JP7082809B2 - Member health condition management system, member health condition management method, and member health condition management program - Google Patents

Description

The present invention relates to a member health condition management system, a member health condition management method, and a member health condition management program, and particularly utilizes a health service provider to grasp the health condition of an individual member belonging to a group. And members related to health management systems, methods, and programs that facilitate evaluation.

Accurately determine the health status of members belonging to a group such as a company or factory, analyze disease sign information, prevent both physical and mental illnesses of the members, and work and society after illness. Supporting the return is to maintain the welfare level of the members, improve productivity, foster and improve trust in the company and the organization to which they belong, and raise awareness of social evaluation that they will take the lead in work style reform. It is an urgent issue to plan.

By working on so-called health management by each company and organization, it is not only possible to safely and safely promote the efforts of the elderly in the workplace, which is an issue in an aging society with a declining birthrate, but also social welfare such as medical expenses and long-term care expenses. Cost efficiency can be improved and contribute to sound national financial management. In addition, by revitalizing the workplace and improving productivity through health promotion, it is expected that the productivity of companies and affiliated organizations will be improved, and the effects of increasing competitiveness, profitability, efficiency, and cost effectiveness will be expected. If this becomes widespread, it will contribute to a dramatic improvement in Japan's industrial competitiveness.

In recent years, health management has attracted attention, and various services using personal health management goods, IT and health devices are widely used. However, even when healthy, there is no centralized support to grasp all health conditions such as non-illness, illness, rehabilitation after illness, return to work, long-term care, etc. and improve QOL (quality of life) according to the condition. rice field. Furthermore, in order for companies and various organizations to continue health management for a long period of time and to stably promote the health of their members, economic rationality of return on investment is essential. However, there is no service or methodology that can verify the effects on organizational activities such as health promotion and productivity, business performance, and profit improvement. Therefore, there is a long-awaited service that has a function of verifying the effect of health promotion on organizational activities.

In Japan, where the birthrate is declining and the population is aging, it is an urgent issue for the elderly to become more competitive in the workplace, but in order to achieve this, it is essential to have a system to prepare for the onset and progression of dementia in the workplace. In addition, due to the progress of employment of the elderly, the number of strokes and myocardial infarctions in the workplace is expected to increase, and it is necessary to have concrete means for grasping the signs of illness and preventing illness, such as risk management, activities of companies and organizations, and It will also be an important issue for the elderly members themselves.

Previously, improving life was considered an individual problem. However, when considering an organization that is a group of individual members, the health status of the members determines the workability, rewardingness, atmosphere, presenteeism, productivity deterioration or improvement due to loyalty to the organization, and further. If it is a company, it is also suggested that it is related to its business performance. Therefore, the demands of society are changing to dare to step into the area of individual lifestyles and encourage improvement. Also, in the national and local governments, the importance of controlling medical expenses of members and relieving stress is increasing. Therefore, the quality of efforts by various organizations to tackle the health management issues of members and the resulting health improvement of members are regarded as corporate social responsibility, and their importance is increasing in terms of management responsibility and compliance. There is.

From such a background, a system and a method for efficiently managing the health condition of a member have been proposed (Patent Documents 1, 2, 3 and the like). However, in the conventional system, method, etc., although it is possible to analyze the health condition of each member, it is not always a configuration that realizes a relative comparison of the health condition in the group. In recent years, statistical data on health conditions are often updated. In addition, new indicators and analysis methods for health status have been developed. Then, if the latest statistics and inspection methods can be quickly incorporated and the latest information can be reflected in the members, it is expected that the improvement effect will be further enhanced.

At present, there are also specialized businesses (health service providers) that collect and analyze health status information and return it to individuals. In particular, the barriers to use have decreased due to network connections, and widespread use is expected. Therefore, the effective utilization of various health condition analysis companies existing on the network and the management of the health condition of the members are efficiently linked, and obesity, eating habits, lack of exercise, inappropriate working hours, sleeping condition, stress Guidance for improving lifestyle habits, such as condition, smoking, and drinking, and various efforts to prevent illness, as well as individual health, non-illness, illness, and need for nursing care. Effective for improving the quality of life (QOL) of the condition, improving and improving the QOL by preventing illness and aggravation, improving the efforts of employers (companies), and improving the members of the entire department. A system, a method, etc. were desired.

Japanese Unexamined Patent Publication No. 2010-191677 Japanese Unexamined Patent Publication No. 2011-123579 Japanese Unexamined Patent Publication No. 2016-20706

The present invention has been made in view of the above points, and is linked to the management of the health condition of the members by utilizing the external health service provider as well as acquiring various health condition information of the members belonging to the members. We provide a member health condition management system, its management method, and management program that contribute to the improvement of quality of life (QOL) and the efforts of employers (companies), as well as the improvement of the member group such as the entire department. It is a thing.

That is, the first aspect is an information storage unit that aggregates and stores health condition information related to members, and an information transmission unit that transmits the health condition information to a plurality of health service providers connected via an Internet line. , The primary analysis data acquisition unit that acquires the primary analysis data based on the health condition information analyzed by the plurality of health service providers from the plurality of health service providers, and a predetermined evaluation based on the primary analysis data. The evaluation standard generation unit that generates the standard, the evaluation standard, and at least one health condition information of one member for the same item as the health condition information related to the primary analysis data used to generate the evaluation standard. A member comprising: a secondary analysis data generation unit for generating secondary analysis data relating to the one member, and a notification unit for notifying the secondary analysis data to the one member. Related to health condition management system.

In the second aspect, the evaluation standard generation unit generates the predetermined evaluation standard through data analysis including big data analysis or machine learning based on the primary analysis data, and the secondary analysis data generation unit generates the predetermined evaluation standard. The member health condition management according to the first aspect, wherein the health condition information of one employee is input to the predetermined evaluation criteria to generate secondary analysis data for the one member. Related to the system.

