JP2022174010A - HEALTH CONDITION EVALUATION SYSTEM, HEALTH CONDITION EVALUATION METHOD, AND PROGRAM - Google Patents

Abstract

The present invention provides a health status evaluation system, a health status evaluation method, and a program that can provide an evaluation method regarding the health status of personnel working in an organization. [Solution] An estimation department that estimates the amount of loss caused by a decrease in the labor productivity of the personnel by calculating at least one of presenteeism loss and absenteeism loss, and evaluates the health condition of the personnel. a calculation unit that calculates an evaluation value based on at least some of the plurality of parameters or at least one of the amount of loss; and a calculation unit that compares past health status data for each person with newly acquired health status data; The present invention includes a factor estimating unit that extracts a parameter that has a large influence on the amount of loss from among changing parameters as a factor that should be improved for the health of the person concerned. [Selection diagram] Figure 4

Description

TECHNICAL FIELD The present invention relates to a health condition evaluation system, a health condition evaluation method, and a program for evaluating the health conditions of employees who belong to an organization and work.

A system has been developed in which questions about lifestyle habits, stress, etc. are given to employees who belong to an organization and work, and the health conditions of the employees are evaluated based on the results. For example, in Patent Document 1, by asking questions about the health condition of employees and calculating the amount of loss caused by the loss of productivity due to the health condition based on the answers, A system is disclosed that presents the risk associated with

Japanese Unexamined Patent Application Publication No. 2020-42548

There is a need for a method of assessing the health condition from different perspectives, in order to estimate not only the amount of loss but also how to improve the health condition of each person.

An object of the present disclosure is to provide a health condition evaluation system, a health condition evaluation method, and a program that can provide a method for evaluating the health condition of employees who belong to an organization and work.

A health condition evaluation system of the present disclosure acquires health condition data having a plurality of parameters including at least presenteeism and absenteeism of a person who belongs to an organization and works, and acquires health condition data. Presenteeism loss as said loss in respect of said presenteeism of said person and absenteeism as said loss in respect of said absenteeism of said person an estimation unit for estimating by calculating at least one of the tissue loss; and a calculation for calculating an evaluation value for evaluating the health condition of the person based on at least part of the plurality of parameters or at least one of the loss amount. , the past health condition data for each person, and the newly acquired health condition data, and among the changing parameters, the parameters that have a large impact on the loss amount are determined as the health of the person. and a factor estimator for extracting factors to be improved for the purpose.

In the health condition evaluation method of the present disclosure, a computer acquires health condition data having a plurality of parameters including at least the presenteeism and absenteeism of a person working in an organization, and Presenteeism loss as said loss in respect of said presenteeism of said person and absenteeism as said loss in respect of said absenteeism of said person estimating by calculating at least one of the tissue loss, and calculating an evaluation value for evaluating the health condition of the person based on at least part of the plurality of parameters or at least one of the loss amount, for each person By comparing the past health data and the newly acquired health data, among the changing parameters, the parameters that have a large impact on the loss amount should be improved for the health of the person. Extract it as a factor.

The program of the present disclosure includes a procedure for obtaining health condition data related to the health condition of a person who belongs to an organization and works, and the amount of loss caused by the decrease in productivity related to the work of the person. A procedure for estimating by calculating at least one of presenteeism loss as the loss amount related to presenteeism and absenteeism loss as the loss amount related to the absenteeism of the person, and evaluating the health condition of the person A procedure for calculating an evaluation value based on at least part of the plurality of parameters or at least one of the loss amount, and comparing the past health condition data of each employee with the newly acquired health condition data. and extracting, from among the changing parameters, parameters that have a large impact on the loss amount as factors that should be improved for the health of the employee.

Advantageous Effects of Invention According to the present disclosure, it is possible to provide a method for evaluating the health status of personnel who belong to an organization and work.

A diagram showing an example of the configuration of a health condition evaluation system Block diagram showing an example of the configuration of an information processing device Functional block diagram for explaining the functions of the control unit Flowchart for explaining health evaluation processing for individuals Flowchart for explaining health evaluation processing for an organization Flowchart for explaining the health evaluation process assuming a change

Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. However, more than necessary detailed description, for example, detailed description of already well-known matters and redundant description of substantially the same configuration may be omitted. It should be noted that the following description and referenced drawings are provided for the understanding of the present disclosure by those skilled in the art and are not intended to limit the scope of the claims of the present disclosure.

[Constitution]
<Health evaluation system 100>
A configuration of a health evaluation system 100 according to an embodiment of the present disclosure will be described with reference to FIG. As shown in FIG. 1 , the health evaluation system 100 includes an information processing device 1 and a user terminal 2 .

