representing voltage and current, high and low signal values ​​as a binary bit set consisting of 0 or 1, or quantum superposition (so-called quantum bits), and communication and calculations can be performed on a circuit in the broad sense.

[0013] Furthermore, a circuit in the broad sense is a circuit realized by at least an appropriate combination of a circuit, circuitry, processor, memory, etc. The processor may be a general-purpose processor or a dedicated circuit. That is, it includes an application specific integrated circuit (ASIC), a programmable logic device (e.g., a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA)), etc.

[0014] <Embodiment> 1. System Configuration The system configuration according to the embodiment will be described below. Fig. 1 is a diagram showing an example of the overall configuration of a medical collaboration system 1. Fig. 1 shows an overview of each device included in the medical collaboration system 1 and users who use those devices. Each overview will be explained as needed, with reference to other figures.

[0015] The medical collaboration system 1 is an information processing system that executes information processing for medical collaboration. Medical collaboration refers to collaboration between related institutions and parties involved in medical care. Related institutions involved in medical care include medical institutions such as hospitals, clinics, special medical institutions, and home medical services, as well as companies closely related to medical care, such as pharmaceutical companies, medical device manufacturers, medical supply manufacturers, pharmaceutical wholesalers, dispensing pharmacies, nursing care facilities, drug discovery venture companies, healthcare service companies, and medical IT companies. Parties involved in medical care include employees of these related institutions.

[0016] The medical cooperation system 1 includes a communication line 2, a cooperation server 10, a medical institution server 20, and a plurality of user terminals 30.

[0017] The linking server 10 is an information processing device that executes information processing for medical collaboration. The linking server 10 has a medical database 3, and stores and accumulates big data including medical institution information and patient information in the medical database 3. The medical institution information is information about the medical institution where the patient receives medical treatment. The patient information is information about the patient receiving medical treatment at the medical institution. The linking server 10 executes various information processing using the accumulated big data. Details of this information processing, medical institution information, and patient information will be described later.

[0018] The linkage server 10 acquires this medical institution information and patient information, for example, from the medical institution server 20. The medical institution server 20 is an information processing device that executes information processing related to the operations of the medical institution. The medical institution server 20 constitutes, for example, a part of an electronic medical record system, a medical accounting system, a patient management system, a test information system, a drug management system, an electronic prescription system, etc., and stores the medical institution information and patient information.

[0019] The user terminal 30 is an information processing device, such as a personal computer or a tablet terminal, whose users are users of the medical collaboration system 1. Users of the user terminal 30 include employees of related medical institutions. The user terminal 30 has a display means and an operation reception means, and performs information processing such as displaying the screen of the medical collaboration system 1 on the display means and receiving operations performed by the user on the operation reception means.

[0020] The linking server 10 executes an authentication process to authenticate a user who uses the user terminal 30. The linking server 10 stores, for example, authentication information (such as a user ID and password) for authenticating a user who uses the medical collaboration system 1, and authenticates a user who inputs the authentication information. By authenticating a user, the linking server 10 can restrict access to data, assign identification information to data input by the user to make the data identifiable, and save settings made by the user.

[0021] The link server 10 also executes display control processing to display images on the user terminal 30. The link server 10 performs processing such as generating and transmitting an HTML (Hyper Text Markup Language) file as the display control processing, and causes the user terminal 30 to display a web page showing a system screen using a browser function. Note that the user terminal 30 may install an application program for using the medical link system 1, and the link server 10 may perform processing such as generating and transmitting display data in the application as the display control processing. The link server 10 performs these display control processing to display various images on the user terminal 30. In other words, the images displayed on the display means of the user terminal 30 can be rephrased as images that the link server 10 causes the user terminal 30 to display.

[0022] 2. Hardware Configuration The hardware configuration according to the embodiment will be described below. 2 is a diagram showing an example of the hardware configuration of link server 10. Link server 10 includes control unit 11, storage unit 12, communication unit 13, and bus 14. Bus 14 electrically connects the various units included in link server 10.

[0023] (Control unit 11) The control unit 11 is, for example, a central processing unit (CPU) not shown. The control unit 11 realizes various functions related to the medical collaboration system 1 by reading out predetermined programs stored in the storage unit 12. In other words, information processing by software stored in the storage unit 12 is specifically realized by the control unit 11, which is an example of hardware, and can be executed as each functional unit included in the control unit 11. Note that the control unit 11 is not limited to being single, and multiple control units 11 may be provided for each function. A combination of these may also be used.

[0024] (Storage unit 12) The memory unit 12 stores various pieces of information defined above. This may be implemented, for example, as a storage device such as a solid state drive (SSD), hard disk drive (HDD), compact disc (CD), or solid state hybrid drive (SSHD) that stores various programs and the like related to the medical collaboration system 1 executed by the control unit 11, or as a memory such as a random access memory (RAM) that stores temporarily required information (arguments, arrays, etc.) related to the program calculations. The memory unit 12 stores various programs, variables, etc. related to the medical collaboration system 1 executed by the control unit 11.

[0025] (Communications Department 13) The communication unit 13 is configured to be able to transmit various electrical signals from the link server 10 to external components. The communication unit 13 is also configured to be able to receive various electrical signals from the external components to the link server 10. More preferably, the communication unit 13 has a network communication function, which allows various information to be communicated between the link server 10 and external devices via the communication line 2.

