JP2006301893A - Health business supporting system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To solve the problem that since health guidance is carried out in the unit of a group, such as a business establishment, a workplace, and a local region to which an insured person belongs, a characteristic for each group is compared and considered, then the group where high efficiency can be expected is selected, and an appropriate business establishment should be selected within the resource of health business which the insured person owns. <P>SOLUTION: A health business supporting system has: a group comparison means for comparing the number of predicted patients and predicted medical expenses, or suppression efficiency of the patients and suppression efficiency of the medical expenses attributed to the health guidance for each group; and a resource comparison means for selecting a group within the resources of a health nurse to select a group to which the health business is applied. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a health business support system that supports the management of a health business carried out by an insurer, a company, a local government or the like.

  Insurers are aiming to improve the health of insured persons and to curb soaring medical expenses, and conduct health services such as health guidance by health nurses for insured persons. As a method for supporting a health service by an insurer, for example, Patent Document 1 introduces a system that evaluates changes in indices before and after the application of a health intervention service using health degree, medical expenses, and the like as indices.

JP2004-341611

In health services, for example, health guidance given by public health nurses, etc. is implemented in units of groups such as establishments, workplaces, and regions where the insured belongs, so after comparing and examining the characteristics of each group, It is necessary to implement for groups that can be expected to be highly effective. However, the above-mentioned conventional example does not consider the means necessary for this.
In addition, since the resources of insurers, such as the number of public health nurses and the budget that can be secured, are limited, it is necessary to operate the health business efficiently and effectively. However, the above-mentioned conventional example does not consider the means necessary for this.

  In order to solve the above-mentioned problems, the health business support system of the present invention is a health business support system that supports the management of a health business carried out by an institution such as an insurer, a company, or a local government. Group information management means for managing information on groups such as regions, medical examination information management means for managing medical examination information generated when individuals belonging to the group have undergone medical examinations, and groups as health businesses An implementation information management means for managing the results of the health guidance implemented; an onset prediction information management means for managing the onset prediction information indicating the relationship between the health status obtained from the items of the medical examination information, the implementation results of the health guidance and the onset of the disease; The onset prediction means for calculating the predicted number of patients by group from the individual's health checkup results and the onset prediction information, and the group for outputting the output to the output means by comparing the predicted number of onset by group. Comparison means is characterized by having a group selecting means for specifying a group to carry out health guidance from the group.

  The health business support system of the present invention is a health business support system that supports the management of health business carried out by organizations such as insurers, companies, and local governments. Group information management means to manage information on health, health examination information management means to manage medical examination information generated by individuals belonging to the group, and the results of health guidance implemented by the group as a health business Information management means for managing medical expenses, information on health expenses obtained from the items of medical examination information, medical expenses prediction information managing means for managing the medical expenses prediction information indicating the relationship between the performance of the health guidance and the medical expenses, A medical cost predicting means for calculating a predicted medical cost for each group from the diagnosis result and the medical cost prediction information; a group comparing means for comparing the medical expenses for each group and outputting them to the output means; It is characterized by having a group selecting means for specifying a group to carry out health guidance from the.

  The health business support system of the present invention is a health business support system that supports the management of health business carried out by organizations such as insurers, companies, and local governments. Group information management means to manage information on health, health examination information management means to manage medical examination information generated by individuals belonging to the group, and the results of health guidance implemented by the group as a health business Information management means for managing the onset, onset prediction information management means for managing the onset prediction information indicating the relationship between the health status obtained from the items of the checkup information, the performance of the health guidance, and the onset of the disease, Medical expenses indicating the relationship between the health status obtained from the items of health checkup information, the results of health guidance, and medical costs Medical cost prediction information management means for managing measurement information, medical cost prediction means for calculating predicted medical expenses for each group based on individual medical checkup results and medical cost prediction information, and comparison of the number of patients with disease occurrence and predicted medical expenses for each group And a group comparing means for outputting to the output means, and a group selecting means for designating a group for performing health guidance from among the groups.

  Furthermore, the health business support system according to the present invention includes resource information management means for managing information on personnel who perform health guidance provided by a group as a health business, information on personnel managed by the resource management means, and group designation means. It is characterized by having resource comparison means for comparing the information of personnel required by the implementation of health guidance for the designated group and outputting it to the output means.

  Further, the health business support system of the present invention is based on the health checkup information managed by the health checkup information management means and the health guidance results managed by the implementation information management means. It is characterized by having an onset prediction information creating means for calculating the disease incidence rate and recording it on the onset rate prediction information management means.

  Furthermore, the health business support system of the present invention comprises: a receipt information management means for managing medical cost information generated by an individual belonging to an organization who has visited a medical institution due to illness; and a medical examination information managed by a medical examination information management means; The medical expenses are estimated by calculating the medical expenses according to the health status based on the medical examination information and the results of the health guidance based on the results of the health business managed by the implementation information management means and the medical expenses information managed by the receipt information management means. It is characterized by having medical cost prediction information creating means for recording in the information management means.

  Furthermore, in the health business support system of the present invention, the onset predicting means obtains the predicted number of onset patients when the onset prediction information management means does not carry out health guidance by obtaining the disease incidence rate of persons who have no health guidance performance. Calculate the number of people who have a history of health guidance from the onset prediction information management means and calculate the number of predicted cases when the health guidance is given by group, and the group comparison means implements health guidance. It is characterized in that the groups are arranged and displayed on the output means in descending order of the difference between the predicted number of onset patients when there is not and the predicted number of onset persons when health guidance is implemented.

  Further, in the health business support system of the present invention, the medical cost predicting means obtains the predicted medical expenses when the medical expenses are not implemented by acquiring the medical expenses of a person who has no health guidance performance from the medical expenses prediction information managing means. Calculate and calculate the expected medical expenses for each group by obtaining the medical expenses of persons who have a history of health guidance from the medical cost prediction information management means and carrying out health guidance, and the group comparison means implements health guidance. It is characterized in that the groups are arranged and displayed on the output means in descending order of the difference between the predicted medical cost when there is not and the predicted medical cost when health guidance is implemented.

  Furthermore, in the health business support system of the present invention, the group comparison means is configured to calculate a difference between the predicted number of onset patients when the health guidance for each group is not implemented and the predicted number of onset patients when the health guidance is implemented. Among them, the onset suppression efficiency divided by the number of persons providing health guidance is calculated and arranged on the output means in descending order of onset suppression efficiency.

  Furthermore, in the health business support system of the present invention, the group comparison means calculates the difference between the medical costs when the health guidance for each group is not implemented and the medical costs when the health guidance is implemented within the group. The medical cost suppression efficiency divided by the number of people who conduct the instruction is calculated, and the medical cost suppression efficiency is arranged in the descending order and displayed on the output means.

  Furthermore, in the health business support system of the present invention, the group selection means uses the resource comparison means to confirm that it is within the range of personnel for health guidance, and selects groups in descending order of onset suppression efficiency. It is said.

  Furthermore, in the health business support system of the present invention, the group selection means uses the resource comparison means to confirm that it is within the range of the staff of health guidance, and selects groups in descending order of medical cost control efficiency. It is said.

  Further, in the health business support system of the present invention, the resource information management means manages information related to the cost required for health guidance, and the resource comparison means performs the medical cost control effect and the health guidance when the health guidance is implemented. It is characterized in that the costs when implemented are compared and displayed on the output means.