The information processing device 1 is a device that performs various types of information processing. The information processing device 1 is, for example, a computer such as a PC (Personal Computer) or a workstation. In the example shown in FIG. 1, the information processing device 1 is communicably connected to a plurality of user terminals 2 via a network NW. The network NW is the Internet or an intranet. In the example shown in FIG. 1, the information processing device 1 is communicably connected to a plurality of user terminals 2 via the network NW, but direct communication without the network NW may be possible.

The user terminal 2 is a device that outputs various information to the user and receives input from the user. The user terminal 2 includes a display device such as a liquid crystal display or an organic EL (Electro-Luminescence) display, and operation devices such as a keyboard, mouse, touch panel, trackball, and the like. Examples of such user terminals 2 include personal digital assistants such as smart phones and tablet terminals, in addition to PCs. Although the health evaluation system 100 includes a plurality of user terminals 2 in the example shown in FIG. 1, the number of user terminals 2 included in the health evaluation system 100 may be one.

In this embodiment, a user is a person who belongs to an organization and works. An example of the organization in this embodiment is a profit-making organization such as a company or corporation. Further, examples of personnel who perform labor in the present embodiment include workers, employees, employees, and other employees who are employed by an organization. In this embodiment, the user is not limited to an employee, and may include an employer, a manager, a manager, or the like who employs or manages an employee.

The health evaluation system 100 acquires information about the user's health condition, evaluates the user's health condition, and also evaluates the health of the organization to which the user belongs. Specifically, the user terminal 2 displays a plurality of question items regarding the health condition of the user, receives input of answers from the user, and transmits information about the answer results to the information processing apparatus 1 . The information processing device 1 evaluates the health condition of the user or the organization based on the information about the answer result, and outputs the evaluation result.

<Information processing device 1>
Next, the configuration of the information processing device 1 will be described with reference to FIG. The information processing device 1 includes a communication unit 11 , an input/output unit 12 , a storage unit 13 and a control unit 14 .

The communication unit 11 is a communication device that communicates with the user terminal 2 .

The input/output unit 12 is an operating device such as a keyboard, mouse, touch panel, or trackball for receiving input of various information, and a display device such as a liquid crystal display or organic EL. It should be noted that the person who inputs information to the information processing device 1 and receives information output from the information processing device 1 is a person who is in a position to manage and understand the health conditions of all personnel belonging to the organization. Specifically, it is a person who belongs to the personnel department of an organization, or a manager or manager of an organization. In the following description, a person who inputs information to the information processing apparatus 1 and receives information output from the information processing apparatus 1 is simply referred to as an administrator.

The storage unit 13 is composed of a large-capacity recording medium such as a HDD (Hard Disk Drive) or SSD (Solid State Drive), and stores various information used for control of the control unit 14 . Details of various information stored in the storage unit 13 will be described later.

The control unit 14 is a processor including, for example, a CPU (Central Processing Unit), a ROM (Read Only Memory), and a RAM (Random Access Memory). The control unit 14 reads programs stored in the ROM, develops them in the RAM, and controls the plurality of user terminals 2 according to the developed programs. The RAM forms a work area that temporarily stores various programs executed by the CPU and data related to the programs. The ROM is composed of a non-volatile memory or the like, and stores various programs and various data used for control.

<Control unit 14>
FIG. 3 is a functional block diagram for explaining functions of the control unit 14. As shown in FIG. As shown in FIG. 3 , the control unit 14 includes an acquisition unit 141 , an estimation unit 142 , a factor estimation unit 143 , a calculation unit 144 and an output unit 145 .

The acquisition unit 141 acquires various data relating to the health conditions of personnel who belong to an organization and work, from acquisition sources corresponding to the respective data. The data acquired by the acquisition unit 141 is hereinafter referred to as health condition data.

The health condition data includes, for example, the gender, age group, job evaluation results, health checkup results, stress check results, attendance data, answers to questions directly asked to the personnel, and the like.

The business evaluation result is data indicating the result of an evaluation performed on the employee by, for example, the personnel department of the organization to which the employee belongs. The acquisition unit 141 acquires the business evaluation result from, for example, the personnel system of the organization to which the staff member belongs.

The health checkup result is data indicating the results of the health checkup required by the Industrial Safety and Health Act. Moreover, the stress check result is data indicating the result of the personnel undergoing the stress check implemented based on the Industrial Safety and Health Act.

Acquisition unit 141 acquires a health checkup result or a stress check result from, for example, a health checkup result server that holds the health checkup results of a plurality of personnel. Alternatively, the acquisition unit 141 may ask the employee a question to confirm the result of the health checkup via the user terminal 2 and cause the employee to operate the user terminal 2 to acquire the result of the health checkup. Alternatively, the acquisition unit 141 may ask the personnel a question for performing the stress check via the user terminal 2, and cause the personnel to operate the user terminal 2 to acquire the stress check result. It should be noted that general items may be adopted as the items included in the stress check, and detailed description thereof will be omitted in the present embodiment.

