JP7395134B1 - Health management support devices and health management support programs - Google Patents

Abstract

The present invention provides a health management support device and a health management support program that support individual health management so that as many individuals as possible can improve their own health management and improve their mental and physical conditions. [Solution] A health management support server 10 uses at least one of health checkup data and pre-symptomatic test data to determine the abnormality level of each of the measurement results of medical checkup items or the test results for pre-symptomatic items. The analysis processing unit 80 includes an analysis processing unit 80 that performs analysis processing, and a recommendation presentation processing unit 90 that presents an explanatory image according to the analysis data generated by the analysis processing unit 80. The analysis processing unit 80 is an individual analysis processing unit that performs analysis processing on each of the health examination data and pre-symptomatic test data to generate individual analysis data, and according to the abnormality level of the measurement result and the abnormality level of the test result, and a composite analysis processing unit that determines a composite abnormality level indicating the degree of abnormality of the health condition. [Selection diagram] Figure 4

Description

The present invention relates to a health management support device and a health management support program that support individual health management.

Traditionally, individuals have measured basic data about their bodies (also called physical data) such as their blood pressure, body temperature, and weight, and based on the results, they have improved their diet, exercised, etc., and thereby improved their own health. Maintaining a good condition (also called health management) is carried out.

In addition, individuals may undergo a health checkup (also referred to as a "health checkup") by a medical institution to examine their own health condition in detail. When an individual undergoes a medical checkup, the individual is required to go to a medical institution, etc., and undergo blood sampling for blood tests, blood pressure measurement, chest X-rays, and the like.

In recent years, mail-in testing services on topics related to health conditions have been provided by private providers. For example, there are urine tests to check for antibodies to Helicobacter pylori, which increases the risk of stomach cancer, and stool tests to measure the state of intestinal flora.

When individuals examine their own health conditions, they can obtain physical data through their own measurements. In addition, when a health checkup or mail-in testing service is performed, data related to the results, such as γ-GTP, creatinine values, blood sugar level, blood pressure, and the presence or absence of Helicobacter pylori (hereinafter referred to as "diagnosis result data") ) will be provided to individuals who have been tested by medical institutions or private businesses.

BACKGROUND ART Conventionally, techniques related to services for individuals using physical data measured by individuals themselves and diagnosis result data have been known. For example, a technique related to a service in which analysis processing results and advice based on measurement data are provided to individuals who have undergone a test is known (for example, see Patent Document 1). In addition, technology related to services that provide advice in accordance with actions to improve health conditions (for example, see Patent Document 2), and services that are provided to individuals using health examination data are determined. A technique is known that increases the incentive for receiving a health checkup by providing a health checkup (for example, see Patent Document 3).

Japanese Patent Application Publication No. 2002-123613 Japanese Patent Application Publication No. 2006-3991 JP 2021-22333 Publication

As mentioned above, services for individuals using body data measured by individuals themselves and diagnosis result data have been known. Therefore, the above-mentioned conventional technology allows individuals to receive advice according to their own measurement results and diagnosis results. Moreover, the incentive to undergo a health checkup is increased.

However, there are some individuals who are reluctant to take care of their own health, such as those who have no or very little interest in their own health status, and individuals who are overconfident about their own health status. Such individuals often do not undergo medical examinations and, if they do, do not review the results. Therefore, even with conventional support such as advice, there may be cases in which the patient is not seen at a medical institution. Furthermore, if an individual has anxiety or if there is a misunderstanding about the results of a health checkup or the meaning of the diagnostic items, the time for the individual to see a medical institution may be delayed.

In order to deal with this situation, in conventional health management, 1) it is cumbersome to confirm whether there is an abnormality in the health condition, 2) the meaning and content of the medical examination results and diagnostic items, and the health status of the diagnostic results are It would be desirable to resolve two issues: the difficulty in understanding the effects of

Further, in the conventional technology, only support such as advice is provided. Therefore, it is desirable to enable as many individuals as possible, including those who are proactive in managing their health as well as those who are passive, to improve their own health management. For example, when a disease is specifically diagnosed as a medical condition, such as diabetes or obesity, it is not only necessary to treat the disease, and provide medical guidance on diet, exercise, and medication, but also to treat patients who are not yet diagnosed with the disease. Guidance on nutrition, diet, and exercise aimed at behavioral change for those at risk of developing the disease or those at high risk of developing the disease, as well as nutritional supplements, foods for specified health uses, health foods, and daily food recommended for maintaining health. Food products are also covered. Through these recommendations and introductions, it is desirable to expand support for individuals to change their behavior to stay healthy, and to do so, it is desirable that data related to health status, such as physical data and diagnostic data, be effectively utilized.

The present invention was made to solve the above problems, and aims to improve the health of individuals so that as many individuals as possible can improve their own health management and improve their mental and physical conditions by effectively utilizing data related to health conditions. The purpose is to provide health management support devices and health management support programs that support management.

In order to solve the above-mentioned problems, the present invention provides a medical checkup that includes data indicating an individual's health condition, including the measurement results of each of a plurality of checkup items such as blood pressure, blood lipids, liver function, and renal function of the individual. Measurement of each health checkup item using at least one of data and pre-symptomatic test data showing the test results of multiple pre-symptomatic items using specimens such as urine, stool, and saliva. An analysis in which an analysis process is performed to determine whether or not the results indicate an abnormality or the degree of abnormality in the test results for each pre-symptomatic item, and the abnormal level indicating the result of the analysis process is included for each health checkup item and non-symptomatic item. An image for explaining the analysis processing means for generating data and the measurement results for each health checkup item or the test results for each presymptomatic item, including an explanation of the contents of the medical checkup item and the presymptomatic item. A stored explanatory image storage means, an image search means for searching for a corresponding explanatory image corresponding to the analysis data generated by the analysis processing means among the explanatory images stored in the explanatory image storage means, and the image. and a presentation means for presenting explanatory image data that can identify the corresponding explanatory image or the recording position of the corresponding explanatory image retrieved by the retrieval means , and the analysis processing means analyzes each of the medical examination data and the pre-symptomatic examination data. An individual analysis processing unit that generates individual analysis data by executing processing , an abnormal level of the measurement result of one of the plurality of health checkup items, and an abnormal level of the test result of one of the plurality of pre-symptomatic items. and a composite analysis processing unit that performs composite analysis processing to determine the degree of abnormality of the health condition using The composite analysis processing unit is characterized by a health management support device that uses data that is subject to composite analysis processing by performing analysis processing on both health checkup data and pre-symptomatic test data among the individual analysis data. do.

The present invention also provides health checkup data that is data indicating an individual's health condition and that shows the measurement results of each of a plurality of checkup items such as blood pressure, blood lipids, liver function, and kidney function, urine, Using at least one of the pre-symptomatic test data showing the test results for each of multiple pre-symptomatic items that are test items related to pre-symptomatic diseases using specimens such as stool and saliva, the measurement results for each of the health checkup items indicate an abnormality. An analysis process that performs an analysis process to determine whether or not the test result is abnormal or not, or the degree of abnormality in the test results for each pre-symptomatic item, and generates analytical data that includes abnormal levels indicating the results of the analysis process for each health checkup item and non-symptomatic item. The processing means and the analysis data generated by the analysis processing means , which are based on different years and months when the health checkup or test from which the health checkup data or pre-symptomatic test data was created, and which are individual The abnormal level continues to be abnormal based on the secular change in the abnormal level of the measurement results of multiple health checkup items or the test results of multiple pre-symptomatic items using multiple pieces of data with equal values. A secular change analysis means that performs a secular change analysis process to extract age-related abnormalities or age-related deterioration where the abnormality level has worsened, and generates secular change analysis data showing the analysis results, and a secular change analysis means that performs a secular change analysis process to extract age-related abnormalities or age-related deterioration where the abnormality level has worsened, and a secular change analysis means that generates secular change analysis data showing the analysis results, An explanatory image storage means storing an explanatory image that explains the test results of each item and includes an explanation of the contents of the medical checkup items and the pre-symptomatic items; An image search means for searching for a corresponding explanation image according to the analysis data generated by the analysis processing means among the explanation images that are available, and a corresponding explanation image or a recording position of the corresponding explanation image searched by the image search means. The analysis processing means includes an individual analysis processing unit that generates individual analysis data by performing analysis processing on each of the health checkup data and the pre-symptomatic test data ; Performing a composite analysis process to determine the degree of abnormality of the health condition using the abnormal level of the measurement result of one of the plurality of health checkup items and the abnormal level of the test result of one of the plurality of pre-symptomatic items, It has a composite analysis processing unit that generates analysis data so as to include a composite abnormality level according to the result of the composite analysis processing, and the composite analysis processing unit is configured such that the analysis processing of the individual analysis data is Provided is a health management support device that uses data that has been subjected to complex analysis processing performed on both data and pre-symptomatic test data .

