Health management system, health management method, and health management program

Abstract

To provide a health management system, health management method, and health management program, capable of supporting an organization for managing the health of a member.SOLUTION: A health management system, etc., includes a state information acquisition part for acquiring information on the health state of a member constituting an organization; a learning model acquisition part for acquiring a learning model previously learning a correspondence relation between the state information and a restriction level showing the level of a work restriction corresponding to the state information; a restriction level generation part for inputting the state information acquired by the state information acquisition part into a learning model to generate a restriction level corresponding to the state information; a storage control part for mapping the state information inputted into the learning model and the restriction level generated by the restriction level generation part to be stored as information on the health management of the member constituting the organization; and an output part for outputting information for the organization or the member on the basis of the health management information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a health management system, a health management method, and a health management program, and more particularly to a health management system, a health management method, and a health management program suitable for an organization to manage the health of its members. [Background technology]

[0002] Businesses and other organizations have an obligation to maintain the health of their employees. For example, Article 3 of the Industrial Safety and Health Act stipulates that business operators must ensure the safety and health of workers in the workplace by creating a comfortable working environment and improving working conditions. Article 13 of the Act also stipulates that business operators of a certain size must appoint an industrial physician and have that person manage the health of their employees.

[0003] Under these circumstances, there are not enough industrial physicians for businesses that require them, and some businesses are concerned that they will not be able to adequately manage the health of their workers. Therefore, health management systems that support employee health management have been proposed (for example, the infectious disease test result information sharing system disclosed in Patent Document 1). The infectious disease test result information sharing system disclosed in Patent Document 1 prevents the spread of infection by having employees who are likely to have contracted an infectious disease refrain from going out, and claims to be able to limit losses to the company to which the employee belongs by allowing the employee to return to work only after it has been confirmed that the employee is no longer shedding bacteria.

[0004] Organizations are required to fulfill their obligation to take care to protect the physical and mental health and safety of their members (duty of care), not only from infectious diseases but also from other factors, and therefore it is desirable for organizations to receive support in managing the health of their employees. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2020-42685 Summary of the Invention [Problem to be solved by the invention]

[0006] Therefore, an object of the present invention is to provide a health management system, a health management method, and a health management program that can support organizations in managing the health of their members. [Means for solving the problem]

[0007] In other words, the health management system of the first aspect is characterized by comprising a status information acquisition unit that acquires status information regarding the health status of members constituting an organization; a learning model acquisition unit that acquires a learning model that has previously learned the correspondence between the status information and a restriction level that indicates the degree of work restriction corresponding to the status information; a restriction level generation unit that inputs the status information acquired by the status information acquisition unit into the learning model and generates a restriction level corresponding to the status information; a memory control unit that associates the status information input into the learning model with the restriction level generated by the restriction level generation unit and stores the result as health management information for the members of the organization; and an output unit that outputs information intended for the organization or members based on the health management information.

[0008] In a second aspect, in the health management system of the first aspect, the output unit may output, as information for the organization, the number or names of members who are unable to work, or the number or names of members who have work restrictions.

[0009] In a third aspect, in the health management system according to the first aspect, the output unit may output information for the member, such as whether the member is able to work, whether the member needs to be restricted from working, or whether the member needs support.

[0010] In a fourth aspect, in the health management system according to the first aspect, the status information may include at least information indicating the results of a health check taken by the member or information indicating the results of a medical interview answered by the member.

[0011] In a fifth aspect, in the health management system according to the fourth aspect, the information indicating the results of the health checkup may include test results for multiple test items in the health checkup, and the learning model may learn the correspondence between the test results and the restriction level determined by a doctor based on the test results.

[0012] In a sixth aspect, in the health management system according to the fifth aspect, the information indicating the results of the medical interview may include answers to a plurality of questions in the medical interview, and the learning model may learn the correspondence between the test results and answers and the restriction level determined by the doctor based on the test results and answers.

[0013] According to a seventh aspect, in the health management system according to the first aspect, the restriction level generating section may generate the restriction level as a plurality of categories.

[0014] In an eighth aspect, in the health management system according to the first aspect, the output unit may output a letter of introduction for introducing the member to a medical institution based on the health management information.

[0015] A ninth aspect is a health management system according to the eighth aspect, further comprising a medical examination result acquisition unit that acquires the medical examination results obtained when a member visits a medical institution for treatment based on a referral letter, a medical examination result transmission unit that transmits the medical examination results to a doctor terminal used by a doctor requested by the organization, and a final restriction level acquisition unit that acquires the final restriction level of the member determined by the doctor based on the medical examination results, and the memory control unit may store the health management information of the members of the organization, including the medical examination results and final restriction level of the member.

[0016] In a tenth aspect, in the health management system according to the ninth aspect, a re-learning unit may be further provided that causes the learning model to learn the correspondence between the status information included in the health management information and the final restriction level of the doctor corresponding to the status information.

[0017] In an eleventh aspect, in the health management system of the first aspect, a notification sending unit is further provided that sends a notification to a doctor terminal used by the doctor to notify the doctor of the implementation of the processing by any or all of the status information acquisition unit, restriction level generation unit, memory control unit, and output unit in order to obtain the doctor's approval for the implementation of the processing by the doctor, and an acceptance / rejection receiving unit that receives the doctor's approval or rejection of the implementation of the processing after receiving the notification, and the status information acquisition unit, restriction level generation unit, memory control unit, and output unit may implement the processing when they receive the doctor's approval for the notification regarding the processing.

[0018] In a twelfth aspect, in a health management system according to the fifth aspect, the learning model is trained to output test results used as the basis for generating the restriction level, among the test results of multiple test items in a health checkup, together with the restriction level, and the restriction level generation unit may input the status information acquired by the status information acquisition unit into the learning model and, when generating a restriction level corresponding to the status information, output the contents of the test results used as the basis for generating the restriction level together with the restriction level.

[0019] The health management method of the thirteenth aspect is characterized in that a computer carries out the following steps: a status information acquisition step of acquiring status information regarding the health status of members constituting an organization; a learning model acquisition step of acquiring a learning model that has previously learned the correspondence between the status information and a restriction level that indicates the degree of work restriction corresponding to the status information; a restriction level generation step of inputting the status information acquired in the status information acquisition step into the learning model to generate a restriction level corresponding to the status information; a memory control step of correlating the status information input into the learning model with the restriction level generated in the restriction level generation step and storing the result as health management information for the members of the organization; and an output step of outputting information intended for the organization or members based on the health management information.

[0020] The health management program of the 14th aspect is characterized in that it causes a computer to perform the following functions: a status information acquisition function for acquiring status information regarding the health status of members constituting an organization; a learning model acquisition function for acquiring a learning model that has previously learned the correspondence between the status information and the restriction level that indicates the degree of work restriction corresponding to the status information; a restriction level generation function for inputting the status information acquired by the status information acquisition function into the learning model and generating a restriction level corresponding to the status information; a memory control function for correlating the status information input into the learning model with the restriction level generated by the restriction level generation function and storing the result as health management information for members of the organization; and an output function for outputting information intended for the organization or members based on the health management information. [Effects of the Invention]

[0021] The health management system etc. of the present invention is characterized by comprising a status information acquisition unit that acquires status information regarding the health status of members constituting an organization; a learning model acquisition unit that acquires a learning model that has been pre-trained to determine the correspondence between the status information and the restriction level that indicates the degree of work restriction corresponding to the status information; a restriction level generation unit that inputs the status information acquired by the status information acquisition unit into the learning model and generates a restriction level corresponding to the status information; a memory control unit that associates the status information input into the learning model with the restriction level generated by the restriction level generation unit and stores the result as health management information for members of the organization; and an output unit that outputs information intended for the organization or members based on the health management information, thereby enabling support for organizations in managing the health of their members. [Brief explanation of the drawings]

[0022] [Figure 1] FIG. 1 is a diagram for explaining an overview of a health management system according to this embodiment. [Figure 2] FIG. 2 is a diagram showing an example of a system configuration diagram including a health management system according to this embodiment. [Figure 3] FIG. 3 is a block diagram showing an example of the hardware configuration of the health management system according to this embodiment. [Figure 4]FIG. 4 is a block diagram showing an example of the functional configuration of a health management system according to this embodiment. [Figure 5] FIG. 5 is a diagram for explaining an example of examination items in a health checkup according to this embodiment. [Figure 6] FIG. 6 is a diagram for explaining an example of a medical interview question according to this embodiment. [Figure 7] FIG. 7 is a table and a diagram for explaining an example of restriction levels generated by the health management system according to this embodiment. [Figure 8] FIG. 8 is a diagram for explaining an example of learning of the learning model according to this embodiment. [Figure 9] FIG. 9 is a diagram showing an example of information for an organization output by the health management system according to this embodiment. [Figure 10] FIG. 10 is a diagram showing an example of a letter of introduction for a member output by the health management system according to this embodiment. [Figure 11] FIG. 11 is a flowchart of the health management program according to this embodiment. [Figure 12] FIG. 12 is a flowchart of the health management program according to the second embodiment. [Figure 13] FIG. 13 is a diagram for explaining an overview of a health management system according to the third embodiment. [Figure 14] FIG. 14 is a block diagram showing an example of the functional configuration of a health management system according to the third embodiment. [Figure 15] FIG. 15 is a flowchart of the health management program according to the fourth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0023] (Overview of Health Management System 10) An overview of a health management system 10 according to an embodiment of the present disclosure will be described with reference to FIG. The health management system 10 is used by an organization for the purpose of managing the health of members 1 that make up the organization. An organization may be a business, company, union, country, local government, civic group such as NPO or NGO, educational corporation, medical corporation, or other corporation, as well as a circle or group whose purpose is private activities. A member 1 is a person who makes up an organization. For example, if the organization is a business, the employees of the business would be members 1. Also, if the organization is a group, the members of the group would be members 1.

[0024] The health management system 10 is an information processing device typified by a personal computer (hereinafter referred to as a PC), and corresponds to a server, a desktop PC, a notebook PC, a tablet PC, a smartphone, etc. The health management system 10 may be used in an on-premise operation form or an off-premise operation form. An on-premise operating mode refers to a mode in which hardware is installed within a company and the system is operated independently, and refers to an operating mode in which the health management system 10 is used by installing the hardware of the health management system 10 within the company's facilities and having users operate it directly or by having users operate it remotely via the information and communication network 40.