The attendance data is the attendance data of the personnel managed by the attendance system of the organization to which the personnel belongs. Attendance data includes net work hours, overtime hours, paid leave acquisition rate, and the like. The acquisition unit 141 acquires attendance data from the attendance system.

The answer result to the question is data indicating the answer result of each person to a plurality of question items made via the user terminal 2 . When the staff member inputs the answer result via the user terminal 2, the question for the staff member is displayed on the display device of the user terminal 2, for example. Then, the answer result is input to the user terminal 2 by the staff operating the operation device of the user terminal 2 while viewing the content of the question displayed on the display device.

In addition, it is desirable that the question to the personnel through the user terminal 2 is repeated at predetermined intervals, such as every week, every month, or every three months. At this time, instead of requesting answers to all question items at once, it is possible to reduce the number of question items at one time and request multiple responses at relatively short intervals. By obtaining answer result information at a high frequency in this way, it is expected that answer results that reflect the actual situation more effectively will be obtained.

The answer result has a plurality of parameters corresponding to the content of answers to a plurality of question items. Examples of multiple parameters included in the response results include presenteeism, absenteeism, unpaid overtime hours, telecommuting hours, subjective quantitative burden, discretion, workplace relationships, subjective physical condition, and subjective well-being. degree.

Presenteeism means a state in which the ability to work is reduced due to a health condition (such as illness or fatigue) while a person is working. Presenteeism as a parameter is a parameter that indicates the extent to which a person's ability to work is reduced due to their health condition while they are working. In the following description, presenteeism as a parameter is simply referred to as presenteeism.

Presenteeism corresponds to the first degree of this disclosure. An example of a specific question about presenteeism would be, "How well do you think you are performing at work these days, compared to when you had no health problems? Please answer as a percentage. ” and the like. In this example, the answer results for presenteeism are given in the form of percentages.

Absenteeism means a state in which a person is less productive in relation to work due to lateness or absenteeism due to health conditions. Absenteeism as a parameter is a parameter that indicates the extent to which a worker's productivity is reduced due to late arrival, early departure, or absence due to health conditions. In the following description, absenteeism as a parameter is simply referred to as absenteeism.

Absenteeism corresponds to the second degree of this disclosure. Specific examples of questions about absenteeism include, "In the last ○ months, how late, left early, or absent from work? Please answer the total number of hours." The period during which the questions are asked may be appropriately set based on the interval between questions to the personnel, and may be, for example, 1 month, 3 months, 6 months, or 1 year. Incidentally, absenteeism as a parameter may be included in the attendance data described above. In the following description, absenteeism as a parameter is the total time of absence due to tardiness, leaving early, or absenteeism per month.

The unpaid overtime hours are values that indicate the length of overtime hours that are not counted as working hours. The unpaid overtime hours as a parameter may be obtained by self-reporting of the personnel, or may be included in the attendance data described above, for example.

Telecommuting time is a value that indicates the time spent working at each employee's home, cafe, library, rental office, etc., rather than at the organization's office. The telecommuting time as a parameter may be obtained by self-reporting of the personnel, or may be included in the attendance data described above, for example.

The subjective degree of quantitative burden is a value that indicates the degree to which an employee perceives the amount of work as a burden. The subjective quantitative burden may be included in the stress check results described above.

The degree of discretion is a value that indicates the degree to which an employee feels that he/she can perform work at his or her own discretion.

The workplace relationship is a value that indicates how comfortable the person's relationship at work is. Workplace relationships as a parameter may be included in the stress check results described above.

The subjective physical condition is a value that indicates the degree to which the employee feels that his or her recent physical condition is good.

Subjective happiness is a value that indicates the degree to which an employee feels happy as a result of belonging to an organization and working.

Note that the parameters included in the answer result information described above are only examples, and the answer result information may include more parameters.

The estimating unit 142 estimates the amount of loss caused by a reduction in labor productivity of the employee due to the health condition of the employee, based on at least part of the health condition data described above. In the present embodiment, the estimator 142 estimates the amount of loss using a predetermined analysis means for presenteeism, absenteeism, and unpaid overtime hours described above. For example, artificial intelligence (AI) is used as the predetermined analysis means.

The estimation unit 142 estimates the amount of loss for each member belonging to the organization, and estimates the total amount of loss, which is the amount of loss for the entire organization. The estimating unit 142 may estimate the total amount of loss by totaling the amounts of loss of all personnel belonging to the organization, for example.