Furthermore, the present invention provides health checkup data that is data indicating an individual's health condition and that shows the measurement results of each of a plurality of checkup items such as the individual's blood pressure, blood lipids, liver function, and kidney function; Using at least one of the pre-symptomatic test data showing the test results for each of multiple pre-symptomatic items that are test items related to pre-symptomatic diseases using specimens such as stool and saliva, the measurement results for each of the health checkup items indicate an abnormality. An analysis process that performs an analysis process to determine whether or not the test result is abnormal or not, or the degree of abnormality in the test results for each pre-symptomatic item, and generates analytical data that includes abnormal levels indicating the results of the analysis process for each health checkup item and non-symptomatic item. The processing means and the analysis data generated by the analysis processing means , which are based on different years and months when the health checkup or test from which the health checkup data or pre-symptomatic test data was created, and which are individual The abnormal level continues to be abnormal based on the secular change in the abnormal level of the measurement results of multiple health checkup items or the test results of multiple pre-symptomatic items using multiple pieces of data with equal values. A secular change analysis means that performs secular change analysis processing to extract secular abnormalities or aging deterioration where the abnormality level has worsened, and generates secular change analysis data showing the analysis results, and analysis data generated by the analysis processing means. Based on age-related abnormalities or age-related deterioration of abnormal levels of measurement results for multiple health checkup items or test results for multiple pre-symptomatic items, using multiple pieces of data from different years and months and from the same individual. a predicted change analysis means that performs a predicted change analysis process to predict a future abnormality level indicating the degree of abnormality in the future of each measurement result or test result, and generates predicted change analysis data indicating the predicted result; and a medical examination item. An explanatory image storage means storing an explanatory image that explains each measurement result or test result of each pre-symptomatic item and includes an explanation of the contents of the medical checkup item and the pre-symptomatic item , and the explanation thereof. an image search means for searching for a corresponding explanation image corresponding to the analysis data generated by the analysis processing means among the explanation images stored in the image storage means; and a corresponding explanation image or corresponding explanation searched by the image search means; the analysis processing means generates individual analysis data by performing analysis processing on each of the health checkup data and the pre-symptomatic test data; determines the degree of abnormality in the health condition using an individual analysis processing unit that performs and a composite analysis processing unit that performs composite analysis processing to generate analysis data that includes a composite abnormality level according to the result of the composite analysis processing, and the composite analysis processing unit The present invention provides a health management support device in which analysis processing is performed on both medical checkup data and pre-symptomatic test data, and uses data that is subjected to complex analysis processing .

The above-mentioned health management support device is a product or service that is useful for improving abnormalities in the measurement results of each health checkup item or abnormalities in the test results for each pre-symptomatic item, or for behavioral changes to improve the abnormality . A recommended product registration means in which the goods and services code, which is the data used to identify Recommended product determining means for determining, as a recommended product, a product or service corresponding to a product/service code associated with a medical examination item or a disease-free item according to the analysis data generated by the analysis processing means among the items. It is preferable to have.

In addition, the above-mentioned health management support device is a product useful for improving abnormalities in the measurement results of each health checkup item , abnormalities in the test results for each pre-symptomatic item, or behavioral changes to improve the abnormalities . Or, a recommended product registration means in which the product/service code, which is data used to specify the service, and medical examination items or pre-symptomatic items are registered in association with each other, and the medical examination items or disease-free items registered in the recommended product registration means. The product or service corresponding to the product/service code associated with the medical checkup item or the pre-symptomatic item according to the analysis data generated by the analysis processing means among the pre-symptomatic items is determined as the first recommended product, and The method further includes a recommended product determining means for determining, as a second recommended product, a product or service corresponding to a product/service code associated with a medical checkup item or a disease-free item according to the secular change analysis data generated by the change analysis means. It is preferable to have.

Furthermore, the above-mentioned health management support device is a product that is useful for improving abnormalities in the measurement results of each health checkup item or abnormalities in the test results for each pre-symptomatic item, or for behavioral changes to improve the abnormalities . Or, a recommended product registration means in which the product/service code, which is data used to specify the service, and medical examination items or pre-symptomatic items are registered in association with each other, and the medical examination items or disease-free items registered in the recommended product registration means. The product or service corresponding to the product/service code associated with the medical checkup item or the pre-symptomatic item according to the analysis data generated by the analysis processing means among the pre-symptomatic items is determined as the first recommended product, and The product or service corresponding to the product/service code associated with the health check item or disease-free item according to the secular change analysis data generated by the change analysis means is determined as the second recommended product , and the predictive change analysis means The method further includes recommended product determining means for determining, as a third recommended product, a product or service corresponding to the product/service code associated with the medical examination item or pre-symptomatic item according to the generated predictive change analysis data. can do.

In the above health management support device, the image search means searches for a secular change explanation image according to the secular change analysis data generated by the secular change analysis means from among the explanation images stored in the explanation image storage means, and presents the image. The means may present the secular change explanatory image together with the corresponding explanatory image or explanatory image data .

In the case of the above-mentioned health management support device, the image search means includes a secular change explanation image and a predicted change analysis according to the secular change analysis data generated by the secular change analysis means among the explanation images stored in the explanation image storage means. The presenting means searches for a predicted change analysis explanation image corresponding to the predicted change analysis data generated by the means, and the presentation means presents the secular change explanation image and the predicted change analysis explanation image together with the corresponding explanation image or explanation image data. is preferred.

Further, the presenting means may present the first recommended product, the second recommended product, and the third recommended product determined by the recommended product determining means together with the corresponding explanatory image or explanatory image data .

The present invention is a health management support program for making a computer function as a health management support device, and the computer is used to collect data indicating the health condition of an individual, such as blood pressure, blood lipids, and liver function of the individual. , health checkup data that shows the measurement results of multiple health checkup items such as kidney function, and Mibyou data that shows the test results of multiple Mibyo items that are test items related to Mibyou using specimens such as urine, stool, and saliva. Using at least one of the test data, perform analysis processing to determine whether the measurement results for each health checkup item show an abnormality or the degree of abnormality in the test results for each pre-symptomatic item, and the results of the analysis processing are performed. an analysis processing means for generating analysis data including abnormal levels indicating abnormalities for each of the health checkup items and the pre-symptomatic items; A corresponding explanatory image corresponding to the analysis data generated by the analysis processing means among the explanatory images stored in the explanatory image storage means in which explanatory images including explanations of the contents of diagnosis items and pre-symptomatic items are stored. The analysis processing means functions as an image retrieval means for retrieving medical examination data, and a presentation means for presenting explanatory image data that can identify the corresponding explanatory image or the recording position of the corresponding explanatory image retrieved by the image retrieval means. , an individual analysis processing unit that generates individual analysis data by executing analysis processing on each of the pre-symptomatic test data; Using the abnormality level of any of the test results, perform a composite analysis process to determine the degree of abnormality in the health condition, and generate analysis data so as to include the composite abnormality level according to the result of the composite analysis process. When functioning as a composite analysis processing unit, analysis processing is performed on both health checkup data and pre-symptomatic test data among individual analysis data, and the data is subject to composite analysis processing. We will provide a health management support program using collected data .

Furthermore, the present invention is a health management support program for causing a computer to function as a health management support device, the computer being used to collect data indicating an individual's health condition, such as blood pressure, blood lipids, and liver function of the individual. , health checkup data that shows the measurement results of multiple health checkup items such as kidney function, and Mibyou data that shows the test results of multiple Mibyo items that are test items related to Mibyou using specimens such as urine, stool, and saliva. Using at least one of the test data, perform analysis processing to determine whether the measurement results for each health checkup item show an abnormality or the degree of abnormality in the test results for each pre-symptomatic item, and the results of the analysis processing are performed. an analytical processing means for generating analytical data including an abnormal level indicating each of the medical examination items and the pre-symptomatic items ; and the analytical data generated by the analytical processing means , wherein the medical examination data or the pre-symptomatic examination data are created. The measurement results of multiple health checkup items or the measurement results of multiple pre-symptomatic items can be calculated using multiple pieces of data from different years and months when the health checkups or tests were performed, and from the same individual. Based on the secular change in the abnormality level of the test results, we perform a secular change analysis process to extract the secular abnormality in which the abnormality level continues to be abnormal or the abnormality level worsens over time, and show the analysis results over time. A secular change analysis means that generates change analysis data, and an image that explains the measurement results of each diagnosis item or the test results of each pre-symptomatic item, including an explanation of the contents of the medical examination item and pre-symptomatic item. an image search means for searching for a corresponding explanation image corresponding to the analysis data generated by the analysis processing means from among the explanation images stored in the explanation image storage means in which the image is stored; By causing the analytical processing means to function as a presentation means for presenting explanatory image data that can identify the corresponding explanatory image or the recording position of the corresponding explanatory image , and to perform analytical processing on each of the medical checkup data and the pre-symptomatic test data. The individual analysis processing unit that generates individual analysis data , the abnormal level of the measurement result of any of the multiple health checkup items , and the abnormal level of the test result of any of the multiple pre-symptomatic items are used to determine the health status. Functions as a composite analysis processing unit that performs composite analysis processing to determine the degree of abnormality and generates analysis data to include the composite abnormality level according to the result of the composite analysis processing. In this case, a health management support program is provided that uses the data of the individual analysis data that has been subjected to the analysis process on both the health checkup data and the pre-symptomatic test data and has been subjected to the combined analysis process .

Furthermore, the present invention is a health management support program for causing a computer to function as a health management support device, the computer being used to collect data indicating an individual's health condition, such as blood pressure, blood lipids, and liver function of the individual. , health checkup data that shows the measurement results of multiple health checkup items such as kidney function, and Mibyou data that shows the test results of multiple Mibyo items that are test items related to Mibyou using specimens such as urine, stool, and saliva. Using at least one of the test data, perform analysis processing to determine whether the measurement results for each health checkup item show an abnormality or the degree of abnormality in the test results for each pre-symptomatic item, and the results of the analysis processing are performed. an analytical processing means for generating analytical data including an abnormal level indicating each of the medical examination items and the pre-symptomatic items ; and the analytical data generated by the analytical processing means , wherein the medical examination data or the pre-symptomatic examination data are created. The measurement results of multiple health checkup items or the measurement results of multiple pre-symptomatic items can be calculated using multiple pieces of data from different years and months when the health checkups or tests were performed, and from the same individual. Based on the secular change in the abnormality level of the test results, we perform a secular change analysis process to extract the secular abnormality in which the abnormality level continues to be abnormal or the abnormality level worsens over time, and show the analysis results over time. The analysis data generated by the secular change analysis means that generates change analysis data and the analysis processing means, which are from different years and months and are the same for each individual, are used to calculate the measurement results or results of each of the multiple health checkup items. Predictive change analysis processing is performed to predict the future abnormality level indicating the degree of abnormality in the future of each measurement result or test result, based on the abnormality level or deterioration over time of the abnormality level of the test results for each of multiple pre -symptomatic items. , a predicted change analysis means for generating predicted change analysis data showing the predicted results, and an image explaining the measurement results of each health checkup item or the test result of each pre-symptomatic item, the an image search means for searching for a corresponding explanatory image according to the analysis data generated by the analysis processing means among the explanatory images stored in the explanatory image storage means in which an explanatory image including an explanation of the content is stored ; , functions as a presentation means for presenting explanatory image data capable of specifying the recording position of the corresponding explanatory image or corresponding explanatory image searched by the image retrieval means, and the analysis processing means is configured to function as a presentation means for presenting explanatory image data that can specify the recording position of the corresponding explanatory image or corresponding explanatory image searched by the image retrieval means , and the analysis processing means An individual analysis processing unit that generates individual analysis data by performing analysis processing on the abnormal level of the measurement result of one of the multiple health checkup items , and an abnormality of the test result of one of the multiple pre-symptomatic items. It functions as a composite analysis processing unit that performs composite analysis processing to determine the degree of abnormality in the health condition using the When functioning as a combined analysis processing unit, health management that uses data that has been subjected to analysis processing on both health checkup data and pre-symptomatic test data and is subject to combined analysis processing, among individual analysis data. Provide support programs.