In the third aspect, the secondary analysis data is aggregated for each member of the first group to which the member belongs to generate the first group analysis data, and the member is different from the member of the first group. A group analysis unit that aggregates the secondary analysis data for each member of the second group to generate the second group analysis data, and the first group analysis data and the second group analysis data are compared with each other. The member health condition management system according to the first or second aspect, comprising an inter-group comparison unit for generating inter-group comparison data comparing the first group and the second group.

A fourth aspect relates to the member health condition management system according to any one of the first to third aspects, which is provided with a digitization conversion unit for quantifying the primary analysis data.

A fifth aspect relates to the member health condition management system according to any one of the first to fourth aspects, which includes a secondary analysis data storage unit for accumulating the secondary analysis data.

A sixth aspect relates to the member health condition management system according to the third aspect, which includes an intergroup comparison data storage unit for accumulating intergroup comparison data.

A seventh aspect is an information storage step in which the computer unit aggregates and accumulates health status information related to members, and information transmission for transmitting the health status information to a plurality of health service providers connected via an Internet line. Predetermined based on the step, the primary analysis data acquisition step of acquiring the primary analysis data based on the health condition information analyzed by the plurality of health service providers from the plurality of health service providers, and the primary analysis data. At least one member's health for the same items as the evaluation criteria generation step that generates the evaluation criteria and the health status information related to the evaluation criteria and the primary analysis data used to generate the evaluation criteria. It is characterized by including a secondary analysis data generation step for generating secondary analysis data for the one member based on the state information, and a notification step for notifying the secondary analysis data to the one member. It relates to the member health condition management method.

The eighth aspect is an information storage function for aggregating and accumulating health condition information about members in a computer unit, and information transmission for transmitting the health condition information to a plurality of health service providers connected via an Internet line. A function and a primary analysis data acquisition function for acquiring primary analysis data based on the health condition information analyzed by the plurality of health service providers from the plurality of health service providers, and predetermined based on the primary analysis data. At least one member's health for the same items as the evaluation criteria generation function that generates the evaluation criteria and the health condition information related to the evaluation criteria and the primary analysis data used to generate the evaluation criteria. It is characterized by executing a secondary analysis data generation function for generating secondary analysis data related to the one member and a notification function for notifying the secondary analysis data to the one member based on the state information. It is related to the member health condition management program.

According to the member health condition management system of the present invention, various health condition information of members belonging to a specific group can be acquired and processed by utilizing an external health service provider, so that the health condition of the members can be efficiently processed. Can be tied to the management of. Then, it becomes possible to contribute to the improvement of the quality of life (QOL) of the members and the improvement of the efforts of managers and employers (companies, etc.). Furthermore, it becomes easy to manage the health condition of the entire group to which the members belong.

In addition, according to the member health condition management method and the member health condition management program of the present invention, the cost is reduced by acquiring various health condition information of the members belonging to the organization and automating the utilization of various health condition materials. However, it is possible to provide high-quality services.

It is a schematic block diagram of the member health state management system of 1st Embodiment. It is a schematic diagram of an aggregate table. It is a schematic diagram which shows the flow of information of 1st Embodiment. It is a schematic block diagram of the member health condition management system of 2nd Embodiment. It is a schematic diagram which shows the flow of information of 2nd Embodiment. It is a flowchart of the member health state management method of 1st Embodiment. It is a flowchart of the member health state management method of 2nd Embodiment.

The member health condition management system 1 of the first embodiment will be described with reference to the schematic configuration diagram of FIG. The member health condition management system 1 (2 described later) of the present invention is carried out by the operator U (see FIGS. 3 and 5) of the system. Here, the "member" in the present invention means an employee of a company (company, factory, business establishment, facility), an employee of a local government / various organizations, a local resident, a person belonging to a predetermined group such as an NPO, and various types. It is defined as a person who belongs to some kind of "group" such as a student / student / child of a school, a resident of a nursing facility, etc.

In the member health condition management system 1, various terminals 11 and 12 are connected to the Internet line 10 (network). Servers 21 and 22 of the websites of health service providers 51 and 52 are also connected to the Internet line 10. Then, a computer unit 100 for executing the main processing of the present invention is connected to the Internet line 10. In this way, in the member health condition management system 1, various devices are connected through a wired or wireless network. Further, storage servers 13 and 14 are added to the Internet line 10. These servers store health status information acquired by medical examinations, as well as potential health status information such as health status information and genetic information acquired from biological information that changes daily. The notation of terminals 11, 12, health service providers 51, 52, their servers 21 and 22, and storage servers 13, 14 is two for convenience of illustration. In reality, the number of connections to the Internet line 10 is not limited to the illustration, and is a plurality.

Terminals 11 and 12 are devices such as pedometers, sphygmomanometers, body temperature monitors, heart rate monitors, glucose meters, fatigue measuring instruments, sleep activity meters, accelerometers, etc. that can measure the state of the human body, including smartphones and tablets. included. Therefore, the result of measuring the state of the human body of the member over time through the above-mentioned device is transmitted to the computer unit 100 through the Internet line 10. The information of the terminals 11 and 12 may be directly transmitted to the computer unit 100 without going through the Internet line 10.

In addition, terminals 11 and 12 have urinalysis, blood test, vision test, hearing test, electrocardiogram test, respiratory volume test, roentgen test, endoscopy, bacterial / virus infection test, DNA test (genome analysis), and blood vessel inner wall. It also includes medical institutions that carry out inspections and various inspection institutions (computers and servers of these institutions). Information such as test results, diagnostic fee statements, electronic medical records, medicine notebooks, mother-child notebooks, etc., which are stored in computers or servers of medical institutions such as terminals, are also stored in the computer section via the Internet line 10. Sent to 100.