In the health business support system of the present invention, the onset prediction information management means is characterized by managing the incidence of diabetes by sex, stratified age, stratified fasting blood glucose level, and health guidance presence or absence Health business support system.
In the health service support system of the present invention, the medical cost prediction information management means manages medical costs related to diabetes according to gender, stratified age, stratified fasting blood glucose level, and health guidance presence / absence. It is characterized by that.

  In the health service support system of the present invention, the group comparing means selects at least one means from means for calculating information for each of the plurality of groups including the onset predictor and the medical expenses predictor. The result is output to the output means.

  The health business support system of the present invention predicts the number of patients with the onset prediction means, and compares the number of patients with the group comparison means, the effect of suppressing onset by health guidance, medical expenses, the effect of suppressing medical expenses by health guidance, etc. Therefore, there is an effect that health guidance can be implemented for a group that can be expected to have a higher effect after comparing and examining the characteristics of each group.

  Further, the health business support system of the present invention displays the necessary resources when the health guidance is implemented for the selected group by the resource comparison means. Among them, there is an effect that the health business can be operated efficiently and effectively.

  Hereinafter, the best mode for carrying out the present invention will be described in detail with reference to the drawings. In the following explanation, an insurer uses this health business support system as an organization that implements a health business, and there are multiple companies and business offices belonging to the insurer, and employees and family members of the company or business office. Take as an example an insured person. Insurers also dispatch health nurses to establishments where there are many people who are at high risk for lifestyle-related diseases as health services, and implement health services to prevent the onset of lifestyle-related diseases by providing health guidance. Take the case as an example. Further, the health business support system of the present invention will be described as an example in which the insurer's person in charge is used in a situation where a business or business establishment that performs health guidance is selected. The person in charge selects the business establishment once a month, and selects the establishment that provides health guidance in the following month. An example will be given in which the selection work is carried out in July 2004 and the office to go to health guidance in August of the following month is selected.

First, the configuration example of FIG. 1, the information example of FIGS. 2 to 7 and the example of the screen of FIG. 8 will be described, and the flow of processing by this configuration will be described using the example of the flowcharts of FIGS.
FIG. 1 is a diagram illustrating a configuration example of a health service support system according to the present invention. The health service support system is operated by a computer and includes an input device 101 such as a mouse and a keyboard, an output device 102 such as a display and a printer, a CPU 103 that calculates and executes a program, and a storage device 104 such as a hard disk and a memory. The storage device 104 manages means comprising programs and data described below, which are read out and executed by the CPU as necessary. The storage device 104 includes a group information management unit 105 that manages the IDs and names of companies and establishments, a resource information management unit 106 that manages information about resources such as the staff of a public health nurse for an insurer to provide health guidance, Onset prediction information management means 107 that is the basis for predicting the onset of lifestyle-related diseases, medical expense prediction information management means 108 that is the basis for predicting medical expenses related to lifestyle-related diseases, and the insured assists the insurer, etc. The medical checkup information management means 109 that manages the medical checkup information that records the results of the medical checkup, such as the Ningen Dock, and the information on the results of the health guidance implemented by the insurer for the insured are recorded. Implementation information management means 110, receipt information management means 111 relating to medical expenses incurred by the insured who visited the hospital, personal information management means 112 for managing the name of the insured individual, The person in charge of health guidance And a selection information managing means 113 for managing the selected information about the plant as 施先. Further, the storage means 104 is a group comparison means 115 for comparing the characteristics of the establishments, and a resource comparison for comparing the health nurse personnel required for the health guidance of the selected establishment with the health nurse personnel possessed by the insurer. Means 116, onset prediction means 117 for predicting the number of people with lifestyle-related diseases according to establishment, medical expense prediction means 118 for predicting the occurrence of medical expenses for each establishment, and health examination information management means 109 The onset prediction information creation means 119 that calculates information for predicting the onset from the diagnosis information and the implementation information of the health guidance managed by the implementation information management means 110, and the health examination information and implementation managed by the health examination information management means 109 Implementation of health guidance and medical cost prediction information creation means 120 for calculating information for predicting medical expenses from information on health guidance managed by information management means 110 and information on medical expenses managed by receipt information management means 111 Designation of establishment And a earthenware pots group selecting means 122.

  FIG. 2 is a diagram illustrating an example of information related to a business office managed by the group information management unit 105. As the information regarding the establishment, information such as the establishment ID 201, the establishment name 202, the location 203, the contact 204 such as a telephone number, and the number of employees 205 of the establishment are managed.

  FIG. 3 is a diagram showing an example of information related to the insured person managed by the personal information management means 112. As shown in FIG. It manages an ID 301 that identifies an individual, a name 302, a gender 303, and an establishment ID 304 of the establishment to which the individual belongs.

  FIG. 4 is a diagram illustrating an example of the medical examination information managed by the medical examination information management unit 109. In addition to the individual ID 401 that identifies the individual, the checkup date 402, the age 403 at the time of the checkup, etc., the blood glucose level 404, blood pressure 405, BMI406, etc. Information such as a determination 407 regarding diabetes and a determination 408 regarding blood pressure is managed.

  FIG. 5 is a diagram illustrating an example of implementation information related to the performance of the health guidance managed by the implementation information management unit 110. Information such as an individual ID 501 for identifying an individual who has received health guidance, an implementation date 502 indicating the date on which health guidance was implemented, and a person in charge 503 indicating a public health nurse who has implemented health guidance are managed.

  FIG. 6 is a diagram illustrating an example of information related to the prediction of the number of affected individuals managed by the onset prediction information management unit 107. Here, as an example of lifestyle-related diseases, diabetes was used as an example, and the incidence of diabetes 604 when health guidance was provided for gender 601, age group 602, blood glucose level 603, and health guidance was not implemented If you are managing a diabetes incidence 605.

  FIG. 7 is a diagram showing an example of information related to the selection of establishments managed by the selection information management unit 113. Information such as the establishment ID 701 for identifying the selected establishment, the date of selection 702, the scheduled date of implementation of health guidance 703, and the person in charge 704 who implements health guidance is managed.

  FIG. 8 is a diagram showing an example of a screen that is displayed on the display of the output device 102 and that selects an establishment that implements health guidance by comparing the predicted number of affected individuals for each establishment. The offices are compared using the table 801 and the graph 802. The table 801 has a selection field 803 for designating establishments that implement health guidance, and designates establishments that implement health guidance by operations such as an operator clicking with a mouse. Furthermore, establishment ID804, establishment name 805, number of establishments by establishment 806, number of predicted cases 807 when health guidance is not implemented, number of forecast cases 808 when health guidance is implemented, suppression of onset of health guidance Display efficiency 809 and so on. In addition, the graph 802 displays the number 810 of cases without health guidance, the number 811 of cases with health guidance, and the onset suppression efficiency 812 for each office. Reference numeral 813 denotes a determination button, which is a button that is clicked when an establishment for carrying out health guidance is specified and determined in the selection field 803. Reference numeral 814 denotes an update button for onset prediction information.

  FIG. 9 is a flowchart showing an example of a process for predicting the number of people with lifestyle-related diseases and selecting a business location where health guidance is performed by comparing business locations in the health business support system of the present invention. When the selection of establishments that provide health guidance is started (901), first, in step 902 for predicting the onset, the number of people who develop diabetes at each establishment is calculated for cases where health guidance is given or not. To do. Details of the prediction method will be described later. Then, in step 903 for comparing groups, a comparison is made between the establishments, and establishments that provide health guidance are selected. Details of the comparison method will be described later. When the decision is made (904), in step 905 for recording selection information, information relating to the selected establishment is recorded in the table of FIG.