As detailed above, by effectively utilizing data related to health conditions, health management support devices and devices that support individual health management and so that as many individuals as possible can improve their own health management and improve their physical and mental conditions. Health management support programs are available.

1 is a system configuration diagram of a health management support system including a health management support server according to a first embodiment of the present invention. FIG. 2 is a block diagram mainly showing the internal configuration of the health management support server. FIG. 2 is a block diagram mainly showing the internal configuration of a user terminal device. 1 is a functional block diagram showing the main configuration of a health management support server according to a first embodiment of the present invention. FIG. FIG. 2 is a functional block diagram showing the main configuration of a medical examination/examination data analysis processing section together with various main storage sections or DBs. FIG. 2 is a functional block diagram showing the main configuration of a registration processing section. FIG. 2 is a functional block diagram showing the main configuration of the recommendation presentation processing section according to the first embodiment of the present invention together with various main storage sections or DBs. It is a diagram showing an example of a record layout of a member master. It is a diagram showing an example of a record layout of a medical examination/examination data storage DB. It is a diagram showing an example of a record layout of an analysis data storage DB. It is a figure showing an example of the database structure of a judgment standard value table. It is a flowchart which shows an example of the operational procedure of member support processing. 3 is a flowchart illustrating an example of an operation procedure of health management support processing. It is a flowchart which shows an example of the operating procedure of individual analysis processing. 3 is a flowchart illustrating an example of an operation procedure of a composite analysis process. It is a flowchart which shows an example of the operational procedure of medical examination data determination processing. It is a flowchart which shows an example of the operational procedure of mail inspection data determination processing. FIG. 2 is a functional block diagram showing the main configuration of a health management support server according to a second embodiment of the present invention. FIG. 3 is a functional block diagram showing the main configuration of a health management support server according to a third embodiment of the present invention. It is a flowchart which shows an example of the operational procedure of a secular change analysis process. It is a flowchart which shows an example of the operational procedure of a predicted change analysis process. FIG. 3 is a functional block diagram showing the main configuration of a recommendation presentation processing section according to a second embodiment of the present invention together with various main storage sections or DBs. FIG. 7 is a functional block diagram showing the main configuration of a recommendation presentation processing unit according to a third embodiment of the present invention, together with various main storage units or DBs. FIG. 3 is a diagram showing an example of a database structure of a recommended product determination table. FIG. 2 is a diagram illustrating an example of processing performed by a health management support server and a user terminal device, and an operation performed by the two in cooperation.

Embodiments of the present invention will be described below with reference to the drawings. Note that the same elements are denoted by the same reference numerals, and duplicate explanations will be omitted.

First embodiment
(Overall configuration of health management support system)
First, the configuration of a health management support system 1 including a health management support server 10 according to a first embodiment of the present invention will be described.

FIG. 1 is a system configuration diagram of a health management support system 1 according to a first embodiment of the present invention. As shown in FIG. 1, the health management support system 1 includes a health management support server (hereinafter also referred to as "support server") 10, and a plurality of user terminal devices 30 (in FIG. 1, a fixed terminal device 30A) operated by a user. and mobile terminal devices 30B, 30C, and 30D), which are connected to each other via the Internet N1.

The support server 10 performs data processing according to a health management support program and provides health management support services to users. The health management support service is a service realized by applying the present invention. The health management support service is a service in which the user is presented with an explanatory image, a recommended product list, and a consultation reservation list according to analysis data, which will be described later. In this embodiment, if the user desires, the support server 10 also provides services other than the health management support service.

The user terminal device 30 receives or transmits data with the support server 10.

In the health management support system 1, the support server 10 executes the following processes a), b), and c), and in c), the explanatory image, the recommended product list, and the consultation reservation list are presented to the user terminal device 30. Ru.
a) Analytical processing to determine whether the measurement results of health checkup items or test results of pre-symptomatic items indicate an abnormality in the user's health condition b) Generate analytical data indicating the results of the analytical processing in a) c) b) ) Presentation of explanatory images, recommended product list, and consultation reservation list according to analysis data of

The user of the user terminal device 30 can know his/her own health condition from the measurement results of health checkup items or the test results of pre-symptomatic items. Furthermore, by referring to the presented explanatory images, the user can understand their meanings. Furthermore, from the recommended product list, the user can also learn about products and services that are useful for improving his or her own health or changing behavior for that purpose. Furthermore, by referring to the consultation reservation list, the user can grasp the opportunity to consult with a specialist or visit a medical institution. From these, even users who are reluctant to manage their health can improve their own health management.

(Configuration of support server 10)
Next, the configuration of the support server 10 will be described with reference to FIG. 2. FIG. 2 is a block diagram mainly showing the internal configuration of the support server 10. As shown in FIG. The support server 10 is a server operated by a specialized business that provides health management support services.

The support server 10 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, and a RAM (Random Access Memory) 13. The CPU 11 operates according to a program stored in the ROM 12, and inputs input data obtained by operating the keyboard 19 and mouse 20 via the KBC (Key board controller) 17 via the main bus 19A. It inputs and outputs signals with other components and controls the overall operation of the support server 10. The CPU 11 operates a registration processing section 60, a medical examination/examination data recording processing section 70, a medical examination/examination data analysis processing section 80, and a recommendation presentation processing section 90, which will be described later, in accordance with a health management support program, which will be described later. The ROM 12 stores a BIOS (Basic Input/Output System), control programs, and permanent data. The RAM 13 stores data used when the CPU 11 operates and programs such as a member support program (a program used to execute member support processing, including the health management support program according to the present invention).

In addition, the support server 10 includes a hard disk drive (HDD) 14, a communication control section 15, a communication processing section 16, and a video controller 18.

The hard disk device 14 stores middleware such as an OS (Operating System), a DBMS (Database Management System), and a Web server program. The hard disk device 14 also stores programs such as a health management support program, various storage units or DBs (databases) shown in FIGS. 4, 5, and 7 necessary for their execution, and other storage units, DBs, and files. It is formed. Here, FIG. 4 is a functional block diagram showing the main configuration of the support server 10 that realizes the health management support service, and FIG. FIG. 7 is a functional block diagram showing the main configuration of the recommendation presentation processing unit 90 together with various main storage units or DB.

As shown in FIGS. 4, 5, and 7, the hard disk drive 14 includes a member master 101, a medical examination/examination data storage DB 103, an analysis data storage DB 104, an analysis data intermediate file 105, and a judgment standard value table (TBL) 110. , a recommended product determination table (TBL) 111, an explanation image DB 112, a consultation reservation DB 113, a recommended product data file 114, an explanation image data file 115, and a recommended product master 116 are formed. Each storage unit, DB, and file will be described later.

The communication control unit 15 operates according to instructions from the CPU 11 to control connection and disconnection of lines for communicating with the user terminal device 30 and a server (not shown). The communication processing unit 16 operates according to instructions from the communication control unit 15 to execute data transmission and reception via the Internet N1.

The video controller 18 controls image display on a display device (not shown) to display screens used for various settings.

The various storage units, DB, and files of the hard disk device 14 will be explained as follows. The member master 101 is a master file that stores data regarding members. In this embodiment, a member is a user who receives health management support services. For example, as shown in FIG. 8, the member master 101 stores a record having eight fields from a member code field 101a to a held points field 101h.

A member code unique to a member is stored in the member code field 101a. The member's name is stored in the member name field 101b. The membership classification field 101c stores the membership classification. The membership classification is data indicating whether the member is the member himself/herself receiving health management support services, his or her family (family member), or furthermore, whether the member is a friend or related person (friend member). The address field 101d and the Tel-No field 101e store the member's address and telephone number, respectively. The gender field 101f and the date of birth field 101g store data indicating the gender of the member (gender data) and date of birth. The held points field 101h stores data indicating points held by the member. Points are earned by members using health management support services (or other optional services).

(Medical examination/examination data storage DB, analysis data storage DB, judgment standard value table)
The medical examination/examination data storage DB 103 stores medical examination/examination data. The medical examination/examination data includes multiple medical examination/examination records. Each medical examination/examination record includes user's medical examination data and mailed examination data. The medical examination/examination data storage DB 103 has a relationship with the member master 101 using the member code as a key, and forms an RDB (Relational Data Base). Both health checkup data and mailed test data are data that indicate the health status of individuals.

Health checkup data is data that shows the user's physical measurement data including height and weight, and measurement results of multiple health checkup items such as blood pressure, blood lipids, liver function, and kidney function, and is not used for medical health checkups. We have data showing the results. Mail inspection data is data indicating the results of a mail inspection. In mail tests, inspection items related to Mibyou (also referred to as Mibyou items) are conducted. Mibyo refers to a state in which a person is moving away from a healthy state, although the disease has not yet developed. Mailed test data is data (also referred to as pre-symptomatic test data in the present invention) indicating the results of tests conducted on the pre-symptomatic items. Mail-in tests use test kits that are sent back and forth by mail (or home delivery). Individuals who wish to undergo a test use the test kit to collect samples such as urine, stool, or saliva and send them to a testing institution. Thereafter, the testing institution will present the test results using that sample. Data indicating the test results is mailed test data.