[0026] 2 has the same hardware configuration as the linking server 10. In the following description of the medical institution server 20, a control unit 21 is assigned a different reference numeral from that of the control unit 11 of the linking server 10.

[0027] 3 is a diagram showing an example of the hardware configuration of the user terminal 30. The user terminal 30 includes a control unit 31, a storage unit 32, a communication unit 33, an input unit 34, an output unit 35, and a bus 36. The bus 36 electrically connects the various units included in the user terminal 30. The control unit 31, the storage unit 32, and the communication unit 33 are similar hardware to the control unit 11, the storage unit 12, and the communication unit 13 shown in FIG. 2, although the specifications, model, etc. may differ.

[0028] (Input unit 34) The input unit 34 has operation acceptance means such as keys, buttons, a touch screen, a mouse, etc., and accepts input operations by the user. The input unit 34 may also have a microphone and have a function to accept voice input by the user.

[0029] (Output unit 35) The output unit 35 has a display means such as a display and a sound emitting means such as a speaker, and displays visual information generated in a manner that is visible to the user, such as a screen, image, icon, text, etc., on the display surface of the display, and outputs sound including voice.

[0030] 3. Information Processing Information processing according to the embodiment will be described below. In the following description, the linking server 10, the medical institution server 20, and the user terminal 30 are described as the subjects of each information processing, but the information processing is executed by at least one processor included in the medical collaboration system 1, i.e., the processor included in the control unit of each device. The medical collaboration system 1, for example, accumulates big data related to patients and executes information provision processing to provide information based on the accumulated big data.

[0031] 4 is an activity diagram showing an example of information provision processing. The information provision processing is initiated, for example, when a medical cooperation agreement is made between a provider of the medical cooperation system 1 and a medical institution to provide services such as tabulation based on information provided by the medical institution. First, the medical institution server 20 extracts the above-mentioned medical institution information from the information stored in the system of the medical institution itself (activity A11).

[0032] The medical institution information includes, for example, the name, type, and address of the medical institution. Types of medical institutions include, for example, general hospitals, university hospitals, specialized hospitals, medical offices, clinics, rehabilitation facilities, and emergency medical facilities. The medical institution server 20 transmits the extracted medical institution information to the linking server 10. The linking server 10 acquires the transmitted medical institution information (activity A12).

[0033] Next, the medical institution server 20 extracts the above-mentioned patient information from the information stored in the system of its own medical institution (activity A21). The patient information includes, for example, the patient's gender, age, type, type, symptoms, and severity of the health problem. A health problem is a problem that occurs in a person's physical or mental health condition, such as illness (disease), injury, or disability. For example, there are various types of illnesses, ranging from colds and influenza to various types of cancer, and there are similarly various types of injuries and disabilities.

[0034] In the example of FIG. 4, the medical institution server 20 extracts patient information regarding health problems that are the subject of neuropsychological testing. Neuropsychological testing is a test that quantifies and objectively evaluates functional disorders such as intelligence, memory, and language. The test involves asking questions about age, date, time, location, calculations, repeating numbers backwards, and memorizing objects, and assigning a score based on the answers. Health problems that are the subject of the test include dementia and mild cognitive impairment.

[0035] A type is a classification of health problems based on their nature or cause. For example, dementia and mild cognitive impairment include Alzheimer's disease, Lewy body dementia, vascular dementia, frontotemporal dementia, and mixed dementia. Symptoms are abnormalities that occur in patients due to health problems. For example, dementia and mild cognitive impairment may exhibit symptoms such as memory loss, disorientation, impaired comprehension and judgment, impaired executive function, and apraxia, agnosia, and aphasia.

[0036] The above patient information is stored in systems owned by the medical institution (electronic medical record system, patient management system, examination information system, etc.) The medical institution server 20 extracts the patient information from the information stored in these systems.

[0037] Next, the medical institution server 20 removes personal identification information from the extracted patient information (activity A22). Personal identification information is information that can identify an individual, such as name information. For a patient living alone, address also serves as personal identification information. An individual may be identified by two or more pieces of information, not just one piece of information. For example, even if a patient lives with their family, an individual can be identified by their address and age. The medical institution server 20 removes from the patient information, for example, name, address, and contact information (mainly mobile phone number), which are generally information that can easily lead to the identification of an individual. The medical institution server 20 may eliminate the operation of A22 by extracting patient information from which personal identification information has been removed from the beginning.

[0038] The medical institution server 20 transmits the patient information excluding the personal identification information to the linking server 10. Note that in A11 and A22, the medical institution information and the patient information are transmitted online to the linking server 10, but for example, this information may be stored in a portable storage device and provided to the linking server 10 offline.

[0039] When the linking server 10 receives the transmitted patient information, it generates unique information based on the received patient information (activity A23). Unique information is unique information that can be generated based on information included in the patient information. In the example of FIG. 4, the linking server 10 generates, as unique information, information on "age category" that can be generated from the patient's "age." For example, the linking server 10 generates age categories such as "childhood" for ages up to 5 years old, "young" for ages 6 to 18 years old, "young" for ages 19 to 39 years old, "middle-aged" for ages 40 to 64 years old, and "elderly" for ages 65 years old and over. Note that the age categories are not limited to the above examples. For example, age categories may be such that "children" are 0-4 years old, "boys" are 5-14 years old, "young adults" are 15-24 years old, "adults" are 25-44 years old, "middle-aged" are 45-64 years old, and anyone above that is "elderly," or they may be divided into 10-year increments such as under-teens, teens, twenties, etc., or they may be divided into 5-year increments.

