Prediction program, prediction device, and prediction method

Abstract

To visualize effect obtained by changing an index regarding the health of a member of an association .SOLUTION: A computer calculates a first predicted value of a performance index for evaluating an effect obtained by improving one of a plurality of health indices, on the basis of the target value of each of a plurality of health indices regarding the health of a plurality of members belonging to an association. The computer outputs first display data for displaying the target value of each of the plurality of health indices and the first predicted value of the performance index. The computer accepts a change indication indicating change of the target value of one or a plurality of health indices and changes the target value of the one or plurality of health indices on the basis of the change indication to thereby generate the target value of the one or plurality of health indices after the change. The computer calculates a second predicted value of the performance index on the basis of the target value of a health index other than the one or plurality of health indices and the target value of the one or plurality of health indices after the change. The computer outputs second display data for displaying the second predicted value of the performance index.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to forecasting techniques. [Background technology]

[0002] Against the backdrop of a declining birthrate and an aging population, problems such as rising national medical expenses, a decline in the working-age population, and increased long working hours are arising.

[0003] Rising national medical expenses can worsen the financial situation of health insurance societies and increase the health insurance premiums borne by companies. A decline in the working-age population can lead to a decline in corporate productivity and an extension of employee employment periods. Therefore, it is increasingly important for employees to stay healthy and work long-term.

[0004] The increase in long working hours increases the risk of lawsuits due to death from overwork, suicide, and industrial accidents, and can also lead to a decline in the brand value of companies. Therefore, employees will become more conscious of their working style.

[0005] In response to issues such as rising national medical expenses, a declining working-age population, and long working hours, the Ministry of Economy, Trade and Industry is promoting corporate health management alongside work style reform. Health management is a management method that regards employee health management or health promotion efforts as an investment and implements them strategically from a management perspective (see, for example, Non-Patent Document 1).

[0006] In order to encourage companies to practice health and productivity management, the Ministry of Economy, Trade and Industry has established a system to evaluate companies that are working on health and productivity management. This system includes the Health and Productivity Management Brand and Health and Productivity Management Excellent Corporations (White 500). The Ministry of Economy, Trade and Industry has also developed a health and productivity management guidebook. This guidebook systematizes what companies should do when working on health and productivity management, and provides examples of indicators that companies can use to evaluate and improve their own health and productivity management efforts.

[0007] Previous domestic research has found a significant correlation between some health cost-related indicators and health-related indicators (see, for example, Non-Patent Document 2). Health cost-related indicators correspond to outcome indicators used in health management guidebooks to evaluate the quality of health management, and health-related indicators indicate risk factors that worsen health cost-related indicators.

[0008] In relation to health management, an augmented collective intelligence system is known that applies augmented collective intelligence to encourage voluntary behavioral changes in each employee and organization aimed at improving health (see, for example, Patent Document 1). An information processing device is also known that accurately predicts attendance risks resulting from stress among employees in an organization (see, for example, Patent Document 2). A member health status management system is also known that utilizes health service providers to easily grasp and evaluate the health status of individual members belonging to a group (see, for example, Patent Document 3).

[0009] There is also a known evaluation processing system that clarifies the impact that maintaining and improving employees' physical and mental health has on corporate performance and presents it specifically as an actual management measure (see, for example, Patent Document 4). There is also a known health condition prediction device that predicts a user's future health condition based on their current health condition (see, for example, Patent Document 5). [Prior art documents] [Patent documents]

[0010] [Patent Document 1] Patent Publication No. 2021-117546 [Patent Document 2] Japanese Patent Publication No. 2020-52757 [Patent Document 3] Japanese Patent Publication No. 2020-13230 [Patent Document 4] Japanese Patent Publication No. 2020-106903 [Patent Document 5] International Publication No. 2017 / 204233 Brochure [Non-patent literature]

[0011] [Non-Patent Document 1] “What is Health and Productivity Management? | Tokyo Chamber of Commerce and Industry,” [online], Tokyo Chamber of Commerce and Industry, [searched October 7, 2021], Internet<URL:https: / / www.tokyo-cci.or.jp / kenkokeiei-club / 01 / > [Non-patent document 2] "Report on the Development and Utilization of Health and Productivity Management Evaluation Indicators," [online], Health and Productivity Management Evaluation Indicators Development and Utilization Consortium, February 2016, [Retrieved October 7, 2021], Internet<URL:https: / / pari.ifi.u-tokyo.ac.jp / unit / H27hpm.pdf> Summary of the Invention [Problem to be solved by the invention]

[0012] For companies seeking to implement health management, it is unclear how much effect can be achieved by improving which of the multiple risk factors related to employee health.

[0013] This problem is not limited to health management in companies, but arises when evaluating the effects of improving health indicators for members of various organizations.

[0014] In one aspect, the present invention aims to visualize the effect of changes in health-related indicators of group members. [Means for solving the problem]

[0015] In one example, the prediction program causes a computer to perform the following process.

[0016] The computer calculates a first predicted value of an outcome index for evaluating the effect of improving any of the health indexes based on target values ​​for each of the health indexes related to the health of multiple members belonging to an organization, and outputs first display data for displaying the target values ​​for each of the health indexes and the first predicted value of the outcome index.

[0017] The computer receives a change instruction to change the target value of one or more health indicators among the target values ​​of each of a plurality of health indicators, and generates changed target values ​​of the one or more health indicators by changing the target value of the one or more health indicators based on the change instruction.