As shown in FIG. 9, in the medical examination/examination data storage DB 103, the medical examination/examination records include a member code field 103a, a gender field 103b, a medical examination month field 103c, a usage classification field 103d, a medical examination data storage section 103e, It has a mail inspection data storage section 103f.

Data common to the member master 101 is stored in the member code field 103a and the gender field 103b, respectively. The medical examination month field 103c stores the year and month of the medical examination or postal examination. The usage classification field 103d stores the usage classification. The usage category is data that indicates whether health checkup data or mailed test data (pre-symptomatic test data) can be used. Three types of data are set for the usage classification: "1", "2", and "3". Usage categories = "1", "2", and "3" indicate that in the health checkup/test record, only health checkup data can be used, and only mailed test data (pre-symptomatic test data) can be used. This shows that both health checkup data and mailed test data (pre-symptomatic test data) are available.

The medical examination data storage unit 103e stores physical measurement data and data indicating measurement results for each of a plurality of medical examination items. In FIG. 9, blood pressure, blood lipids, liver function, and kidney function are illustrated as health checkup items, but other items may also be included.

The medical examination data storage section 103e has a physical measurement data field 103g, a blood pressure data field 103h, a blood lipid data field 103j, a liver function data field 103k, and a renal function data field 103m. Physical measurement data field 103g, blood pressure data field 103h, blood lipid data field 103j, liver function data field 103k, and renal function data field 103m contain measurement results of physical measurement, blood pressure, blood lipids, liver function, and kidney function, respectively. Data (physical data, health checkup data) indicating this is stored.

The postal test data storage unit 103f stores pre-symptomatic test data indicating test results for each of a plurality of pre-symptomatic items. In the mail test, a specimen collected by an individual user is used, and in this embodiment, urine, saliva, feces, blood, and sperm are exemplified as specimens, but other specimens may also be included. . Data indicating the results of tests using urine, saliva, stool, blood, and sperm (pre-symptomatic test data) are stored in the urine test data field 103p, saliva test data field 103q, stool test data field 103r, and blood test data field 103s, respectively. , is stored in the sperm test data field 103t. The pre-symptomatic test data can be data indicating test results for pre-symptomatic items, such as the blood test data field 103s shown in FIG. Furthermore, as in the urine test data field 103p shown in FIG. 9, determined data can be used as pre-symptomatic test data. The determined data is generated by each testing institution making a determination on the test results for the disease-free items. For example, five levels of data such as "5", "4", "3", "2", and "1" (the larger the value, the higher the degree of abnormality) can be used. The determined data in this case indicates the degree of abnormality and thus corresponds to the abnormality level.

The analysis data storage DB 104 stores analysis data. The analysis data is data indicating the result of analysis processing using the medical examination/examination data stored in the medical examination/examination data storage DB 103. As shown in FIG. 10, the analysis data includes a plurality of records including analysis records r11 to r15 and r21 to r25. In the analysis process, which will be described in detail later, the abnormality level of each measurement result or test result is determined for each measurement result of the health checkup item or the test result of each pre-symptomatic item. The analysis data storage DB 103, like the medical checkup/examination data storage DB 103, has a relationship with the member master 101 using the membership code as a key, and forms an RDB (Relational Data Base).

In the analysis data storage DB 104, the analysis record is stored in the medical examination analysis data storage section 104e and the mail examination along with the membership code field 103a, medical examination month field 103c, and usage classification field 103d, which are similar to those in the medical examination/examination data storage DB 103. It has an analysis data storage section 104f and a composite analysis data storage section 104g.

The medical examination analysis data storage unit 104e stores abnormal levels of measurement results for each medical examination item. The medical examination analysis data storage section 104e has a BMI determination data field 104d, a blood pressure determination field 104h, a blood lipid determination field 104j, a liver function determination field 104k, and a renal function determination field 104m. These stores store abnormal levels of measurement results of BMI, blood pressure, blood lipids, liver function, and kidney function, respectively.

The mail test analysis data storage unit 104f stores abnormal levels of measurement results for each non-symptomatic item. In this embodiment, urine, saliva, feces, blood, and sperm are assumed to be the specimens. The mail test analysis data storage section 104f has storage sections corresponding to each specimen, that is, a urine test data field 104p, a saliva test data field 104q, a stool test data field 104r, a blood test data field 104s, and a sperm test data field 104t. It is provided.

The composite analysis data storage unit 104g stores data (composite abnormality level) indicating the results of the composite analysis processing described later. The composite analysis data storage section 104g has a first analysis item section 104ga and a second analysis item section 104gb. The first analysis item section 104ga stores a composite abnormality level regarding oxidized LDL. The second analysis item section 104gb stores a composite abnormality level related to CKD (chronic kidney disease).

The determination reference value table 110 is used for medical examination examination determination. The health checkup test determination is a determination regarding the measurement results of each health checkup item or the test results of each pre-symptomatic item. In the health checkup test determination, it is determined whether each measurement result or test result indicates an abnormality in the health condition.

The determination reference value table 110 has a data structure as shown in FIG. As shown in FIG. 11, the determination reference value table 110 includes a determination item storage section 110a and a reference value storage section 110b, both of which are associated with each other. The determination item storage section 110a has a medical examination item section 110e and a mail examination item section 110f. In the medical examination item section 110e, items corresponding to the medical examination analysis data storage section 104e are set. Items corresponding to the mail test analysis data storage section 104f are set in the mail test item section 110f. A reference value storage section 110b is set for each item. The reference value storage section 110b has a reference value data section 110c and a unit data section 110d. The reference value data section 110c is set with data that serves as a reference when determining the degree of abnormality regarding the measurement results of each medical checkup item and the test result of each pre-symptomatic item. In the unit data section 110d, data indicating the standard unit is set. Note that in the reference value data section 110c, reference value data is set in the blood test reference value data 110g in order to cope with the fact that the mailed test data is not determined data.

Next, the registration processing section 60, medical examination/examination data recording processing section 70, medical examination/examination data analysis processing section 80, and recommendation presentation processing section 90 will be explained.

(Registration processing unit, medical examination/examination data recording processing unit, medical examination/examination data analysis processing unit)
The registration processing unit 60 includes a member registration unit 61, as shown in FIG. The member registration unit 61 transmits a member registration form (not shown) (input screen data used for member registration) to the user terminal device 30, and causes the member master 101 to store the data input using the member registration form.

The medical examination/examination data recording processing unit 70 transmits a medical examination/examination data recording form (not shown) to the user terminal device 30, and performs medical examination/examination on the data input using the medical examination/examination data recording form. It is stored in the data storage DB 103.

The medical examination/examination data record form has an input section for the membership code, an input section for the month of the medical examination, and the usage category (including a pull-down menu, for example), an input section for each medical examination item of the medical examination data, and an input section for the mailed examination data. It has an input section for each inspection item. When data corresponding to each input section (medical examination data indicating measurement results, mailed examination data indicating test results) is input, the input data is stored in the corresponding field of the medical examination/examination data storage DB 103. Ru. As shown in FIG. 9, the medical examination/examination data storage DB 103 stores medical examination/examination data including medical examination/examination records r1, r2, r3, r4, r12, and r13. For example, health checkup/examination records r1 and r2 have health checkup months of "January 2019" and "January 2020", respectively, and both use categories are "1", so health checkup data is not entered. However, the mail inspection data has not been entered. Furthermore, the medical examination/examination record r3 is a record in which the medical examination month is "January 2021" and the usage category is "3", so medical examination data and mailed examination data are input. The medical examination/examination record r4 has the medical examination month "July 2021" and the usage category "2", so mail examination data has been input, but no medical examination data has been input.

The medical examination/examination data analysis processing section 80 includes an individual analysis processing section 81 and a composite analysis processing section 82, as shown in FIG. The individual analysis processing unit 81 executes individual analysis processing to be described later. In the individual analysis process, medical examination/examination data stored in the medical examination/examination data storage DB 103 is used. In the individual analysis process, a medical checkup test determination is performed according to the data set in the determination reference value table 110. The individual analysis processing unit 81 generates individual analysis data indicating the result of the individual analysis processing, and writes the individual analysis data to the analysis data intermediate file 105. Although not shown, the individual analysis data has a layout common to the analysis data. The composite analysis processing unit 82 generates analysis data by executing a composite analysis process to be described later using the individual analysis data of the analysis data intermediate file 105. The generated analysis data is stored in the analysis data storage DB 104.

The recommendation presentation processing unit 90 executes presentation processing of explanation images, etc., which will be described later, according to the analysis data stored in the analysis data storage DB 104. As shown in FIG. 7, the recommendation presentation processing section 90 includes a recommendation processing section 91, a list creation section 92, an explanation image search processing section 93, a consultation reservation processing section 94, and a presentation processing section 95.

The recommendation processing unit 91 accesses the recommended product determination table 111 using the analysis data stored in the analysis data storage DB 104, and extracts the corresponding product or service.

As shown in FIG. 24, the recommended product determination table 111 has an item data section 111b and a recommended product registration section 111a, both of which are stored in association with each other. Items corresponding to the analysis record are set in the item data section 111b. The recommended product registration section 111a includes a product registration section 111a1 and a service registration section 111a2. Data (product code) used to identify recommended products is registered in the product registration section 111a1. Data (service code) used to identify recommended services is registered in the service registration unit 111a2. Recommended products or services are products (for example, supplements) or services (for example, sports facilities) that are recommended as effective for improving measurement results or test results when they are abnormal. For example, in the case of an abnormality such as high blood pressure, recommended products include supplements that have the effect of lowering blood pressure, and recommended services include sports facilities suitable for exercise.