[0025] On the other hand, an off-premises operation mode refers to an operation mode in which health management system 10 is constructed using a system within hardware operated and managed by another company, and is operated, managed, etc., and made available to users via information and communication network 40. An example of an off-premises operation mode is constructing health management system 10 using cloud services from other companies, such as AWS (registered trademark) from Amazon (registered trademark) and Azure (registered trademark) from Microsoft (registered trademark). The health management system 10 may be managed and operated by the organization itself, or the organization may use a health management system 10 operated by another organization to manage the health of its members 1.

[0026] The health management system 10 acquires the results of the self-test 2 taken by the member 1 as status information indicating the health condition of the member 1. Based on the acquired status information, the health management system 10 generates a restriction level indicating the degree of work restriction for the member 1. The restriction level generated by the health management system 10 is processed and provided to both or either the organization and the member 1 as processed information. Self-examination 2 refers to a situation in which a member 1 checks his or her own physical and health condition by undergoing a medical checkup or medical examination at a medical institution such as a hospital or health center, and by answering questions given at the medical institution.

[0027] The restriction level indicates the degree of work restriction on member 1, and may be generated as multiple categories, for example, divided into three categories (low level 3, medium level 4, and high level 5). Low level 3 indicates that member 1 is able to work normally and is the assessment that indicates that member 1 is receiving the least support. Medium level 4 is an assessment that indicates that restrictions on Member 1's work or support for Member 1 is necessary. High level 5 is an assessment that indicates that member 1 is unable to work and that member 1 needs support from a medical institution or other institution. The restriction levels are not limited to being divided into three categories, but may be generated into five or ten categories, for example.

[0028] (System configuration of health management system 10) An example of the system configuration of the health management system 10 will be described with reference to Fig. 2. Fig. 2 is a diagram showing an example of the system configuration including the health management system 10. The health management system 10 shown in FIG. 2 is intended for both on-premise and off-premise operation. The health management system 10 is connected to an information communication network 40 and is capable of two-way data communication with other devices such as a doctor terminal 27, group terminals 20 (20a, 20b), and member terminals 30 (30a, 30b, 30c). The information and communication network 40 is an information and communication line including the Internet and an intranet, and allows two-way data communication.

[0029] The organization uses organization terminal 20, which is an information processing device, to connect to health management system 10 via information communication network 40. Organization terminal 20 is used to obtain information output by health management system 10 and also to input information to health management system 10. An organization may connect to the health management system 10 using multiple organization terminals 20; for convenience, two organization terminals 20 (organization terminal 20a and organization terminal 20b) are shown in Figure 2, but the number of organization terminals 20 may be one, or three or more. Furthermore, it is envisioned that the health management system 10 may be used by one organization or by multiple organizations. When the health management system 10 is used by multiple organizations, each organization may use a different learning model 18, or multiple organizations may share a single learning model 18. An organization selects a doctor to manage the health of its members. This doctor uses doctor terminal 27, an information processing device, to connect to health management system 10 via information and communication network 40. For convenience, in this embodiment, only one doctor terminal 27 is shown in FIG. 2, but this is not limiting and multiple doctor terminals 27 may be connected to health management system 10 via information and communication network 40.

[0030] Member 1 connects to health management system 10 via information communication network 40 using member terminal 30 (30a, 30b, 30c), which is an information processing device. Member terminal 30 is used to acquire information output by health management system 10 and also to input information to health management system 10. Multiple members 1 may connect to the health management system 10 using their own member terminals 30; for convenience, three member terminals 30 (member terminal 30a, member terminal 30b, and member terminal 30c) are shown in Figure 2, but the number of member terminals 30 may be two or less, or four or more.

[0031] Member 1 and the organization can share information using member terminal 30 and organization terminal 20. For example, the organization can send information regarding member 1's work restrictions to member terminal 30 using organization terminal 20. Furthermore, member 1 can send, for example, the results of self-test 2 and the results of a medical interview that he or she has taken to organization terminal 20 using member terminal 30. If the results of self-test 2 and the results of a medical interview are on paper, member 1 imports them as electronic data into member terminal 30 using an input device such as a keyboard, scanner, or digital camera.

[0032] (Hardware configuration of health management system 10) The hardware configuration of the health management system 10 will be described with reference to Fig. 3. Fig. 3 is a block diagram showing an example of the hardware configuration of the health management system 10. The health management system 10 includes a communication interface 10a, a read only memory (ROM) 10b, a random access memory (RAM) 10c, a storage unit 10d, a processing unit 10e, and an input / output interface 10f. The calculation unit 10e may include a central processing unit (CPU), a micro processing unit (MPU), a graphics processing unit (GPU), etc., and may be realized by a logic circuit (hardware) formed on an integrated circuit (IC (Integrated Circuit) chip, LSI (Large Scale Integration)), etc., or a dedicated circuit. Since the group terminal 20, the doctor terminal 27, and the member terminal 30 are information processing devices similar to the health management system 10, their hardware configurations are similar to those of the health management system 10.

[0033] The communication interface 10a has a function of transmitting and receiving data to and from other devices via the information communication network 40. The memory unit 10d can be used as a recording device, and stores the health management program described below, the OS (Operating System) required for the health management system 10 to operate, various applications, and various data used by the applications. Furthermore, memory unit 10d may be constructed as a database, and memory control unit 16, which will be described later, may store in memory unit 10d the health management information for each member 1. A database is suitable for organizing and managing large amounts of data, and when there are a large number of members 1, it becomes possible to efficiently organize, manage, and analyze the health management information of all members 1.

[0034] Health management system 10 stores a health management program in storage unit 10d and loads the health management program into a main memory configured with RAM 10c etc. Calculation unit 10e accesses the main memory into which the health management program has been loaded and executes the health management program. The input / output interface 10f transmits and receives data to and from devices external to the health management system 10. The external devices refer to an input device 11 and an output device 12 that input and output data to and from the health management system 10. The input device 11 includes a keyboard 11a, a mouse 11b, a scanner 11c, and a camera 11d, while the output device 12 includes a monitor 12a, a printer 12b, and a speaker 12c.

[0035] Members 1 and organizations can operate and input information into the health management system 10 using input devices 11, such as a keyboard 11a, a mouse 11b, a scanner 11c, and a camera 11d. Furthermore, the member 1 and the organization can view and listen to the information output by the health management system 10 using the output device 12, which includes the monitor 12a, the printer 12b, and the speaker 12c.

[0036] (Functional configuration of health management system 10) Next, the functional configuration of the health management system 10 will be described with reference to Fig. 4. Fig. 4 is a block diagram showing an example of the functional configuration of the health management system 10. By executing the health management program described below, the health management system 10 has functional units such as a status information acquisition unit 13, a learning model acquisition unit 14, a restriction level generation unit 15, a memory control unit 16, an output unit 17, a medical examination result acquisition unit 80, a medical examination result transmission unit 81, a final restriction level acquisition unit 82, a re-learning unit 83, a notice transmission unit 84, and an acceptance / rejection acceptance unit 85 in the calculation unit 10e.

[0037] The condition information acquisition unit 13 acquires information relating to the health conditions of the members 1 constituting the group as condition information. The condition information of the member 1 includes at least information indicating the results of a health check that the member 1 has undergone, or information indicating the results of a medical interview that the member 1 has undergone.

[0038] The information showing the results of the health checkup will be described with reference to Fig. 5. Fig. 5 is a diagram for explaining an example of test items 22 for the health checkup. The information indicating the results of the health checkup is the evaluation 25 of the actual measured values ​​24 for each test item 22 of the health checkup. The information indicating the results of the health checkup may include the evaluation 25 of the actual measured values ​​24 for all of the multiple test items 22 of the health checkup, or may include only the evaluation 25 of the actual measured value 24 for a specific representative test item 22.

[0039] The health checkup result table 21 shown in FIG. 5 shows a list of the health checkup results. The health checkup result table 21 includes test items 22 such as a physical examination (BMI, vision, hearing), blood pressure test, anemia test (hemoglobin concentration, red blood cell count), liver function test (AST (GOT), ALT (GPT), γ-GPT), blood lipid test (LDL cholesterol, HDL cholesterol, triglyceride), blood sugar test, and urine test. A reference value 23 is set for each of the test items 22. The evaluation 25 of the actual measurement value 24 of the health checkup may be evaluated, for example, in three stages of normal / caution required / abnormal by comparison with the reference value 23, or in four stages of 1, 2, 3, and 4 using numbers, or in five stages of A, B, C, D, and E using letters. The information showing the results of the health checkup is the evaluation 25 for each test item 22 of the health checkup.

[0040] If the actual measurement value 24 is within the range of the reference value 23, it is evaluated as normal. Furthermore, the actual measurement value 24 is evaluated as requiring caution if the deviation from the reference value 23 is within a predetermined range. This predetermined range is set in advance for each test item 22. Moreover, if the deviation of the actual measurement value 24 from the reference value 23 exceeds a predetermined range, the actual measurement value 24 is evaluated as abnormal.

[0041] The information indicating the results of the medical interview will be described with reference to Fig. 6. Fig. 6 is a diagram for explaining an example of test items in the medical interview. The information indicating the results of the medical interview is the answers of member 1 for each test item on the medical questionnaire 31. The information indicating the results of the medical interview may include the answers of member 1 for all of the multiple test items on the medical questionnaire 31, or may include only the answers of member 1 for a representative specific test item. The medical questionnaire 31 shown in FIG. 6 includes a name entry field 32, a chief complaint entry field 33, a medical history entry field 34, a medication history entry field 35, an allergy information entry field 36, and a pregnancy status entry field 37. Member 1 writes down the answers to the medical interview on medical interview sheet 31. Medical interview sheet 31 is a form on which the medical interview is written, and may be provided to member 1 as a paper medium so that member 1 can fill it out using a writing implement, or may be provided to member 1 as an electronic medium such as hypertext so that member 1 can input the answers using member terminal 30.

[0042] The learning model acquisition unit 14 acquires a learning model 18 that has previously learned the correspondence between status information and restriction levels that indicate the degree of work restriction corresponding to the status information. In this embodiment, the learning model 18 is trained in advance by backpropagation using a generative adversarial network (GAN), as described below (see FIG. 8 ). The learning model 18 may be stored in the storage unit 10d, ROM 10b, or RAM 10c of the health management system 10. Alternatively, the learning model 18 may be stored in an external storage device instead of being built into the health management system 10. In this case, the health management system 10 is connected to the storage device in which the learning model 18 is stored via the information and communication network 40.