In addition to this, member managers (employees of companies, etc.) and contractors who outsource information security, compliance, stress checks, etc., management departments of organizations such as industrial doctors, health insurance associations, personnel departments, or sites Working hours, smoking history, vehicle operating time, machine operation time, dangerous work engagement time, computer keying status record related to stress status determination, work content, web search history, email transmission / reception status analysis Computers and servers of companies that have accumulated data on labor such as travel distance and time, history of enrollment location, number of contacts, etc. are also included in terminals 11 and 12. These labor management results are also transmitted to the computer unit 100 via the Internet line 10.

The server 21 of the website 25 of the health service provider 51 stores information such as the data of the web page 31, the processing content related to the provided service, the processing result, and the server 22 of the website 26 of the health service provider 52. , Web page 32, processing contents related to the provided service, processing results, and other information data are stored. Therefore, it is possible to browse the web page 31 of the health service provider 51 and the web page 32 of the health service provider 52 and access their servers 21 and 22 through the Internet line 10. Of course, the number of websites, web pages, and related servers is not limited to the illustration, but is multiple. These websites and web pages are limited to those containing information on health conditions and the content provided based on them, as will be described later.

Here, the health condition information is an index that can quantitatively grasp the physical condition of the member. For example, daily walking count by pedometer, high and low blood pressure by sphygmomanometer, daily blood pressure fluctuation, body temperature change by body temperature meter, heart rate fluctuation, blood pressure level fluctuation within a certain period, physiological irregularity (body temperature change), fatigue. Accumulation of blood pressure by a pedometer, quality and time of sleep by a sleep activity meter, and amount of exercise by an sphygmomanometer. In addition, the amount of sugar and protein in urine by urinalysis, blood glucose level (daily change) by blood test, blood cell volume, cholesterol level, liver function, antibody level, sex hormone level, visual value by visual test, hearing test Hearing level by ECG, heartbeat by ECG, detection of arrhythmia, expiratory volume by respiratory volume test, lung and bone abnormality detection by urinalysis, esophagus, stomach, rectal roughness by endoscopy, bacterial / virus infection test The type and degree of infection by DNA test (genome analysis), the presence or absence of disease-prone genome sequences, and the risk of onset. Furthermore, it is information on the hours and periods during which labor can be managed, such as monthly working hours, smoking history (years of smoking), monthly vehicle operating hours, monthly machine operating hours, and monthly dangerous work engagement hours.

For the index expressed as a concrete numerical value, the numerical value is used. Regarding the index that is left to the relative quality such as the presence or absence of arrhythmia, for example, 3 stages (high risk: 3 points, medium risk: 2 points, low risk: 1 point), or the rejection judgment (+1 or -1), etc. The divided numerical points of are given in advance. In this way, all individual health status information is quantified. Then, the processing of various operations in the computer unit 100 becomes easy.

A health service provider is a business operator that acquires the health status information of the above-mentioned members through an internet line, etc., and analyzes the appropriateness and bias of the health status information of the members, and provides various applications. The operator, etc. For example, when the website of a health service provider is accessed by a smartphone or the like and the daily blood pressure is input, the risk of illness or the like is determined from the fluctuation state of the blood pressure. The service content provided differs depending on the health service provider, and the health service provider can be used properly.

Health service providers include providers of various business contents if the above-mentioned analysis can be performed. For example, manufacturers and distributors of health foods, foods for specified health uses, various nutritional supplements, etc., service providers such as sports clubs, meditation, yoga, relaxation, acupuncture and moxibustion, EAP (employee) for persons with specific diseases and precursory symptoms. Assistant program), psychotherapy, medical peripheral treatments such as rehabilitation, providers of counseling, etc. These are just examples, and of course also include providers of other business content. In FIG. 3, which will be described later, the operator of the member health condition management system of the present invention and the health service provider are described as examples of different persons. In addition to this form, the case where the operator of the member health condition management system of the present invention and the health service provider are the same entity is also included.

The computer unit 100 connected to the Internet line 10 is equipped with a processor such as a CPU, an ASIC, an FPGA, or the like and has an arithmetic function, and a storage unit for storing various data related to the above-mentioned health condition information and necessary programs. 102 (including ROM, RAM, HDD, SSD, etc.) is provided. Further, an output unit 300 for outputting various data is also provided. Further, although not shown, an interface for inputting / outputting data, a reading unit for reading data, a buffer, a display unit such as a display, etc. are also provided. Generally, the computer unit 100 is composed of a known personal computer, a server, or the like.

The computer unit 100 (and the calculation unit 101) of the first embodiment includes an information storage unit 110, an information transmission unit 120, a primary analysis data acquisition unit 130, a digitization conversion unit 140, an evaluation standard generation unit 150, and a secondary analysis data generation. It corresponds to the unit 160, the secondary analysis data storage unit 170, and the notification unit 210. The storage unit 102 provided in the computer unit 100 stores various data such as health condition information (D0), primary analysis data (D1), evaluation criteria (D2), secondary analysis data (D3), and regression analysis formula (D4). Remember. It also stores various data, programs, etc. necessary for operating the system.

The output unit 300 outputs (transmits) various information such as the calculation result in the computer unit 100 (and the calculation unit 101), that is, the notification to the individual member and the result of comparing the data to the employee (company) of the member. )do. Specifically, a printer that prints on paper, a mail server that notifies individual members by e-mail, message applications, etc., corporate personnel, general affairs staff, industrial doctors, various types of health promotion and disease prevention for some information. , Notify the servers of various external organizations responsible for medical care and nursing care, management servers, etc. as appropriate. Each part will be described below.