[0040] The linking server 10 acquires the patient information transmitted in A22 plus the unique information generated in A23 as patient information (activity A24). The linking server 10 then associates the medical institution information acquired in A12 with the patient information acquired in A24, stores them in the medical database 3, and accumulates them as big data for medical collaboration (activity A25).

[0041] FIG. 5 is a diagram showing an example of accumulated big data. FIG. 5 shows medical institution information and patient information stored in the medical database 3. The medical institution information stores, for example, the name of the medical institution (such as "Hospital A" and "Clinic B"), type (such as "General Hospital" and "Clinic"), and address. The patient information stores, for example, the acquisition date, the patient's gender, age, age category, type, form, symptoms, and severity of the health problem. The acquisition date indicates, for example, the date on which the patient information was acquired. Note that the accumulated big data may include data other than the above-mentioned data, or may include partially different content.

[0042] Here, each piece of patient information is stored in association with medical institution information, but is not stored in association with all other patient information. For example, "gender" is associated with "acquisition date" and "age," but is not associated with other patient information such as "age category," "type of health problem," "type," "symptoms," and "level of symptoms." Furthermore, "type of health problem" is associated with "acquisition date," "age category," "type," "symptoms," and "level of symptoms," but is not associated with "gender" and "age."

[0043] The operations from A11 to A25 are performed repeatedly, for example, periodically or irregularly, in accordance with the agreement with the medical institution described above. When the linkage server 10 acquires new patient information from a certain medical institution in A24, for example, the currently stored patient information is left as past patient information, and the new patient information is newly stored as current patient information in A25. This is because the stored patient information does not contain information that can identify individuals, making it impossible to associate information about the same patient. In the example of FIG. 5, only current patient information is shown, but it is assumed that past patient information is also stored in the medical database 3, associated with, for example, "Hospital A" and "Clinic B."

[0044] Next, an operation of a user requesting information provision based on the big data accumulated as described above will be described. First, the user terminal 30 displays a request screen (activity A31). Fig. 6 is a diagram showing an example of a request screen. The example in Fig. 6 shows a request screen C1 in the case where aggregated information obtained by aggregating big data is provided. The request screen C1 displays a character string saying "Please enter the aggregation conditions," an input field D11 for the name of the medical institution to be specified as the aggregation target, an input field D12 for the first condition group, an input field D13 for the second condition group, and a confirm button B11.

[0045] In the input field D12 for the first condition group, "type of health problem," "type," "symptoms," "degree of symptoms," and "age category" can be input as aggregation conditions. In the input field D13 for the second condition group, "age" and "gender" can be input as aggregation conditions. Only one of the first condition group and the second condition group can be selected, and a check box indicating the selected condition group is displayed. In the example of Figure 6, "Hospital A" has been input as the name of the medical institution, and the first condition group has been selected.

[0046] Each input field D12 and D13 allows the input of aggregation conditions using an OR condition, allowing the input of a list of conditions to be aggregated. Each input field D12 and D13 also has a corresponding selection field D14 and D15 for selecting the type of condition. The types of conditions include "graph conditions" and "narrowing conditions." "Graph conditions" are conditions under which the respective aggregation results are displayed in a graph. "Narrowing conditions" are conditions for narrowing down the data that will be the basis for aggregation.

[0047] In the example of FIG. 6, all of the "narrowing conditions" are "ALL," so all patient information associated with "Hospital A" is subject to aggregation. However, if, for example, "Alzheimer's type" is entered as the "type," the patient information associated with "Hospital A" that includes "Alzheimer's type" as the "type" is narrowed down to be subject to aggregation. Furthermore, the "narrowing conditions" can also be entered by listing them using an "OR" condition. The narrowing conditions may be selectable in a pull-down format, or may be selectable using a search or prediction function.

[0048] 6, the "graph condition" is "dementia, mild cognitive impairment," so the tabulation results of patient information that includes "dementia" as a type of health problem and the tabulation results of patient information that includes "mild cognitive impairment" as a type of health problem are displayed on the graph. Note that when "dementia, mild cognitive impairment" is specified as the "graph condition," the tabulation results of patient information for "normal" patients are also displayed, which will be explained later.

[0049] The user terminal 30 accepts input operations into each input field and an operation on the OK button B11 as a request operation (activity A32). Upon accepting the request operation, the user terminal 30 generates request data indicating the content of the accepted request and transmits it to the linking server 10 (activity A33).

[0050] The link server 10 accepts the request indicated by the transmitted request data (activity A41) and generates the requested aggregated information (activity A42). The following describes the case where aggregated information is requested as described in Figure 6. The link server 10 reads out, from the medical database 3, medical institution information and patient information of the medical institution designated as the aggregation target.

[0051] Next, the linking server 10 narrows down the retrieved patient information to patient information that matches the requested narrowing-down conditions. In the example of Figure 6, the linking server 10 does not narrow down the patient information because all of the "narrowing-down conditions" are "ALL". Next, the linking server 10 tally up each of the patient information items that include "dementia" and "mild cognitive impairment" as health problem types specified in the "graph conditions". Here, the tallying of "normal" patients will be explained.