The recommendation processing unit 91 extracts data indicating abnormality from the analysis record (abnormality level in the medical examination analysis data storage unit 104e = “9”, abnormality level in the mail examination analysis data storage unit 104f = “5”, composite analysis). The data storage unit 104g extracts items (also referred to as “abnormal items”) for which the abnormality level is set to “7” to “9”.The recommendation processing unit 91 also stores item data in the extracted abnormal items. The section 111b is searched, and for the hit item, the code (product code, service code) of the set product or service is acquired from the recommended product registration section 111a.Then, the recommendation processing section 91 Information about the product or service (name, price, manufacturer, etc.) corresponding to the product code or service code is obtained from the recommended product master 116, and the obtained information is used to generate recommended product data. The data is written to the product data file 114.

The list creation unit 92 reads recommended product data from the recommended product data file 114, edits it, and generates a recommended product list L1.

The explanatory image search processing unit 93 searches the explanatory image DB 112 for an explanatory image corresponding to the abnormal item, and writes the obtained explanatory image to the explanatory image data file 115. Along with or instead of the explanatory image, data that can specify the recording position of the explanatory image (for example, URL: Uniform Resource Locator, also referred to as explanatory image data) may be written to the explanatory image data file 115.

The explanatory image can be a still image or a moving image. The explanatory image is an image that explains the measurement results of the health checkup items or the test results of the pre-disease items (health checkup/test data in the health checkup/test data storage DB 103). It includes explanations of the contents and medical meaning of each health checkup item and test item, as well as their normal values, abnormal values, and abnormal levels. Furthermore, images related to diseases that are expected to develop if there is an abnormality can also be included in the explanatory images. Text and figures are used for the explanatory image, and in the case of a still image, an image including a cartoon is preferable.

The consultation reservation processing unit 94 executes processing related to a user's consultation reservation. The consultation reservation processing unit 94 accesses the consultation reservation DB 113 and obtains contact information (phone number, website address, e-mail address, etc.) of the specialist (doctor, nurse, pharmacist, etc.) associated with the abnormal item. ). The consultation reservation processing unit 94 generates a consultation reservation list L2 using the extracted data.

The presentation processing unit 95 executes presentation processing of the recommended product list L1, the explanatory image data file 115, and the consultation reservation list L2. The presentation processing unit 95 presents the recommended product list L1, the explanatory image data file 115, and the consultation reservation list L2 by transmitting them to the user terminal device 30.

(Configuration of user terminal device 30)
As shown in FIG. 1, the user terminal device 30 is equipped with a connection environment to the Internet N1 and can communicate with the support server 10. Note that in the following description, a mobile terminal device 30B (portable notebook personal computer) is assumed as the user terminal device 30, but the user terminal device 30 may be a fixed terminal device 30A. The fixed terminal device 30A is, for example, a stationary personal computer. The mobile terminal devices 30C and 30D are assumed to be, for example, a high-performance mobile phone (also referred to as a smartphone) or a tablet-type terminal device.

As shown in FIG. 3, the user terminal device 30 includes a CPU 31, a ROM 32, a RAM 33, a data storage section 34, and a liquid crystal display section 35. Further, the user terminal device 30 includes a voice conversion processing section 36, a communication control section 37, a communication processing section 38a, a wireless communication section 38b, a speaker 39, and a microphone 40.

The CPU 31 operates according to a program stored in the ROM 32 and controls the operation of the user terminal device 30 as a whole. The ROM 32 stores a control program executed by the CPU 31. The RAM 33 stores a communication control program for data communication, data necessary for the CPU 31 to execute the program, and the like.

The data storage unit 34 stores various data and application programs. The liquid crystal display section 35 is an image display means that includes an LCD (Liquid Crystal Display) and its driving section, and displays images such as characters, figures, and symbols. The audio conversion processing section 36 expands the audio data and outputs it to the speaker 39, while converting and compressing the analog audio signal input from the microphone 40 into digital audio data and inputting it to the communication processing section 38a. The communication control unit 37 operates in response to instructions from the CPU 31 and controls connection and disconnection of lines for data communication. The communication processing unit 38a operates according to instructions from the communication control unit 37 to execute data transmission and reception via the Internet N1. The wireless communication unit 38b is a wireless communication means that transmits and receives data wirelessly under the control of the communication control unit 37. The speaker 39 is an audio output means that outputs audio, and the microphone 40 inputs audio such as the contents of a user's conversation and converts it into an electrical signal.

Then, the CPU 31 operates a list acquisition section 401, a list display control section 402, an explanation image acquisition section 403, and an image display control section 404, which will be described later, in accordance with a recommended product acquisition program, which will be described later (see FIG. 25). As a result, a recommended product list L1, a consultation reservation list L2, and an explanatory image, which will be described later, are displayed on the liquid crystal display section 35 of the user terminal device 30.

(Operation details of the health management support system)
Next, with reference to FIGS. 12 to 17 as well as FIGS. 4 and 5, the operation contents of member support processing including health management support processing by the support server 10 will be described.

In the support server 10, the CPU 11 executes member support processing according to the member support program. Since the health management support program according to the present invention is incorporated into the member support program, the health management support process is executed by executing the member support process.

When the health management support process is executed, the CPU 11 sets the determination reference value according to the data input using the user terminal device 30 (mainly data including medical examination data, mailed test data, and usage classification). While accessing the table 110, the recommended product determination table 111, etc., the medical examination/examination data analysis processing section 80 and the recommendation presentation processing section 90 are operated. As a result, health management support processing is executed. Note that the health management support program is a program for realizing the medical checkup/examination data analysis processing section 80 and the recommendation presentation processing section 90 in the support server 10, and for causing the support server 10 to function as an analysis processing means and a presentation means. .

When the support server 10 executes the member support process, the CPU 11 executes the process according to the flowchart shown in FIG. 12 according to the member support program. FIG. 12 is a flowchart illustrating an example of the operating procedure of the member support process executed by the CPU 11. Note that "S" in FIGS. 12, 13, etc. is an abbreviation for step.

When starting the member support process, the CPU 11 first performs a member registration process in step 1. Here, the member registration section 61 operates and transmits a member registration form to the user terminal device 30. When the user uses the user terminal device 30 to input data according to the member registration form, data such as the member code and member name necessary for registering a member to receive health management support services is sent from the user terminal device 30. The data is transmitted to the support server 10, and the member registration unit 61 stores the data in the member master 101.

Subsequently, the process proceeds to step 2, and medical examination data recording processing is executed. Here, the medical examination/examination data recording processing section 70 operates and transmits the medical examination/examination data recording form to the user terminal device 30. When the user uses the user terminal device 30 to perform data input processing according to the medical examination/examination data recording form, the medical examination data and mailed examination data are transmitted from the user terminal device 30 to the support server 10. Then, the medical examination/examination data recording processing section 70 stores those data in the medical examination/examination data storage DB 103.

Subsequently, when the process proceeds to step 3, the CPU 11 executes a health management support process, which will be described later, according to the health management support program. Thereafter, the process proceeds to step 4, where it is determined based on the input data from the user terminal device 30 whether or not to use the optional service. If it is determined that the optional service is to be used, the process proceeds to step 5, and if it is not determined that the optional service is to be used, the process proceeds to step 6.

In step 5, optional service processing is executed. Optional services are services related to member support and are separate from health management support services. Optional services are performed according to member requests. For example, optional services include the provision of information on health management, the use of a chat function regarding health management, remote medical examinations at medical institutions, and vaccinations.

When step 5 is completed, the process proceeds to step 6, where point calculation processing is executed. In the point calculation process, points acquired by the user by using the health management support service and other optional services are added, and points are subtracted by using the points. When step 6 ends, the member support process ends.

(Health management support processing: S3)
The CPU 11 executes health management support processing according to the flowchart shown in FIG. The details of the health management support processing are as follows.

When the CPU 11 starts the health management support process, the process proceeds to step 11. In step 11, the medical examination/examination data analysis processing section 80 operates according to instructions from the CPU 11. As described above, the medical examination/examination data analysis processing section 80 includes the individual analysis processing section 81 and the composite analysis processing section 82 (see FIG. 5). By operating the individual analysis processing section 81, the individual analysis processing of step 11 is executed. Thereafter, the composite analysis processing section 82 operates to execute the composite analysis process of step 12. The medical examination/examination data analysis processing section 80 has a function as an analysis processing means according to the present invention.

(Individual analysis processing: S11)
In the individual analysis process, analysis data is generated by executing an analysis process using the medical examination/examination data stored in the medical examination/examination data storage DB 103. The individual analysis processing is executed by the individual analysis processing section 81 according to the flowchart shown in FIG.

Then, as shown in FIG. 14, when the individual analysis processing section 81 starts the individual analysis generation process, the process proceeds to step 21. In step 21, the medical examination/examination record stored in the medical examination/examination data storage DB 103 is read. Processing then proceeds to step 22. In step 22, it is determined whether or not the usage category of the read health checkup/examination record is "1". When the usage category is "1", the process proceeds to step 23, and when the usage category is not "1", the process proceeds to step 24. In step 23, a medical examination data determination process, which will be described later, is executed, and in step 24, a mail examination data determination process, which will be described later, is executed. Following step 24, the process proceeds to step 25. In step 25, it is determined whether the usage category is "3". When the usage category is "3", the process proceeds to step 23, and when the usage category is not "3", the process proceeds to step 26. When the process proceeds to step 26, a process is executed in which an output record (a record corresponding to individual analysis data, including BMI judgment data, health checkup judgment data, and test judgment data to be described later) is written to the analysis data intermediate file 105. Then, the process proceeds to step 27. In step 27, it is determined whether it is the end of the record (whether the last record has been read or not), and if it is the end of the record, the individual analysis process ends, but if it is not the end of the record, the process returns to step 21. The above process is repeated.

(Medical examination data judgment process: S23)
The medical examination data determination process is executed by the individual analysis processing unit 81 according to the flowchart shown in FIG.