The estimating unit 142 calculates a presenteeism loss Lp as a loss amount related to presenteeism by the following formula (1). The presenteeism loss Lp is an estimated amount of loss caused by presenteeism in one year, and is an example of the first degree of the present disclosure. In equation (1), the value of presenteeism as an answer result is represented by p (%), and the average annual income of employees is represented by yi (yen, dollar, euro, etc.).
Lp = (100-p) x yi (1)

The estimation unit 142 also calculates an absenteeism loss La as a loss amount related to absenteeism by the following equation (2). The absenteeism loss La is an estimate of the amount of loss incurred in one year due to absenteeism and is an example of the second degree of the present disclosure. In formula (2), the value of absenteeism as the answer result is a (total hours per month), and the income amount per hour of the staff (average annual income converted to per hour) is hw (yen, dollar, euro, etc.).
La=a×hw×12 (2)
In the formula (2), a×hw corresponds to one month's loss, so by multiplying by "12", 12 months=1 year's worth is calculated.

Then, the estimator 142 estimates the total loss amount Lt based on the presenteeism loss Lp and the absenteeism loss La. The estimation unit 142 may calculate the loss amount Lt by simply adding the presentity loss Lp and the absenteeism loss La, or using the presentity loss Lp and the absenteeism loss La to calculate the loss amount Lt. It may be calculated by a scientific method.

The factor estimating unit 143 estimates factors that greatly affect the amount of loss based on at least part of the health condition data described above and the amount of loss estimated by the estimating unit 142 . In the following description, the factors estimated by the factor estimation unit 143 are referred to as health problem factors.

The factor estimating unit 143 compares, for example, the past health condition data of each employee with the health condition data newly acquired by the acquiring unit 141, and determines that, among the changing parameters, the impact on the amount of loss is large. Extract possible parameters. The factor estimating unit 143 may use, for example, a pre-created trained model for estimating factors that have a large impact on the amount of loss, and extract parameters that are factors. A trained model created in advance is stored in the storage unit 13, for example.

A method for extracting parameters that are considered to have a large impact on the amount of loss will be described with specific examples.

As described above, the amount of loss is calculated based on the presenteeism loss related to presenteeism and the absenteeism loss related to absenteeism. Then, presenteeism as a parameter is acquired based on the results of answers to questions asked to personnel. Presenteeism is the answer to the question, "How well do you think you are performing at your job these days compared to when you had no health problems?" It is a value that fluctuates depending on the state of health (such as sickness or fatigue). In other words, various health parameters of personnel at the time of answering questions on presenteeism have a large impact on presenteeism as a parameter and, in turn, on presenteeism loss. .

Based on the above, by using the following means, it is possible to extract parameters that have a large impact on presenteeism loss and, in turn, a large amount of loss.

(Means 1)
A multiple regression model is constructed with each parameter of the health condition data of multiple personnel as an explanatory variable and the loss amount as an objective variable. Extract.

(Means 2)
We constructed a model using a decision tree algorithm such as a decision tree, random forest, gradient boosting method, etc. with each parameter of the health condition data of multiple personnel as an explanatory variable and the loss amount as the objective variable, and obtained variable importance Based on the degree or partial dependence graph, parameters that have a large impact on the loss amount of the target personnel are extracted.

(Means 3)
By estimating the graph of the network structure between health condition data parameters using a Bayesian network, we calculate the probability of which parameter has an impact on the loss amount through what path, and determine the parameter with the greatest impact. Extract.

Returning to the description of FIG. The calculation unit 144 calculates various evaluation values for evaluating the health condition based on at least part of the health condition data. Examples of evaluation values include objective happiness, compatibility, retirement risk, compatibility after transfer, happiness after transfer, and the like. Note that the calculation unit 144 further calculates an overall evaluation value, which is an evaluation value for the entire organization, based on the evaluation value for each member belonging to the organization. The overall evaluation value will be described later in the health evaluation process for the entire organization (see FIG. 6).

Objective happiness is a value that indicates the objectively estimated degree of happiness of an employee by working in an organization. The calculation unit 144 calculates the objective happiness level based on at least one of gender, age group, attendance data, stress check result, and answer result, for example. As a more detailed example, the calculator 144 uses the following parameters to calculate the objective happiness level. That is, the calculation unit 144 uses the net working hours and overtime hours from the attendance data. In addition, the calculation unit 144 calculates, from the stress check results, a value indicating how much support is received from the boss, a value indicating how much support is received from colleagues, and values from family and friends. A value that indicates the extent to which support can be received for work is used. Also, the calculation unit 144 uses presenteeism, absenteeism, and human relations in the workplace from the answer results. Note that the calculation unit 144 may separately calculate the objective happiness level related to work and the objective happiness level related to private life.

More specifically, the calculation unit 144 calculates the objective happiness level D _happiness , for example, using the following formula (3).

D _happiness = α ₁ × V _sex + α ₂ × V _age + α ₃ × D _attendance + α ₄ × R _stress + α ₅ × R _reply (3)

Thus, the calculation unit 144 calculates the objective happiness level by linearly combining each parameter. Note that α ₁ , α ₂ , α ₃ , α ₄ , and α ₅ are coefficients that are appropriately set. The calculation unit 144 may use a coefficient set by the administrator, or have the administrator input the validity of the objective happiness calculated in the past, and bring the objective happiness with high validity closer to the calculated coefficient. The coefficient may be determined as follows.