[0018] The computer calculates a second predicted value of the outcome index based on the target values ​​of the health indexes other than one or more health indexes among the target values ​​of each of the plurality of health indexes and the changed target values ​​of the one or more health indexes, and outputs second display data for displaying the second predicted value of the outcome index. [Effects of the Invention]

[0019] According to one aspect, it is possible to visualize the effects of changes in indicators related to the health of group members. [Brief explanation of the drawings]

[0020] [Figure 1] FIG. 1 is a functional configuration diagram of a prediction device according to an embodiment. [Figure 2] 10 is a flowchart of a prediction process. [Figure 3] FIG. 1 is a configuration diagram of a health management support system. [Figure 4] FIG. 2 is a functional configuration diagram of the support device. [Figure 5] FIG. 10 is a diagram illustrating weight information. [Figure 6] FIG. 1 illustrates a risk assessment. [Figure 7] FIG. 10 is a diagram showing a simulation screen. [Figure 8] FIG. 10 is a diagram showing a target value change window. [Figure 9] 10 is a flowchart of a prediction process performed by the assistance device. [Figure 10] FIG. 10 is a diagram showing a category setting screen. [Figure 11] FIG. 2 is a hardware configuration diagram of an information processing device. DETAILED DESCRIPTION OF THE INVENTION

[0021] Hereinafter, the embodiments will be described in detail with reference to the drawings.

[0022] 1 shows an example of the functional configuration of a prediction device according to an embodiment. The prediction device 101 in FIG. 1 includes a calculation unit 111 and an output unit 112.

[0023] Fig. 2 is a flowchart showing an example of prediction processing performed by prediction device 101 of Fig. 1. First, calculation unit 111 calculates a first predicted value of an outcome index for evaluating the effect of improving any of a plurality of health indexes based on target values ​​for each of the plurality of health indexes related to the health of a plurality of members belonging to the organization (step 201). Then, output unit 112 outputs first display data for displaying the target values ​​for each of the plurality of health indexes and the first predicted value of the outcome index (step 202).

[0024] Next, calculation unit 111 receives a change instruction to change the target value of one or more of the plurality of health indices (step 203). Then, calculation unit 111 changes the target value of one or more health indices based on the change instruction, thereby generating changed target values ​​of the one or more health indices (step 204).

[0025] Next, calculation unit 111 calculates a second predicted value of the outcome index based on the target values ​​of the health indexes other than the one or more health indexes and the changed target values ​​of the one or more health indexes (step 205). Then, output unit 112 outputs second display data for displaying the second predicted value of the outcome index (step 206).

[0026] The prediction device 101 in FIG. 1 can visualize the effects of changes in health-related indicators of group members.

[0027] Fig. 3 shows an example configuration of a health management support system including prediction device 101 of Fig. 1. The health management support system of Fig. 3 includes a terminal device 301 and a support device 302. Support device 302 corresponds to prediction device 101 of Fig. 1.

[0028] Terminal device 301 is an information processing device of a user, and support device 302 is an information processing device of a business that provides health management support services to the user. The user is, for example, a person in charge of a company that practices health management. Support device 302 may be a server on the cloud.

[0029] The terminal device 301 and the support device 302 communicate with each other via a communication network 303. The communication network 303 is, for example, a wide area network (WAN) or a local area network (LAN).

[0030] Based on a user's instruction, terminal device 301 accesses support device 302 and requests the provision of a health and productivity management support service. In response to the request, support device 302 transmits display data for displaying a screen for the health and productivity management support service to terminal device 301, and terminal device 301 displays the screen for the health and productivity management support service using the received display data. The user uses the health and productivity management support service by performing predetermined operations on the displayed screen.

[0031] The health management support service visualizes and displays multiple risk factors and multiple outcomes related to the health of multiple employees belonging to a company. A company is an example of an organization, and multiple employees belonging to a company are an example of multiple members belonging to an organization.

[0032] Outcomes are indicators for evaluating the quality of health management, and risk factors are indicators that show factors that pose a risk of worsening outcomes. Risk factors correspond to health indicators related to the health of multiple members belonging to an organization, and outcomes correspond to performance indicators for evaluating the effects of improving any of the health indicators.

[0033] Risk factors include, for example, physical risk factors, psychological risk factors, lifestyle risk factors, and work-related risk factors. Physical risk factors include, for example, obesity, blood pressure, blood sugar level, lower back pain, and health checkup implementation rate. Psychological risk factors include, for example, stress response, stress check implementation rate, job satisfaction, and high-stress individuals.

[0034] Lifestyle risk factors include, for example, exercise frequency, sleep duration, smoking, breakfast, drinking, etc. Work-related risk factors include, for example, workload, overtime hours, support from superiors and colleagues, discretion, work engagement, and working long hours.

[0035] Examples of outcomes include absenteeism, presenteeism, turnover rate, and medical expenses. Absenteeism and presenteeism are indicators of the productivity of all employees. Absenteeism represents the loss of productivity due to absence from work, and presenteeism represents the loss of productivity due to physical or mental illness.

[0036] For example, absenteeism is measured by the average number of days absent due to illness for all employees, presenteeism is measured by the average productivity for all employees, turnover rate is measured by the percentage of employees who left the company in a year, and medical expenses is measured by the average medical expenses for all employees.

[0037] Risk factors are indicators that affect outcomes and are indicators that make it easy to see the effects of health management measures.