[0052] For example, patients with the health problem type "dementia" and "mild cognitive impairment" are patients who have received medical treatment for those health problems, but do not necessarily exhibit symptoms. This includes patients who have been diagnosed as normal as a result of medical examination or treatment. For patients diagnosed as normal, "normal" is stored as the "symptom level." Therefore, the linkage server 10 counts patient information that includes "dementia" and "mild cognitive impairment" as the health problem type and that includes "normal" as the symptom level as "normal." In other words, the number of patient information items for "dementia" and "mild cognitive impairment" does not include the number of "normal" patient information items.

[0053] Link server 10 generates aggregate information that associates each aggregation condition with the number of patient records aggregated based on each aggregation condition (activity A42). Link server 10 generates a response screen for the request based on the generated aggregate information (activity A43). Link server 10 transmits screen data showing the generated response screen to user terminal 30. User terminal 30 displays the response screen shown in the transmitted screen data (activity A44).

[0054] FIG. 7 is a diagram showing an example of a response screen. In the example of FIG. 7, a display field D21 for the name of a medical institution and a counting result image E21 are displayed on the response screen C2 as the counting result. The name of the medical institution specified in FIG. 6 ("Hospital A" in the example of FIG. 7) is displayed in the display field D21. The counting result image E21 is an image showing the number of patient information items containing "dementia," "mild cognitive impairment," and "normal," and a pie chart showing the proportion of those numbers. By looking at the response screen C2, the user can understand the number of patients with dementia and mild cognitive impairment at Hospital A and the number of patients who have been diagnosed as normal after receiving treatment for those health problems.

[0055] Fig. 8 is a diagram showing another example of a response screen. In the example of Fig. 8, a counting result image E22 showing the number of pieces of patient information about male patients and the number of pieces of patient information about female patients at Hospital A is displayed on response screen C2 as the counting result. By looking at response screen C2 shown in Fig. 8, the user can understand the number of male patients and female patients at Hospital A. Note that, as explained in Fig. 5, "gender" is not associated with "type of health problem" or the like, so it is not possible to count, for example, the number of pieces of patient information about male and female dementia patients.

[0056] FIG. 9 is a diagram showing another example of a response screen. In the example of FIG. 9, a response screen C2 displays a display field D22 for "Filtering Conditions" in addition to a display field D21 for "Name of Medical Institution." In the example of FIG. 9, "dementia" or "mild cognitive impairment" is displayed as a filtering condition in the display field D22. In addition, the response screen C2 displays a counting result image E23 in which data is collected using "age category" as a graph condition. In other words, the counting result image E23 shows the number of patient information items by age category among patients with dementia or mild cognitive impairment who are receiving treatment at Hospital A. By viewing the response screen C2 shown in FIG. 9, the user can understand the number of patients by age category who are receiving treatment for dementia or mild cognitive impairment at Hospital A.

[0057] Fig. 10 is a diagram showing another example of a response screen. In the example of Fig. 10, a display field D23 for the names of multiple medical institutions and a count result image E24 showing the number of patient information cases including dementia, mild cognitive impairment, and normal patients receiving treatment at each medical institution are displayed on the response screen C2. By looking at the response screen C2 shown in Fig. 10, the user can understand the number of patients with dementia, mild cognitive impairment, and normal patients at multiple hospitals.

[0058] As described above, the counting conditions are not limited to one medical institution as in the example of Fig. 10, but may be set to two or more medical institutions. Furthermore, the counting conditions may be set to a first group of conditions as in the example of Fig. 7, or a second group of conditions as in the example of Fig. 8. Furthermore, the counting conditions are set using or conditions in the counting condition input field shown in Fig. 6, but may also be set using and conditions, and these may be switchable.

[0059] As described above, the link server 10 functions as an example of an acquisition unit that acquires medical institution information and patient information. Medical institution information is information about the medical institution where the patient receives treatment. Patient information is information about the patient excluding information that can identify the patient. As described in FIG. 4, the link server 10 acquires patient information from the medical institution server 20 excluding information that can identify individuals. The link server 10 then functions as an example of a storage unit that associates and stores the acquired medical institution information with patient information about patients who received treatment at the medical institution indicated by the medical institution information. As shown in FIG. 5, the link server 10 stores the medical institution information and patient information in the medical database 3, thereby associating these pieces of information with each other.

[0060] If a medical institution provides patient information that includes personally identifiable information to an external business without the patient's permission, this constitutes a leak of personal information. On the other hand, it is possible to provide patient information with the patient's consent, but it is difficult and time-consuming to obtain consent from all patients. In the medical collaboration system 1, patient information that does not include personally identifiable information is accumulated, so this does not constitute a leak of personal information. Therefore, it is easier for medical institutions to provide patient information compared to when accumulating patient information that includes personally identifiable information.

[0061] Furthermore, the patient information accumulated by the link server 10 includes type information indicating the type of health problem for which the patient is receiving medical treatment. In the example of FIG. 4 etc., the health problem includes subjects for neuropsychological testing. The subjects for neuropsychological testing include at least dementia or mild cognitive impairment. According to this embodiment, it is possible to accumulate big data on patients receiving medical treatment for health problems related to cognitive function.