When the individual analysis processing unit 81 starts the medical examination data determination process, the process proceeds to step 42. In step 42, BMI (Body Mass Index) is calculated. The BMI is calculated using the height and weight data in the physical measurement data field 103g according to the following formula 1.
Formula 1: BMI=weight/(height x height)

Next, when the process proceeds to step 43, it is determined whether the BMI calculated in step S42 satisfies the reference value. In this case, the individual analysis processing unit 81 reads reference value data (<25) regarding BMI from the determination reference value table 110. The individual analysis processing unit 81 determines whether the BMI calculated in S42 satisfies the reference value data (<25). In FIG. 16, when the BMI satisfies the reference value data (<25), the process proceeds to step 44, but when the BMI does not satisfy the reference value data (<25), the process proceeds to step 45. In steps 44 and 45, "1" and "9" are respectively set in the BMI determination data as abnormal levels. Note that the BMI determination data is data set in the BMI determination data field 104d.

The abnormality level is data indicating the degree of abnormality of a medical checkup item (or test item). In the individual analysis process, "1" or "9" is set as the abnormality level of the medical checkup item. "1" indicates normality, and "9" indicates abnormality.

Next, when the process proceeds to step 46, it is determined whether or not the measurement data of each medical examination item from the blood pressure data field 103h to the renal function data field 103m satisfies the respective reference value data. At this time, the individual analysis processing unit 81 reads the standard value data corresponding to each medical examination item from the judgment standard value table 110, compares each measurement data with the standard value data, and determines whether the respective standard value data is satisfied. Determine whether or not. Then, when each measurement data satisfies the respective reference value data, the process proceeds to step 47, but when each measurement data does not satisfy the respective reference value data, the process proceeds to step 48. In steps 47 and 48, "1" and "9" are respectively set as abnormal levels in the medical examination determination data. Next, when the process proceeds to step 49, it is determined whether or not there is measurement data, and if there is measurement data, the process returns to step 46, but if there is no measurement data, the medical examination data determination process ends. The health checkup determination data is data set in each determination field of the blood pressure determination field 104h, the blood lipid determination field 104j, the liver function determination field 104k, and the renal function determination field 104m.

(Postal inspection data determination process: S24)
The mail inspection data determination process is executed by the individual analysis processing unit 81 according to the flowchart shown in FIG.

When the individual analysis processing unit 81 starts the mailed test data determination process, the process proceeds to step 52, where it is determined whether the pre-symptomatic test data is determined data. When the pre-symptomatic test data is determined data (for example, when the pre-symptomatic test data is any of "5", "4", "3", "2", or "1"), step 57 is performed. After execution, the process proceeds to step 56, but if the pre-symptomatic test data is not determined data, the process proceeds to step 53. When proceeding to step 57, the pre-symptomatic test data is determined data and there is no need for processing to determine the abnormality level, so the pre-symptomatic test data is set as the test determination data.

When the process proceeds to step 53, using the reference value data of the judgment reference value table 110, the pre-symptomatic test data of each test item from the urine test data field 103p to the sperm test data field 103t is determined from among the respective reference value data. , it is determined whether or not it conforms to a reference value corresponding to a normal value (also referred to as a normal reference value; for example, <50 of 110 g of blood test reference value data). When each pre-symptomatic test data conforms to the respective normal reference values, the process proceeds to step 54, but when each pre-symptomatic test data does not conform to the respective normal reference values, the process proceeds to step 55. In step 54, "1" is set in the inspection determination data as an abnormal level. In step 55, data ("2" to "5") corresponding to each reference value is set as the abnormal level in the inspection determination data. Next, when the process proceeds to step 56, it is determined whether or not there is pre-symptomatic test data, and if there is pre-symptomatic test data, the process returns to step 52, but if there is no pre-symptomatic test data, mail test data is determined. Processing ends. The test determination data is data set in the urine test data field 104p, saliva test data field 104q, stool test data field 104r, blood test data field 104s, and sperm test data field 104t.

(Composite analysis processing: S12)
In the composite analysis process, analysis data is generated by executing an analysis process using the individual analysis data stored in the analysis data intermediate file 105. The composite analysis process is executed according to the flowchart shown in FIG.

Then, as shown in FIG. 15, when the composite analysis processing section 82 starts the composite analysis process, the process proceeds to step 31. In step 31, individual analysis data from the analysis data intermediate file 105 is read. Processing then proceeds to step 32. In step 32, it is determined whether or not the usage category of the individual analysis data record is "3". When the usage category is "3", the process proceeds to step 33, but when the usage category is not "3", the process proceeds to step 37.In the present embodiment, in the composite analysis process, any of the medical examination items A composite abnormality level, which will be described later, is determined according to the abnormality level of the measurement result of data and postal inspection analysis data are required.Therefore, records with usage classification "3" are subject to complex analysis processing, but records whose usage classification is not "3" are subject to complex analysis processing. Not considered.

When the process proceeds to step 33, it is determined whether or not there is abnormal data (data with abnormal level = "9") in the target item among the medical examination items of the medical examination analysis data. If there is abnormal data, the process proceeds to step 34, but if there is no abnormal data, the process returns to step 31. The target items in step 33 are the items to be subjected to the complex analysis process among the medical examination items, and in this embodiment, "LDL" in the blood lipid determination field 104j and "LDL" in the renal function determination field 104m. "creatinine", "urine protein", and "eGFR" are set as target items. When the process proceeds to step 34, it is determined whether or not there is abnormal data (data with abnormal level = "5") in the target item among the non-symptomatic items of the mailed test analysis data. If there is abnormal data, the process proceeds to step 35, but if there is no abnormal data, the process returns to step 31. The target item in step 34 is an item that is a target of the composite analysis process among the pre-symptomatic items, and in this embodiment, "oxidative stress" in the urine test field 104p is set as the target item.

Then, when the process proceeds to step 35, a composite abnormality level is determined according to the abnormality level of the target item of the medical checkup item and the abnormality level of the target item of the non-disease item, and the determined composite abnormality level corresponds to It is set in the analysis item section. The composite abnormality level is data indicating the degree of abnormality in a health condition. In this embodiment, three levels of data ("9", "8", and "7"; the larger the numerical value, the greater the degree of abnormality) is assumed. The above-mentioned abnormality level is data indicating the degree of abnormality of each medical checkup item or pre-symptomatic item, and is therefore different from the composite abnormality level.

For example, in FIG. 10, in the analysis record r15, the abnormal level of "LDL" in the blood lipid determination field 104j is "9" and the abnormal level of "oxidative stress" in the urine test field 104p is "5". Data corresponding to both abnormality levels (in this embodiment, "7") is determined to be a composite abnormality level. In addition, in the analysis records r23 and r24, the abnormal level is "9" for all three items "creatinine", "urine protein", and "eGFR" in the renal function determination field 104m, and the "oxidative stress" level in the urine test field 104p is "9". Since the abnormality level of " is "5", data corresponding to both abnormality levels ("8" in this embodiment) is determined to be the composite abnormality level. Note that, as corresponding analysis item sections, a first analysis item section 104ga and a second analysis item section 104gb are provided in the composite analysis data storage section 104g. In the first analysis item section 104ga, a composite abnormality level is set according to the abnormality level in the blood lipid determination field 104j and the abnormality level in the urine test field 104p. A composite abnormality level according to the abnormality level of the renal function determination field 104m and the abnormality level of the urine test field 104p is set in the second analysis item section 104gb. (“LDL”: Low Density Lipoprotein).

Following step 35, the process proceeds to step 36, where the process of writing the output record to the analysis data storage DB 104 is executed, and the process proceeds to step 37. In step 37, it is determined whether it is the end of the record (whether the last record has been read or not), and if it is the end of the record, the composite analysis process ends, but if it is not the end of the record, the process returns to step 31. The above process is repeated.

As described above, the composite analysis data storage section 104g has first and second analysis item sections 104ga and 104gb. For each of them, a composite abnormality level regarding oxidized LDL and CKD is set. In this embodiment, the degree of abnormality in the health condition is determined for two items, oxidized LDL and CKD, by a composite analysis process. The degree of abnormality regarding oxidized LDL is determined by a composite analysis according to the abnormal level of the LDL measurement result and the abnormal level of the oxidative stress test result. Further, the degree of abnormality related to CKD is determined by a composite analysis according to the abnormal level of the measurement result of renal function and the abnormal level of the test result of oxidative stress.

When the composite analysis process is completed, the process proceeds to step 13 in FIG. In step 13, the recommendation processing unit 91 and list creation unit 92 operate according to instructions from the CPU 11.

(Recommended product list creation process: S13)
In the recommended product list creation process, the recommendation processing section 91 (see FIG. 7) of the recommendation presentation processing section 90 operates as a recommended product determining means. The recommendation processing unit 91 generates recommended product data using the analysis data stored in the analysis data storage DB 104, and writes the recommended product data into the recommended product data file 114 (see FIG. 7). When the recommendation processing unit 91 reads the analysis data from the analysis data storage DB 104, it extracts health checkup items and non-sickness items that are abnormal items. Next, the recommendation processing unit 91 sets each of the medical examination items and/or pre-symptomatic items extracted as abnormal items as search keys, and refers to the recommended product determination table 111. Then, the recommendation processing unit 91 acquires the products or services (product code or service code) associated with each of the medical examination items and pre-symptomatic items, and determines them as the first recommended products. In addition, the recommendation processing unit 91 obtains information regarding the product or service (for example, product name, service name, price, function, manufacturer, instruction manual, etc.) according to the obtained product code or service code from the recommended product master 116. Recommended product data is generated by acquiring the acquired information and editing the acquired information as appropriate. Products and services that correspond to the recommended product data are recommended as being useful for improving measurement results of abnormal items or test results for non-disease items (abnormal results indicating that the abnormal level is not normal) or for behavioral changes for that purpose. goods and services.

When the list creation section 92 operates following the recommendation processing section 91, it reads recommended product data from the recommended product data file 114, edits it as appropriate, and creates a recommended product list L1. Once the recommended product list L1 is created, in FIG. 13, step 13 ends and the process proceeds to step 14, where an explanatory image generation process is executed.