[0038] Fig. 4 shows an example of the functional configuration of the support device 302 in Fig. 3. The support device 302 in Fig. 4 includes a calculation unit 411, a communication unit 412, and a storage unit 413. The calculation unit 411 and the communication unit 412 correspond to the calculation unit 111 and the output unit 112 in Fig. 1, respectively.

[0039] The storage unit 413 stores risk factor statistical information 421, outcome statistical information 422, and weight information 423. The risk factor statistical information 421 includes statistical data for each of a plurality of risk factors, and the outcome statistical information 422 includes statistical data for each of a plurality of outcomes. The weight information 423 is information indicating the weight of each of a plurality of risk factors for each outcome.

[0040] Figure 5 shows an example of the weight information 423 in Figure 4. Each row in Figure 5 represents a risk factor, and each column represents an outcome. The numerical value in each cell represents the weight of the risk factor indicated by the row for the outcome indicated by the column.

[0041] In this example, weights are set for each risk factor for absenteeism, presenteeism, and medical costs. For example, the weight of exercise frequency for absenteeism is 0.174. The weights for each risk factor for employee turnover can be set in a similar manner.

[0042] The terminal device 301 transmits a simulation request to the support device 302 based on a user instruction, and the communication unit 412 receives the simulation request from the terminal device 301 .

[0043] Based on the received simulation request, the calculation unit 411 calculates the current score value of each risk factor using the risk factor statistical information 421, and calculates the latest current value of each outcome using the outcome statistical information 422. The current score value of a risk factor is an example of an actual measurement value of a health index.

[0044] Next, the calculation unit 411 uses the risk factor statistical information 421 and the weight information 423 to calculate the predicted PA at the end of the fiscal year for each outcome according to the following formula.

[0045] PA = (Σ(W × (R1 - R2))) × O (1)

[0046] In equation (1), W represents the weight of each risk factor for the outcome. The calculation unit 411 obtains W from the weight information 423.

[0047] In formula (1), R1 represents the current statistical value of each risk factor, and R2 represents the reference statistical value of each risk factor. The calculation unit 411 calculates R1 using the statistical data for the current year included in the risk factor statistical information 421, and calculates R2 using the statistical data for the previous year included in the risk factor statistical information 421, for example.

[0048] The statistical value of a risk factor may be the percentage of employees with health risks among all employees, or the mean, median, or mode of the risk assessment of all employees. Σ(W×(R1-R2)) represents the sum of W×(R1-R2) for all risk factors.

[0049] FIG. 6 shows an example of risk assessments for psychological risk factors, lifestyle risk factors, and work-related risk factors for each employee. The response value for each risk factor represents the response obtained from the employee, and the risk assessment represents an assessment value calculated from the response value. The risk factor statistical information 421 includes the response value or risk assessment for each risk factor for each employee.

[0050] The risk assessment is expressed as a real number ranging from 0 to 1. The higher the risk assessment, the greater the risk to health, and the lower the risk assessment, the smaller the risk to health. Response values ​​can be obtained in a similar manner to calculate risk assessments for other risk factors not shown in Figure 6.

[0051] In equation (1), O represents the baseline value of the outcome. The baseline values ​​for absenteeism, presenteeism, and medical expenses are determined, for example, from the results of existing research and surveys. The baseline value for turnover rate is calculated, for example, using statistical data from the previous year included in outcome statistical information 422.

[0052] Next, the calculation unit 411 calculates the influence I of each risk factor on each outcome using the following formula.

[0053] X = (W × (R1 - R2)) × O (2) I=X / Σ(X) (3)

[0054] In equation (2), X represents the contribution of each risk factor to PA in equation (1), and Σ(X) represents the sum of X for all risk factors. The influence I is an example of the influence of a health indicator on an outcome indicator.

[0055] Next, the calculation unit 411 calculates the predicted value PB of each outcome for the target score of each of the multiple risk factors using the following formula.

[0056] ΔO=(Σ(W×ΔR))×O (4) PB=PA+ΔO (5)

[0057] In equation (4), ΔR represents the difference between the current score and the target score for each risk factor, and ΔO represents the difference for each outcome. Σ(W × ΔR) represents the sum of W × ΔR for all risk factors. The predicted value PB corresponds to the first predicted value of the outcome indicator.

[0058] By calculating the predicted value PB of each outcome using equations (4) and (5), it is possible to accurately determine the predicted value PB when the target score for each risk factor is achieved.

[0059] Next, the calculation unit 411 generates display data D1 for displaying the current and target scores for each risk factor, the latest current value, end-of-year predicted PA, predicted value PB, and end-of-year target for each outcome, and the impact I of each risk factor on each outcome. The display data D1 corresponds to the first display data. The end-of-year target for an outcome is an example of a target value for a performance indicator.

[0060] The communication unit 412 transmits the display data D1 to the terminal device 301, and the terminal device 301 displays a simulation screen using the received display data D1.

[0061] 7 shows an example of a simulation screen. The risk factor chart 701 includes a radar chart 711-1 of physical and psychological indicators, a radar chart 711-2 of lifestyle indicators, and a radar chart 711-3 of work-related indicators. The physical and psychological indicators correspond to physical risk factors and psychological risk factors, the lifestyle indicators correspond to lifestyle risk factors, and the work-related indicators correspond to work-related risk factors.