  FIG. 10 is a diagram showing an example of detailed processing contents of the onset number predicting step 902, showing an example of processing executed by the onset prediction means 117. When predicting an onset person, the onset prediction unit 117 first acquires the onset prediction information shown in FIG. 6 from the onset prediction information management unit 107 (1001). Next, the medical examination information as shown in FIG. 4 is obtained from the medical examination information management means 109, and the personal information as shown in FIG. 3 is obtained from the personal information management means 112 (1002). Here, the medical checkup date 402 acquires medical checkup information from April 2004 to June 2004, and acquires personal information of each person. Then, a health guidance implementation history as shown in FIG. 5 managed by the implementation information management means 110 is acquired, and information is integrated as to whether each health check-up examinee has received health guidance in the past (1003). Next, the onset prediction information in FIG. 6 includes gender (male and female), age group in their 30s, 40s, and 50s, blood glucose level less than 110, 110 to 125, with and without health guidance, respectively. Since it is hierarchized, it counts how many people are in which hierarchy by establishment based on the obtained medical examination information and implementation information (1004). For example, at ABC Ironworks of establishment IDJ0001, there are 20 men, age 40s, blood glucose level 110-125 and no health guidance, and men, age 40s, blood glucose level 110-125, health guidance Count the number of people that fit all combinations of conditions, such as 15 people. Then, the number of onset patients by group is calculated from the counted number of groups by class and the onset prediction information of FIG. 6 (1005). For example, in ABC Ironworks of establishment IDJ0001, there are 20 men, people in their 40s, blood sugar level 110-125, and no health guidance, so the incidence of 605 in the case where there is no health guidance of the same class condition Multiply by 50% and the estimated number of onset is 10. Also, multiplying the same 20 people by 15% of the incidence 604 when there is health guidance, the predicted number of onset is 3 people. In addition, for men who have been providing health guidance in the past and who have an age of 40s and a blood sugar level of 110 to 125, multiply by 15% of the onset rate 604 when there is health guidance, and the predicted number of onset patients is 2.25. Become a person. From this result, the number of predictive onset patients who did not give health guidance was 12.25 people who did not give health guidance, and predicted onset when health guidance was given to men in the ABC Iron Works, those in their 40s and blood sugar levels 110-125 The number of persons is 5.25, and by adding this to the calculation results of other genders, ages, and blood glucose levels, the number of people who developed health guidance when not providing health guidance for each establishment Calculate

  FIG. 11 is a diagram showing an example of detailed processing contents of the group comparison step 903, and shows an example of processing contents executed by the group comparison means 115. In the group comparison step 903, first, the group comparison means 115 acquires information such as the establishment name and the number of people from the group information management means 105 (1101). The offices are compared with each other by receiving the predicted number of onset patients as a result of the onset predicting step 902 executed by the onset predicting means 117. First, onset suppression efficiency is calculated from the predicted number of onset patients (1102). Here, the difference between the predicted number of people without health guidance and the number of people with health guidance is divided by the number of establishments that need guidance, and the number of people with health guidance is suppressed. Calculate efficiency. For example, if the number of employees at ABC Ironworks is 120, the predicted number of patients with no health guidance is 30, and the predicted number of patients with health guidance is 20 (30-20 ) / 120, the onset suppression efficiency is 0.08 onset suppression per coach. This is requested for each office.

  Next, the group comparison means 115 displays the establishment name, the estimated number of affected persons at the establishment, the onset suppression efficiency, etc. on the display of the output device 102 as a screen as shown in FIG. 8 (1103). In FIG. 8, as described above, as the table 801, the number of persons 805, the predicted number of affected persons 807 and 808, the onset suppression efficiency 809, and the like are displayed in descending order by the onset suppression efficiency for each establishment. The graph 802 also displays the predicted number of onset patients 810 when health guidance is not implemented, the number 811 of episodes when health guidance is implemented, and the onset suppression efficiency 812. The person in charge who selects the establishment that provides health guidance compares the establishments from this screen and selects the establishment (1104). In this case, the person in charge compares the predicted number of patients with onset and the suppression efficiency of the onset, selects DEF food sales with the highest onset suppression efficiency, and clicks the selection field 803 in FIG. Designate the office as the destination. When the enter button 813 is pressed, the group comparison step 903 is ended, and information relating to the selected DEF food sales is recorded in the selection information in FIG.

  As described above, the health business support system of the present invention predicts the number of affected individuals for each establishment by the onset prediction means, compares the establishments by the group comparison means, and displays them in a table or graph. The person who is predicted to have many illnesses is able to give health guidance. In addition, the group comparison means calculates the health-onset suppression rate and displays it in a table or graph in descending order of the onset suppression rate. There is an effect that can be.

Next, an example will be described in which the data shown in FIG. 12, the screen shown in FIG. 13, and the flowcharts shown in FIGS.
FIG. 12 is a diagram illustrating an example of medical cost prediction information managed by the medical cost prediction information management unit 108. For each gender 1201, age group 1202, and blood glucose level 1203, the medical expenses 1204 when there is health guidance and the medical expenses 1205 when there is no health guidance are managed.

FIG. 13 is a diagram showing an example of a screen that is displayed on the display of the output device 102 and that selects an establishment that provides health guidance by comparing predicted medical expenses for each establishment.
The offices are compared using Table 1301 and Graph 1302. Table 1301 shows the selection field 1303 for specifying the place where health guidance is to be implemented, the place ID 1304, the place name 1305, the number of people by place 1306, the expected medical expenses 1307 when not carrying out health guidance, and the health guidance The predicted medical cost 1308 and the medical cost control efficiency 1309 of the health guidance are displayed. The graph 1302 displays the predicted medical cost 1310 when the health guidance is not performed, the predicted medical cost 1311 when the health guidance is performed, the medical cost restraint efficiency 1312, and the like for each establishment. 1313 is a decision button, which is a button that is clicked when establishing a place to conduct health guidance. Reference numeral 1314 denotes a medical cost prediction information update button.

  FIG. 14 is a flowchart showing an example of a process for predicting a medical cost for lifestyle-related diseases and selecting a business location where health guidance is performed by comparing business locations in the health business support system of the present invention. The basic flow is the same as in the case of predicting the onset person in FIG. 9, but the step 1402 for predicting medical expenses and the step 1403 for comparing groups are different, and this is shown in the flowcharts of FIGS. 15 and 16, respectively. I will explain it. FIG. 15 is a flowchart showing detailed processing of the medical cost prediction step. FIG. 16 is a flowchart showing detailed processing of the group comparison step in the case where medical cost prediction is used. When the office selection process based on the medical cost prediction in FIG. 14 is started (1401), the medical cost prediction unit 118 first executes a medical cost prediction step 1402. The medical cost prediction step 1402 performs processing in the order shown in FIG. First, the medical cost prediction unit 118 acquires the medical cost prediction information shown in FIG. 12 from the medical cost prediction information management unit 108 (1501). The medical cost prediction information is obtained for each medical cost for each gender, age group, and blood glucose level, with and without medical guidance. A method for deriving this will be described later.