[0062] In addition, neuropsychological testing can be performed on a variety of conditions, including but not limited to the above, such as other neurodegenerative diseases, stroke, cerebrovascular disease, head trauma, psychiatric disorders, learning disabilities, developmental disorders, and other neurological disorders. In either case, big data on patients undergoing neuropsychological testing can be accumulated. Health problems can also include but are not limited to the above, such as infectious diseases, chronic diseases, cancer, lifestyle-related diseases, genetic diseases, environmental diseases, and trauma. In either case, data can be compiled based on the type of health problem.

[0063] Furthermore, the patient information includes type information indicating the type of health problem, as well as information indicating one or more of the type indicated by the type information, the number of patients of that type, or the severity of symptoms of those patients. In the example of Figure 5, the type of health problem, the type of type, and the severity of symptoms of patients of that type are stored as patient information in the medical database 3. Furthermore, information directly indicating the number of patients is not stored, but the number of instances of the same type stored as a "health problem type" indicates the number of patients of that type. In this way, by accumulating various information as patient information, it is possible to perform aggregation, etc. from various perspectives.

[0064] The aggregation, etc., may take into account changes from past conditions to the current condition (for example, the number of patients who currently have dementia out of the number of patients who had mild cognitive impairment seven years ago), and the period of the data to be aggregated (in years or months) may be arbitrarily selected. Furthermore, data on changes in the condition (such as worsening of symptoms) (for example, the percentage of patients whose condition has improved or worsened) may be a factor to be considered when predicting changes in the aggregation, etc. Furthermore, the period of the data used in the aggregation may be displayed or confirmed when the prediction results are displayed, indicating which period of data was referenced (for example, as a notation such as "data reference from January 2020 to December 2023").

[0065] Furthermore, patient information includes a first item of information and a second item of information. The second item of information is information that makes it easier to narrow down an individual compared to the first item of information. In the example of Figure 5, the type, type, symptoms, and severity of the health problem are the first item of information, and the patient's gender and age are the second item of information. The type of health problem (type of illness or disability, etc.), type, and severity of symptoms cannot be easily determined by looking at the patient, so even if the aggregated information is known, it is difficult to narrow down which patient is being identified. On the other hand, the gender and age can be roughly determined by looking at the patient, so if a person who knows the aggregated information were to look at the patient, it would be easy to narrow down which patient is being identified.

[0066] Therefore, the linking server 10 (an example of a storage unit) stores the information of the first item without associating it with the information of the second item. In the example of Fig. 5, the linking server 10 stores the first item "type of health problem" and the second item "gender and age" in the medical database 3 without associating them with each other. According to this mode, it is possible to make it more difficult to narrow down individuals from the big data compared to when all patient information is stored in association with each other.

[0067] In the example of Figure 5, "age" is stored as the second item of information, while "age category" is stored as the first item of information. The age categories of child, young, adult, middle-aged, and elderly cover a wide range of ages, making it difficult to narrow down individuals compared to when the age is known. On the other hand, for example, by aggregating patient information narrowed down by other information in the first item by age category, it is possible to grasp the number of cases of the important factor of age category from various perspectives such as the type of health problem.

[0068] The linking server 10 also functions as an example of an output unit that outputs generated information generated based on the accumulated medical institution information and patient information. The linking server 10 (an example of an output unit) outputs, as generated information, aggregated information about commonalities shared by patients who have received medical treatment at a medical institution. The aggregated information is information indicating, for example, the number of aggregated cases of patient information about patients with a common type of health problem such as illness, the number of aggregated cases of patient information about patients with a common type of illness, or the number of aggregated cases of patient information about patients in a common age category.

[0069] In the example of Fig. 7, the linking server 10 generates aggregated information that is aggregated about the patient information of patients who have a common type of health problem. In the example of Fig. 8, the linking server 10 generates aggregated information that is aggregated about the patient information of patients who have a common gender, and in the example of Fig. 9, the linking server 10 generates aggregated information that is aggregated about the patient information of patients who have a common age group. Note that the commonalities shared by the patients are not limited to these, and may also be, for example, age, type of health problem, symptoms, and severity of symptoms.

[0070] In the above example, aggregated information is generated for each medical institution, but this is not limited thereto. Aggregated information that summarizes patient information for patients at two or more medical institutions may also be generated. For example, aggregated information may be generated for two or more medical institutions, such as medical institutions in the same region, medical institutions of the same type, or medical institutions of the same size. In the above example, the aggregated results are displayed in a pie chart, but this is not limited thereto. The aggregated results may also be displayed in a bar graph, a bar chart, text, or the like. In either case, the user can grasp the trends of patients receiving treatment at the medical institution by viewing the output aggregated information.

[0071] Furthermore, the generated information is not limited to aggregate information. For example, the link server 10 may generate information in which information extracted from accumulated big data is appropriately arranged as the generated information. Furthermore, the link server 10 may generate analysis information indicating the results of analyzing the accumulated big data as the generated information. For example, the link server 10 may analyze the accumulated big data to predict situations that will occur in the future, and generate information indicating the prediction results as the analysis information.

[0072] For example, the link server 10 functions as an example of an aggregation prediction unit that predicts future aggregation of the above commonalities (commonalities shared by patients who have received medical treatment at a medical institution) based on the history of aggregated information. As described above, past patient information is also accumulated in the medical database 3. The link server 10, for example, generates past aggregated information based on past patient information, or stores aggregated information generated in the past. Then, the link server 10 predicts future aggregated information based on the past aggregated information using well-known prediction methods such as time series analysis, regression analysis, or Bayesian estimation. The link server 10 may also make predictions using AI (artificial intelligence) that has learned the past aggregated information. The link server 10 (an example of an output unit) further outputs the predicted results of future aggregation as generated information.