[0062] The radar chart 711-1 for physical and psychological indicators includes risk factors such as obesity, blood pressure, blood sugar level, lower back pain, health checkup participation rate, stress response, stress check participation rate, job satisfaction, and high-stress individuals. The radar chart 711-2 for lifestyle indicators includes risk factors such as exercise frequency, sleep time, smoking, breakfast, and alcohol consumption. The radar chart 711-3 for work-related indicators includes risk factors such as workload, overtime hours, support from superiors and colleagues, discretion, work engagement, and working long hours.

[0063] Each radar chart 711-i (i = 1 to 3) represents the score of each risk factor. The score of a risk factor is expressed as a number ranging from 0 to 100. The larger the number, the smaller the risk to health, and the smaller the number, the greater the risk to health.

[0064] The polygonal line 712-i represents the current score of each risk factor, and the polygonal line 713-i represents the target score of each risk factor. Therefore, for risk factors whose polygonal line 712-i is located inside the polygonal line 713-i, the current score is lower than the target score, and therefore it is desirable to improve them.

[0065] By displaying the current score and the target score for each risk factor, the user can easily understand how far the current score deviates from the target score.

[0066] Outcome 702 includes bar graphs 721-1 and 722-1 for absenteeism and bar graphs 721-2 and 722-2 for presenteeism. Outcome 702 further includes bar graphs 721-3 and 722-3 for turnover rate and bar graphs 721-4 and 722-4 for medical costs.

[0067] Each bar graph 721-j (j=1 to 4) represents the latest current value of the outcome, and each bar graph 722-j represents the predicted PA for the outcome at the end of the fiscal year. Horizontal line 723-j represents the target for the outcome at the end of the fiscal year, and horizontal line 724-j represents the predicted value PB for the outcome.

[0068] By displaying the year-end target for the outcome, the user can easily understand how much the predicted PA and predicted PB at the end of the year deviate from the year-end target.

[0069] In this example, PA and PB are calculated using 14 risk factors among those shown in the risk factor chart 701. The 14 risk factors are obesity, back pain, stress response, job satisfaction, exercise frequency, sleep time, smoking, breakfast, alcohol consumption, workload, overtime hours, support from superiors and colleagues, discretion, and work engagement.

[0070] The achievement rate is expressed using the difference ΔQ between the current latest value and the target at the end of the fiscal year, and the ratio of ΔQ to the target at the end of the fiscal year. The number in parentheses represents the ratio, and the number to the left of the parentheses represents ΔQ.

[0071] The impact on outcome 703 includes a bar graph 731-1 for absenteeism, a bar graph 731-2 for presenteeism, a bar graph 731-3 for turnover rate, and a bar graph 731-4 for medical costs.

[0072] Each bar graph 731-j (j=1 to 4) includes 14 regions color-coded with colors C1 to C14. Each region corresponds to the contribution of a risk factor, and the ratio of the length of each region to the overall length of the bar graph 731-j represents the impact I of each risk factor on the outcome. Therefore, the larger the percentage occupied by an region, the greater the impact I of the corresponding risk factor.

[0073] Color C1 represents obesity, color C2 represents back pain, color C3 represents stress response, color C4 represents job satisfaction, color C5 represents exercise frequency, color C6 represents sleep duration, color C7 represents smoking, color C8 represents breakfast, color C9 represents drinking, color C10 represents workload, color C11 represents overtime, color C12 represents support from superiors and colleagues, color C13 represents discretion, and color C14 represents work engagement.

[0074] By displaying the impact I of each risk factor on the outcome, the user can easily identify risk factors that have a significant effect on improving the outcome.

[0075] When the user uses a pointing device such as a mouse to hover over the name of a risk factor in each radar chart 711-i, a target value change window appears. By performing a predetermined operation in the target value change window, the user can change the target value of the score of the risk factor indicated by the broken line 713-i.

[0076] 8 shows an example of a target value change window when the sleep time in radar chart 711-2 is hovered over. The user can change the target value by clicking buttons 801 to 804.

[0077] In this example, each time button 801 is clicked, the target value decreases by 1, and each time button 802 is clicked, the target value increases by 1. Each time button 803 is clicked, the target value decreases by 0.1, and each time button 804 is clicked, the target value increases by 0.1.

[0078] The user can change the target score values ​​of multiple risk factors by repeatedly hovering over the radar charts 711-1 to 711-3. When the user changes the target score values ​​of one or multiple risk factors, the terminal device 301 transmits a change instruction indicating the change operation to the support device 302, and the communication unit 412 receives the change instruction from the terminal device 301.

[0079] The calculation unit 411 accepts the received change instruction and generates a changed target value by changing the target value of the score of the risk factor indicated by the change instruction.The calculation unit 411 then uses the changed target value to calculate the predicted value PB of each outcome according to equations (4) and (5).The predicted value PB calculated using the changed target value corresponds to the second predicted value of the outcome index.

[0080] For risk factors indicated by change instructions, the difference between the current score of the risk factor and the target score after the change is used as ΔR in (4).For risk factors that have not been changed, the difference between the current score of the risk factor and the target score is used as ΔR.

[0081] Next, the calculation unit 411 generates display data D2 for displaying the target values ​​of the risk factor scores after the change and the predicted values ​​PB calculated using the target values ​​after the change. The display data D2 corresponds to the second display data.

[0082] The communication unit 412 transmits the display data D2 to the terminal device 301. Using the received display data D2, the terminal device 301 changes the target value of the score of the risk factor indicated by the broken line 713-i to the changed target value, and changes the predicted value PB of each outcome indicated by the horizontal line 724-j to the changed predicted value PB. This changes the shape of the broken line 713-i of the radar chart 711-i that includes the changed risk factor, and fluctuates the position of the horizontal line 724-j of each outcome.