[0043] The restriction level generation unit 15 inputs the state information acquired by the state information acquisition unit 13 into the learning model 18, and generates a restriction level corresponding to the state information. The restriction level generation unit 15 inputs status information regarding the health status of the member 1 constituting the organization, which is acquired by the status information acquisition unit 13, into the learning model 18 acquired by the learning model acquisition unit 14, and generates a restriction level for the member 1 corresponding to this status information. The restriction level generating unit 15 generates restriction levels as a plurality of divisions.

[0044] The restriction levels will be described with reference to Fig. 7. Fig. 7 is a table and diagram for explaining an example of restriction levels generated by the health management system 10, in which Fig. 7(a) is a table 41 showing an example of restriction level classification, and Fig. 7(b) is a diagram for explaining four elements 45 that make up the restriction level.

[0045] The restriction level is divided into three major categories 41a: low level 3, medium level 4, and high level 5. Low level 3 is further divided into three subdivisions 41b: low level 3-1, low level 3-2, and low level 3-3. Medium level 4 is further divided into five sub-divisions, 41b: medium level 4-4, medium level 4-5, medium level 4-6, medium level 4-7, and medium level 4-8. High level 5 is further divided into two sub-divisions, high level 5-9 and high level 5-10, as sub-division 41b. That is, the restriction level is divided into three major categories 41a and ten minor categories 41b, each category indicating the degree of work restriction on the member 1. Specifically, the restriction level specifies for each category whether work is possible 45a, whether there are working conditions 45b, whether support is required 45c, and the type of support 45d.

[0046] Next, the four elements 45 constituting the restriction level subdivision 41b will be described with reference to Fig. 7(b). The four elements 45 include whether work is possible 45a, whether there are working conditions 45b, whether support is necessary 45c, and the type of support 45d. The restriction level generation unit 15 generates instructions 41c based on the status information of subsection 41b by selecting the following options set for each of the following: whether work is possible 45a, whether work conditions are present 45b, whether support is required 45c, and type of support 45d.

[0047] The options for work availability 45a include: able to work normally (a-1), work restrictions (a-2), and unable to work (requires re-examination) (a-3). Work restrictions (a-2) mean that normal working hours are not possible, but working with restrictions on working hours or place of work, such as reduced working hours or working from home, is possible.

[0048] The options for whether or not there are working conditions 45b include no working conditions (b-1), working conditions (b-2), and conditions for specific work (b-3). The presence or absence of working conditions 45b means whether or not there are conditions required for the member 1 to work. Working conditions (b-2) means that there are conditions imposed on the member 1 to work, such as night shifts, working outdoors, overtime, or a ban on working on holidays. Conditions for specific work (b-3) means that there are conditions required for the member 1 to perform specific work, such as requiring a passenger when the member 1 drives a vehicle, or limiting working at heights to two hours per day.

[0049] The options for 45c regarding whether support is needed include: needing support without delay (c-1), not able to work until support is received (c-2), able to work once support is received (c-3), and needing support immediately (c-4). The need for support 45c refers to the need for support from a doctor such as an industrial physician, a public health nurse, or a medical institution. Support refers to Member 1 receiving advice, guidance, or treatment from a person with specialized knowledge, and includes medical treatment not only for the recovery of physical functions, but also for mental and emotional recovery.

[0050] Options for type of support 45d include self-care (d-1), line care (d-2), in-house expert support (d-3), and external expert support (d-4). The type of support 45d means the type of medical treatment that the member 1 receives. Line care (d-2) refers to mental health measures in the workplace, etc., in which a manager or supervisor of Member 1 provides individual guidance, consultation, or improvements to the work environment to Member 1.

[0051] Instructions 41c based on the condition information divided into 10 categories in subsection 41b are generated by combining patterns of options from the four elements 45 (45a, 45b, 45c, 45d) described above. Instructions 41c based on the condition information are used as instructions to member 1 and as information on the health condition of member 1 to be provided to the organization.

[0052] Each of the instructions 41c based on the state information in the restriction level subsection 41b is generated by combining options of the above-mentioned four restriction level elements 45 (45a, 45b, 45c, 45d), as shown in the note 41d. The low level 3-1 is generated by combining (a-1) and (b-1). The lower level 3-2 is generated by combining (a-1), (b-2), (c-1), and (d-1). The lower level 3-3 is generated by combining (a-1), (b-2), (c-1), and (d-1). The intermediate level 4-4 is generated by combining (a-1), (b-2), (c-1), and (d-3). The intermediate level 4-5 is generated by combining (a-1), (b-2), (c-1), and (d-3). The intermediate level 4-6 is generated by combining (a-1), (b-2), (c-1), and (d-4). The intermediate levels 4-7 are generated by combining (a-2), (b-3), (c-2), and (d-3). The intermediate levels 4-8 are generated by combining (a-2), (b-3), (c-2), and (d-4). High levels 5-9 are generated by combining (a-3), (b-2), (c-3), and (d-4). High levels 5-10 are generated by combining (a-3), (b-2), (c-4), and (d-4).

[0053] The degree of work restriction is a judgment derived by the restriction level generation unit 15 based on certain specialized knowledge regarding whether work is possible, whether there are work restrictions if possible, and whether support is needed for member 1. The condition information includes at least information indicating the results of a health check that the member 1 has undergone or information indicating the results of a medical interview that the member 1 has answered. The learning model 18 learns in advance the correspondence between the condition information relating to the health condition of the members 1 constituting the organization and the restriction level indicating the degree of work restriction corresponding to this condition information. The information indicating the results of the health checkup is status information regarding the health condition of the members 1 that make up the organization, and includes the test results of multiple test items 22 in the health checkup, and the learning model 18 learns the correspondence between these test results and the restriction level determined by a doctor based on the test results of the health checkup. Physicians include industrial physicians and other physicians. The doctor determines the restriction level in consideration of the test results of multiple test items 22 in the health checkup. The learning model 18 performs machine learning using a combination of the test results of the health checkup and the restriction level determined by the doctor in consideration of the test results of the health checkup as learning data, and learns the pattern by which the doctor determines the restriction level. The learning model 18 may perform machine learning on the above learning data to learn patterns of how a doctor combines the options of the four elements 45 (45a, 45b, 45c, 45d) to determine a restriction level, taking into account the test results of a health checkup.

[0054] The information indicating the results of the medical interview may include answers to multiple questions in the medical interview, and the learning model 18 may learn the correspondence between the medical examination test results and the answers to the medical interview and the restriction level determined by a doctor based on the test results and answers. That is, the learning model 18 may learn the restriction level determined by the doctor taking into consideration not only the test results of the health checkup but also the answers to the medical interview of the member 1. The learning model 18 performs machine learning using the combination of the test results of the health checkup and the answers to the medical interview, and the restriction level determined by the doctor in consideration of these, as learning data, and learns the pattern by which the doctor determines the restriction level. The learning model 18 may perform machine learning on the above learning data to learn patterns of how a doctor combines the options of the four elements 45 (45a, 45b, 45c, 45d) to derive a restriction level, taking into account the test results of a health checkup and answers to a medical interview.

[0055] (About learning model 18) Learning of the learning model 18 will be described with reference to Fig. 8. Fig. 8 is a diagram for explaining an example of learning of the learning model 18 according to this embodiment. In this embodiment, the learning model 18 uses a generative adversarial network (GAN) and is trained by backpropagation. The generative adversarial network performs training using two networks: a learning model 18 that generates a constraint level and a discriminator 18. Backpropagation is used in this training process. The learning data used for this learning is a large number of restriction levels generated by the learning model 18 based on status information, which is information about the health status of member 1, as shown in Figure 8(a), and a large number of restriction levels derived by a doctor such as an industrial physician based on this status information.

[0056] First, as shown in Fig. 8(a), the classifier 19 is trained. From the training data, a combination of the restriction level derived by the doctor and the condition information on which it is derived is extracted, and this combination is input to the classifier 19. The classifier 19 is trained to distinguish this doctor's restriction level as genuine. Next, as shown in Fig. 8(a), a combination of the restriction level generated by the learning model 18 and the state information on which it is based is extracted from the learning data, and this combination is input to the classifier 19. The classifier 19 is trained to distinguish the restriction level of this learning model 18 from a fake. 8(a) is learned using the backpropagation method. That is, the parameters of the classifier 19 are adjusted by backpropagating the classification result of the classifier 19 to the classifier 19 so that the value of the error function of the classifier 19 becomes zero.

[0057] Next, as shown in Figure 8(b), the learning model 18 is trained. State information is input to the learning model 18, and the restriction level generated by the learning model 18 is input to the classifier 19. The learning model 18 is trained by the backpropagation method, which backpropagates the classification results of the classifier 19 until the classifier 19 starts to mistakenly classify this as the real thing. By learning the learning model 18 as described above, the knowledge of a doctor such as an industrial physician regarding the condition information, which is information about the health condition, can be copied to the learning model 18. In addition, the learning model 18 may be trained to output the test items of the health check or medical interview that were used as the basis for generating the restriction level, along with the restriction level, from the input health check test results and medical interview answers. In this case, the restriction level generation unit 15 inputs the state information acquired by the state information acquisition unit 13 into the learning model 18, and when generating a restriction level corresponding to the state information, outputs the contents of the state information used as the basis for generating the restriction level together with the restriction level. In this embodiment, when generating a restriction level corresponding to the input status information, the learning model 18 may be trained to output any or all of the reference values ​​23, actual measured values ​​24, and evaluations 25 of the health checkup test items 22 included in the status information together with the restriction level as evidence for the generation of the restriction level. Furthermore, in this embodiment, when generating a restriction level corresponding to the input condition information, the learning model 18 may be trained to output the answers of member 1 for each test item on the questionnaire 31 included in the condition information together with the restriction level as evidence for the basis for generating the restriction level. Furthermore, in this embodiment, when generating a restriction level corresponding to the input status information, the learning model 18 may be trained to output, along with the restriction level, which option was selected for each of the four elements 45 that make up the restriction level subdivision 41b, namely, whether work is possible 45a, whether working conditions are present 45b, whether support is required 45c, and type of support 45d, as evidence of the basis for generating the restriction level.