The information storage unit 110 aggregates and stores health status information related to the members. As described above, the aggregation of the health status information is executed from the terminals 11 and 12 through the Internet line 10 or directly by inputting to the computer unit 100. Then, the information storage unit 110 aggregates a plurality of types of health state information for the plurality of members and stores (stores) them in the storage unit 102. The form of aggregating the health condition information regarding the members may be the form of an aggregate table as shown in FIG. 2 below for each person or for a plurality of persons collectively.

The schematic diagram of FIG. 2 is an example of an aggregate table, and is a table composed of matrices. According to this example, the target members in the row are "gender (male: M, female: F)", "age (year)", "height (cm)", "weight (kg)", "1 day". Items of health condition information such as "average number of steps (steps)", "average sleeping time (hours) per day", and "working hours (hours) per month" are aggregated. Although not specifically shown in FIG. 2, the time point at which various health status information is aggregated becomes a problem. Therefore, for each health condition information, the acquisition "year / month / day" (YY / ZZ day of 20XX) and, if necessary, the time are also added to each item.

Then, the numerical values of the above-mentioned health condition information items for each member are also aggregated in the column. As a result, the items of health status information and the status of the members can be listed as a corresponding table. In addition, since all are digitized, post-processing (data processing) is easy. The items of health status information in the aggregate table are not limited to the illustrations, but are arbitrary and can be selected and rearranged as necessary. For example, it may be changed as appropriate depending on the manager of the member, the type of business of the employer, or the priority item. For example, it is possible to add health condition information such as blood cholesterol level (its evaluation point).

The information transmission unit 120 transmits health status information to a plurality of health service providers 51 and 52 connected via the Internet line 10.

The flow of each information will be described with reference to the schematic diagram of FIG. The member health condition information (D0) is transmitted from the member M (including the group to which the member belongs) to the operator U of the member health condition management system 1 of the present invention. Health status information (D0) is aggregated and accumulated by the same operator. Then, the health condition information (D0) is transmitted from the operator to the health service provider 51. Since the illustration is complicated, the only business operator is the health service provider 51.

The primary analysis data acquisition unit 130 acquires primary analysis data (D1) based on the health state information (D0) analyzed by the plurality of health service providers 51 from the plurality of health service providers 51 and 52. The primary analysis data (D1) is data processed on the health service provider side so that the health condition information (D0) can be easily compared with each other in each person. For example, calculation of mean value, variance, standard deviation, etc., and calculation of deviation value, etc. are applicable.

In FIG. 3, a plurality of health service providers 51 and 52 that have received the health status information (D0) generate primary analysis data (D1) based on the health status information (D0). Then, the primary analysis data (D1) is transmitted from the health service providers 51 and 52 to the operator U.

The digitization conversion unit 140 digitizes the primary analysis data (D1). This quantification is ambiguous and stepwise, such as "4 grades of excellent, good, acceptable, and unacceptable", "3 grades of A, B, and C", and "2 grades of no problem and caution". Add a numerical value to the result. For example, numerical values such as 10, 6, 3, 0 points are given to the stepwise results. This process is executed by the operator U (see FIG. 3).

The evaluation standard generation unit 150 generates a predetermined evaluation standard (D2) based on the primary analysis data (D1). The evaluation standard (D2) is a standard for mutual comparison adjusted from the digitized primary analysis data (D1) based on a predetermined process. In particular, when there are a plurality of types of primary analysis data (D1), an evaluation criterion (D2) is generated in order to comprehensively process these primary analysis data. The generation of the evaluation standard (D2) is not limited, and it is appropriate as long as a predetermined reference value is utilized for each primary analysis data (D1). For example, the totalization of the amount of deviation between the predetermined reference value for each primary analysis data (D1) and the primary analysis data (D1) of each member is taken into consideration. Further, as the evaluation standard (D2), it is also possible to use a mathematical formula such as a simple first-order regression equation. Further, the evaluation standard generation unit 150 generates the evaluation standard (D2) through data analysis such as statistics, estimation method, AI, machine learning, etc. that handle a large number such as big data analysis based on the primary analysis data (D1). For this, the machine learning unit 320, the AI unit 330, etc. (see FIG. 1 and the like) are used.

More accurately, in grasping the comprehensive health condition information of individual members, the evaluation standard generation unit 150, as an example, is based on a plurality of types of primary analysis data (D1) (with a predetermined reference value related thereto as a variable). ) Execute multiple regression analysis and generate regression analysis formula (D4). The regression analysis formula (D4) is stored (stored) in the storage unit 102 of the operator U (see FIG. 3).

For example, when obtaining the regression analysis formula ( _D4-1 ) and its score value (SM1), "BMI", "systolic blood pressure at the time of diagnosis", and "diastolic diastole at the time of diagnosis" are obtained from the primary analysis data (D1). Select "Blood pressure" and "Fasty blood glucose level at the time of diagnosis". In this case, the evaluation standard generation unit 150 "score value (SM1) of the member ( _M ) = {α1 × BMI + α2 × systolic blood pressure at the time of diagnosis + α3 × diastolic blood pressure at the time of diagnosis + α4 × fasting period at the time of diagnosis. A regression analysis formula (D4-1) with "blood pressure level + α0}" is generated. In the equation, α1, α2, α3, and α4 are coefficients, and α0 is a constant.

Alternatively, when calculating the regression analysis formula ( _D4-2 ) and its score value (SM2), select "BMI", "average number of steps per day", and "average sleep time per day" from the health status information. do. In this case, the evaluation standard generation unit 150 uses a regression analysis formula in which the score value (SM2) of the member ( _M ) = {β1 × BMI + β2 × average number of steps per day + β3 × average sleep time per day + β0}. (D4-2) is generated. In the equation, β1, β2, and β3 are coefficients, and β0 is a constant.