  Next, the medical cost predicting means 118 acquires the medical examination information as shown in FIG. 4 from the medical examination information management means 109 and the personal information as shown in FIG. 3 from the personal information management means 112 (1502). Then, the medical cost predicting means 118 acquires information related to the implementation results of the health guidance as shown in FIG. 5 from the implementation information management means 110, and integrates it with the medical examination information, personal information, and personal IDs 301, 401, 501 ( 1503). Next, based on information on gender 1201, age class 1202, blood glucose level class 1203, and health guidance presence / absence information classified by medical cost forecast information, the number of health check information by class for each establishment 304 Count up (1504). For example, at ABC Ironworks of establishment IDJ0001, there are 20 men, age 40s, blood glucose level 110-125 and no health guidance, and men, age 40s, blood glucose level 110-125, health guidance Count the number of people that fit all combinations of conditions, such as 15 people. Then, the medical cost for each group is calculated from the counted number of groups and the medical cost prediction information shown in FIG. 12 (1505).

  For example, here, there are 20 men, age 40s, blood sugar level 110-125, and no health guidance at ABC Ironworks of establishment IDJ0001. Cost 1205 is applied, and the predicted medical cost is 20 people x 30000 yen / person = 600000 yen. In addition, if the same 20 people have health guidance, the medical cost 1204 will be applied, resulting in 20 people x 20000 yen / person = 400000 yen. In addition, for men who have been providing health guidance in the past and for 15 people in their 40s age and blood sugar level of 110 to 125, the medical cost 1204 when there is health guidance is applied, and the predicted medical cost is 15 people × 20000 yen / Person = 300,000 yen. Based on this result, the estimated medical expenses for the ABC Iron Works male, those in their 40s, and blood sugar levels 110-125, were 300,000 yen + 600,000 yen = 900,000 yen. The estimated medical cost in this case is 300000 yen + 400000 yen = 700000 yen. This is calculated in the same way for the other classes, and the medical expenses for cases where health guidance is given and not given for each establishment are calculated by adding them for each establishment. This completes the medical cost prediction step 1402.

  Next, in step 1403 for comparing groups, a comparison is made between the establishments, and establishments that provide health guidance are selected. In the group comparison step 1403, the group comparison means 115 executes processing according to the flowchart shown in FIG. First, the group comparison means 115 acquires information such as the establishment name 202 and the number of people 205 from the group information management means 105 (1601). Then, the difference between the medical cost when the health guidance is not performed as a result of the medical cost prediction step 1402 and the medical cost when it is performed is obtained (1602). For example, if the medical expenses for ABC Iron Works's health guidance are 2000000 yen and the medical expenses for the health guidance are 150,000 yen, the difference is 500,000 yen. Next, the medical cost control efficiency of health guidance is calculated from the difference in predicted medical costs (1603). Since ABC Iron Works has 120 employees, the above-mentioned difference in estimated medical costs of 500,000 yen / 120 people results in a medical cost control efficiency of 4166 yen / person per medical instruction. This is calculated for each office. Next, the group comparison means 115 displays the establishment name, the predicted medical cost of the establishment, the medical cost suppression efficiency, etc. on the output device 102 as a screen as shown in FIG. 13 (1604). In FIG. 13, as the table 1301, the business establishment ID 1304, the business establishment name 1306, the number of people 1306, the predicted medical expenses 1307 and 1308, the medical expenses restraint efficiency 1309, etc. are displayed in descending order of the medical expenses restraint efficiency.

  The graph 1302 also displays the predicted medical cost 1310 when the health guidance is not performed, the predicted medical cost 1311 when the health guidance is performed, and the medical cost suppression efficiency 1312. The person in charge who selects the establishment compares the characteristics of the establishment from this screen and selects the establishment (1605). Here, the person in charge compares the predicted medical cost and the medical cost suppression efficiency, selects the GHI drug with the highest medical cost suppression efficiency, and clicks the selection field 1303 with a mouse or the like. Then, in the group selection means 122, the selection information management means 113 records the selected GHI chemical information in the selection information of FIG.

  As described above, the health business support system of the present invention predicts the medical expenses for each establishment by the medical expense prediction means, compares the establishments by the group comparison means, and displays them in a table or graph. The person can receive health guidance from an establishment that is expected to have high medical expenses. Furthermore, since the group comparison means calculates the medical cost control efficiency of the health guidance and displays it in a table or graph in order of the high medical cost control efficiency, select the establishment with the high medical cost control efficiency and implement the health guidance There is an effect that can be.

  Next, when comparing establishments from various angles and selecting the establishments to be instructed while checking the resources for health guidance held by the insurer, FIG. 17, FIG. 18, FIG. This will be described with reference to FIG. FIG. 17 is a diagram showing an example of a screen for selecting an establishment while comparing with the insurer's resource in the health service support system of the present invention. Multiple selection criteria can be selected as selection criteria. There are a button 1701 for comparing with predictive onset persons, a button 1702 for comparing with predicted medical expenses, a button 1703 for comparing with medical examination results, etc. You can choose from. Then, a table 1704 that displays the selected offices and a graph 1705 that displays the number of the selected offices are displayed. Table 1704 displays the establishment ID 1706, establishment name 1707, and number 1708 of the selected establishment, and the graph shows the maximum number of persons 1710 that can be instructed by the public health nurse belonging to the insurer and the selected establishment Number of people (number of people who provide health guidance) 1709, 1710, etc. are displayed.

  FIG. 20 is a diagram showing an example of resource information for the health business managed by the resource information management means 106. Here, a list of public health nurses belonging to the insurer is shown. Information such as public health nurse ID 2001, public health nurse name 2002, number of public health nurses who can be in charge of 2003, and area in charge of 2004 is recorded.

  FIG. 18 is a flowchart showing an example of processing in the case of selecting establishments by comparing establishments using a plurality of selection criteria and comparing them with resources held by an insurer. The person in charge who selects the establishment subject to health guidance for the insurer performs the operation. In selecting a business office, first, the screen of FIG. 17 is displayed on the output device 102, and the person in charge selects the selection criteria. At this time, the screen of FIG. 17 is in a state where the information on the office is not displayed in the table 1704 or the graph 1705. Then, if the person in charge wants to select an establishment from the viewpoint of suppressing the onset of the disease, the person in charge clicks the button 1701 to be selected from the onset prediction with the input device 101 such as a mouse and selects it. . Then, first, the screen of FIG. 8 is displayed, and the number of patients onset prediction step 1802 is started. Predicting the number of patients in step 1802, the detailed processing is performed by the procedure described in FIG. 10 described above, and when health guidance is provided for each establishment, the number of patients who have not been implemented can be calculated. Ask. Then, the group comparison step 1803 is executed. In the group comparison step 1803, processing is executed according to the above-described procedure of FIG. When the person in charge selects DEF food sales as an establishment with high onset suppression efficiency and presses a decision button 813 (1804), the screen returns to the screen of FIG. 17 and starts the resource comparison step 1808. In the resource comparison step 1808, the resource comparison unit 116 executes the process according to the flowchart shown in FIG. FIG. 19 is a flowchart showing detailed processing of the resource comparison step 1808.