[0058] The storage control unit 16 associates the state information input to the learning model 18 with the restriction level generated by the restriction level generation unit 15 and stores them as health management information for the member 1 of the organization. That is, the memory control unit 16 associates status information, which is information regarding the health condition of member 1, with the restriction level generated by the restriction level generation unit 15 based on this status information, and stores this in the memory unit 10d as health management information for member 1 of the organization. When there are multiple members 1, the health management information includes information on multiple members 1.

[0059] The output unit 17 outputs information for the group and information for the member 1 based on the health management information. The output unit 17 outputs, as information for the organization, the number or names of members 1 who are not able to work, or the number or names of members 1 who have work restrictions.

[0060] The information for the organization output by the output unit 17 will be described with reference to Fig. 9. Fig. 9 is a diagram showing an example of information for the organization output by the health management system 10. Figure 9(a) shows data extracted and output from health management information for the number of members 1 of an organization who are able to work normally 51, have work restrictions 52, are unable to work 53, are undergoing re-examination (separate) 54, and have not yet undergone 55. The normal work available 51 indicates the number of members 1 who are determined by the restriction level generating unit 15 to be normal work available (a-1) among the members 1 stored in the health management information. The work restriction 52 indicates the number of members 1 who are determined to be subject to the work restriction (a-2) by the restriction level generation unit 15 among the members 1 stored in the health management information. Unable to work 53 indicates the number of members 1 who have been determined by the restriction level generation unit 15 to be unable to work (a-3) among the members 1 stored in the health management information. Re-examination (separate) 54 indicates the number of members 1 stored in the health management information who have been determined to be unable to work (a-3) by the restriction level generation unit 15 and who have reported that they are currently undergoing re-examination or separate examination. Not yet performed 55 indicates the number of members 1 stored in the health management information who have been determined to be unable to work (a-3) by the restriction level generation unit 15 and who have not reported having undergone a re-examination or a separate examination.

[0061] By obtaining the above information from the health management system 10, an organization can grasp the overall health status of the members 1 that make up the organization, and by understanding this health status, it can formulate the organization's management policies and plans.

[0062] The table in Figure 9(b) shows the health status of each member 1 of the organization, and is generated based on data extracted from the health management information stored in the memory unit 10d. Figure 9(b) includes a column for name 60, a column for availability to work 61, a column for availability of working conditions 62, a column for instructions based on health status 63, a column for type of support 64, and a column for progress status 65.

[0063] Name 60 displays the name of the member 1 who has been determined by the restriction level generation unit 15 to be unable to work (a-3) or restricted from working (a-2). The availability of work 61 displays the availability of work 45a of the four elements 45 that make up the restriction level subdivision 41b. The presence or absence of working conditions 62 displays the presence or absence of working conditions 45b of the four elements 45 that make up the subdivision 41b of the restriction level. The instructions 63 based on the health condition are displayed as instructions 41c based on the condition information of the restriction level subsection 41b. The instructions 63 are a message sent from the health management system 10 to the member terminal 30 by email or push notification to the member 1. The support type 64 displays the support type 45d of the four elements 45 that make up the restriction level subsection 41b. The progress status 65 is based on a report from the member 1 who has received the instruction 41c based on the status information. If the member 1 is required to report the progress status regarding the support received by the member 1, the member 1 reports the progress status of the support received at a medical institution or the like to the health management system 10 from the member terminal 30.

[0064] The output unit 17 outputs a letter of introduction 70 for introducing the member 1 to a medical institution based on the health management information. The output unit 17 references the member 1, the condition information that is information about the health condition of the member 1, and the health management information that associates the restriction level generated by the restriction level generation unit 15, and outputs the letter of introduction 70. The output unit 17 may output the letter of introduction 70 as a paper medium or as an electronic medium. When the letter of introduction 70 is output as a paper medium, the contents of the letter of introduction 70 are printed on paper and output. When the letter of introduction 70 is output as an electronic medium, it may be output in a format similar to a document printed on paper, such as an electronic document format such as PDF (registered trademark: Portable Document Format), or may be output as a text file.

[0065] The letter of introduction 70 will be described with reference to Fig. 10. Fig. 10 is a diagram showing an example of the letter of introduction 70 for the member 1 output by the health management system 10. The letter of introduction 70 includes the addressee of the letter of introduction 71, the name etc. of the member 1 72, and the reason for introduction 73.

[0066] The addressee 71 of the letter of introduction lists the name of the doctor or other person who will introduce the member 1. The addressee 71 of the letter of introduction 70 varies depending on the reason for introduction 73, and is divided into industrial physician, internist, surgeon, and the like. The name etc. 72 of member 1 displays the name, sex, date of birth, address, telephone number, affiliation, etc. of member 1. The reason for referral 73 displays the symptoms that caused member 1 to be referred to a medical institution. The reason for referral 73 displays the reason why the restriction level generation unit 15 determined that support for member 1 is necessary, and for example, displays the test item 22 in which the actual measurement value 24 of the health check was abnormal. Furthermore, the output unit 17 may describe the risk of the member 1 developing the disease in the letter of introduction 70 using the upper and lower limits of a 95% confidence interval.

[0067] The medical examination result acquisition unit 80 acquires the medical examination results obtained when the member 1 goes to a medical institution for medical treatment based on the referral letter 70. The member 1 goes to the medical institution referred in the referral letter 70 and receives the medical examination results. The member 1 converts the results into electronic data using the member terminal 30 and transmits them to the health management system 10. The examination results are converted into electronic data by peripheral devices such as a keyboard, mouse, scanner, and digital camera used on the member terminal 30. When member 1 is prescribed medicine by a medical institution, information about the prescribed medicine is also converted into electronic data and transmitted to health management system 10 together with the medical examination results. The health management system 10 acquires the medical examination results sent from the member terminal 30 by the medical examination result acquisition unit 80.

[0068] The examination result sending unit 81 sends the examination result to the doctor terminal 27 used by the doctor who has received the request from the organization. A doctor requested by an organization may be a doctor who belongs to the organization, or a doctor appointed by the organization to manage the health of its members. The medical examination result sending unit 81 sends the medical examination results of the member 1 acquired by the medical examination result acquiring unit 80 to the doctor terminal 27 to provide them to the doctor. When the medical examination result acquisition unit 80 acquires information on the medication prescribed to member 1 along with the medical examination result of member 1, the medical examination result transmission unit 81 transmits the medication information along with the medical examination result to the doctor's terminal 27, and also provides the doctor with the medication information.

[0069] The final restriction level acquisition unit 82 acquires the final restriction level of member 1 determined by a doctor based on the examination results. The final restriction level refers to the restriction level determined as the degree of work restriction for Member 1. The doctor requested by the organization is responsible for determining the restriction level that indicates the degree of work restriction for Member 1. The doctor will determine the final restriction level for member 1 after reviewing the results of the consultation when member 1 visits the medical institution referred in the referral letter 70. When member 1 is prescribed medication by a medical institution, the doctor will review both the medication information and the consultation results to determine the final restriction level for member 1. Member 1 is required to follow the doctor's decision on the final restriction level.

[0070] The storage control unit 16 stores the health management information of the member 1 of the organization, including the medical examination results and final restriction level of the member 1. The memory control unit 16 stores the medical examination results of member 1 acquired by the medical examination result acquisition unit 80 and the final restriction level of member 1 acquired by the final restriction level acquisition unit 82, together with the health management information of member 1. When member 1 is prescribed medication by the medical institution referred in the referral letter 70, the memory control unit 16 stores the medication information for member 1 acquired by the medical examination result acquisition unit 80 in addition to member 1's health management information. The memory control unit 16 associates health status information, the restriction level indicating the degree of work restriction, medical examination results, medication information, and the final restriction level, and stores them in the memory unit 10d as health management information for each member 1 of the organization. By connecting to the health management system 10 via the information and communication network 40 using the organization terminal 20, the organization can refer to the health management information stored in the memory unit 10d and to the health status information, the restriction level indicating the degree of work restriction, medical examination results, medication information, and the final restriction level stored for each member 1.

[0071] The re-learning unit 83 causes the learning model 18 to learn the correspondence between the condition information included in the health management information and the final restriction level of the doctor corresponding to the condition information. The learning model 18 uses the re-learning unit 83 to learn the correspondence between the condition information relating to the health condition of the member 1 and the final restriction level determined by a doctor requested by the organization. The learning model 18 is trained by the re-learning unit 83 by associating the final restriction level with status information related to the health condition of member 1, so that the learning model 18 can accurately generate a restriction level corresponding to the status information of member 1. This is because the final restriction level is a highly reliable restriction level for member 1 that was finally determined by a doctor commissioned by the organization based on the results of a medical examination conducted by member 1 at a medical institution based on the referral letter 70.

[0072] The advance notice sending unit 84 may send an advance notice to the doctor terminal 27 used by the doctor to notify the doctor of the execution of processing in order to obtain the doctor's approval for the execution of processing by any or all of the status information acquisition unit 13, restriction level generation unit 15, memory control unit 16, and output unit 17. The physician is a physician who has been commissioned by the organization.

[0073] The approval / disapproval receiving unit 85 may receive approval or disapproval of the execution of the process from the doctor who has received the advance notice. The state information acquisition unit 13, the restriction level generation unit 15, the memory control unit 16, and the output unit 17 may perform the processing when the doctor's consent to the advance notice of the processing is received. The advance notice sending unit 84 and the acceptance / rejection receiving unit 85 are used when the health management system 10 is operated under the supervision of a doctor requested by an organization. By placing the operation of the health management system 10 under the supervision of a doctor, it is possible to avoid dangers such as the health management system 10 becoming uncontrollable.