The calculation of the reference value in the evaluation reference generation unit 150 can be further supplemented as follows. For example, at the age of 20, the score of a person whose medical examination data is all within the standard value (and, for example, within the standard value and within ± 10% of the median) is 800 points, and the score value of all those who deviate from the standard value is. Determined as 200 to 0 points (depending on the degree of deviation). The reference value is set in the range of 0 to 1000 points by adding the 24-hour vital data and the result of the genetic test (collectively referred to as other health condition information).

Using the deduction calculation formula for each evaluation item of the person who deviates from the score value specified above, the evaluation standard generation unit 150 obtains the calculation formula for calculating the individual score value, and the medical examination data for each individual and other health conditions. Calculate the final score value by entering information.

In multiple regression analysis, first, the explanatory variables are simply fixed. Then, calculate which value the coefficient and constant of the multiple regression equation should be for the score value of an individual to be the closest to the calculated individual score, and recalculate from the result, which of the explanatory variables is how much. It is used to judge whether it works for the individual.

When generating the regression analysis formula (D4) in the evaluation standard generation unit 150, the items of the primary analysis data (D1) are narrowed down in advance to decipher the relationship with the specific items, and further, the primary analysis data (D1). ) Can be grasped closely between items. Therefore, epidemiological advice can be given after the fact based on the relationship between the obtained score value and the actual health condition of the member. Furthermore, by prescribing the items of the primary analysis data (D1) in advance, it is possible to efficiently calculate the score value while preventing the items and the calculation amount from increasing more than necessary. Of course, the regression analysis formula (D4) is not limited to the above formula, and health condition information and the accompanying primary analysis data (D1) can be added or changed at any time according to the operating status of the system. .. In addition, a correction coefficient for ensuring continuity after the change can be added.

The secondary analysis data generation unit 160 has health condition information (D0) related to the evaluation standard (D2) and the primary analysis data (D1) used to generate the evaluation standard (D2, and further, the regression analysis formula of D4). ) And at least one member's health condition information for the same item, the secondary analysis data (D3) for the one member is generated.

When the secondary analysis data (D3) is generated, the primary analysis data (D1) of each member is substituted into the evaluation criterion (D2) described above. For example, the evaluation criteria (D2) including "age, blood pressure fluctuation, number of steps per day" are set to numerical values (measured values) of health condition information of one member (may be primary analysis data (D1)). Information of "50 years old, blood pressure 100 to 170, 4000 steps a day" is substituted, and the validity of each health condition information according to the age can be output as secondary analysis data (D3).

Further, when the regression analysis formula (D4) is used as the evaluation standard (D2), the numerical value (measured value) of the health condition information of one member in the regression analysis formula (D4) (the primary analysis data (D1) may also be used. Is) is substituted. Therefore, the score value ( _SM ) can be calculated as the secondary analysis data (D3). For example, when obtaining a score value (SM1) to be the secondary analysis data (D3) of the regression analysis formula (D4-1) of a certain member ( _M ), the measured value of the health condition information of the member (M). "BMI", "systolic blood pressure at the time of diagnosis", "diastolic blood pressure at the time of diagnosis", and "fasting blood pressure level at the time of diagnosis" are substituted into the regression analysis formula (D4-1). The score value (SM2) of the secondary analysis data (D3) of the health condition standard calculation formula ( _D4-2 ) can also be calculated by substitution in the same manner.

Furthermore, when generating the above-mentioned evaluation criteria (D2) (including the regression analysis formula (D4)), a specific "health service provider" such as "I take two designated foods for specified health use every day." Information on taking the product of "" is also taken into consideration. In this case, the coefficients of the calculation formula and the regression formula are used after the optimum values are obtained in the composition simulation of the corresponding formula.

In addition, in the generation of the regression analysis formula (D4) based on the primary analysis data (D2) in the evaluation standard generation unit 150, the generation of the secondary analysis data (D3) in the secondary analysis data generation unit 160, etc., the computer unit 100 The machine learning unit 320, AI unit 330, etc. (see FIG. 1 and the like) incorporated in the above are also utilized. Therefore, the evaluation criteria (D2) and the secondary analysis data (D3) can be generated by machine learning, reinforcement learning, etc. through statistics dealing with a large number of big data analysis, estimation methods, AI utilization, and the like.

The secondary analysis data storage unit 170 stores the secondary analysis data (D4) generated through a series of processes. After that, the output unit 300, etc., is sent to the members themselves, their employers / managers, industrial physicians, and for some information, to the servers of various external organizations responsible for various health promotion, disease prevention, medical care, and long-term care. , Including written printing, notify by e-mail, message application, etc. The method of output or the like is appropriate. This information is also stored in the secondary analysis data storage unit 170 for a certain period of time for improvement confirmation by tracking over time, if necessary.

According to the judgment and evaluation based on the above-mentioned secondary analysis data (D3), the individual health condition information of each member is compared with the national, local, and group statistics. Therefore, individual members can be aware of the quality of individual health status information and work on improvement.

The notification unit 210 notifies the secondary analysis data (D3) obtained from the series of processes from the operator U to the member M (see FIG. 3). This notification is executed through the output unit 300 of FIG. In addition to outputting information to the members themselves and the above-mentioned related parties, health status information (D0), primary analysis data (D1), secondary analysis data (D2), etc. are also compared and visualized daily and provided to users. do. Then, the user can be linked to the action by knowing the method and progress of health promotion. The output unit 300 can complement the so-called PDCA function and the like. In this way, it is possible to make recommendations (proposals) for action plans, and the effects can be further enhanced.

For efficient proposals from the notification unit 210 (output unit 300), the computer unit 100 further includes a rule engine unit 310, a machine learning unit 320, and an AI unit 330. The rule engine unit 310 analyzes the influence of the efforts of individual members (groups in the embodiment described later) for a predetermined period (daily, one month, etc.) on individual judgment data (D3), score values, and the like. Estimate what kind of efforts should be taken first to promote health, prevent diseases, and prevent the aggravation of diseases, and what kind of combination is effective. For example, the rule engine unit 310 derives whether jogging should be 10 minutes every day or 1 hour every other day. The rule engine unit 310 has a function of automatically generating and providing an optimum corresponding menu for each member. In addition, the risk analysis of overweight is performed, and warnings such as "Do not work any further" are notified to the relevant members through the output unit. The machine learning unit 320 and the AI unit 330 execute operations such as big data analysis and advanced estimation by machine learning, reinforcement learning, and the like.