  First, the resource comparison unit 116 acquires information about resources from the resource information management unit 106 (1901). Here, the public health nurse ID 2001, the number of persons in charge 2003, etc. are acquired from the table of FIG. 20 managed by the resource information management means 106. Next, the resource and the current selection status are compared and displayed (1902). Here, on the screen of FIG. 17, the establishment ID 1706, establishment name 1707, and number 1708 of the DEF food sales selected earlier are displayed in Table 1704, and 1709 indicating 120 persons is displayed on the graph. Also, the total number of public health nurses in charge 2003 from the resource information acquired in the resource information acquisition step 1901 is displayed on the graph as the maximum number of possible teachers 1710. Next, the resource comparison unit 116 checks the number of selected business establishments and the maximum number of persons who can be instructed (1903). Here, since the number of selected establishments is less than the maximum number of persons capable of teaching 500, the resource comparison step is finished as it is.

  The person in charge looks at this graph 1705 and still has enough resources for public health nurses, so he continues to select offices (1809). Here, the predicted medical cost button 1702 is pressed as a comparison from the medical cost side. Then, the screen of FIG. 13 is displayed and the medical cost prediction step 1805 is started. The medical cost prediction step 1805 is a step executed by the medical cost prediction means. The processing is executed according to the procedure described with reference to the flowchart of FIG. 15 described above, and the medical costs when the health guidance is performed and not performed are calculated. Then, in the group comparison step 1809, the group comparison means 115 executes the processing in the flowchart shown in FIG. 16 to calculate the medical cost restraint efficiency and the like, and the business locations such as the predicted medical costs 1307 and 1308, the medical cost restraint efficiency 1309, etc. Are displayed in descending order of medical cost control efficiency. The person in charge selects the GHI drug with the highest medical cost control efficiency. When the person in charge presses the enter button 1313 (1807), the screen returns to the screen of FIG. 17 and the resource comparison step 1808 is started. In the resource comparison step 1808, the resource comparison means 116 compares and displays the number of selected offices and the number that can be instructed by a public health nurse, as before. Here, information on GHI medicine is added as the second office, and the number of persons 1710 is additionally displayed on the graph. The person in charge has more room for the maximum number of teachers, so select the criteria again and proceed with the selection of establishments.

  Also, if the number of establishments selected in group comparison steps 1803 and 1804 is larger than the maximum number of public health nurses who can be instructed, the lack of public health nurse resources in step 1903, which checks whether it is within the resource range of resource comparison step 1808 Or a message prompting you to reduce selected offices. The person in charge looks at this message and makes a decision such as re-selecting the office or adjusting the number of public health nurses. When all the establishments are selected, the person in charge presses the decision button 1709. The selection information management means records the finally determined office information in the selection information of FIG. 7 (1810), and ends.

  As described above, the health business support system of the present invention compares and displays the number of persons instructed in the selected group by the resource comparison means and the number of instructors that can be instructed by the insurer, so that the establishment is selected. There is an effect that the person in charge can check that the number of persons providing health guidance does not exceed the number that can be provided. Furthermore, since the health business support system of the present invention can compare establishments using a plurality of criteria such as prediction of the number of multiple onset patients, prediction of medical expenses, the person in charge can compare groups from various angles, Effective and effective group can be selected as the target of health services.

  Next, an example of a method for creating the onset prediction information of FIG. 6 managed by the onset prediction information management means 107 will be described. FIG. 21 is a flowchart showing an example of a procedure for creating the onset prediction information in the onset prediction information creating means 119. This process starts when the update button 814 of the predictor predictive knowledge on the screen of FIG. 8 is pressed. First, the onset prediction information creation unit 119 acquires medical examination information for a predetermined number of years in the past from the medical examination information management unit 109 (1901). Here, for example, medical examination information for six years is acquired, and among the medical examination information of FIG. 4 managed by the medical examination information management means 109, information for six years before fiscal 2003 (from fiscal 1998 to fiscal 2003). Get medical examination information. Next, the implementation information of health guidance for a predetermined number of years in the past is acquired from the implementation information management means 110 (1902). Here, among the implementation information of FIG. 5 managed by the implementation information management means 110, the health guidance implementation information for six years before the same year is acquired. And the medical examination information acquired previously and implementation information are integrated, and it is distinguished whether the person who received the medical examination has the experience of receiving health guidance in the past. Then, the state at the first medical examination is divided into classes, and the disease incidence is calculated for each combination of classes (1903). Here, the onset prediction information as shown in Fig. 6 is created. Based on the 1998 health checkup information, gender 303 is set for men and women, age 403 at the time of the checkup is divided into three classes, 30s, 40s, and 50s. The value 404 is divided into two classes of less than 110 and 110 or more and 125 or less, and further divided into those who have received guidance from those who have received guidance from the health guidance implementation information (Fig. 5). Then, information on the onset of the disease is extracted from the medical examination information.

  In addition, when the blood glucose level 404 is 126 or more, the case where the doctor's judgment 407 regarding diabetes is under treatment is defined as diabetes onset, and a person who does not develop diabetes at the first time is taken out. The number of people who belonged to the group and the number of people who developed the disease were determined. And about the group which combined each class about sex, age, a blood glucose level, and the presence or absence of health guidance, an onset rate is calculated | required by dividing the number of onset persons by the number of persons belonging to a group. Finally, the onset prediction information creating unit 119 records the calculated onset rate in the onset prediction information management unit 107, and updates the contents of the onset rate 604 with guidance and the onset rate 605 without guidance in FIG. 1904). By executing the above procedure, the incidence rate within 5 years can be created as the onset prediction information in FIG. For example, the onset prediction information creating unit 119 executes this process again every year and updates the onset prediction information using new data, so that the prediction can always be made with the latest tendency.

  As described above, the health business support system of the present invention can create the onset prediction information using the medical examination information managed by the insurer by the onset prediction information creating means 119. There exists an effect which can utilize the onset prediction information according to the characteristic of onset. Furthermore, by regularly updating the onset prediction information by the onset prediction information creating means 119, there is an effect that the prediction can always be performed based on the latest trend. Furthermore, since diabetes has different risks of subsequent onset depending on gender, age, and blood glucose level, the effectiveness and efficiency of health guidance vary depending on the gender, age, and blood glucose level of the person receiving health guidance. Therefore, by predicting the number of affected persons using the incidence rate of each sex, age, and blood glucose level as described above, the number of affected persons can be predicted more accurately. As a result, it is possible to predict the number of patients reflecting the characteristics of people belonging to the business establishment, and it is possible to select a business establishment that can expect a higher health guidance effect.

  Next, an example of a method for creating the medical cost prediction information of FIG. 12 managed by the medical cost prediction information management means 108 will be described with reference to FIGS. FIG. 22 is a diagram showing an example of receipt information managed by the receipt information management unit 111. As shown in FIG. The receipt information is information on a medical fee detail issued from the medical institution to the insurer for medical expenses when receiving medical care at the medical institution. It manages an individual ID 2201 that identifies an individual, a medical treatment date and time 2202, a billing score 2203 that is information related to medical expenses, and information 2204, 2205, and 2206 that are related to the name of the disease for which medical care was performed. FIG. 23 is a flowchart illustrating an example of a procedure for creating medical cost prediction information in the medical cost prediction information creating unit 120. This process starts when the medical cost prediction information update button 1314 in FIG. 13 is pressed. First, medical examination information for a predetermined number of years in the past is acquired from the medical examination information management means (2301). Here, for example, medical examination information for six years is acquired, and among the medical examination information of FIG. 4 managed by the medical examination information management means 109, information for six years before fiscal 2003 (from fiscal 1998 to fiscal 2003). Get medical examination information. Next, among the implementation information shown in FIG. 5 managed by the implementation information management means 110, the health guidance implementation information for six years before FY 2003 is acquired (2302). Then, the previously obtained medical examination information and the implementation information are integrated to distinguish whether the person who has undergone the medical examination has received health guidance in the past (2302). Next, the receipt information for a predetermined period of the person corresponding to the acquired medical examination information is acquired from the receipt information management means 111 (2303).