[0074] The learning model 18 is trained to output the test results used as the basis for generating the restriction level, among the test results of multiple test items in a health checkup, along with the restriction level, and the restriction level generation unit 15 may input the status information acquired by the status information acquisition unit 13 into the learning model 18 and, when generating a restriction level corresponding to the status information, output the contents of the test results used as the basis for generating the restriction level along with the restriction level. In this embodiment, when the restriction level generation unit 15 inputs the status information acquired by the status information acquisition unit 13 into the learning model 18 to generate a restriction level corresponding to the status information, the restriction level generation unit 15 may be trained to output any or all of the reference values ​​23, actual measured values ​​24, and evaluations 25 of the health checkup test items 22 included in the status information together with the restriction level as evidence for the basis for generating the restriction level. Furthermore, in this embodiment, when the restriction level generation unit 15 inputs the status information acquired by the status information acquisition unit 13 into the learning model 18 to generate a restriction level corresponding to the status information, the restriction level generation unit 15 may be trained to output the answers of member 1 for each test item in the questionnaire 31 included in the status information together with the restriction level. Furthermore, in this embodiment, when the restriction level generation unit 15 inputs the status information acquired by the status information acquisition unit 13 into the learning model 18 to generate a restriction level corresponding to the status information, the restriction level generation unit 15 may be trained to output, together with the restriction level, which option was selected for each of the four elements 45 that make up the sub-division 41b of the restriction level, namely, whether work is possible 45a, whether working conditions are present 45b, whether support is required 45c, and the type of support 45d, as evidence of the basis for generating the restriction level. When generating a restriction level for member 1, the health management system 10 can provide the organization or member 1 with evidence of the basis for generating the restriction level, so that the organization and member 1 can accept the restriction level with peace of mind.

[0075] (Health management program and health management method) Next, a health management program according to one embodiment of the present invention will be described together with a health management method with reference to Fig. 11. Fig. 11 is a flowchart of the health management program according to one embodiment of the present invention. The health management method is executed by calculation unit 10e of health management system 10 based on the health management program.

[0076] The health management program includes a state information acquisition step S13, a learning model acquisition step S14, a restriction level generation step S15, a storage control step S16, and an output step S17.

[0077] The health management program causes the calculation unit 10e of the health management system 10 to perform functions such as a status information acquisition function, a learning model acquisition function, a restriction level generation function, a memory control function, and an output function. These functions are executed in the order shown in the flowchart of Figure 11, but the order can also be changed as appropriate. Note that each function overlaps with the description of the various functional units of the health management system 10 described above, and therefore a detailed description thereof will be omitted.

[0078] The status information acquisition function acquires information relating to the health status of the members 1 constituting the group as status information (S13: status information acquisition step). The learning model acquisition function acquires a learning model that has previously learned the correspondence between status information and restriction levels that indicate the degree of work restriction corresponding to the status information (S14: learning model acquisition step). The restriction level generation function inputs status information into a learning model 18 that has previously learned the correspondence between status information and restriction levels that indicate the degree of work restrictions based on the status information, and generates a restriction level (S15: restriction level generation step). The storage control function stores the member 1, the status information, and the restriction level in association with each other as health management information for the member 1 of the organization (S16: storage control step). The output function outputs information for the group and information for the member 1 based on the health management information (S17: output step).

[0079] (Health management program and health management method according to the second embodiment) Next, a health management program according to the second embodiment will be described together with a health management method with reference to Fig. 12. Fig. 12 is a flowchart of the health management program according to the second embodiment. The health management information processing program of Figure 12 is another embodiment of the health management program of Figure 11, and differs from the health management program of Figure 11 in that it has added a medical examination result acquisition step S80, a medical examination result transmission step S81, a final restriction level acquisition step S82, a re-learning step S83, a notice transmission step S84, and an acceptance / rejection acceptance step S85.

[0080] The health management program according to FIG. 12 will be described below together with the health management method. The health management program of FIG. 12 includes a status information acquisition step S13, a learning model acquisition step S14, a restriction level generation step S15, a memory control step S16, an output step S17, a medical examination result acquisition step S80, a medical examination result transmission step S81, a final restriction level acquisition step S82, a re-learning step S83, a notice transmission step S84, and an acceptance / rejection acceptance step S85. The health management program of Fig. 12 causes the calculation unit 10e to realize a status information acquisition function, a learning model acquisition function, a restriction level generation function, a memory control function, an output function, a medical examination result acquisition function, a medical examination result transmission function, a final restriction level acquisition function, a re-learning function, a preliminary notification transmission function, and an acceptance / rejection acceptance function. Note that these functions are executed in the order shown in the flowchart of Fig. 12, but the order can be changed as appropriate. The various functions overlap with the description of the health management program of Fig. 11 above, and therefore the overlapping description will be omitted.

[0081] The examination result acquisition function acquires the examination results obtained when the member 1 goes to a medical institution for treatment based on the referral letter 70 (S80: examination result acquisition step).

[0082] The examination result transmission function transmits the examination result to the doctor terminal 27 used by the doctor who has received the request from the organization (S81: examination result transmission step).

[0083] The final restriction level acquisition function acquires the final restriction level of member 1 determined by the doctor based on the examination results (S82: final restriction level acquisition step).

[0084] The re-learning function causes the learning model 18 to learn the correspondence between the condition information included in the health management information and the doctor's final restriction level corresponding to the condition information (S83: re-learning step).

[0085] The advance notice sending function sends an advance notice to the doctor's terminal 27 used by the doctor to notify him of the execution of processing in order to obtain the doctor's approval for the execution of processing by any or all of the status information acquisition unit 13, restriction level generation unit 15, memory control unit 16, and output unit 17 (S84: advance notice sending step).

[0086] The approval / disapproval reception function receives approval / disapproval of the doctor who has received the advance notice regarding the implementation of the process (S85: approval / disapproval reception step).

[0087] According to the above-described embodiment, the learning model 18 is formed using a generative adversarial network, and the learning model 18 can be efficiently trained by using the backpropagation method, which can efficiently train neural networks.

[0088] Furthermore, according to the above-described embodiment, the restriction level generation unit 15 generates the restriction level by taking into consideration not only the results of the health check but also the results of the medical interview, so that the restriction level can be generated by taking into more detailed consideration the actual health condition of member 1.

[0089] Furthermore, according to the above-described embodiment, the health management system 10 outputs the number and names of members 1 who are unable to work (a-3) and have work restrictions (a-2) as information for the organization and communicates this information to the organization, allowing the organization to grasp the overall health status of the members 1 who make up the organization and to provide individual support to members 1 who are unable to work (a-3) and have work restrictions (a-2).

[0090] Furthermore, according to the above-described embodiment, the health management system 10 generates a restriction level for member 1 based on status information, and can classify member 1's health status into low level 3, medium level 4, and high level 5 before consulting a doctor such as an industrial physician, thereby reducing the burden on doctors.

[0091] (Health management system 10 according to the third embodiment) Next, a health management system 10 according to a third embodiment of the present disclosure will be described with reference to FIGS. FIG. 13 is a diagram for explaining an overview of a health management system 10 according to the third embodiment, and FIG. 14 is a block diagram showing an example of the functional configuration of the health management system 10 according to the third embodiment. The health management system 10 of the third embodiment is used by an organization 100 for the purpose of managing the health of the members 1 that make up the organization, similar to the health management system 10 of the present embodiment described with reference to Figures 1 to 10. In the following description of the health management system 10 according to the third embodiment, only the differences from the health management system 10 according to the present embodiment will be described, and the commonalities will be denoted by the same reference numerals as those used in the description of the health management system 10 according to the present embodiment, and the description thereof will be omitted. The health management system 10 according to the third embodiment generates a restriction level indicating the degree of work restriction on the member 1 based on the acquired status information, similar to the health management system 10 according to the present embodiment. The health management system 10 according to the third embodiment further differs from the health management system 10 according to the present embodiment in that it generates improvement plan information that indicates an improvement plan for improving the symptoms of the member 1.

[0092] First, an overview of a health management system 10 according to the third embodiment will be described with reference to FIG. An organization 100 such as a business that is a user of the health management system 10 has an obligation to maintain the health of its employees, who are its members 1. By using the health management system 10, the organization 100 can perform higher quality health management of its members 1. A PDCA cycle 101 for health management of a member 1 of an organization 100 shown in FIG. 13 will be described. The PDCA cycle 101 for health management of a member 1 of an organization 100 is made up of four elements: Plan, Do, Check, and Act. In Plan 102, the organization 100 makes a plan for the health management of the member 1. In the third embodiment, the organization 100 utilizes the health management system 10 to also manage improvement plans for the symptoms of the members 1. The term "entrust" (Do) 103 means that when executing a plan made by the organization 100, the health management system 10 is used instead of entrusting it to a specialist such as an industrial physician. In Check 104, the health management system 10 checks the reports described below for each member 1 to confirm whether the health management plan for all members 1 of the organization 100 is being implemented properly. In the improvement (Act) 105, based on the results of the confirmation 104, necessary improvements are made to the plan for health management of all the members 1 of the organization 100.

[0093] Next, the operation of the health management system 10 used by the organization 100 will be described with reference to FIG. The health management system 10 manages the health of the member 1 by linking SOAP with the PDCA cycle.

[0094] (About SOAP for Health Management System 10) SOAP is a concept that involves assessing a patient's condition based on subjective and objective information and formulating an optimal treatment plan. It consists of four elements: Subject, Object, Assessment, and Plan. The subject refers to the patient's awareness, that is, the patient's subjective symptoms and experiences. In the health management system 10, this refers to acquiring the subjective information and awareness 108 of the member 1. The object refers to objective data, observation results, and test results that indicate the patient's condition. In the health management system 10, it refers to obtaining objective information and objective findings 109 of the member 1. Objective findings refer to symptoms that can be objectively perceived by someone other than the patient, and refer to symptoms that can be objectively detected through hospital tests, physical examinations such as palpation and visual inspection by a doctor, imaging tests (X-rays, MRIs, etc.), and medical tests (blood tests, nerve conduction tests, etc.). Objective findings may also include symptoms that can be detected by the patient's self-examination results, examinations using a patient checklist, and observations by a third party. Even if the patient is not aware of the symptoms, symptoms that can be objectively perceived by someone other than the patient are included in objective findings. Assessment refers to evaluating the patient's condition based on the acquired subjective and objective information. In the health management system 10, this refers to the evaluation level generation 110, which means generating an evaluation level that indicates the degree of work restriction of the member 1. The "Plan" refers to creating an improvement plan based on an evaluation of the patient's condition. In the health management system 10, this refers to the "Improvement Plan Generation 111" and refers to creating improvement plan information for the symptoms of the member 1. The member 1 implements the improvement plan under the guidance of the medical institution. The implementation of the improvement plan by the member 1 is treated as plan execution 113 in the health management system 10.