Further, the rule engine unit 310, the machine learning unit 320, and the AI unit 330 analyze the disease risk from the individual health condition information, the primary analysis data, and the secondary analysis data, and further detect the sign of the disease, and the members themselves. And for the employers / managers of the members, industrial doctors, and some information, e-mails and messages, including written printing, to the servers of various external organizations responsible for various health promotion, disease prevention, medical care, and nursing care. It is possible to notify by an application or the like.

Next, the member health condition management system 2 of the second embodiment will be described with reference to FIG. The member health condition management system 2 of the second embodiment is a system that makes it possible to grasp the trend of the entire group by comparing the group to which the member belongs with other groups. In the member health condition management system 2, the Internet line 10, terminals 11, 12, health service providers 51, 52 and their servers 21 and 22, storage servers 13, 14, computer unit 100, output unit 300, rule engine unit. The 310, the machine learning unit 320, the AI unit 330, and the like are the same as the system of the first embodiment.

The computer unit 100 (and the calculation unit 101) of the second embodiment includes an information storage unit 110, an information transmission unit 120, a primary analysis data acquisition unit 130, a numerical conversion conversion unit 140, an evaluation standard generation unit 150, and a secondary analysis data generation. It corresponds to the unit 160, the group analysis unit 180, the group comparison unit 190, the intergroup comparison data storage unit 200, and the notification unit 210. The storage unit 102 provided in the computer unit 100 includes health state information (D0), primary analysis data (D1), evaluation criteria (D2), secondary analysis data (D3), regression analysis formula (D4), and first group analysis. Various data such as data (D5), second group analysis data (D6), and intergroup comparison data (D7) are stored. It also stores various data, programs, etc. necessary for operating the system. In the member health condition management system 2 of the second embodiment, the description of the part common to the system of the first embodiment will be omitted.

The group analysis unit 180 aggregates the secondary analysis data (D3) for each member of the first group to which the member belongs and generates the first group analysis data (D5). Further, the secondary analysis data (D3) for each member of the second group, which is different from the member of the first group, is similarly aggregated to generate the second group analysis data (D6). The terms of the first group and the second group are expressions for convenience in distinguishing between groups. Even if the group to be compared is further expanded to a plurality of groups (third group, fourth group, etc.), comparison between groups is possible by the same repetition.

As described above, the secondary analysis data (D3) for one member is quantified. Therefore, when aggregating the secondary analysis data (D3) relating to one member, the group analysis unit 180 collects the secondary analysis data (D3) of a part or all of the members constituting the group as a total or an average (D3). Mathematical (or statistical) processing such as additive mean, synergistic mean, or harmonic mean), median calculation, etc. is performed to generate the first group analysis data (D5).

Further, regarding the second group (various groups such as different departments, different companies, different local governments, etc. in the same company) different from the first group, the group analysis unit 180 is also a member of the second group. The second group analysis data (D6) is generated from the secondary analysis data (D3) of a part or all of the above through mathematical (or statistical) processing. By doing so, it becomes possible to grasp the tendency of all the members belonging to the group depending on the amount of the numerical value.

The group comparison unit 190 compares the first group analysis data (D5) and the second group analysis data (D6), and generates intergroup comparison data (D7) comparing the first group and the second group. That is, the group comparison unit 190 compares the numerical values of both the first group analysis data (D5) and the second group analysis data (D6). Comparisons between numerical values are usually calculated as "differences". The first group analysis data (D5), the second group analysis data (D6), the intergroup comparison data (D7), and the like are also stored (stored) in the storage unit 102.

The intergroup comparison data storage unit 200 accumulates intergroup comparison data (D7). The first group analysis data (D5), the second group analysis data (D6), the intergroup comparison data (D7), and the like are also stored (stored) in the storage unit 102.

FIG. 5 is a schematic diagram showing a data flow corresponding to the member health state management system 2 of the second embodiment. As can be understood from the figure, from the secondary analysis data (D3) stored (accumulated) in the operator U, the first group analysis data (D5), the second group analysis data (D6), and comparison between groups. The data (D7) is generated, and the intergroup comparison data (D7) is notified from the operator U to the member (the group to which the member belongs).

For example, the first group analysis data (D5) for the members belonging to the first group is a numerical value "87.43" from the calculation by the arithmetic mean, and the second group analysis data (D6) for the members belonging to the second group. Is a numerical value "85.67" from the calculation by the arithmetic mean. In this case, the difference between the two, that is, the intergroup comparison data (D7) is "1.76" (numerical difference, difference value). Therefore, through the numerical comparison (including the positive / negative relationship) of the intergroup comparison data (D7), the difference from the better group can be concretely grasped, and the improvement points of the group from the current situation are recognized. .. By repeating such comparisons with other groups, it becomes possible to grasp the relativization and improvement items with other groups.

The interpopulation comparison data (D7) derived from the first group analysis data (D5) and the second group analysis data (D6) is, as can be seen from its origin, a comparison between relatively limited items. Therefore, the inter-population comparison data (D7) is effective when comparing specific health status information within a population with specific health status information of another population. Furthermore, the comparison between groups can be extended to a comprehensive quality comparison based on the mutual relationship between multiple health status information of individual members. Epidemiological findings also accumulate as the number of comparative samples increases between populations. Therefore, the way of future utilization will expand.