  Here, from the receipt information in FIG. 22 managed by the receipt information management means 111, the medical date 2002 obtains information such as the personal ID 2201, the number of claims 2203, the disease names 2204, 2205, 2206, etc. . Then, the personal ID 401 of the medical examination information acquired previously matches the personal ID 2201 of the receipt information. Next, the state at the time of the first medical examination is divided into classes, and medical expenses are calculated for each combination of classes (2304). Here, because the medical cost prediction information as shown in FIG. 12 is created, the person who is not diabetic at the first time is taken out from the 1998 medical examination information, and the gender 303 is divided into two men and women, and the age at the time of the examination is 403 Is divided into three classes of 30s, 40s, and 50s, blood sugar level 404 is divided into two classes of less than 110 and 110 to 125, and guidance is given from the health guidance implementation information (Figure 5) of those persons Divided into those who received it and those who did not. Furthermore, only the claim score 2203 of the receipt information described as diabetes in the disease names 2204, 2205, and 2206 is extracted, and the total score for one year is calculated. In addition, for a group that combines different classes of age, blood glucose level, and health guidance, the total number of claims is divided by the number of people belonging to the class combination, and the medical care per person when health guidance is given Calculate the medical expenses per person when there is no cost and health guidance. Finally, the calculated medical cost is recorded in the medical cost prediction information management means 108, and the contents of the medical cost 1204 with guidance and the medical cost 1205 without guidance in FIG. 12 are updated (2305). Through the above procedure, information for predicting medical expenses in five years can be created as the medical cost prediction information in FIG. Further, for example, the medical cost prediction information creating unit 120 updates the medical cost prediction information every year, so that prediction based on the latest trend can always be performed.

  As described above, the health service support system of the present invention can create medical cost prediction information using medical checkup information and receipt information managed by the insurer by the medical cost prediction information creation means 120. There is an effect that it is possible to use medical cost prediction information according to the characteristics of the insurer's disease onset. Furthermore, by regularly updating the medical cost prediction information by the medical cost prediction information creating means 120, there is an effect that the prediction can always be performed based on the latest trend. Furthermore, since diabetes has different risks of subsequent onset depending on gender, age, and blood glucose level, the effectiveness and efficiency of health guidance vary depending on the gender, age, and blood glucose level of the person receiving health guidance. Therefore, as described above, the medical cost can be predicted more accurately by using the medical cost for each class of gender, age, and blood glucose level as the medical cost prediction information. This makes it possible to predict medical expenses that reflect the characteristics of people belonging to the establishment, and has the effect of selecting an establishment that can expect a higher health guidance effect.

  In the embodiment described so far, the case where the affected person and the medical expenses are compared for each establishment and the establishment for performing the health guidance is selected has been described. However, the selection may be made based on other criteria. For example, when the button 1703 for comparing from the medical examination result shown in FIG. 17 is pressed, the tendency of the medical examination result for each office may be compared. For example, compare the tendency of each health examination result by establishment, such as the relationship between age and number of people at each establishment, histograms for laboratory values such as blood glucose level and blood pressure, and the distribution of the number of persons for judgment results by doctors such as diabetes and blood pressure. May be. Also, the presence or absence of health guidance may be compared, for example, displaying the number of people who have received health guidance and the number of people who have not received health guidance for each office. In addition, the number of users of high medical expenses, the number of hospitalizations, etc. may be used. Thereby, there is an effect that the person in charge who selects the office can select and compare the office from various angles.

  In the examples described so far, the case where the person in charge of selecting an establishment selects the establishment by comparison is described as an example. However, the establishment where the group selection means 122 automatically performs health guidance. May be selected. For example, the group selection means 122 calculates the number of cases by establishment by the onset prediction means 117, and further arranges them in descending order of the onset suppression efficiency by the group comparison means 115, and the resource comparison means 116 can take charge of the public health nurse While confirming that the number of people is within the range, establishments may be automatically selected in order from the top of the onset suppression efficiency. In addition, the group selection means 122 predicts the medical expenses for each office by the medical cost prediction means 118, and further arranges them in order of the medical cost suppression efficiency by the group comparison means 115, and the resource comparison means 116 can be in charge of the public health nurse. Establishments may be automatically selected in order from the top of the medical cost control efficiency while confirming that the number is within the number of people. Moreover, both onset suppression efficiency and medical expenses suppression efficiency are calculated | required, and both may give a score from the top with high efficiency, and you may select in order with a favorable score. By selecting an office automatically by these methods, there is an effect that the burden on the person in charge of selecting the office can be reduced.

  In the embodiment described so far, the case where the predicted number of onset patients and the onset suppression efficiency are displayed has been described. However, the difference between the case where health guidance is performed and the case where health guidance is not performed may be displayed as the prediction effect. Moreover, although the Example demonstrated so far demonstrated the case where prediction medical expenses and medical expenses suppression efficiency were displayed, you may display the difference with the case where it does not carry out health guidance as a prediction effect. By these methods, the person in charge who selects the establishment that implements health guidance has the effect of being able to select the establishment that is expected to have a high effect of suppressing health guidance onset or a high medical cost reduction effect. Moreover, although the example demonstrated so far demonstrated the method of arranging and displaying in order with high onset suppression efficiency and medical expenses suppression efficiency, you may make it choose so that it may arrange in another item. By this method, the person in charge of selecting a business establishment is compared not only with efficiency, but also with various items such as the order of the most likely to be affected, the order of the expected medical expenses, and the order of the most effective implementation. There is an effect that the office can be selected.

  In the embodiments described so far, diabetes has been described as an example of a disease for predicting the onset and medical expenses, but other diseases may be used. In particular, the same can be done using various lifestyle-related diseases such as hyperlipidemia, hypertension and obesity. As a result, the person in charge of selecting a business establishment can select an establishment with high implementation efficiency and high implementation efficiency from the viewpoint of various lifestyle-related diseases.

  In the embodiments described so far, the case where the number of public health nurses is used as resource information has been described, but other information may be used. For example, the resource comparison unit 116 may compare the resource for each area by comparing the information of the assigned area 2004 in the resource information with the information of the location 203 of the group information. Thereby, there is an effect that finer selection such as placement of public health nurses can be performed. Also, budget information such as personnel costs of public health nurses is managed as resource information, and the resource comparison unit compares the budget information with the medical cost reduction effect information obtained by the medical cost prediction unit 118 and the group comparison unit 115. Therefore, you may make it select the establishment whose reduction effect is higher than the cost of health guidance. This has the effect of supporting the health management of the health worker.