[0095] (About the PDCA cycle of the health management system 10) The health management system 10 has the member 1 execute the improvement plan generated by SOAP in accordance with the PDCA cycle, and makes necessary improvements to the improvement plan based on the progress observation. The PDCA cycle of the health management system 10 consists of four elements: Plan, Do, Check, and Act. Plan refers to the above-mentioned SOAP Plan, and as described above, in the health management system 10, it refers to improvement plan generation 111. When the member 1 implements the improvement plan, the health management system 10 acquires agreement information, such as a consent form (described later), from the member 1 in order to reach a consensus with the member 1 regarding the improvement plan. Therefore, in the health management system 10, after the improvement plan generation 111, consensus formation and consent form 112 is executed. Do refers to the member 1 executing the improvement plan, and is handled in plan execution 113 of the health management system 10. Note that the member 1 executes the improvement plan under the guidance of a medical institution, so the member 1 will be examined at the medical institution to execute the improvement plan. When the member 1 needs to execute the improvement plan under the guidance of a specific medical institution, the health management system 10 issues a referral letter 70 together with the improvement plan information. Check refers to checking the plan being implemented, which in the health management system 10 is follow-up observation 114. When performing follow-up observation 114 in the implementation of the improvement plan for member 1, the health management system 10 acquires follow-up observation information, which will be described later, from the medical institution where member 1 has visited. Act refers to improving the plan based on the results of the confirmation, which in the health care system 10 is improvement 115.

[0096] The health management system 10 acquires the implementation record 116 created by the medical institution based on the implementation of the improvement plan for the member 1. Then, the health management system 10 creates a report for the organization 100 based on the acquired implementation record 116 in the report / processing information 117, and the created report information is output to the organization 100 and saved 118. If the prepared report is inspected and any abnormalities are found, it will be treated as Accident 119, and the member 1 involved in the report will be required to consult a specialist or other medical professional.

[0097] (Functional Configuration of Health Management System 10 According to Third Embodiment) The functional configuration of the health management system 10 according to the third embodiment will be described with reference to Fig. 14. Fig. 14 is a block diagram showing an example of the functional configuration of the health management system 10 according to the third embodiment. The health management system 10 of the third embodiment executes the health management program of the fourth embodiment described below, and is equipped with functional units such as a status information acquisition unit 13, a learning model acquisition unit 14, a restriction level generation unit 15, an improvement plan generation unit 125, a memory control unit 16, an output unit 17, an agreement acquisition unit 126, a progress observation acquisition unit 127, a progress observation acquisition determination unit 128, a medical consultation recommendation generation unit 129, an implementation record acquisition unit 130, and a report creation unit 131 in the calculation unit 10e.

[0098] (Status information acquisition unit 13) The condition information acquisition unit 13 acquires condition information relating to the health conditions of the members 1 constituting the group 100 . The condition information acquisition unit 13 may acquire condition information relating to the health conditions of the members 1 constituting the group 100 in the subjective information / awareness 108 and the objective information / objective findings 109 shown in FIG. The storage control unit 16 stores the condition information associated with the member 1 as part of the member 1's health management information in the storage unit 10d.

[0099] The status information includes subjective information and objective information. Subjective information is information based on member 1's awareness regarding his / her health. Subjective information refers to information based on the awareness of member 1, and is information based on member 1's judgment, actions, or experiences. For example, a questionnaire about sleep apnea is distributed to employees of the organization 100 who are members 1, and the state information acquiring unit 13 acquires subjective information by collecting the questionnaires. The member 1 may answer the questionnaire using the member terminal 30, and the state information acquisition unit 13 acquires the questionnaire answers from the member terminal 30 to thereby acquire subjective information. The questionnaire may also be distributed to members 1 as an electronic medium such as an electronic file or a web page.

[0100] Objective information is information based on objective findings regarding member 1's health. Objective findings for member 1 refer to symptoms related to member 1's health that can be objectively perceived by someone other than member 1. For example, objective findings for Member 1 refer to symptoms of Member 1 that can be objectively identified through hospital examinations, physical examinations such as palpation and visual inspection by a doctor, imaging tests (X-rays, MRIs, etc.), and medical tests (blood tests, nerve conduction tests, etc.). In addition, objective findings for member 1 may include symptoms of member 1 that can be captured by member 1's self-examination results, member 1's examination using a checklist, and a third party's observation of member 1, etc. Even if member 1 is not aware of a symptom, if a person other than member 1 can objectively perceive the symptom, it is included in the objective findings for member 1. The objective information may be, for example, information on the condition of an employee of the organization 100 who is a member 1, judged by an expert such as a doctor, or information obtained by the member 1 performing a self-examination. The condition information acquisition unit 13 collects the objective information by collecting the information judged by an expert such as a doctor or the results of the member 1's self-examination.

[0101] (Learning model acquisition unit 14) The learning model acquisition unit 14 acquires a second learning model that has previously learned the correspondence between the condition information and a plan (improvement plan) for improving symptoms that is prepared by a specialist such as a doctor based on the condition information. The second learning model is machine-learned using supervised learning, i.e., the second learning model is trained with the correct answer given to the training data. Specifically, the second learning model is prepared with a large amount of condition information constituting the learning data, and for each piece of condition information, supervised learning is performed using a large number of data sets obtained by annotating the data to provide a symptom improvement plan created by a doctor or other expert as the correct answer.

[0102] Alternatively, as shown in Table 1 below, a doctor or other expert may prepare an improvement plan in advance corresponding to each disease name and restriction level, and the second learning model may machine-learn the correspondence between the situation information and the disease name and restriction level corresponding to the situation information in advance. Specifically, a doctor or other expert will create improvement plan A(3-1) to improvement plan A(5-10) for disease name A, improvement plan B(3-1) to improvement plan B(5-10) for disease name B, and improvement plan C(3-1) to improvement plan C(5-10) for disease name C, as shown in Table 1. For example, by inputting situation information into the second learning model, it is determined that member 1's illness is disease name A and the work restriction level is low level 3-1.In this case, improvement plan A (3-1) is selected as the improvement plan for member 1 based on Table 1. Furthermore, by inputting situation information into the second learning model, it is determined that member 1's illness is disease name B and the work restriction level is medium level 4-5, and in this case, improvement plan B (4-5) is selected as the improvement plan for member 1 based on Table 1. Furthermore, by inputting situation information into the second learning model, it is determined that member 1's illness is disease name C and the work restriction level is high level 5-9, and in this case, improvement plan C (5-9) is selected as the improvement plan for member 1 based on Table 1.

[0103] [Table 1]

[0104] (Restriction level generation unit 15) The restriction level generation unit 15 inputs the state information acquired by the state information acquisition unit 13 into the learning model 18, and generates a restriction level corresponding to the state information. The 10 restriction levels described above are described by a combination of major division 41a and minor division 41b. For example, "Low Level 3" in Low Level 3-1 indicates major division 41a, and "1" in Low Level 3-1 indicates minor division 41b. Also, "Mid Level 4" in Mid Level 4-5 indicates major division 41a, and "5" in Mid Level 4-5 indicates minor division 41b. As described above, the state information input to the restriction level generation unit 15 according to the second embodiment includes subjective information and objective information. The restriction level generation unit 15 generates restriction level information indicating the degree of work restriction on the member 1 based on the subjective information and the objective information. Specifically, the restriction level generation unit 15 selects one of low level (normal work possible) (3-1 to 3), medium level (work restriction) (4-4 to 8), and high level (work not possible) (5-9 to 10) based on the subjective information and the objective information. The restriction level generating unit 15 may generate restriction level information corresponding to the state information in the evaluation level generating unit 110 shown in FIG.

[0105] (Improvement plan generation unit 125) The improvement plan generating unit 125 inputs the state information acquired by the state information acquiring unit 13 into the second learning model, and generates improvement plan information indicating an improvement plan for the symptoms of the member 1. Alternatively, the improvement plan generation unit 125 may input the status information acquired by the status information acquisition unit 13 into the second learning model to generate the disease name and restriction level information for member 1's illness, and then use Table 1 to select an improvement plan associated with the generated disease name and restriction level. If the member 1 needs to be introduced to a specific medical institution in order to implement the improvement plan, the improvement plan generating unit 125 generates a letter of introduction 70 for the specific medical institution together with the improvement plan information.

[0106] (Memory control unit 16) The storage control unit 16 associates the state information input to the second learning model with the improvement plan information generated by the improvement plan generating unit 125 and stores them as health management information of the member 1 of the organization 100. That is, the memory control unit 16 stores the state information and the improvement plan information in association with each other in the memory unit 10d as part of the health management information of the member 1. In this embodiment, the storage control unit 16 stores information acquired, generated, or output by the health management system 10 in the storage unit 10d.

[0107] (Output section 17) The output unit 17 outputs improvement plan information as information for the member 1 based on the health management information. That is, the output unit 17 outputs the improvement plan information to the member terminal 30 through the information communication network 40. In addition, when the improvement plan generation unit 125 generates the letter of introduction 70 together with the improvement plan information, the output unit 17 outputs the improvement plan information and the letter of introduction 70 as information for the member 1.

[0108] (Agreement acquisition section 126) The agreement acquisition unit 126 acquires agreement information indicating the agreement of the member 1 with respect to the improvement plan information. That is, the agreement acquisition unit 126 acquires agreement information indicating the agreement of the member 1 with respect to the improvement plan information from the member terminal 30 via the information communication network 40. The agreement acquisition unit 126 may acquire agreement information indicating the agreement of the member 1 with respect to the improvement plan information from the agreement formation / agreement document 112 shown in FIG. It should be noted that if the agreement between the member 1 and the organization 100 on the improvement plan information is reached verbally or in writing, the agreement acquisition unit 126 is not an essential component. The storage control unit 16 associates the condition information input to the second learning model with the agreement information acquired by the agreement acquisition unit 126 and stores them as health management information of the member 1 of the organization 100. That is, the memory control unit 16 associates the state information input to the second learning model with the agreement information acquired by the agreement acquisition unit 126, and stores the information as part of the health management information of the member 1 in the memory unit 10d.