[0083] The user reviews the target value for achieving the end-of-year target indicated by the horizontal line 723-j by repeatedly changing the target value for the risk factor score while watching the fluctuating horizontal line 724-j.

[0084] For example, in the case of absenteeism, the target value of the risk factor score is changed so that the position of horizontal line 724-1 is at the same height as horizontal line 723-1 or lower than horizontal line 723-1. Then, the user considers measures to improve the risk factor so that the changed target value is achieved.

[0085] In the case of presenteeism, the target value of the risk factor score is changed so that the position of horizontal line 724-2 is at the same height as horizontal line 723-2 or higher than horizontal line 723-2. The user then considers measures to improve the risk factor so that the changed target value is achieved.

[0086] In the case of turnover rate, the target value of the risk factor score is changed so that the position of horizontal line 724-3 is at the same height as horizontal line 723-3 or lower than horizontal line 723-3. Then, the user considers measures to improve the risk factors so that the changed target value is realized.

[0087] In the case of medical expenses, the target value of the risk factor score is changed so that the position of horizontal line 724-4 is at the same height as horizontal line 723-4 or lower than horizontal line 723-4. Then, the user considers measures to improve the risk factors so that the changed target value is achieved.

[0088] The user can save the target value of the risk factor score after the change by clicking button 705, and can reset the change operation and return to the previously saved state by clicking button 704.

[0089] The health and productivity management support system in Figure 3 can visualize and present to users the changes in predicted outcomes when target values ​​for risk factors related to employee health are changed. This allows users to intuitively grasp the effects of improving risk factors, making it easier to consider health and productivity management measures.

[0090] Fig. 9 is a flowchart showing an example of the prediction process performed by the support device 302 of Fig. 4. First, the calculation unit 411 generates display data D1 based on a simulation request received from the terminal device 301 (step 901).

[0091] In step 901, the calculation unit 411 calculates the current value of the score for each risk factor using the risk factor statistical information 421, and calculates the latest current value of each outcome using the outcome statistical information 422. Then, the calculation unit 411 uses the risk factor statistical information 421 and the weight information 423 to calculate the predicted PA at the end of the fiscal year for each outcome according to formula (1).

[0092] Furthermore, the calculation unit 411 calculates the influence I of each risk factor on each outcome using equations (2) and (3), and calculates the predicted value PB of each outcome relative to the target score for each of the multiple risk factors using equations (4) and (5).The calculation unit 411 then generates display data D1 for displaying the calculation results.

[0093] Next, the communication unit 412 transmits the display data D1 to the terminal device 301 (step 902). The terminal device 301 uses the received display data D1 to display a simulation screen.

[0094] When the user changes the target score value of any of the risk factors on the simulation screen, the communication unit 412 receives a change instruction from the terminal device 301 (step 903).

[0095] Next, the calculation unit 411 accepts the received change instruction (step 904) and generates changed target values ​​by changing the target values ​​of the risk factor scores indicated by the change instruction (step 905).The calculation unit 411 then uses the changed target values ​​to calculate the predicted value PB of each outcome according to equations (4) and (5), and generates display data D2 for displaying the changed target values ​​and the predicted value PB calculated using the changed target values ​​(step 906).

[0096] Next, the communication unit 412 transmits the display data D2 to the terminal device 301 (step 907). The terminal device 301 uses the received display data D2 to change the simulation screen.

[0097] The support device 302 can also cause the terminal device 301 to display a category setting screen showing the outcomes of some employees, instead of a simulation screen showing the outcomes of the entire company.

[0098] FIG. 10 shows an example of a category setting screen. The user can use the setting button 1011 of the filter 1001 to narrow down the displayed content by employee category. Categories include fiscal year, headquarters, business establishment, age group, gender, job type, employee classification, etc. The headquarters and business establishment represent organizations within the company. In this example, the displayed content is narrowed down to fiscal year 2019.

[0099] The user can select a category using the settings button 1012 of the category 1002. In this example, headquarters is selected.

[0100] A category list showing multiple groups belonging to the selected category is displayed in the left pane 1003. In this example, the names of multiple headquarters are displayed, and among them, test headquarters 10 is selected.

[0101] Tables 1004 and 1005 show the current scores for risk factors for employees at test headquarters 10 compared to the average scores for employees at all headquarters included in the category list.

[0102] Table 1004 contains information on the top five risk factors for which the testing headquarters 10 performed particularly well. Table 1005 contains information on the bottom five risk factors for which the testing headquarters 10 performed particularly poorly. The result value represents the current score of the testing headquarters 10, and the average value represents the average value of all headquarters. The ranking represents the ranking of the testing headquarters 10 among all headquarters.

[0103] Impact on outcome 1006 includes bar graphs 1021-1 to 1021-4 and bar graphs 1022-1 to 1022-4. Bar graph 1021-1 represents the average value of the impact of each risk factor on absenteeism at all headquarters, and bar graph 1022-1 represents the impact of each risk factor on absenteeism at test headquarters 10.

[0104] Bar graph 1021-2 represents the average value of the impact of each risk factor on presenteeism for all headquarters, and bar graph 1022-2 represents the impact of each risk factor on presenteeism for test headquarters 10.