  In the embodiments described so far, the case where the computer is configured by one computer apparatus has been described as an example. However, each unit in FIG. 1 may be distributed to a plurality of computer apparatuses. For example, a medical examination information server that manages medical examination information by the medical examination information management means 109, a health guidance information server that manages the history of health guidance by the implementation information management means 109, and a receipt that manages receipt information by the receipt information management means 111 Health having the other means of FIG. 1 such as information server, group information management means 105, resource information management means 106, onset prediction information management means 107, medical expense prediction information management means 108, personal information management means, selection information management means 113 An operation terminal having a business support server, an input device, and an output device may be provided and distributed. By making the health business support server independent, there is an effect that the load on the computer can be distributed.

  In the embodiment described so far, the case of using the onset rate with respect to the combination of the medical examination results by class as the onset prediction information has been described, but other methods may be used as long as the information predicts the onset. For example, information related to the prediction formula of the onset person obtained by a statistical method such as multivariate analysis may be used. Moreover, although the Example demonstrated so far demonstrated the case where the sum total of the medical expenses per person with respect to the combination of the medical examination result according to a class was used as medical expenses prediction information, if it is information which estimates medical expenses, other methods May be used. For example, information on a medical cost prediction formula obtained by a statistical method may be used. Any method can be used as long as it is a method for predicting the onset and a method for predicting medical expenses.

  In the embodiments described so far, the case where health guidance is given by a public health nurse for each business place has been described, but other health programs may be implemented for each business place. For example, various health services such as health consultations by doctors, walking programs, distribution of health information by letters and emails, and fitness club benefits can be selected for each office as described in the case of health guidance.

  In the embodiment described so far, the case where the insurer selects the establishment when providing health guidance to the insured is described as an example, but when the group belonging to the insurer is a region, You may make it select an area. In addition, when the insurer is a corporate health insurance association, in addition to the establishments described so far, it may be selected in units of workplaces. Moreover, you may use it for the use of selecting the area for the relationship between the local government and the residents, not the relationship between the insurer and the insured, or selecting the workplace for the relationship between the company and the employee. The health business support system of the present invention can be used not only for the activities of insurers but also for health business carried out by companies and local governments. In particular, when an organization such as a company that does not have receipt information uses it, it can be used in a configuration that excludes the medical cost prediction information creation means 120, the medical cost prediction information management means 108, the medical cost prediction means 118, etc. from FIG. Efficient health guidance can be implemented from the prediction of the number of patients. In addition, the health business support system of the present invention may be used by a business operator who is entrusted with an organization such as a local government, a company, or an insurer to implement a health business. In this case, by receiving the medical examination information and the receipt information from the group and storing them in the medical examination information management means 109 and the receipt information management means 111, it is possible to select a business office that provides health guidance.

  In the embodiment described so far, the case where the office is selected in July 2004 has been described as an example. However, this can be repeated every month. At this time, for example, in the case of performing the selection work in the following August, the establishment selected at the time of July is read from the selection information management means 113, and the group comparison means 116 removes the establishment. You may make it select. Also, establishments that have already provided health guidance from April to July 2004 may be read from the implementation information management means 109 and removed by the group comparison means 115. As a result, establishments that have already been selected in the same year or establishments that have already provided health guidance can be excluded from the selection targets for health guidance, and target establishments can be selected from establishments that have not been provided with health guidance.

The figure which shows the example of 1 structure of the health-business assistance system of this invention. The figure which shows an example of the information regarding the establishment which the group information management means 105 manages. The figure which shows an example of the information regarding the insured individual which the personal information management means 112 manages. The figure which shows an example of the medical examination information which the medical examination information management means 109 manages. The figure which shows an example of the implementation information regarding the performance which implemented the health guidance which the implementation information management means 110 manages. The figure which shows an example of the information regarding the prediction of the number of onset patients which the onset prediction information management means 107 manages. The figure which shows an example of the information regarding the selection of the establishment which the selection information management means 113 manages. The figure which shows an example of the screen which selects the establishment which implements health guidance in the screen displayed on the display of the output device 102 by comparing the predicted number of onset persons for each establishment. The flowchart which shows an example of the process which estimates the number of the onset of a lifestyle-related disease, and selects the establishment which implements health guidance by the comparison of establishments. The flowchart which shows an example of the process which the onset prediction means 117 performs by the detailed process content of the onset person number prediction step 902. FIG. The flowchart which shows an example of the processing content which the group comparison means 115 performs by the detailed processing content of the group comparison step 903. The figure which shows an example of the medical expenses prediction information which the medical expenses prediction information management means 108 manages. The figure which shows an example of the screen which selects the establishment which implements health guidance by comparing the predicted medical expenses for each establishment on the screen displayed on the display of the output device. The health service support system of this invention WHEREIN: The flowchart which shows an example of the process which estimates the medical expenses of a lifestyle-related disease and selects the establishment which implements health guidance by the comparison of establishments. The flowchart which shows the detailed process of a medical expenses prediction step. The flowchart which shows the detailed process of a group comparison step in the case of using medical expenses prediction. The figure which shows an example of the screen which selects an establishment, comparing with the resource of an insurer in the health business support system of this invention. The flowchart which shows an example of the process in the case of selecting an establishment, comparing an establishment using a some selection criteria, and also comparing with the resource which an insurer holds. 10 is a flowchart showing detailed processing of a resource comparison step 1808. The figure which shows an example of the resource information for health business which the resource information management means 106 manages. The flowchart which shows an example of the procedure which produces onset prediction information in the onset prediction information creation means 119. The figure which shows an example of the receipt information which the receipt information management means 111 manages. 9 is a flowchart showing an example of a procedure for creating medical cost prediction information in the medical cost prediction information creating unit 120.

Explanation of symbols

101 ... Input device, 102 ... Output device, 103 ... CPU, 104 ... Storage device, 105 ... Group information management means, 106 ... Resource information management means, 107 ... Onset Predictive information management means, 108 ... Medical cost prediction information management means, 109 ... Medical examination information management means, 110 ... Implementation information management means, 111 ... Receipt information management means, 112 ... Personal information Management means 113 ... Selection information management means 115 ... Group comparison means 116 ... Resource comparison means 117 ... Development prediction means 118 ... Medical cost prediction means 119 ... Onset Prediction information creation means, 120 ... Medical cost prediction information creation means, 122 ... Group selection means, 201, 304, 701 ... Establishment ID, 202 ... Establishment name, 203 ... Location, 204 ... Contact, 205 ... Number of people, 301, 401, 501, 2201 ... Personal ID, 302 ... Name, 303 ... Gender, 402 ... Consultation , 403 ... Age at consultation, 404 ... Blood glucose level, 405 ... High blood pressure, 406 ... BMI, 407 ... Diabetes judgment, 408 ... Blood pressure judgment, 502 ... Health guidance Day, 503 ... Health instructor, 601, 1201 ... Gender, 602,1202 ... Age class, 603,1203 ... Blood glucose class, 604 ... Guided incidence, 605 ...・ Training rate without guidance, 702 ... Selection date, 703 ... Scheduled to be implemented, 704 ... Person in charge ID, 801 ... Comparison of establishments (prediction of onset), 802 ... Comparison graph of establishments (Predicted case), 813, 1313 ... Decision button, 814 ... Update button for predictive case knowledge, 1204 ... Medical expenses with guidance, 1205 ... Medical expenses without guidance, 1301 ... Business Comparison table (medical cost prediction), 1302 ... Establishment comparison graph (medical cost prediction), 1314 ... Update button for medical cost prediction knowledge, 1701 ... Selection criteria prediction onset button, 1702 ... Selection criteria Medical cost button, 1703 ... Checkup result button, 1704 ... Selected office table, 1705 ... Resource comparison graph, 1709 ... Decision button, 1710 ... Maximum guidance number display mark, 2001 · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · Name of public health nurse, 2003 · Number of people in charge, 2004 · Areas in charge · 2202 · Date of medical treatment · 2203 · · · Number of claims, 2204, 2205, 2206 ... disease name.