[0109] The follow-up observation acquisition unit 127 acquires follow-up observation information created by a medical institution when the member 1 is examined at the medical institution based on the improvement plan. Follow-up observation means observing how the condition of member 1 changes over time while the member 1 is implementing the improvement plan. The medical institution will conduct follow-up observation of member 1 and create follow-up observation information. The follow-up information may be a reply from the medical institution to the referral letter 70. The state in which member 1 is implementing the improvement plan may be referred to as plan execution 113 shown in Fig. 13, and the follow-up observation acquisition unit 127 may acquire follow-up observation information created by a medical institution when member 1 visits a medical institution based on the improvement plan in follow-up observation 114 shown in Fig. 13. The medical institution transmits the created follow-up observation information to the health management system 10 via an information and communication network. The follow-up observation acquisition unit 127 acquires follow-up observation information sent from a medical institution via an information communication network. The storage control unit 16 associates the condition information input to the second learning model with the follow-up observation information acquired by the follow-up observation acquisition unit 127 and stores them as health management information of the member 1 of the organization 100. That is, the memory control unit 16 stores the condition information and the follow-up observation information in association with each other in the memory unit 10d as part of the health management information of the member 1.

[0110] The follow-up observation acquisition determination unit 128 determines whether or not the follow-up observation information of the member 1 who generated the improvement plan information has been acquired within a predetermined period of time. That is, the follow-up observation acquisition determination unit 128 determines whether or not the follow-up observation information of the member 1 created by the medical institution has been acquired from the medical institution or the member 1 within a predetermined period of time. The follow-up observation information may be a reply from the medical institution to the referral letter 70.

[0111] When the follow-up observation acquisition determination unit 128 determines that follow-up observation information for member 1 has not been acquired within a specified period, the consultation recommendation generation unit 129 generates consultation recommendation information that encourages member 1 to visit a medical institution based on the improvement plan. The "predetermined period" may be a period that has been agreed upon in advance with the medical institution. The follow-up observation information may be a reply from the medical institution to the referral letter 70, and the consultation recommendation generation unit 129 may generate consultation recommendation information that encourages member 1 to visit a medical institution based on the improvement plan when the follow-up observation acquisition determination unit 128 determines that a reply from the medical institution to the referral letter 70 has not been received within a specified period of time. When the follow-up observation acquisition determination unit 128 determines that follow-up observation information has not been acquired within a specified period, the memory control unit 16 associates the condition information input into the second learning model with medical examination recommendation information indicating a recommendation for medical examination for member 1, and stores the result as health management information for member 1 of group 100. That is, the memory control unit 16 associates the status information input into the second learning model with medical examination recommendation information indicating a recommendation for medical examination for member 1, and stores the information in the memory unit 10d as part of the health management information for member 1 of the organization 100. The output unit 17 outputs information encouraging the member 1 to undergo medical examination as information directed to the member 1 based on the health management information. That is, the output unit 17 outputs consultation recommendation information to the member terminal 30 via the information and communication network 40. Encouraging a medical examination means encouraging member 1 to visit a medical institution and implement an improvement plan. In improvement 115 of Figure 13, if the consultation recommendation generation unit 129 and the output unit 17 determine that follow-up observation information for member 1 has not been obtained within a specified period, they may generate consultation recommendation information that recommends member 1 to visit a medical institution based on the improvement plan, and output the consultation recommendation information to the member terminal 30 via the information and communication network 40.

[0112] The implementation record acquisition unit 130 acquires the implementation record of the improvement plan created based on the implementation of the improvement plan by the member 1 as implementation record information. That is, the implementation record acquisition unit 130 acquires implementation record information created based on the implementation of the improvement plan for the member 1 from the medical institution via the information communication network 40. Implementation record information refers to information created by a medical institution that records the details of the implementation of member 1's improvement plan, and includes member 1's personal information, test results, and personal information that requires consideration. The implementation record acquiring unit 130 may acquire implementation record information created based on the implementation of the improvement plan by member 1 in the implementation record 116 of FIG.

[0113] The report creation unit 131 creates report information indicating a report for the organization 100 based on the implementation record information. That is, the report creation unit 131 creates report information indicating a report to be sent to the organization 100 as processed information of the implementation record information acquired by the implementation record acquisition unit 130 . The storage control unit 16 associates the condition information input to the second learning model with the report information created by the report creation unit 131 and stores them as health management information for the member 1 of the organization 100. That is, the memory control unit 16 associates the status information input into the second learning model with the report information created by the report creation unit 131 and stores them in the memory unit 10d as part of the health management information of the member 1. The output unit 17 outputs the report information created by the report creation unit 131 as part of the health management information of the member 1 and as information for the organization 100. That is, the output unit 17 outputs the report information of the member 1 to the group terminal 20 via the information communication network 40 based on the health management information stored in the storage unit 10d. The report creation unit 131 and the output unit 17 may create report information indicating a report intended for the organization 100 in the report / processing information 117 of Figure 13, and output the report information of member 1 to the organization terminal 20 via the information and communication network 40. In addition, in save 118 of Figure 13, the memory control unit 16 associates the status information input into the second learning model with the report information created by the report creation unit 131 and stores it in the memory unit 10d as part of the health management information of member 1.

[0114] (Health management program and health management method according to the fourth embodiment) Next, a health management program according to the fourth embodiment will be described together with a health management method with reference to Fig. 15. Fig. 15 is a flowchart of the health management program according to the fourth embodiment. The health management method and health management program of the fourth embodiment, like the health management method and health management program of this embodiment, generates a restriction level indicating the degree of work restriction on member 1 based on the acquired status information. The health management method and health management program of the fourth embodiment differ from the health management method and health management program of the present embodiment in that it further generates improvement plan information that indicates an improvement plan for improving the symptoms of member 1.

[0115] The health management method according to the fourth embodiment is executed by the calculation unit 10e of the health management system 10 according to the third embodiment based on the health management program according to the fourth embodiment. The health management program includes a status information acquisition step S13, a learning model acquisition step S14, a restriction level generation step S15, an improvement plan generation step S125, a memory control step S16, an output step S17, an agreement acquisition step S126, a follow-up observation acquisition step S127, a follow-up observation acquisition determination step S128, a medical examination recommendation generation step S129, a medical examination record information acquisition step S130, and a report creation step S131.

[0116] The health management program according to the fourth embodiment causes the calculation unit 10e of the health management system 10 according to the second embodiment to realize functions such as a status information acquisition function, a learning model acquisition function, a restriction level generation function, an improvement plan generation function, a memory control function, an output function, an agreement acquisition function, a follow-up observation acquisition function, a follow-up observation acquisition determination function, a consultation recommendation generation function, a consultation record information acquisition function, and a report creation function. These functions are executed in the order shown in the flowchart of FIG. 15, but the order can also be changed as appropriate. Note that each function overlaps with the description of the various functional units of the health management system 10 according to the second embodiment, and therefore detailed description thereof will be omitted.

[0117] The status information acquisition function acquires status information relating to the health status of the members 1 who make up the group 100 (S13: status information acquisition step). The state information includes subjective information and objective information.

[0118] The learning model acquisition function acquires a first learning model that has been pre-trained to determine the correspondence between status information and the restriction level indicating the degree of work restriction corresponding to the status information, and a second learning model that has been pre-trained to determine the correspondence between status information and a symptom improvement plan created by an expert based on the status information (S14: learning model acquisition step).

[0119] The restriction level generation function inputs the state information acquired by the state information acquisition function into the learning model 18, and generates a restriction level corresponding to the state information (S15: restriction level generation step).

[0120] The improvement plan generation function inputs the state information acquired by the state information acquisition function into the second learning model, and generates improvement plan information indicating an improvement plan for the symptoms of member 1 (S125: improvement plan generation step).

[0121] The memory control function associates the status information input into the first learning model with the restriction level generated by the restriction level generation function and stores it as health management information for member 1 of organization 100, and associates the status information input into the second learning model with the improvement plan information generated by the improvement plan generation function and stores it as health management information for member 1 of organization 100 (S16: memory control step).

[0122] The output function outputs information for the group or its members based on the health management information (S17: output step).

[0123] The agreement acquisition function acquires agreement information indicating the agreement of the member 1 with respect to the improvement plan information (S126: agreement acquisition step).

[0124] The follow-up observation acquisition function acquires follow-up observation information created by a medical institution when the member 1 visits the medical institution based on the improvement plan (S127: follow-up observation acquisition step).

[0125] The follow-up observation acquisition determination function determines whether or not the follow-up observation information of the member 1 who generated the improvement plan information has been acquired within a predetermined period (S128: follow-up observation acquisition determination step).

[0126] The medical examination recommendation generation function generates medical examination recommendation information that encourages member 1 to visit a medical institution based on the improvement plan when the follow-up observation acquisition determination function determines that follow-up observation information for member 1 has not been acquired within a specified period (S129: medical examination recommendation generation step).

[0127] The implementation record acquisition function acquires, as implementation record information, an implementation record of the improvement plan created based on the implementation of the improvement plan by member 1 (S130: implementation record acquisition step).

[0128] The report creation function creates report information indicating a report to be sent to the organization 100 based on the implementation record information (S131: report creation step).

[0129] According to the health management system 10 of the above embodiment, SOAP and PDCA are linked to evaluate the health of member 1 of the organization 100 based on an evaluation level, and member 1 can be made to implement a symptom improvement plan corresponding to member 1's condition information.

[0130] Furthermore, according to the health management system 10 of the above-described embodiment, when the member 1 implements the improvement plan, agreement information such as a consent form is obtained from the member 1, so that an agreement can be reached between the organization 100 and the member 1 regarding the improvement plan.

[0131] Furthermore, according to the health management system 10 of the above-described embodiment, it is possible to process the implementation record, which is a record of the implementation of the improvement plan for member 1 created by the medical institution, and create a report for the organization 100.

[0132] Furthermore, according to the health management system 10 of the above-described embodiment, when the medical examination recommendation generating unit 129 and the output unit 17 determine that follow-up observation information of member 1 has not been obtained within a specified period, medical examination recommendation information is generated and output to the member terminal 30, thereby making it possible to lead member 1 to improve his / her symptoms with a higher probability.