[0105] Bar graph 1021-3 represents the average value of the influence of each risk factor on the turnover rate of all headquarters, and bar graph 1022-3 represents the influence of each risk factor on the turnover rate of test headquarters 10.

[0106] The bar graph 1021-4 represents the average value of the degree of influence of each risk factor on the medical expenses of all headquarters, and the bar graph 1022-4 represents the degree of influence of each risk factor on the medical expenses of the test headquarters 10.

[0107] By viewing the category setting screen, the user can compare the risk factors and impact levels of the group selected in the category list with the average values ​​for all groups, allowing the user to quickly grasp the characteristics of the data for the selected group.

[0108] The configuration of prediction device 101 in Fig. 1 is merely an example, and some of the components may be omitted or changed depending on the use or conditions of prediction device 101. The configurations of the health management support system in Fig. 3 and support device 302 in Fig. 4 are merely examples, and some of the components may be omitted or changed depending on the use or conditions of the health management support system.

[0109] 2 and 9 are merely examples, and some processes may be omitted or changed depending on the configuration or conditions of the prediction device 101 or the health management support system. Instead of employees belonging to a company, changes in the predicted values ​​of outcomes when target values ​​of risk factors are changed may be visualized for members belonging to another organization such as a non-profit organization or an administrative organization.

[0110] The weight information 423 shown in Figure 5 is just an example, and the weight information 423 changes depending on the risk factors and outcomes. The risk assessment shown in Figure 6 is just an example, and the risk assessment changes depending on the risk factors.

[0111] The simulation screen shown in Figure 7, the target value change window shown in Figure 8, and the category setting screen shown in Figure 10 are merely examples, and some of the information displayed on the screen may be omitted or changed depending on the purpose or conditions of the health management support system.

[0112] For example, on the simulation screen of Fig. 7, if it is not necessary to display the latest outcome value and the predicted PA at the end of the fiscal year, bar graph 721-j and bar graph 722-j can be omitted. If it is not necessary to display the influence I of each risk factor on the outcome, band graph 731-j can be omitted.

[0113] Fig. 11 shows an example of the hardware configuration of an information processing device used as the prediction device 101 in Fig. 1, the terminal device 301 in Fig. 3, and the support device 302 in Fig. 4. The information processing device in Fig. 11 includes a CPU (Central Processing Unit) 1101, a memory 1102, an input device 1103, an output device 1104, an auxiliary storage device 1105, a media drive device 1106, and a network connection device 1107. These components are hardware and are connected to each other by a bus 1108.

[0114] The memory 1102 is, for example, a semiconductor memory such as a read-only memory (ROM) or a random access memory (RAM), and stores programs and data used in processing. The memory 1102 may operate as the storage unit 413 in FIG.

[0115] 1 by executing a program using the memory 1102. The CPU 1101 also operates as the calculation unit 411 in FIG. 4 by executing a program using the memory 1102.

[0116] The input device 1103 is, for example, a keyboard, a pointing device, etc., and is used for inputting instructions or information from an operator. The output device 1104 is, for example, a display device, a printer, etc., and is used for outputting inquiries or instructions to an operator and processing results. The output device 1104 may operate as the output unit 112 in FIG. 1.

[0117] The auxiliary storage device 1105 is, for example, a magnetic disk device, an optical disk device, a magneto-optical disk device, a tape device, etc. The auxiliary storage device 1105 may be a hard disk drive or a solid state drive (SSD). The information processing device stores programs and data in the auxiliary storage device 1105 and can use them by loading them into the memory 1102. The auxiliary storage device 1105 may operate as the storage unit 413 in FIG. 4.

[0118] The medium drive device 1106 drives the portable recording medium 1109 and accesses the recorded contents thereof. The portable recording medium 1109 is a memory device, a flexible disk, an optical disk, a magneto-optical disk, etc. The portable recording medium 1109 may be a CD-ROM (Compact Disk Read Only Memory), a DVD (Digital Versatile Disk), a USB (Universal Serial Bus) memory, etc. An operator can store programs and data in the portable recording medium 1109 and load them into the memory 1102 for use.

[0119] In this way, the computer-readable recording medium that stores the program and data used in the processing is a physical (non-transitory) recording medium such as the memory 1102, the auxiliary storage device 1105, or the portable recording medium 1109.

[0120] The network connection device 1107 is a communication interface circuit that is connected to the communication network 303 and performs data conversion associated with communication. The information processing device receives programs and data from external devices via the network connection device 1107 and can use them by loading them into the memory 1102. The network connection device 1107 may operate as the output unit 112 in FIG. 1 or the communication unit 412 in FIG. 4.

[0121] It is not necessary for the information processing device to include all of the components shown in Figure 11, and some components may be omitted depending on the purpose or conditions of the information processing device. For example, if an interface with an operator is not required, the input device 1103 and the output device 1104 may be omitted. If the portable recording medium 1109 or the communication network 303 is not used, the medium drive device 1106 or the network connection device 1107 may be omitted.

[0122] Although the disclosed embodiments and their advantages have been described in detail, those skilled in the art may make various modifications, additions, and omissions without departing from the scope of the invention as clearly set forth in the claims.