When the explanatory image generation process is executed, the explanatory image search processing section 93 of the recommendation presentation processing section 90 is activated. The explanatory image search processing unit 93 executes a search process of the explanatory image DB 112 by setting the medical examination items and/or pre-symptomatic items extracted as abnormal items as search keys, and searches the corresponding explanatory image (or its explanatory image). (also referred to as explanatory image data) is obtained using the URL of the server where the image is stored. Furthermore, when the explanatory image search processing unit 93 writes the acquired explanatory image data into the explanatory image data file 115, in FIG. 13, step 14 is completed and the process proceeds to step 15, where a consultation reservation process is executed.

When the consultation reservation process is executed, the consultation reservation processing section 94 of the recommendation presentation processing section 90 is activated. The consultation reservation processing unit 94 sets the medical examination items and/or pre-symptomatic items extracted as abnormal items as search keys, refers to the consultation reservation DB 113, and selects the corresponding consultation reservation destination (doctor, nurse, pharmacist, etc.). Obtain information (phone number, email address, URL of a consultation reservation site, etc., also referred to as consultation reservation information) of a registered dietitian or other specialist. Further, the consultation reservation processing unit 94 appropriately edits the obtained consultation reservation destination information to create a consultation reservation list L2. When the consultation reservation list L2 is created, in FIG. 13, step 15 is completed and the process proceeds to step 16, where list/image presentation processing is executed.

The list/image presentation process is executed by the presentation processing section 95 of the recommendation presentation processing section 90. When the presentation processing section 95 operates, the communication control section 15 and the communication processing section 16 operate, and data communication is executed with the user terminal device 30. Subsequently, the presentation processing unit 95 presents the recommended product list L1 to the user terminal device 30. As shown in FIG. 25, in the user terminal device 30, a list acquisition unit 401 and a list display control unit 402 operate according to instructions from the CPU 31 to acquire a recommended product list L1 presented from the support server 10, and The product list L1 is displayed on the liquid crystal display section 35.

Further, the presentation processing unit 95 reads the explanatory image data stored in the explanatory image data file 115 and transmits the explanatory image data to the user terminal device 30, thereby presenting the explanatory image. As shown in FIG. 25, in the user terminal device 30, the explanatory image acquisition unit 403 operates according to instructions from the CPU 31 to acquire explanatory image data transmitted from the support server 10.

Furthermore, the presentation processing unit 95 presents the consultation reservation list L2 to the user terminal device 30. As shown in FIG. 25, in the user terminal device 30, a list acquisition unit 401 and a list display control unit 402 operate according to instructions from the CPU 31 to acquire a consultation reservation list L2 presented from the support server 10, and obtain the consultation reservation list L2. The reservation list L2 is displayed on the liquid crystal display section 35.

As described above, in the health management support system 1 having the support server 10 according to the embodiment of the present invention, users (members) can use the user terminal device 30 to input their own medical examination data and test data. I can do it. The support server 10 performs analysis processing to determine the abnormality level for each health checkup item and pre-symptomatic item (examination item). If an abnormal health checkup item or non-disease item is found in the analysis process, we will provide products and services accordingly (for example, if your blood pressure is higher than the standard value and the abnormality level = "9", A recommended product list L1 is presented that includes supplements effective in lowering blood pressure, facilities suitable for exercise, etc. In addition, we also provide explanatory images that include measurement results for health checkup items, test results for pre-disease items, normal values, abnormal values, etc., and a consultation reservation list that is effective for making consultation reservations with specialists such as doctors, nurses, pharmacists, and registered dietitians. is presented. The recommended product list L1 may include supplements as well as commercially available medicines. In such a case, it is preferable that purchasing medicines using the recommended product list L1 and consulting with a specialist such as a pharmacist (online consultation) are combined. This is because it is desirable for individuals to be required to consult and confirm with a doctor or pharmacist before purchasing medicines.

By the way, with the conventional technology, 1) it is troublesome to check whether there is an abnormality in the health condition, and 2) it is difficult to understand the meaning of the medical examination results, the diagnostic items, and the influence of the diagnostic results on the health condition. For some reason, individuals were unable to improve their health care.

In this regard, in the support server 10 according to the present embodiment, analysis processing is performed on the measurement results of each health checkup item and the test results of pre-symptomatic items, so that the individual can check whether or not there is an abnormality in each item. There is no need to perform troublesome confirmations. In addition, if an individual refers to the presented explanatory image, it is possible to understand the meaning of the health checkup items and disease-free items and the degree of abnormality. When an abnormality is found, it is also possible to learn about products and services that are useful for improving the problem and changing behavior accordingly. As a result, even users who are reluctant to manage their health can easily grasp their own health status, improve their own health management, and improve their mental and physical condition. As a result, individuals become more interested in their health conditions, and individuals are motivated to visit medical institutions.

In this way, the health management support service provided by the support server 10 expands support more than ever before, allowing as many individuals as possible to improve their own health management and improve their physical and mental conditions. The health management support service provided by the support server 10 is extremely effective, especially for individuals who are reluctant to manage their health.

Furthermore, since the support server 10 performs a composite analysis process in addition to the individual analysis process, it is possible to find out abnormalities (or even the possibility of an abnormality) regarding the health condition that cannot be detected by the individual analysis process alone. If an abnormality is found, explanatory images, products, and services used to explain the problem are presented, and it is also possible to make an appointment for a consultation. In this respect, the health management support service provided by the support server 10 is extremely effective even for individuals who are proactive about health management.

In the individual analysis process by the support server 10, the analysis process is executed for each of the measurement results of the medical checkup items and the test results of the pre-symptomatic items. Through individual analysis processing, the abnormality level of each of the health checkup items and pre-symptomatic items is grasped.

However, for example, it is extremely difficult to understand the degree of abnormality regarding oxidized LDL and the degree of abnormality regarding CKD through individual analysis processing. For example, in the analysis records r11, r12, and r13 of the analysis data in FIG. 10, "9" is set as the abnormal level for "LDL" in the blood lipid determination field 104j. From these, it is understood that the user with the member code "A001" has a high level of abnormality regarding "LDL" (also referred to as bad cholesterol). As shown in analysis records r11 and r12, the user with member code "A001" had annual medical checkups in January 2019 and January 2020, and it was understood that he had a high level of abnormality in "LDL". Ru. However, since the user with the member code "A001" has not undergone a mail inspection (use classification="1"), the analysis records r11 and r12 are excluded from the composite analysis process.

Thereafter, as shown in the analysis records r13 and r15, when the user with the membership code "A001" undergoes a mail examination in the same year and month as the health checkup, an analysis record with the usage category="3" is generated. Then, as shown in the analysis record r15, "5" is set as the abnormal level in "oxidative stress" in the urine test field 104p. From this, it can be understood that the user with the member code "A001" has a high degree of abnormality in "oxidative stress."

"Oxidative stress" is determined according to the amount of active oxygen in the body. Therefore, the fact that "5" is set as the highest abnormal level means that there is a considerable amount of active oxygen present in the body. When active oxygen increases, it combines with "LDL" and becomes oxidized LDL. When oxidized LDL enters the blood vessel wall, it is taken up by macrophages, a type of white blood cell, and foam cells are formed. It is known that if this situation continues, arteriosclerosis occurs. Arteriosclerosis is considered to be a cause of serious diseases such as myocardial infarction and cerebral infarction.

As described above, an increase in oxidized LDL, which is the cause of arteriosclerosis, is detected by the support server 10 executing the complex analysis process. In this way, the combined analysis process has the effect of providing the user with an opportunity to find and deal with abnormalities that cannot be found in the individual analysis process, thereby providing the user with an opportunity to maintain a good state of health. be.

Second Embodiment Next, the configuration of a health management support server 210 (a health management support system including a health management support server 210) according to a second embodiment will be described with reference to FIGS. 18 and 22. A health management support system (not shown) according to the second embodiment includes a health management support server 210 shown in FIG. 18 instead of the health management support server 10. The health management support server 210 is different from the health management support server 10 in that it includes a secular change analysis processing section 120, a secular change analysis DB 121, and the recommendation presentation processing shown in FIG. 22 instead of the recommendation presentation processing section 90. The difference is that a portion 190 is included.

The aging analysis processing unit 120 performs aging analysis processing, which will be described later, to generate aging analysis data. Further, the aging analysis processing unit 120 writes the generated aging analysis data into the aging analysis DB 121. In the secular change analysis process, a secular abnormality or a secular deterioration, which will be described later, is extracted based on the secular change in the abnormal level of the measurement result of each health checkup item or the abnormal level of the abnormal level of the test result of each pre-symptomatic item.

(Aging analysis processing)
The secular change analysis processing unit 120 then executes the secular change analysis process according to the flowchart shown in FIG. When the secular change analysis processing unit 120 starts the secular change analysis process, the process proceeds to step 61, and the analysis data from the analysis data storage DB 104 is read. Next, when the process proceeds to step 62, the analysis data is sorted in the order of membership code>medical examination month>usage category. In the subsequent step 63, it is determined whether or not there is data that satisfies the aging extraction conditions described later in the analysis data record (analysis record). If there is data that satisfies the aging extraction conditions, the process proceeds to step 64, and if there is no data that satisfies the aging extraction conditions, the processing proceeds to step 65. In step 64, secular change analysis data including the relevant items (medical examination items, inspection items) is written into the secular change analysis DB 121. In step 65, it is determined whether there is any remaining analysis data. If there is any remaining analysis data, the process returns to step 63, but if there is no remaining analysis data, the aging analysis process ends.

For example, as shown in FIG. 10, in analysis records r21, r22, r23, and r24, the user with member code "G007" selects the items ("creatinine", "urine protein", "eGFR") in the renal function determination field 104m. "9" is set as the abnormal level. There are multiple analysis records in which "9" is set as the abnormality level for the same item. From these analysis records, secular abnormalities (a state in which the abnormality level continues to be "9", which is not normal) are extracted.