In equation (3), V _sex is a dummy variable representing sex, V _age is a dummy variable representing age group, D _attendance is attendance data, R _stress is a stress check result, and R _reply is a response result.

The attendance data D _attendance may be calculated, for example, by dividing the net working hours for one year by the prescribed working hours for one year. Also, the stress check result R _stress may be calculated, for example, as the total score of the three areas included in the stress check among the stress check results of the target personnel. Also, the answer result may be calculated as, for example, the total score of all the items of the scores pre-assigned to each answer to the question item.

In addition to the parameters shown in the above formula (3), the calculation unit 144 also calculates objective happiness evaluations from co-workers and superiors, communication tools such as chats used for business in the organization, and the number of target personnel. The objective happiness may be calculated by further considering happiness evaluation based on written content and emotional evaluation based on the face image of the target person taken with a camera or the like.

The calculation unit 144 may indicate the objective happiness level, for example, by how many points it is out of 100 points, or by a plurality of stages (for example, high, medium, low) depending on whether it is above or below a predetermined reference value. 3 steps).

The degree of compatibility is a value that indicates the compatibility between personnel and an organization. More specifically, the degree of compatibility is a value that indicates the degree to which the ability is utilized or the degree to which the ability is demonstrated when an employee works in an organization. The calculation unit 144 calculates the degree of compatibility based on at least one of gender, age group, attendance data, job evaluation results, stress check results, answer results, and objective happiness, for example. As a more detailed example, the calculator 144 uses the following parameters to calculate the degree of compatibility. That is, the calculation unit 144 uses the paid acquisition rate of the attendance data. In addition, the calculation unit 144 calculates, among the stress check results, a value indicating how much work load is a burden, a value indicating how much work can be controlled by oneself, and how much support is received from a boss. Use a value that indicates whether you are able to do it, and a value that indicates how much you can receive support from your colleagues. Further, the calculation unit 144 uses subjective happiness and subjective physical condition among the answer results.

The degree of compatibility includes the subjective degree of compatibility calculated using the subjective degree of happiness of each member and the objective degree of compatibility calculated using the objective degree of happiness.

More specifically, the calculation unit 144 calculates the degree of compatibility D _compatible , for example, using the following equation (4).

D _compatible = β ₁ × V _sex + β ₂ × V _age + β ₃ × D _attendance + β ₄ × R _stress + β ₅ × R _reply + β ₆ × R _operation + β ₇ × D _skill (4)

Thus, the calculation unit 144 calculates the objective happiness level by linearly combining each parameter. β ₁ , β ₂ , β ₃ , β ₄ , β ₅ , β ₆ , and β ₇ are coefficients set appropriately. The calculation unit 144 may use a coefficient set by the administrator, or may cause the administrator to input the validity of the degree of compatibility calculated in the past, so that the objective happiness with a high degree of validity approaches the calculated coefficient. A coefficient may be determined.

In formula (4), V _sex is a dummy variable representing gender, V _age is a dummy variable representing age group, D _attendance is attendance data, R _stress is stress check result, R _reply is response result, and R _operation is work evaluation result. , D _skill is the skill fitness.

The attendance data D _attendance may be calculated, for example, by dividing the net working hours for one year by the prescribed working hours for one year. Also, the stress check result R _stress may be calculated as the total score of the three areas included in the stress check among the stress check results of the target personnel. Also, the answer result may be calculated as the total score of all the items of the scores pre-assigned to each answer to the question item.

Also, the business evaluation result R _operation can be obtained by converting the business evaluation of the target personnel into a score (score conversion between 0 and 100). Also, the skill matching degree D _skill can be obtained by scoring the matching rate between the skill possessed by the target employee and the skill required for the work that the employee is in charge of. The skill suitability D _skill is an objective score given by the superior or colleague of the target personnel.

The calculation unit 144 may indicate the degree of compatibility by, for example, how many points out of 100 points, or may indicate it in a plurality of stages depending on whether it is greater than or less than a predetermined reference value.

The degree of retirement risk is a value that indicates the risk that the employee will retire in the near future. The calculation unit 144 calculates the retirement risk level based on, for example, at least one of the job evaluation result, the stress check result, the response result, and the objective happiness level. As a more detailed example, the calculator 144 uses the following parameters to calculate the retirement risk level. That is, the calculation unit 144 uses, from the stress check results, a value indicating how much work is subjectively burdensome, and a value indicating how much work can be controlled by oneself. Also, the calculation unit 144 uses absenteeism among the answer results.