[0073] Fig. 11 is a diagram showing an example of a request screen for analysis information. The request screen C3 shown in Fig. 11 displays the character string "Please enter the analysis conditions.", an input field D31 for the name of the medical institution to be specified as the analysis target, an input field D32 and a selection field D34 for the first condition group, an input field D33 and a selection field D35 for the second condition group, an input field D36 for the analysis content, and a confirm button B31. The input fields D31, D32, and D33 and the selection fields D34 and D35 are input fields and selection fields similar to the input fields D11, D12, and D13 and the selection fields D14 and D15 shown in Fig. 6, and input and selection are made to specify the aggregation target to be analyzed.

[0074] The input field D36 is an input field for inputting the content to be analyzed. For example, the content to be analyzed includes the target to be predicted and the period for which the prediction is desired. The target to be predicted is, for example, aggregated information. The period for which the prediction is desired is, for example, a specific time point or period in the future. In the example of FIG. 11, the analysis content "Prediction of aggregated information for the next five years" is input. When the enter button B31 is operated, the linkage server 10 extracts the big data specified in D31 to D35, including the history, and performs analysis using the content input in D36.

[0075] Fig. 12 is a diagram showing an example of analysis information. In the example of Fig. 12, a display field D41 for the name of a medical institution and an analysis result image E41 are displayed on a response screen C4 as analysis results. The name of the medical institution specified in Fig. 11 ("Hospital A" in the example of Fig. 12) is displayed in the display field D41. The analysis result image E41 is an image that displays a pie chart showing the proportion of the number of cases of patient information for "dementia," "mild cognitive impairment," and "normal" patients in four different ways: now, one year from now, three years from now, and five years from now.

[0076] In the example of FIG. 12, the analytical information is information showing predictions for three future time points, but is not limited to this, and may be information showing predictions for two or fewer time points or four or more time points. Furthermore, the analytical information is not limited to a pie chart, and may be information showing predictions in the form of a bar graph, a line graph, or text. In either case, information showing future trends of the patient (in the example of FIG. 12, dementia is on the rise and normal is on the decline) can be provided to the user.

[0077] Furthermore, the medical institution information may include information about the location of the medical institution, as in the example of Figure 5. In this case, the linking server 10 may function as an example of a population prediction unit that predicts the population in an area including the location of the medical institution. The linking server 10 (an example of an output unit) then further outputs the results of the population prediction as generated information. The user terminal 30 displays the output population prediction results.

[0078] FIG. 13 is a diagram showing an example of a population prediction result. In the example of FIG. 13, a response screen C5 displays a display field D51 for the name of a medical institution, a counting result image E51, and a population prediction result image E52 as the counting result. "Hospital A" is displayed in the display field D51, and counting result image E51 is a pie chart showing the proportion of the number of patient information cases for "dementia," "mild cognitive impairment," and "normal" patients. Population prediction result image E52 is a line graph showing the predicted results of the population composition ratio by age in the area including the location of Hospital A.

[0079] By viewing the population forecast results in addition to the current aggregated results, users can predict future trends in patients at Hospital A. For example, if the elderly population increases, the number of cases of dementia and mild cognitive impairment is predicted to increase, and if the elderly population decreases, the number of cases of dementia and mild cognitive impairment is predicted to decrease. Note that population forecasts are not limited to age distribution ratios, and may also include forecasts of the total population of the region, household composition, in-migration and out-migration trends, income distribution, or housing environment. In either case, it is easier to predict future increases or decreases in the number of patients than if population forecast results were not output.

[0080] As described above, the linkage server 10 outputs various generated information such as extracted patient information, aggregated information, analytical information, and population-related forecast information. According to this aspect, the accumulated information can be used more effectively than when the generated information is not output.

[0081] Furthermore, the linking server 10 (an example of an output unit) outputs the generated information to a destination corresponding to at least one of a medical institution, a pharmaceutical company, a medical device manufacturer, or a dispensing pharmacy. In the example of FIG. 4 etc., the linking server 10 outputs the generated information to the user terminal 30 that has transmitted the request data. Note that the linking server 10 is not limited to this so-called pull-type output, and may, for example, output the generated information in a push-type manner to a destination predetermined for each user of the medical linking system 1.

[0082] The generated information output to each destination is displayed on the user terminal 30 and provided to the user at each destination. This allows, for example, medical institutions to understand trends regarding the patients they treat. Pharmaceutical companies and medical device manufacturers can use this information as a reference for products and services to recommend to medical institutions. Dispensing pharmacies can also understand trends in prescriptions issued by medical institutions, which can be used as a reference for pharmaceuticals to stock. In this way, by outputting the generated information, big data can be used for a variety of purposes.

[0083] <Example of change: Excluding personally identifiable information> In the above example, medical institution server 20 removed personally identifiable information, but link server 10 may also remove personally identifiable information. For example, if an agreement between the operator of medical collaboration system 1 and a medical institution stipulates that patient information be provided on the premise that the operator will remove personally identifiable information, link server 10 can remove personally identifiable information from the patient information transmitted from medical institution server 20 and acquire the patient information from which the personally identifiable information has been removed. In short, as long as the stored patient information does not contain personally identifiable information, any procedure for removing personally identifiable information may be used.