[0123] The following notes are further provided regarding the embodiment described with reference to FIGS. (Appendix 1) calculating a first predicted value of an outcome index for evaluating the effect of improving any one of a plurality of health indexes based on target values ​​for each of a plurality of health indexes related to the health of a plurality of members belonging to the organization; outputting first display data for displaying the target values ​​of each of the plurality of health indicators and the first predicted value of the outcome indicator; accept a change instruction to change the target value of one or more of the plurality of health indices; generating changed target values ​​of the one or more health indicators by changing the target values ​​of the one or more health indicators based on the change instruction; Calculating a second predicted value of the outcome index based on target values ​​of health indexes other than the one or more health indexes among the target values ​​of each of the plurality of health indexes and the changed target values ​​of the one or more health indexes; outputting second display data for displaying a second predicted value of the outcome index; A prediction program that causes a computer to execute the process. (Appendix 2) the first display data includes data for displaying actual measured values ​​of each of the plurality of health indices; A prediction program as described in Appendix 1, characterized in that the process of calculating the first predicted value of the outcome index includes a process of calculating the first predicted value of the outcome index based on target values ​​for each of the multiple health indexes, actual measured values ​​for each of the multiple health indexes, and weight information indicating the weights of each of the multiple health indexes. (Appendix 3) 3. The prediction program according to claim 1, wherein the first display data includes data for displaying a target value of the performance index. (Appendix 4) A prediction program according to any one of appendices 1 to 3, characterized in that the first display data includes data for displaying the degree of influence of each of the plurality of health indicators on the outcome indicator. (Appendix 5) A prediction program described in any one of Appendices 1 to 4, characterized in that each of the multiple health indicators is an indicator related to physical risk factors related to the health of the multiple members, psychological risk factors related to the health of the multiple members, lifestyle risk factors related to the health of the multiple members, or work-related risk factors related to the health of the multiple members. (Appendix 6) A prediction program described in any one of Appendices 1 to 5, characterized in that the performance indicator is an indicator related to the productivity of the multiple members, the turnover rate of the multiple members, or the medical expenses of the multiple members. (Appendix 7) a calculation unit that calculates, based on target values ​​of multiple health indicators related to the health of multiple members belonging to the organization, a first predicted value of an outcome indicator for evaluating the effect of improving any of the multiple health indicators; receives a change instruction to change the target values ​​of one or more health indicators among the multiple health indicators, changes the target values ​​of the one or more health indicators based on the change instruction to generate changed target values ​​of the one or more health indicators; and calculates a second predicted value of the outcome indicator based on target values ​​of health indicators other than the one or more health indicators among the multiple health indicators and the changed target values ​​of the one or more health indicators; an output unit that outputs first display data for displaying the target values ​​of the plurality of health indexes and the first predicted value of the outcome index before the change instruction is accepted, and outputs second display data for displaying the second predicted value of the outcome index after the second predicted value of the outcome index is calculated; A prediction device comprising: (Appendix 8) the first display data includes data for displaying actual measured values ​​of each of the plurality of health indices; The prediction device described in Appendix 7, characterized in that the calculation unit calculates a first predicted value of the outcome index based on target values ​​of each of the multiple health indexes, actual measured values ​​of each of the multiple health indexes, and weight information indicating the weights of each of the multiple health indexes. (Appendix 9) 9. The prediction device according to claim 7, wherein the first display data includes data for displaying a target value of the performance index. (Appendix 10) 10. The prediction device according to any one of appendices 7 to 9, wherein the first display data includes data for displaying the degree of influence of each of the plurality of health indicators on the outcome indicator. (Appendix 11) A prediction device described in any one of Appendices 7 to 10, characterized in that each of the multiple health indicators is an indicator related to physical risk factors related to the health of the multiple members, psychological risk factors related to the health of the multiple members, lifestyle risk factors related to the health of the multiple members, or work-related risk factors related to the health of the multiple members. (Appendix 12) The prediction device described in any one of Appendices 7 to 11, characterized in that the performance indicator is an indicator related to the productivity of the multiple members, the turnover rate of the multiple members, or the medical expenses of the multiple members. (Appendix 13) calculating a first predicted value of an outcome index for evaluating the effect of improving any one of a plurality of health indexes based on target values ​​for each of a plurality of health indexes related to the health of a plurality of members belonging to the organization; outputting first display data for displaying the target values ​​of each of the plurality of health indicators and the first predicted value of the outcome indicator; accept a change instruction to change the target value of one or more of the plurality of health indices; generating changed target values ​​of the one or more health indicators by changing the target values ​​of the one or more health indicators based on the change instruction; Calculating a second predicted value of the outcome index based on target values ​​of health indexes other than the one or more health indexes among the target values ​​of each of the plurality of health indexes and the changed target values ​​of the one or more health indexes; outputting second display data for displaying a second predicted value of the outcome index; A prediction method characterized in that the processing is performed by a computer. (Appendix 14) the first display data includes data for displaying actual measured values ​​of each of the plurality of health indices; A prediction method as described in Appendix 13, characterized in that the process of calculating the first predicted value of the outcome indicator includes a process of calculating the first predicted value of the outcome indicator based on target values ​​for each of the multiple health indicators, actual measured values ​​for each of the multiple health indicators, and weight information indicating the weights of each of the multiple health indicators. (Appendix 15) 15. The prediction method according to claim 13, wherein the first display data includes data for displaying a target value of the performance indicator. (Appendix 16) A prediction method described in any one of Appendices 13 to 15, characterized in that the first display data includes data for displaying the degree of influence of each of the multiple health indicators on the outcome indicator. (Appendix 17) 17. A prediction method according to any one of appendices 13 to 16, wherein each of the plurality of health indicators is an indicator relating to a physical risk factor relating to the health of the plurality of members, a psychological risk factor relating to the health of the plurality of members, a lifestyle risk factor relating to the health of the plurality of members, or an employment-related risk factor relating to the health of the plurality of members. (Appendix 18) 18. The prediction method according to any one of appendices 13 to 17, wherein the performance indicator is an indicator related to the productivity of the plurality of members, the turnover rate of the plurality of members, or the medical expenses of the plurality of members. [Explanation of symbols]