Claims (16)

A health business support system that supports the operation of health business conducted by organizations such as insurers, companies, and local governments.
Group information management means for managing information related to groups such as offices, workplaces, and regions belonging to the group;
Medical examination information management means for managing medical examination information generated by an individual belonging to the group receiving a medical examination;
Implementation information management means for managing the results of health guidance implemented by the group as a health business to the individual;
Onset prediction information management means for managing the onset prediction information indicating the relationship between the health condition obtained from the items of the medical examination information, the performance of the health guidance, and the onset of the disease;
Onset prediction means for calculating the number of predicted onset for each group from the individual checkup results and the onset prediction information,
A group comparison means for comparing the number of predicted cases by group and outputting to the output means;
A health business support system comprising group selection means for designating a group for performing health guidance from the group. A health business support system that supports the operation of health business conducted by organizations such as insurers, companies, and local governments.
Group information management means for managing information related to groups such as offices, workplaces, and regions belonging to the group;
Medical examination information management means for managing medical examination information generated by an individual belonging to the group receiving a medical examination;
Implementation information management means for managing the results of health guidance implemented by the group as a health business to the individual;
Medical cost prediction information management means for managing medical cost prediction information indicating the relationship between the health condition obtained from the items of the medical examination information, the performance of the health guidance, and the medical cost;
Medical cost prediction means for calculating a predicted medical cost for each group from the medical checkup result of the individual and the medical cost prediction information;
A group comparison means for comparing the medical expenses for each group and outputting them to the output means;
A health business support system comprising group selection means for designating a group for performing health guidance from the group. A health business support system that supports the operation of health business conducted by organizations such as insurers, companies, and local governments.
Group information management means for managing information related to groups such as offices, workplaces, and regions belonging to the group;
Medical examination information management means for managing medical examination information generated by an individual belonging to the group receiving a medical examination;
Implementation information management means for managing the results of health guidance implemented by individuals as a health business by the group;
Onset prediction information management means for managing the onset prediction information indicating the relationship between the health condition obtained from the items of the medical examination information, the performance of the health guidance, and the onset of the disease;
Onset prediction means for calculating the number of predicted onset for each group from the individual checkup results and the onset prediction information,
Medical cost prediction information management means for managing medical cost prediction information indicating the relationship between the health condition obtained from the items of the medical examination information, the performance of the health guidance, and the medical cost;
Medical cost prediction means for calculating a predicted medical cost for each group from the medical checkup result of the individual and the medical cost prediction information;
A group comparison means for comparing the number of disease-onset persons and the predicted medical expenses for each group and outputting them to an output means;
A health business support system comprising group selection means for designating a group for performing health guidance from the group. The health business support system according to any one of claims 1 to 3, wherein the group is managed by resource information management means for managing information related to a person who provides health guidance as a health business by the group, and the resource management means. A health business support system comprising resource comparison means for comparing information on personnel and information on personnel required by the implementation of health guidance for the group designated by the group designation means and outputting the information to output means . 5. The health business support system according to claim 1, 3 or 4, based on medical examination information from medical examination information managed by the medical examination information management means and results of health guidance managed by the implementation information management means. A health business support system, characterized by having an onset prediction information creating means for calculating a disease onset rate for each health condition and the presence or absence of the results of health guidance and recording the onset rate prediction information management means. 5. The health service support system according to claim 2, 3 or 4, wherein a receipt information management means for managing medical cost information generated by an individual belonging to the group receiving a medical institution due to illness, and the medical examination information From the medical examination information managed by the management means, the results of the health business managed by the implementation information management means, and the medical cost information managed by the receipt information management means, the health condition based on the medical examination information and the health guidance A health business support system comprising medical cost prediction information creating means for calculating medical costs according to the presence or absence of results and recording the medical cost prediction information management means. 6. The health service support system according to claim 1, claim 3, or claim 4 or claim 5, wherein the onset predicting means calculates a disease onset rate of a person who has no health guidance record from the onset predictive information managing means. Calculate the predicted number of patients who have not obtained health guidance and obtain the disease incidence rate of persons with health guidance results from the onset prediction information management means, and predict the onset when health guidance is conducted. The number of persons is calculated for each group, and the group comparison means calculates the groups in descending order of the difference between the predicted number of onset patients when the health guidance is not performed and the predicted number of patients when the health guidance is performed. A health business support system characterized by being displayed side by side on the output means. 7. The health service support system according to claim 2, claim 3, or claim 4 or claim 6, wherein the medical cost prediction means is a medical cost for a person who has no health guidance record from the medical cost prediction information management means. Predicted medical expenses when health guidance is obtained by calculating the predicted medical expenses when health guidance is not carried out and acquiring medical expenses of persons with health guidance results from the medical cost prediction information management means Expenses are calculated for each group, and the group comparison means arranges the groups in the descending order of the difference between the predicted medical expenses when the health guidance is not performed and the predicted medical expenses when the health guidance is performed. A health business support system characterized by display. 8. The health business support system according to claim 7, wherein the group comparison unit is configured to predict the number of people who have developed the disease when the health guidance for each group has not been implemented and the number of people who have the disease that has undergone the health guidance. A health business support system characterized by calculating the onset suppression efficiency obtained by dividing the difference by the number of persons performing health guidance in the group and displaying the difference on the output means in the descending order of the onset suppression efficiency. 9. The health business support system according to claim 8, wherein the group comparison means calculates a difference between medical costs when the health guidance for each group is not implemented and medical costs when the health guidance is implemented. A health business support system, wherein medical cost suppression efficiency divided by the number of persons who perform health guidance in the group is calculated and displayed on the output means in order of decreasing medical cost suppression efficiency. The health business support system according to claim 9, wherein the group selection means uses the resource comparison means to confirm that the group selection means is within the range of personnel for health guidance, and selects groups in descending order of the onset suppression efficiency. Health business support system characterized by selection. 11. The health business support system according to claim 10, wherein the group selection means uses the resource comparison means to confirm that it is within the range of personnel for health guidance, and the groups in descending order of the medical cost control efficiency. Health business support system characterized by selecting 5. The health service support system according to claim 4, wherein the resource information management means manages information related to a cost necessary for the health guidance, and the resource comparison means suppresses the medical cost when the health guidance is performed. A health business support system characterized in that the effect and the cost when health guidance is implemented are compared and displayed on the output means. The health business support system according to claim 1 or 3, wherein the onset prediction information management means includes gender, stratified age, stratified fasting blood glucose level, and onset of diabetes according to health guidance presence / absence. Health business support system characterized by managing rates. The health care support system according to claim 2 or claim 3, wherein the medical cost prediction information management means includes sex, stratified age, stratified fasting blood glucose level, and health guidance presence / absence, A health service support system characterized by managing medical expenses related to diabetes. 4. The health service support system according to claim 3, wherein the group comparison means includes at least one means from means for calculating information for each of the plurality of groups including the onset patient prediction means and the medical cost prediction means. A health business support system characterized by selecting and outputting the result to an output means.

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