[0084] <Example of change: Personally identifiable information> In the above example, the patient's name, address, and contact information are excluded as personally identifiable information, but this is not limited to this. For example, email addresses, dates of birth, bank account numbers, credit card numbers (e.g., information registered in a medical institution's system for payment of medical fees), or work information may also be excluded as personally identifiable information. In short, any information that may be used to identify an individual may be excluded as personally identifiable information.

[0085] <Example of change: proprietary information> In the above example, the patient's age category was generated as unique information from the patient information and included in the first condition group, but this is not limited to this. For example, the patient's residential area, occupation, years of service, or years of outpatient visits may also be included as unique information in the first condition group. In short, any information that is not personally identifiable information and is more difficult to narrow down an individual than the information included in the second condition group may be generated as unique information from the patient information and included in the first condition group.

[0086] <Example of change: Analysis information> In the above example, the linking server 10 generates and outputs information predicting future aggregation results as analytical information, but this is not limited to this. For example, the linking server 10 may compare the population and number of patients in the area including the location of the medical institution with the surrounding areas, analyze whether the number of patients in the area including the location of the medical institution is high or low, and output the result as analytical information. The linking server 10 may also compare the population and number of patients in the city, ward, town, or village including the location of the medical institution with a city, ward, town, or village of the same size, and output similar analytical information.

[0087] The linking server 10 may also analyze the causes of differences in the aggregated information compared to other regions (for example, a significantly higher proportion of dementia patients) and output the results as analytical information. This analysis may be performed using well-known methods such as regression analysis, analysis of variance, or residual analysis. The linking server 10 may also have an AI learn the aggregated information of each region and use the AI ​​to analyze the causes of the differences. In short, the linking server 10 may generate and output the results of an analysis based on big data of patient information and well-known analytical methods as analytical information.

[0088] <Example of change: Generated information> In the above example, information extracted from big data, aggregated information, analytical information, and population forecast information are output as generated information, but this is not limited to this. For example, when patient information associated with a certain medical institution satisfies a specific notification condition, notification information that notifies a user associated with the medical institution of this may be generated and output. In short, any information that can be generated based on big data may be generated as generated information.

[0089] <Example of variation: Variation of composition> The configuration (overall configuration, hardware configuration, functional configuration, etc.) shown in FIG. 1 etc. is an example, and other configurations may be used as long as they are not inconvenient for implementation. For example, the linking server 10 may be distributed across two or more devices, or may be provided in the form of SaaS (Software as a Service) or a cloud computing system. In short, as long as the necessary information processing is performed in the entire medical linking system 1, the devices that execute that information processing may have any configuration.

[0090] The output destination of information or data (hereinafter referred to as "information, etc.") may be another device, a display, a memory unit (including an internal memory unit and an external memory unit), an email address, an account of another system, etc. Acquisition of information, etc. includes acquiring information, etc. generated by the device itself, as well as acquiring information, etc. transmitted from another device. The table, etc. (table, database, etc.) in which parameters are associated is not limited to the illustrated table, etc., and the number of parameters may be reduced or increased. Furthermore, information, etc. corresponding to parameters may be obtained using a mathematical formula, a conditional formula, etc., without using a table, etc.

[0091] The above-described embodiments are information processing devices such as the linking server 10 and the user terminal 30, and information processing systems such as the medical collaboration system 1 including the linking server 10 and the user terminal 30. However, they may also be information processing methods. The information processing methods include the same steps as those executed by the information processing system. The above-described embodiments may also be programs. The programs cause a computer to execute the same steps as those executed by the information processing system.

[0092] <Additional Notes> Furthermore, it may be provided in the following aspects.

[0093] (1) An information processing system having one or more processors, wherein the processor acquires medical institution information and patient information in an acquisition step, the medical institution information being information about the medical institution where the patient receives treatment, and the patient information being information about the patient excluding information that can identify the patient, and in a storage step, the acquired medical institution information is stored in association with the patient information about patients who received treatment at the medical institution indicated by the medical institution information.

[0094] According to this aspect, it is possible to make it easier for medical institutions to provide patient information.

[0095] (2) In the information processing system described above in (1), the patient information includes type information indicating the type of health problem for which the patient is receiving treatment.

[0096] According to this aspect, it is possible to perform aggregation based on the type of health problem.

[0097] (3) In the information processing system described in (2) above, the health problem includes a subject of neuropsychological testing.

[0098] According to this embodiment, it is possible to accumulate big data on patients who undergo neuropsychological testing.

[0099] (4) In the information processing system described in (3) above, the subjects of the neuropsychological test include at least dementia or mild cognitive impairment.

[0100] According to this embodiment, it is possible to accumulate big data on patients receiving medical treatment for health problems related to cognitive function.

[0101] (5) In the information processing system described in any one of (2) to (4) above, the patient information includes, in addition to the type information, information indicating one or more of the type of the type indicated by the type information, the number of patients of that type, or the degree of symptoms of the patients.

[0102] According to this embodiment, it is possible to perform aggregation and the like from various perspectives.