More specifically, the calculation unit 144 calculates information about past retirees in the organization (information including at least one of job evaluation results, stress check results, answer results, and objective happiness levels, and the date of retirement). Then, a retirement probability prediction model after N months is constructed, and the output value when each parameter of the target personnel is input to the prediction model is used as the retirement risk degree.

The calculation unit 144 may indicate the degree of retirement risk by, for example, how many points it is out of 100 points, or may indicate it in a plurality of stages depending on whether it is greater than or less than a predetermined reference value.

Post-transfer happiness is an objective degree of happiness when it is assumed that an employee has been transferred to another department, another work location, or another occupation. The calculation unit 144 calculates post-transfer happiness based on at least one of gender, age group, attendance data, stress check results, and answer results, and data on the workplace environment of the transfer destination. For example, the information about the working environment of the transfer destination is input in advance by the manager and stored in the storage unit 13, or is input by the manager at the time of calculating the post-transfer happiness level.

More specifically, the calculation unit 144 may calculate the post-change happiness level as follows. That is, the calculation unit 144 predicts the objective happiness based on each parameter such as the department, place of work, occupation, etc. of each employee based on the objective happiness of all employees in the organization to which the target employee belongs. An objective happiness prediction model may be constructed, and the output value when the target personnel parameter is input to the prediction model is calculated as the post-transfer happiness.

The degree of compatibility after transfer is the degree of compatibility when it is assumed that the personnel has been transferred to another department, another place of work, another occupation, or the like. The calculation unit 144 calculates post-transfer compatibility based on at least one of gender, age group, attendance data, job evaluation results, stress check results, answer results, and objective happiness, as well as information on the workplace environment of the transfer destination. Calculate degrees.

More specifically, the calculation unit 144 may calculate the post-change compatibility degree as follows. That is, the calculation unit 144 is a compatibility prediction model that predicts the compatibility based on each parameter such as the department, work location, and occupation of each employee based on the compatibility of all employees in the organization to which the target employee belongs. is constructed, and the output value when the parameter of the target personnel is input to the prediction model is calculated as the degree of compatibility after the transfer.

Returning to the description of FIG. The output unit 145 outputs various evaluation values calculated by the calculation unit 144 . The output destination to which the output unit 145 outputs the evaluation value is the above-described terminal used by the administrator.

[Health evaluation process]
Next, the health evaluation process executed by the information processing apparatus 1 will be described with reference to FIGS. 4 to 6. FIG. First, with reference to FIG. 4, health evaluation processing for an individual will be described.

In step S<b>1 , the information processing device 1 acquires health condition data regarding one person to be processed from the user terminal 2 , another server device, or the like. The health condition data includes, as described above, the gender, age group, work evaluation results, health checkup results, stress check results, attendance data, and answers to questions directly asked to the personnel. be

In step S<b>2 , the information processing apparatus 1 calculates the presentity loss and absenteeism loss of the person to be processed, thereby estimating the amount of loss related to the individual person. The amount of loss calculated in step S2 is, as described above, the amount of loss caused by a decrease in the productivity of the employee's work due to the health condition of the employee, and uses at least part of the health condition data. It is estimated by calculating presenteeism loss and absenteeism loss.

In step S3, the information processing apparatus 1 estimates health problem factors for each employee using a pre-prepared trained model or the like based on at least a portion of the health condition data and the amount of loss estimated in step S2. do.

In step S4, the information processing apparatus 1 calculates the degree of compatibility between the person to be processed and the organization to which the person belongs, based on at least part of the health condition data. The health condition data used in step S4 includes the subjective happiness answered by the person to be processed. degree of compatibility.

In step S5, the information processing apparatus 1 calculates the retirement risk degree of the processing target personnel based on at least part of the health condition data.

In step S6, the information processing device 1 outputs the evaluation results generated in steps S2 to S5. Note that the output destination is the terminal used by the administrator, as described above. Specifically, the information processing apparatus 1 may output the evaluation result by sending an e-mail or the like with data indicating the evaluation result attached.

Note that the information processing apparatus 1 may output all the evaluation results to an output destination when the health condition data regarding the person to be processed is acquired in step S1. The evaluation result may be output to the output destination only when the Examples of predetermined conditions include a loss amount greater than a threshold value, a degree of compatibility lower than a predetermined threshold value, or a retirement risk degree higher than a predetermined threshold value.

By outputting the health evaluation results of such individuals and targets to the administrator, the administrator can identify employees in the organization who are in poor health and who are likely to have troubles at work. , or who are at high risk of leaving the organization. As a result, the manager can take measures such as arranging measures to improve the health condition of the personnel, contacting the superiors of the personnel, etc., in order to resolve problems that the personnel are presumed to be having. It becomes possible.