[0133] The medical interview in the above-described embodiment may refer to asking member 1 about symptoms, medical history, oral medications, family history, allergy history, travel history, lifestyle habits, etc., and may refer to the content of the questions used during this medical interview. Furthermore, the medical interview is not limited to this, and may also refer to asking about the actual state of member 1's life, such as the member's work content, interpersonal relationships at work, hobbies, preferences, diet, and friendships, and may refer to the content of the questions used during this medical interview. The medical interview may also be referred to as question, question content, survey, interview, inquiry, medical history taking, medical interview, etc. The main complaint in the above embodiment refers to the reason why the member 1 is in trouble, what he wants to convey, the main points of his argument, etc. The main complaint may also be rephrased as an offer, a complaint, an assertion, etc.

[0134] The present invention is not limited to the health management system 10, health management method, and health management program according to the above-described embodiments, and can be embodied in various other modified or applied examples without departing from the spirit of the present invention as set forth in the claims. Furthermore, although the term "information" is used in the above-described embodiments, the term "information" can be replaced with "data," and the term "data" can be replaced with "information." [Explanation of symbols]

[0135] 1 member 2 Self-examination 3. Low Level 4. Medium level 5. High Level 10 Health Management System 10a Communication Interface 10b ROM 10c RAM 10d storage section 10e Calculation unit 10f Input / Output Interface 11 Input Devices 11a keyboard 11b Mouse 11c scanner 11d camera 12 Output Devices 12a Monitor 12b printer 12c speaker 13 Status information acquisition unit 14 Learning model acquisition unit 15 Restriction level generation unit 16 Memory control unit 17 Output section 18 Learning Model 19 Classifier 20 Group terminals 20a Group Terminal a 20b Group terminal b 21 Health checkup results sheet 22 Inspection items 23 Reference Value 24 Actual measurements 25 ratings 27 Doctor's terminal 30 Member terminals 30a Member terminal a 30b Member terminal b 30c Member terminal c 31 Questionnaire 32 Name and other information entry field 33 Main complaint 34 Medical history entry column 35 Medication history entry section 36 Allergy information section 37 Pregnancy status entry field 40 Information and Communication Networks 41 Restriction Level Classification Table 41a Broad division 41b Subdivision 41c Instructions based on state information 41d Notes 45 4 elements 45a Availability of work 45b Whether or not there are working conditions 45c Need for care? 45d Types of Care 51 Available for regular work 52 Work Restrictions 53 Unable to work 54 Re-examination in progress 55 Not implemented 60 Name, 61 Availability of work 62 Working conditions 63 Health-Based Instructions 64 Support Types 65 Progress 70 Letter of Introduction 71 Addressee of letter of introduction 72 Names of members, etc. 73 Reason for referral 80 Medical examination result acquisition unit 81 Medical examination result transmission unit 82 Final Restriction Level Acquisition Unit 83 Re-learning section 84 Advance Notice Transmission Unit 85 Acceptance / Rejection Department 100 organizations 101 PDCA Cycle 102 Plan 103 Leave it to me (Do) 104 Check 105 Improvement (Act) 108 Subjective Information / Awareness (Subject) 109 Objective Information / Objective Findings 110 Assessment Generation 111 Improvement Plan (Plan) 112 Consensus formation / Informed consent 113 Plan Execution (Do) 114 Check 115 Improvement (Act) 116 Medical Institution Records 117 Report / Processing Information (Inform) 118 Save 119 Accident 125 Improvement Plan Generation Unit 126 Agreement Obtaining Department 127 Follow-up Observation Department 128 Follow-up Observation Acquisition and Determination Department 129 Medical Examination Recommendation Generation Department 130 Medical record information acquisition unit 131 Report Writing Department

Claims

1. a status information acquisition unit that acquires status information relating to the health status of members of the organization; a learning model acquisition unit that acquires a learning model that has previously learned the correspondence between the status information and a restriction level that indicates the degree of work restriction corresponding to the status information; a restriction level generation unit that inputs the state information acquired by the state information acquisition unit into the learning model and generates the restriction level corresponding to the state information; a storage control unit that associates the status information input to the learning model with the restriction level generated by the restriction level generation unit and stores the information as health management information for the members of the organization; an output unit that outputs information for the organization or the members based on the health management information; A health management system comprising:

2. The health management system according to claim 1, characterized in that the output unit outputs, as information for the organization, the number or names of the members who are unable to work, or the number or names of the members who have work restrictions.

3. The health management system according to claim 1, characterized in that the output unit outputs, as information for the member, whether the member is able to work, whether the member needs to be restricted in work, or whether the member needs support.

4. 2. The health management system according to claim 1, wherein the condition information includes at least information indicating the results of a health check that the member has undergone or information indicating the results of a medical interview that the member has answered.

5. the information indicating the results of the health checkup includes test results of a plurality of test items in the health checkup; The health management system according to claim 4 , wherein the learning model learns a correspondence relationship between the test results and the restriction level determined by a doctor based on the test results.

6. the information indicating the result of the medical interview includes answers to a plurality of questions in the medical interview; The health management system according to claim 5 , wherein the learning model learns a correspondence relationship between the test results and the answers and the restriction level determined by a doctor based on the test results and the answers.

7. The health management system according to claim 1 , wherein the restriction level generating unit generates the restriction levels as a plurality of categories.

8. The health management system according to claim 1 , wherein the output unit outputs a letter of introduction for introducing the member to a medical institution based on the health management information.

9. a medical examination result acquisition unit that acquires the medical examination results obtained when the member goes to a medical institution for medical treatment based on the referral letter; a medical examination result sending unit that sends the medical examination results to a doctor terminal used by a doctor requested by the organization; a final restriction level acquisition unit that acquires the final restriction level of the member determined by the doctor based on the examination results; Further provided with 9. The health management system according to claim 8, wherein the storage control unit stores the health management information of the members of the organization, including the medical examination results of the members and the final restriction level.

10. The health management system according to claim 9, further comprising a re-learning unit that causes the learning model to learn the correspondence between the status information included in the health management information and the final restriction level of the doctor corresponding to the status information.

11. a notification sending unit that sends a notification to a doctor terminal used by the doctor to notify the doctor of the execution of the processing of any or all of the processing of the status information acquisition unit, the restriction level generation unit, the memory control unit, and the output unit; an approval / disapproval receiving unit that receives approval or disapproval of the doctor who has received the advance notice for carrying out the process; Further provided with The health management system of claim 1, characterized in that the status information acquisition unit, the restriction level generation unit, the memory control unit, and the output unit carry out the processing when the doctor's consent to the advance notice regarding the processing is received.

12. the learning model is trained to output, among test results of a plurality of test items in the health checkup, the test results used as a basis for generating the restriction level together with the restriction level; The health management system of claim 5, characterized in that when the restriction level generation unit inputs the status information acquired by the status information acquisition unit into the learning model to generate the restriction level corresponding to the status information, it outputs the contents of the test results used as the basis for generating the restriction level together with the restriction level.

13. the status information includes subjective information and objective information; The subjective information is information based on the member's subjective awareness regarding the member's health, 2. The health management system according to claim 1, wherein the objective information is information based on objective findings regarding the health of the member.

14. the learning model acquisition unit acquires a second learning model that has previously learned a correspondence relationship between the condition information and a symptom improvement plan created by a specialist based on the condition information; an improvement plan generation unit that inputs the state information acquired by the state information acquisition unit into the second learning model and generates improvement plan information indicating an improvement plan for the symptoms of the member; the storage control unit associates the status information input to the second learning model with the improvement plan information generated by the improvement plan generation unit and stores the information as health management information of the members of the organization; The health management system according to claim 13 , wherein the output unit outputs the improvement plan information as information for the member based on the health management information.

15. further comprising an agreement acquisition unit that acquires agreement information indicating agreement of the members with respect to the improvement plan information; The health management system described in claim 14, characterized in that the memory control unit associates the status information input into the second learning model with the agreement information acquired by the agreement acquisition unit and stores them as health management information for the members of the organization.

16. a follow-up observation acquisition unit that acquires follow-up observation information created by a medical institution when the member visits a medical institution based on the improvement plan; a follow-up observation acquisition determination unit that determines whether the follow-up observation information of the member who generated the improvement plan information has been acquired within a predetermined period; a medical examination recommendation generating unit that generates medical examination recommendation information that recommends the member to receive a medical examination at a medical institution based on the improvement plan when the follow-up observation acquisition determining unit determines that the follow-up observation information of the member has not been acquired within a predetermined period of time; Further provided with The storage control unit the condition information input to the second learning model and the follow-up observation information acquired by the follow-up observation acquisition unit are associated with each other and stored as health management information of the members of the organization; When the follow-up observation acquisition determination unit determines that the follow-up observation information has not been acquired within a predetermined period, the condition information input to the second learning model is associated with medical examination recommendation information indicating a medical examination recommendation for the member, and the association member health management information is stored; The health management system according to claim 14 , wherein the output unit outputs the consultation recommendation information as information directed to the member based on the health management information.

17. an implementation record acquisition unit that acquires, as implementation record information, an implementation record of the improvement plan created based on the implementation of the improvement plan by the member; a report creation unit that creates report information indicating a report to be sent to the organization based on the implementation record information; Further provided with the storage control unit associates the condition information input to the second learning model with the report information created by the report creation unit and stores the information as health management information of the members of the organization; The health management system according to claim 14 , wherein the output unit outputs the report information created by the report creation unit based on the health management information as information for the organization.

18. The computer a status information acquisition step of acquiring status information relating to the health status of members constituting the organization; a learning model acquisition step of acquiring a learning model that has been pre-trained to determine the correspondence between the status information and a restriction level that indicates the degree of work restriction corresponding to the status information; a restriction level generation step of inputting the state information acquired in the state information acquisition step into the learning model and generating the restriction level corresponding to the state information; a storage control step of associating the status information input to the learning model with the restriction level generated in the restriction level generation step and storing the information as health management information for the members of the organization; an output step of outputting information for the organization or the members based on the health management information; A health management method characterized by carrying out the following.

19. On the computer, a status information acquisition function for acquiring status information regarding the health status of members constituting the organization; a learning model acquisition function that acquires a learning model that has previously learned the correspondence between the status information and a restriction level that indicates the degree of work restriction corresponding to the status information; a restriction level generation function that inputs the state information acquired by the state information acquisition function into the learning model and generates the restriction level corresponding to the state information; a storage control function that associates the status information input to the learning model with the restriction level generated by the restriction level generation function and stores the information as health management information for the members of the organization; an output function for outputting information for the organization or the members based on the health management information; A health management program characterized by demonstrating the

Images