[0103] (6) In the information processing system described in any one of (1) to (5) above, the patient information includes a first item of information and a second item of information, the second item of information being information that is easier to narrow down an individual than the first item of information, and in the storage step, the processor stores the first item of information and the second item of information without associating them.

[0104] According to this aspect, it is possible to make it difficult for individuals to be narrowed down from big data.

[0105] (7) In the information processing system described in any one of (1) to (6) above, in the output step, the processor outputs generated information generated based on the accumulated medical institution information and patient information.

[0106] According to this embodiment, it is possible to utilize the accumulated information.

[0107] (8) In the information processing system described in (7) above, in the output step, the processor outputs aggregated information compiled about common characteristics shared by patients who received treatment at the medical institution as the generated information.

[0108] According to this embodiment, it is possible to grasp the tendency of the patient.

[0109] (9) In the information processing system described in (8) above, in the aggregation prediction step, the processor predicts future aggregation of the common points based on the history of the aggregation information, and in the output step, further outputs the predicted result of the future aggregation as the generated information.

[0110] According to this embodiment, it is possible to provide information indicating future patient trends.

[0111] (10) In the information processing system described in any one of (7) to (9) above, the medical institution information includes information regarding the location of the medical institution, and the processor, in the population prediction step, makes a prediction regarding the population in an area including the location of the medical institution, and in the output step, further outputs the result of the population prediction as the generated information.

[0112] According to this embodiment, it is possible to easily predict future increases and decreases in the number of patients.

[0113] (11) In the information processing system described in any one of (7) to (10) above, in the output step, the processor outputs the generated information to a destination corresponding to at least one of the medical institution, pharmaceutical company, medical device manufacturer, or dispensing pharmacy.

[0114] According to this aspect, big data can be utilized for a variety of purposes.

[0115] (12) An information processing method, in which a processor included in an information processing system executes each step of the information processing system described in any one of (1) to (11) above.

[0116] According to this aspect, it is possible to make it easier for medical institutions to provide patient information.

[0117] (13) A program that causes a computer to execute each step of the information processing system according to any one of (1) to (11) above.

[0118] According to this aspect, it is possible to make it easier for medical institutions to provide patient information. Of course, this is not the case. Furthermore, the above-described embodiments and modifications may be combined in any desired manner.

[0119] Finally, while various embodiments of the present invention have been described, these are presented by way of example only and are not intended to limit the scope of the invention. The novel embodiments may be embodied in various other forms, and various omissions, substitutions, and modifications may be made without departing from the spirit of the invention. The embodiments and their modifications are intended to be included within the scope and spirit of the invention, as well as within the scope of the inventions and their equivalents as defined in the appended claims. [Explanation of symbols]

[0120] 1: Medical collaboration system 2: Communication line 3: Medical database 10: Collaboration server 11: Control section 20: Medical institution server 21: Control unit 30: User terminal 31: Control unit

Claims

1. An information processing system comprising one or more processors, the processor: In the acquisition step, medical institution information and patient information are acquired; The medical institution information is information about a medical institution where a patient receives medical treatment, The patient information is information about the patient excluding information that can identify the patient, In the storing step, the acquired medical institution information and the patient information about patients who received medical treatment at the medical institution indicated by the medical institution information are stored in association with each other. Information processing system.

2. 2. The information processing system according to claim 1, the patient information includes type information indicating the type of health problem for which the patient is receiving medical treatment; Information processing system.

3. 3. The information processing system according to claim 2, The health problem includes a subject of neuropsychological testing. Information processing system.

4. 4. The information processing system according to claim 3, The subjects of the neuropsychological test include at least dementia or mild cognitive impairment. Information processing system.

5. 3. The information processing system according to claim 2, The patient information includes, in addition to the type information, information indicating one or more of the type of the type indicated by the type information, the number of patients of the type, or the degree of symptoms of the patients. Information processing system.

6. 2. The information processing system according to claim 1, The patient information includes first and second items of information, and the second item of information is information that makes it easier to narrow down an individual compared to the first item of information, the processor: In the storing step, the information of the first item and the information of the second item are stored without being associated with each other. Information processing system.

7. 2. The information processing system according to claim 1, the processor: In the output step, generated information generated based on the accumulated medical institution information and the accumulated patient information is output. Information processing system.

8. 8. The information processing system according to claim 7, the processor: In the output step, aggregated information on commonalities shared by patients who have received medical treatment at the medical institution is output as the generated information. Information processing system.

9. 9. The information processing system according to claim 8, the processor: In the aggregation prediction step, a prediction of a future aggregation of the common points is made based on the history of the aggregation information; In the output step, the prediction result of the future aggregation is further output as the generation information. Information processing system.

10. 8. The information processing system according to claim 7, the medical institution information includes information about the location of the medical institution; the processor: In the population prediction step, a prediction is made regarding the population in an area including the location of the medical institution; In the output step, the result of the population prediction is further output as the generated information. Information processing system.

11. 8. The information processing system according to claim 7, the processor: In the output step, the generated information is output to a destination corresponding to at least one of the medical institution, the pharmaceutical company, the medical device manufacturer, and the dispensing pharmacy. Information processing system.

12. An information processing method, comprising: The processor of the information processing system Executing each step of the information processing system according to any one of claims 1 to 11. Information processing methods.

13. A program, A computer is caused to execute each step of the information processing system according to any one of claims 1 to 11. program.

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