Further, the above-mentioned storage unit 102 accumulates various data so that the transition of improvement by an individual member, daily changes by organization and group, and comparison before and after the efforts as an organization can be made. Therefore, the estimation accuracy by the rule engine unit 310, the machine learning unit 320, and the AI unit 330 is improved, and the notification from the output unit 300 (notification unit) based on the estimation accuracy is also possible.

In the embodiment of the present invention, as disclosed in FIGS. 3 and 5, the operator U of the member health condition management system and the health service providers 51 and 52 are described as examples of different persons. In addition to the embodiment, the case where the operator U of the member health condition management system of the present invention and the health service providers 51 and 52 are the same entity is also included.

Furthermore, the operator of the member health condition management system determines what attributes of users (beneficiaries of the service, requesters of the service) regarding the effectiveness of the services and products provided by each health service provider. Analyze data with monitoring to see how effective or ineffective it is in health promotion and disease prevention. Then, the result of the monitoring and data analysis by the operator is transmitted to the user. In addition to being able to determine the optimal health promotion service, ingredients, behavior, and selection of service providers, users are also informed of various proposals and recommendations.

At the same time, the operator of the member health condition management system feeds back the information in which the personal information is deleted to the health service provider. As a result of the feedback, health service providers know how effective the products, products, services, etc. produced by each provider are, and what kind of effect each individual or group has for a specific attribute. It is possible to verify whether or not it is. That is, through feedback from the operator of the member health condition management system to the health service provider, the health service provider can utilize it for their own product development.

First, the member health condition management method of the first embodiment will be described together with the member health condition management program by using the flowchart of FIG. The member health condition management method of the first embodiment is executed by the computer unit 100 based on the member health condition management program. The member health condition management program of the first embodiment has an information storage function, an information transmission function, and a primary analysis data acquisition function for the computer unit 100 of the member health condition management system 1 of the first embodiment shown in FIG. , Numerical conversion function, evaluation standard generation function, secondary analysis data generation function, secondary analysis data storage function, notification function, etc. are executed. Each of these functions is performed in the order shown. Since each function overlaps with the description of the member health condition management system 1 described above, the details will be omitted.

The flowchart of FIG. 6 is the overall flow of the member health state management method of the first embodiment, and is an information storage step (S110), an information transmission step (S120), a primary analysis data acquisition step (S130), and a digitization conversion step. (S140), an evaluation standard generation step (S150), a secondary analysis data generation step (S160), a secondary analysis data storage step (S170), and a notification step (S210) are provided. In addition, the member health condition management method also includes various steps (not shown) such as storage, storage, calling, and calculation.

The information storage function aggregates health status information (D0) related to members (S110; information storage step). The information storage function is executed by the computer unit 100 (see FIG. 1) of the member health state management system 1 of the first embodiment.

The information storage function transmits health status information (D0) to a plurality of health service providers 51 and 52 connected via the Internet line 10 (S120; information storage step). The information transmission function is executed by the computer unit 100.

The primary analysis data acquisition function acquires the primary analysis data (D1) based on the health condition information (D0) analyzed by the plurality of health service providers 51 and 52 from the plurality of health service providers 51 and 52 (S130). Primary analysis data acquisition step). The primary analysis data acquisition function is executed by the computer unit 100.

The digitization conversion function digitizes the primary analysis data (D1) (S140; digitization conversion step). The digitization conversion function is executed by the computer unit 100.

The evaluation standard generation function generates a predetermined evaluation standard (D2) based on the primary analysis data (D1) (S150; evaluation standard generation step). The evaluation standard generation function is executed by the computer unit 100.

The secondary analysis data generation function is configured for the same items as the evaluation standard (D2) and the health condition information (D0) related to the primary analysis data (D1) used to generate the evaluation standard (D2). Based on at least one member's health status information (D0), secondary analysis data (D3) for the one member is generated (S160; secondary analysis data generation step). The secondary analysis data generation function is executed by the computer unit 100.

The secondary analysis data storage function accumulates secondary analysis data (S170; secondary analysis data storage step). The secondary analysis data storage function is executed by the computer unit 100.

The notification function notifies one member of the secondary analysis data (D3) (S210; notification step). The notification function is executed by the computer unit 100.

Next, using the flowchart of FIG. 7, the member health condition management method of the second embodiment will be described together with the member health condition management program. The member health condition management method of the second embodiment is executed by the computer unit 100 based on the member health condition management program. The member health condition management program of the second embodiment has an information storage function, an information transmission function, and a primary analysis data acquisition function for the computer unit 100 of the member health condition management system 2 of the second embodiment of FIG. , Digitization conversion function, evaluation standard generation function, secondary analysis data generation function, secondary analysis data storage function, group analysis function, intergroup comparison function, intergroup comparison data storage function, notification function, etc. .. Each of these functions is performed in the order shown. Since each function overlaps with the description of the member health condition management systems 1 and 2 described above, the details will be omitted.

The flowchart of FIG. 7 is the overall flow of the member health state management method of the second embodiment, and is an information storage step (S110), an information transmission step (S120), a primary analysis data acquisition step (S130), and a digitization conversion step. (S140), Evaluation Criteria Generation Step (S150), Secondary Analysis Data Generation Step (S160), Secondary Analysis Data Accumulation Step (S170), Group Analysis Step (S180), Group Comparison Step (S190), Intergroup Comparison Data It includes various steps of a storage step (S200) and a notification step (S210). In addition, the member health condition management method also includes various steps (not shown) such as storage, storage, calling, and calculation. Since the execution and processing of each step are the same as the description of each part in the computer unit 100 described above, duplicate description will be omitted.

The group analysis function aggregates the secondary analysis data (D3) for each member of the first group to which the member belongs to generate the first group analysis data (D5), and also generates the first group analysis data (D5). The secondary analysis data (D3) is aggregated for each member of the second group different from the members to generate the second group analysis data (D6) (S180; group analysis step). The group analysis function is executed by the computer unit 100 (see FIG. 4) of the member health condition management system 2 of the second embodiment.