In addition, in the health evaluation process, the information processing apparatus 1 may output advice for improving the evaluation result of the employee in addition to performing the health evaluation of the employee. For example, if there is an employee with a relatively low degree of compatibility, the information processing device 1 determines which of the parameters used to calculate the degree of compatibility, namely attendance data, stress check results, answer results, job evaluation results, and skill suitability It is only necessary to determine whether it is connected to the low degree of compatibility, and output a message to improve the parameter that is the cause of the low degree of compatibility. Note that the advice or message output by the information processing apparatus 1 is appropriately selected from, for example, a plurality of advices or messages that have been input in advance so as to improve the parameter that is the cause of the low compatibility. Just do it. Alternatively, the information processing apparatus 1 constructs a learning model, which inputs parameters and outputs advice as to what kind of advice should be given in order to improve the degree of compatibility, based on past data, Advice may be generated using the learning model.

In addition, the information processing apparatus 1 automatically outputs a proposal as to whether or not each employee should be transferred and to where they should be transferred, based on the post-transfer happiness level and the post-transfer compatibility level. You may make it

For example, when any of the health evaluation results is low, the information processing apparatus 1 automatically sets an interview between a predetermined person other than the manager and the relevant personnel, and sends an email to the occupational health nurse and the relevant personnel. You can let me know. For example, if the objective happiness level is lower than a predetermined value, an interview between the occupational health nurse and the employee may be set. Also, for example, if the degree of compatibility is lower than a predetermined value, an interview between the person in charge of the personnel department and the relevant employee may be set.

Next, referring to FIG. 5, the health evaluation processing for the entire organization will be described.

In step S11, the information processing apparatus 1 sums up the loss amount of all the personnel belonging to the organization and estimates the total loss amount.

In step S12, the information processing apparatus 1 calculates the health problem factors of the entire organization based on at least a portion of the health condition data of all employees belonging to the organization and the total loss amount obtained by summing in step S11. presume. Health problem factors for the entire organization are factors that should be improved for the health of all personnel belonging to the organization. As in step S3, the information processing apparatus 1 may estimate health problem factors of the entire organization using a pre-prepared learned model or the like.

In step S13, the information processing apparatus 1 calculates an objective degree of happiness for each member belonging to the organization based on at least part of the health condition data. In addition, in the health evaluation processing for individuals shown in FIG. 4, the subjective degree of happiness is included in the answer results from each member, so the objective degree of happiness is not calculated. On the other hand, in the health evaluation processing for the entire organization shown in FIG. 5, in order to calculate a more objective degree of compatibility in the latter step S14, the objective degree of happiness for each member is calculated.

In step S<b>14 , the information processing apparatus 1 uses at least part of the health condition data and the objective happiness level to calculate the degree of compatibility for each member of the organization.

In step S15, the information processing device 1 outputs the evaluation results generated in steps S11 to S14.

By outputting such health evaluation results for the entire organization and targets to the administrator, the administrator can think about what improvement measures should be presented to the entire organization. can.

The information processing apparatus 1 may aggregate the health evaluation results of employees belonging to an organization who belong to different industries, occupations, or business categories, for example, and output the results to the administrator. As a result, the manager can grasp the health evaluation result for each industry, occupation, or business condition of his/her own organization, and can consider improvement for the industry, occupation, or business condition with a lower evaluation value than others.

Furthermore, with reference to FIG. 6, the health evaluation process will be described assuming that the personnel belonging to the organization has been transferred within the organization.

In step S21, the information processing apparatus 1 acquires the objective happiness level and compatibility level generated in steps S13 and S14 of FIG. 5 regarding the personnel to be processed.

In step S22, the information processing apparatus 1 acquires information about the environment of the transfer destination when it is assumed that the processing target personnel is transferred.

In step S23, the information processing apparatus 1 calculates post-change happiness based on at least part of the health condition data, objective happiness, and information about the environment of the change destination.

In step S<b>24 , the information processing apparatus 1 calculates post-change compatibility based on at least part of the health condition data, the post-change happiness level, and information about the environment of the transfer destination.

In step S25, the information processing device 1 outputs the evaluation results generated in steps S22 to S24.

By outputting the results of the health evaluation of the individual assumed to be transferred to the administrator, the administrator can objectively determine whether or not to transfer each employee. become.

INDUSTRIAL APPLICABILITY The present disclosure is useful in a health condition evaluation system that evaluates the health conditions of employees who work for an organization.