[0124] 101 Prediction Device 111, 411 Calculation section 112 Output section 301 Terminal Equipment 302 Support equipment 303 Communication Network 412 Communications Department 413 Storage section 421 Risk Factor Statistics 422 Outcome Statistics 423 Weight Information 701 Risk Factor Chart 702 Outcomes 703, 1006 Impact on outcomes 704, 705, 801-804 buttons 711-1~711-3 Radar chart 712-1~712-3, 713-1~713-3 broken line 721-1~721-4, 722-1~722-4 Bar graph 723-1~723-4, 724-1~724-4 Horizontal line 731-1~731-4, 1021-1~1021-4, 1022-1~1022-4 Bar graph 1001 Filter 1002 categories 1003 Left Pane Tables 1004 and 1005 1011, 1012 setting buttons 1101 CPU 1102 memory 1103 Input Device 1104 Output Device 1105 Auxiliary storage device 1106 Media drive unit 1107 Network connection device 1108 Bus 1109 Portable recording media

Claims

1. calculate a sum of values ​​obtained by multiplying the difference between the current score and the target score of each of a plurality of health indicators relating to the health of a plurality of members belonging to the organization by weight information set for an outcome indicator for evaluating the effect of improving each of the health indicators, calculate a multiplication value by multiplying the sum by a reference value of the outcome indicator, and calculate a first predicted value of the outcome indicator by adding the predicted value of the outcome indicator at the end of the fiscal year to the multiplication value; outputting first display data for displaying the target values ​​of each of the plurality of health indicators and the first predicted value of the outcome indicator; accept a change instruction to change the target values ​​of one or more of the plurality of health indices; generating changed target values ​​of the one or more health indicators by changing the target values ​​of the one or more health indicators based on the change instruction; calculating a second predicted value of the outcome index using the same calculation formula as the first predicted value, using target values ​​of health indexes other than the one or more health indexes among the target values ​​of each of the plurality of health indexes and the changed target values ​​of the one or more health indexes; outputting second display data for displaying a second predicted value of the outcome index; A prediction program that causes a computer to execute the process.

2. 2. The prediction program according to claim 1, wherein the first display data includes data for displaying a current score for each of the plurality of health indices as an actual measurement value for each of the plurality of health indices.

3. 3. The prediction program according to claim 1, wherein the first display data includes data for displaying a target value of the performance index.

4. The computer is further caused to execute a process of calculating the contribution of each of the plurality of health indicators to the end-of-year predicted value of the performance indicator by multiplying the sum of the difference between the current statistical value of each health indicator and the reference statistical value of each health indicator multiplied by the weight information as the influence of each of the plurality of health indicators on the performance indicator, and multiplying the sum of the value of the reference value of the performance indicator; 4. The prediction program according to claim 1, wherein the first display data includes data for displaying each calculated influence degree.

5. a calculation unit that calculates a first predicted value of the outcome indicator by calculating a sum of values ​​obtained by multiplying the difference between the current score and the target value of each of a plurality of health indicators related to the health of a plurality of members belonging to the organization by weight information set for an outcome indicator for evaluating the effect of improving each of the health indicators, multiplying the sum by a reference value of the outcome indicator, and adding the predicted value of the outcome indicator at the end of the fiscal year to the multiplied value; receives a change instruction to change the target value of one or more health indicators among the target values ​​of the plurality of health indicators, changes the target value of the one or more health indicators based on the change instruction, thereby generating changed target values ​​of the one or more health indicators; and calculates a second predicted value of the outcome indicator using the same calculation formula as for the first predicted value, using the target values ​​of health indicators other than the one or more health indicators among the target values ​​of the plurality of health indicators and the changed target value of the one or more health indicators; an output unit that outputs first display data for displaying the target values ​​of the plurality of health indicators and a first predicted value of the outcome indicator before the change instruction is accepted, and outputs second display data for displaying the second predicted value of the outcome indicator after the second predicted value of the outcome indicator is calculated; A prediction device comprising:

6. calculate a sum of values ​​obtained by multiplying the difference between the current score and the target score of each of a plurality of health indicators relating to the health of a plurality of members belonging to the organization by weight information set for an outcome indicator for evaluating the effect of improving each of the health indicators, calculate a multiplication value by multiplying the sum by a reference value of the outcome indicator, and calculate a first predicted value of the outcome indicator by adding the predicted value of the outcome indicator at the end of the fiscal year to the multiplication value; outputting first display data for displaying the target values ​​of each of the plurality of health indicators and the first predicted value of the outcome indicator; accept a change instruction to change the target values ​​of one or more of the plurality of health indices; generating changed target values ​​of the one or more health indicators by changing the target values ​​of the one or more health indicators based on the change instruction; calculating a second predicted value of the outcome index using the same calculation formula as the first predicted value, using target values ​​of health indexes other than the one or more health indexes among the target values ​​of each of the plurality of health indexes and the changed target values ​​of the one or more health indexes; outputting second display data for displaying a second predicted value of the outcome index; A prediction method characterized in that the processing is performed by a computer.

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