The intergroup comparison function compares the first group analysis data (D5) and the second group analysis data (D6) to generate intergroup comparison data (D7) comparing the first group and the second group (S190). Intergroup comparison step). The intergroup comparison function is executed by the computer unit 100 (see FIG. 4).

The intergroup comparison data storage function accumulates intergroup comparison data (D7) (S200; intergroup comparison data storage step). The intergroup comparison data storage function is executed by the computer unit 100 (see FIG. 4).

The member health condition management program is provided as stored in a computer-readable storage medium. The storage medium stores the program in a "non-temporary tangible medium". The storage medium includes any suitable storage medium such as HDD, SDD, or a suitable combination of two or more thereof. The storage medium may be volatile, non-volatile, or a combination of volatile and non-volatile. The storage medium is not limited to these examples, and may be any device or medium as long as the program can be stored.

The terminals 11 and 12 and the computer unit 100 of the member health state management system 1 read out the program stored in the storage medium, and execute the read program to execute the functions of the plurality of functional units shown in each embodiment. Can be realized. Further, the program can also be provided to the terminals 11 and 12 via an arbitrary transmission medium (communication network or the like). The terminals 11 and 12 and the computer unit 100 realize the functions of the plurality of functional units shown in each embodiment by executing, for example, a program downloaded via the Internet or the like.

The program can be implemented using, for example, a markup language such as an object-oriented programming language. At least a part of the processing in the terminals 11 and 12 and the computer unit 100 may be realized by cloud computing composed of one or more computers.

The member health condition management system and the member health condition management method of the present invention acquire various health condition information of the members to which the member belongs and utilize an external health service provider to manage and work on the health condition of the members. Can contribute to the improvement of. Furthermore, it can contribute to the management of health condition and improvement of efforts in the group to which the members belong. Therefore, it is possible to give more detailed advice to individual members or to the group to which they belong.

1, 2 Members Health status management system 10 Internet line 11, 12 Terminal 13, 14, 21, 22, Server 25, 26 Website 31, 32 Web page 51, 52 Health service provider 100 Computer department 101 Calculation unit 102 Storage unit 110 Information storage unit 120 Information transmission unit 130 Primary analysis data acquisition unit 140 Numerical conversion unit 150 Evaluation standard generation unit 160 Secondary analysis data calculation unit 170 Secondary analysis data storage unit 180 Group analysis unit 190 Group comparison unit 200 Intergroup comparison data Accumulation unit 210 Notification unit 300 Output unit 310 Rule engine unit 320 Machine learning unit 330 AI unit

Claims (8)

The information storage department that collects and accumulates health status information about members,
An information transmission unit that transmits the health status information to a plurality of health service providers connected via an internet line, and
A primary analysis data acquisition unit that acquires primary analysis data based on the health condition information analyzed by the plurality of health service providers from the plurality of health service providers.
An evaluation standard generation unit that generates a predetermined evaluation standard based on the primary analysis data,
Based on the evaluation criteria and at least one member's health status information for the same item as the health status information related to the primary analysis data used to generate the evaluation criteria, the two regarding the one member. A secondary analysis data generator that generates secondary analysis data,
A member health condition management system comprising a notification unit for notifying the first member of the secondary analysis data. The evaluation standard generation unit generates the predetermined evaluation standard through big data analysis or data analysis including machine learning based on the primary analysis data.
The secondary analysis data generation unit inputs the health condition information of the one member into the predetermined evaluation standard to generate secondary analysis data for the one member.
The member health condition management system according to claim 1. The secondary analysis data is aggregated for each member of the first group to which the member belongs to generate the first group analysis data, and the member of the second group different from the member of the first group is generated. The group analysis unit that aggregates the secondary analysis data and generates the second group analysis data for each person in
Claim 1 or 2 includes an intergroup comparison unit that compares the first group analysis data with the second group analysis data and generates intergroup comparison data comparing the first group and the second group. Described member health condition management system. The member health condition management system according to any one of claims 1 to 3, which is provided with a digitization conversion unit for digitizing the primary analysis data. The member health condition management system according to any one of claims 1 to 4, further comprising a secondary analysis data storage unit for accumulating the secondary analysis data. The member health condition management system according to claim 3, further comprising an intergroup comparison data storage unit for accumulating intergroup comparison data. The computer department
Information accumulation steps that aggregate and accumulate health status information about members,
An information transmission step for transmitting the health status information to a plurality of health service providers connected via an internet line, and
The primary analysis data acquisition step of acquiring the primary analysis data based on the health condition information analyzed by the plurality of health service providers from the plurality of health service providers, and
An evaluation standard generation step that generates a predetermined evaluation standard based on the primary analysis data, and
With respect to the one member, based on the evaluation criteria and at least one health condition information of one member for the same items as the health condition information related to the primary analysis data used to generate the evaluation criteria. Secondary analysis data generation step to generate secondary analysis data,
A member health condition management method comprising a notification step for notifying the first member of the secondary analysis data. In the computer department
An information storage function that aggregates and accumulates health status information about members,
An information transmission function that transmits the health status information to multiple health service providers connected via the Internet line, and
A primary analysis data acquisition function for acquiring primary analysis data based on the health condition information analyzed by the plurality of health service providers from the plurality of health service providers.
An evaluation standard generation function that generates a predetermined evaluation standard based on the primary analysis data, and
With respect to the one member, based on the evaluation criteria and at least one health condition information of one member for the same items as the health condition information related to the primary analysis data used to generate the evaluation criteria. Secondary analysis data generation function to generate secondary analysis data,
A member health condition management program characterized by executing a notification function for notifying the first member of the secondary analysis data.

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