100 health evaluation system 1 information processing device 11 communication unit 12 input/output unit 13 storage unit 14 control unit 141 acquisition unit 142 estimation unit 143 factor estimation unit 144 calculation unit 145 output unit 2 user terminal

Claims (14)

an acquiring unit that acquires health condition data having a plurality of parameters including at least presenteeism and absenteeism of a person working in an organization;
Presenteeism loss as said loss in respect of said presenteeism of said person and absenteeism as said loss in respect of said absenteeism of said person an estimating unit for estimating by calculating at least one of the tism losses;
a calculation unit that calculates an evaluation value for evaluating the health condition of the person based on at least part of the plurality of parameters or at least one of the loss amount;
Compare the past health condition data of each employee with the newly obtained health condition data, and improve the parameters that have a large impact on the loss amount among the changing parameters for the health of the employee. a factor estimating unit for extracting factors to be
A health assessment system comprising: The calculation unit uses the objective happiness level D _happiness , which is a degree that objectively indicates how happy the person is by belonging to the organization and performing the work, as the evaluation value in the formula (1 ),
The health condition evaluation system according to claim 1.
D _happiness = α ₁ × V _sex + α ₂ × V _age + α ₃ × D _attendance + α ₄ × R _stress + α ₅ × R _reply (1)
Here, V _sex is a dummy variable representing sex, V _age is a dummy variable representing age group, D _attendance is attendance data, R _stress is stress check result, R _reply is answer result, and α ₁ -α7 are coefficients. The calculation unit calculates a retirement risk degree indicating a degree of risk that the employee will retire within a predetermined period based on at least a part of the objective happiness and the health condition data of past retirees. Calculated as the evaluation value using a retirement probability prediction model constructed based on health condition data,
The health condition evaluation system according to claim 2. The calculation unit calculates, as the evaluation value, a degree of compatibility D _compatible that indicates the degree to which the ability of the person is being utilized when the person works in the organization, using formula (2).
The health condition evaluation system according to claim 1.
D _compatible = β ₁ × V _sex + β ₂ × V _age + β ₃ × D _attendance + β ₄ × R _stress + β ₅ × R _reply + β ₆ × R _operation + β ₇ × D _skill (2)
However, V _sex is a dummy variable representing gender, V _age is a dummy variable representing age group, D _attendance is attendance data, R _stress is stress check result, R _reply is answer result, _Operation is work evaluation result, D _skill is Skill fitness, β ₁ -β ₇ are coefficients. The acquisition unit acquires the health condition data including the answer results of the person's answers to a plurality of question items related to the health condition of the person.
The health condition evaluation system according to any one of claims 1 to 4. The acquisition unit acquires the answer result to the question item regarding the first degree indicating the degree of deterioration of the ability related to the work due to the health condition while performing the work. do,
The health condition evaluation system according to claim 5. The acquisition unit acquires the answer result to the question item regarding a second degree indicating the extent to which the productivity of the employee has decreased due to lateness, early leaving, or absence due to the health condition. do,
The health condition evaluation system according to claim 5. The acquisition unit acquires the answer result to the question item regarding the unpaid overtime hours, which indicates the length of overtime hours not recorded as working hours.
The health condition evaluation system according to claim 5. The acquisition unit acquires the answer result to the question item about the working-at-home hours indicating the length of working-at-home.
The health condition evaluation system according to claim 5. The acquisition unit acquires the health condition data including results of health examinations performed on the personnel.
The health condition evaluation system according to claim 1. The calculation unit calculates, based on a change in the environment when the personnel is transferred within the organization, a transfer destination happiness index indicating the degree of happiness of the personnel at the transfer destination by performing the labor. Objective happiness that predicts the degree of objective happiness based on each parameter such as department, place of work, job type, etc. Calculated as the evaluation value using a degree prediction model,
The health condition evaluation system according to claim 2. The calculation unit calculates a post-transfer compatibility value that indicates the degree to which the personnel's ability related to labor is utilized at the transfer destination based on changes in the environment when the personnel is transferred within the organization. , using a compatibility prediction model that predicts the compatibility based on each parameter such as the department, work location, occupation, etc. of each person, which is pre-built based on the compatibility of all personnel belonging to the organization, calculated as an evaluation value,
The health condition evaluation system according to claim 4. the computer
Acquiring health condition data having a plurality of parameters including at least presenteeism and absenteeism of a person working in an organization,
Presenteeism loss as said loss in respect of said presenteeism of said person and absenteeism as said loss in respect of said absenteeism of said person Estimated by calculating at least one of the tism losses,
calculating an evaluation value for evaluating the health condition of the person based on at least part of the plurality of parameters or at least one of the loss amount;
Compare the past health condition data of each employee with the newly obtained health condition data, and improve the parameters that have a large impact on the loss amount among the changing parameters for the health of the employee. extracted as a factor to be
Health status assessment method. A procedure for obtaining health condition data regarding the health condition of personnel who belong to the organization and work;
Presenteeism loss as said loss in respect of said presenteeism of said person and absenteeism as said loss in respect of said absenteeism of said person a procedure for estimating by calculating at least one of the tism losses;
a step of calculating an evaluation value for evaluating the health condition of the person based on at least part of the plurality of parameters or at least one of the loss amount;
Compare the past health condition data of each employee with the newly obtained health condition data, and improve the parameters that have a large impact on the loss amount among the changing parameters for the health of the employee. A procedure for extracting as a factor to be
A program that causes a computer to run

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