In addition, in analysis records r13, r14, and r15, the user with member code "A001" has abnormal levels set to "3", "4", and "5" in "oxidative stress" in the urine test data field 104p. ing. In analysis records r13, r14, and r15, the abnormality level is changing so as to become progressively worse. Users with member code "G007" also have "3", "4", and "5" set as abnormal levels for "oxidative stress" in analysis records r25, r22, and r23, and the abnormal levels gradually worsen. It is changing like this. In these cases, analysis records r25, r22, and r23 have secular changes in which the abnormality level gradually worsens, so aging deterioration (a state in which the abnormality level gradually worsens) is extracted from these analysis records. .

Extracting the above aging abnormality or aging deterioration corresponds to the case where the aging extraction condition is satisfied. In that case, secular change analysis data including the relevant health checkup items and test items is generated from the relevant analysis record.

As shown in FIG. 22, compared to the recommendation presentation processing section 90, the recommendation presentation processing section 190 performs recommendation processing instead of the recommendation processing section 91, explanation image search processing section 93, and consultation reservation processing section 94. The difference is that it includes a section 191, an explanation image search processing section 193, and a consultation reservation processing section 194.

The recommendation processing unit 191 is different from the recommendation processing unit 91 in that in addition to the analysis data storage DB 104, a secular change analysis DB 121 is input. The explanation image search processing section 193 differs from the explanation image search processing section 93 in that in addition to the analysis data storage DB 104, a secular change analysis DB 121 is input. The consultation reservation processing section 194 differs from the consultation reservation processing section 94 in that in addition to the analysis data storage DB 104, a secular change analysis DB 121 is input.

Since the recommendation processing unit 191 receives the secular change analysis DB 121 in addition to the analysis data storage DB 104, it determines a product or service according to the secular change analysis data of the secular change analysis DB 121 as the second recommended product. Therefore, products or services according to the secular change analysis data are also listed on the recommended product list L1 as second recommended products and presented to the user terminal device 30. The second recommended product is a product or service based on secular change analysis data, so it is recommended to improve measurement results or test results (abnormal results) that indicate abnormality or deterioration over time and that the abnormality level is not normal. Goods or services useful for behavioral change. Further, an explanation image according to the secular change analysis data is written as a secular change explanation image in the explanation image data file 115, and a reservation destination according to the secular change analysis data is also added to the consultation list L2. The secular change explanation image is presented to the user terminal device 30 together with the explanation image.

As described above, the health management support server 210 (health management support system including the health management support server 210) according to the second embodiment generates secular change analysis data. Abnormalities may not be detected using only health checkup data or pre-symptomatic test data. Even if an abnormality is found in individual health checkup data or pre-symptomatic test data, users who are passive about their health often do not take any action such as visiting a medical institution.

However, if an abnormality is found multiple times, the abnormality continues, and even if the degree of the abnormality does not change, there is a possibility that some other abnormality exists. For example, the user with the member code "G007" continues to have "9" set in the renal function determination field 104m in the analysis records r21, r22, and r23. Therefore, it is assumed that the decline in renal function is not temporary, but is becoming chronic. Since such a health condition is extracted by the health management support server 210, the user can grasp the health condition from the recommended product list L1 and the explanatory image and manage his or her own health condition. Note that in the health management support server 210 as well, similarly to the support server 10 according to the first embodiment, purchasing of medicines using the recommended product list L1 and consultation with experts such as pharmacists (online consultation) are provided as a set. It is preferable that the

Third Embodiment Next, the configuration of a health management support server 310 (a health management support system including a health management support server 310) according to a third embodiment will be described with reference to FIGS. 19 and 23. A health management support system (not shown) according to the third embodiment includes a health management support server 310 shown in FIG. 19 instead of the health management support server 210. The health management support server 310 is different from the health management support server 210 in that it includes a predicted change analysis processing section 130, a predicted change analysis DB 131, and the recommendation presentation processing shown in FIG. 23 instead of the recommendation presentation processing section 90. The difference is that a portion 290 is included.

The predicted change analysis processing unit 130 performs predicted change analysis processing and generates predicted change analysis data. Further, the predicted change analysis processing unit 130 writes the generated predicted change analysis data into the predicted change analysis DB 131. In the predicted change analysis process, a future abnormality level is predicted based on the analysis data, based on the abnormality level of the measurement result of each health checkup item or the abnormality level of the test result of each pre-symptomatic item. The future abnormality level is data indicating the degree of abnormality in the future of each measurement result or inspection result.

(Predicted change analysis processing)
The predicted change analysis processing unit 130 then executes the predicted change analysis process according to the flowchart shown in FIG. The predicted change analysis process is different from the above-described secular change analysis process in that steps 73 and 74 are executed instead of steps 63 and 64.

After steps 61 and 62 are executed in the same way as in the secular change analysis process, the process proceeds to step 73. In step 73, the predicted change analysis processing unit 130 determines whether or not there is data that satisfies the prediction generation conditions described later in the analysis data record (analysis record). If there is data that satisfies the prediction generation conditions, the process proceeds to step 74, and if there is no data that satisfies the prediction generation conditions, the process proceeds to step 65. Further, when the process proceeds to step 74, predicted change analysis data including the corresponding items (medical examination items, test items) is written into the predicted change analysis DB 131. After step 74, step 65 is executed similarly to the secular change analysis process.

For example, as shown in FIG. 10, in the analysis records r21, r22, r23, and r24, the user with the membership code "G007" has the medical examination items ("creatinine", "urinary protein", "eGFR") in the renal function determination field 104m. "9" indicating that it is not normal is set as the abnormal level. There are multiple analysis records in which "9" is set. The past four analysis records r21, r22, r23, and r24, including the latest analysis record r24, are "January 2019," "January 2020," "January 2021," and "January 2022." This is analysis data of medical checkup data measured four times each year. This result indicates that the future abnormality level (the degree of abnormality of the measurement results in the future, for example, the abnormality level in the future of "January 2023" not shown) of the renal function determination field 104m is also not normal. " is predicted. Predicted change analysis data is generated to include the results of such prediction.

In addition, in the latest analysis record r24 and the previous analysis record r23, the user with the member code "G007" has the highest abnormality level "5" in "oxidative stress" in the urine test data field 104p. " is set. Therefore, the future abnormality level regarding "oxidative stress" in the urine test data field 104p can also be predicted to be "5". Predicted change analysis data is generated to include the results of such prediction.

As described above, the existence of an analysis record whose future abnormality level can be predicted (for example, two or more consecutive analysis records from the most recent) corresponds to a case where the prediction generation condition is satisfied. In that case, predicted change analysis data including the applicable health checkup items and test items is generated from the applicable analysis record.

As shown in FIG. 23, compared to the recommendation presentation processing section 190, the recommendation presentation processing section 290 performs recommendation processing instead of the recommendation processing section 191, explanation image search processing section 193, and consultation reservation processing section 194. The difference is that it includes a section 291, an explanation image search processing section 293, and a consultation reservation processing section 294.

The recommendation processing unit 291 is different from the recommendation processing unit 191 in that a predicted change analysis DB 131 is input in addition to the analysis data storage DB 104 and the secular change analysis DB 121. The explanation image search processing section 293 is different from the explanation image search processing section 193 in that a predicted change analysis DB 131 is inputted in addition to the analysis data storage DB 104 and the secular change analysis DB 121. The consultation reservation processing section 294 is different from the consultation reservation processing section 194 in that a predicted change analysis DB 131 is inputted in addition to the analysis data storage DB 104 and the secular change analysis DB 121.

In addition to the analytical data storage DB 104 and the secular change analysis DB 121, the recommended change analysis DB 131 is input to the recommendation processing unit 291, so that the recommendation processing unit 291 selects the product or service according to the predicted change analysis data of the predicted change analysis DB 131 as the third recommended product. Determine as. Therefore, the product or service according to the predicted change analysis data is also listed on the recommended product list L1 as the third recommended product and presented to the user terminal device 30. The third recommended product is a product or service based on predicted change analysis data, so it is useful for improving measurement results or test results (abnormal results) that indicate that the abnormal level is not normal from now to the future, or for behavioral changes for that purpose. goods or services. Further, an explanation image according to the predicted change analysis data is written as a predicted change analysis explanation image in the explanation image data file 115, and a reservation destination according to the predicted change analysis data is also added to the consultation list L2. The predicted change analysis explanation image is presented to the user terminal device 30 together with the secular change explanation image and the explanation image. Note that in the health management support server 310 as well, similarly to the support server 10 according to the first embodiment, purchasing of medicines using the recommended product list L1 and consultation with experts such as pharmacists (online consultation) are provided as a set. It is preferable that the

As described above, predicted change analysis data is generated in the health management support server 310 (health management support system including the health management support server 310) according to the third embodiment. When an abnormal level that is not normal has continued for the past several years, such as in the analysis records r21, r22, r23, and r24 mentioned above, it is predicted that the same abnormal level will continue in the next measurement result. can do. For example, in the case of CKD, it is recognized that the disease has developed when renal function decline continues for three years, so the predictive change analysis process by the health management support server 310 is effective in such cases. As in analysis records r21, r22, r23, and r24, abnormalities in renal function appear in multiple measurements, and as in analysis records r23 and r24, abnormalities in CKD due to composite analysis processing (complex abnormality level = "8") ) appears twice, and if the same CKD abnormality appears one more time, there is a high possibility that the onset of CKD will be confirmed. The predictive change analysis process performed by the health management support server 310 allows abnormalities in the health condition to be ascertained before such a situation occurs, thereby ensuring an opportunity to deal with it in advance. Therefore, the health management support server 310 makes it possible to realize even more effective health management.

(Modified example)
Furthermore, the user terminal device may be a high-performance mobile phone instead of a notebook computer. Note that the health management support program executed by the CPU 11 can be recorded on various recording media such as a magnetic recording medium, CD-ROM, and DVD, and can also be downloaded from a server (not shown) via a network. The support server 10 is not only configured by a server device, but can also be configured by a virtual server or the like built on a cloud computing service.

Further, in the above embodiment, the composite abnormality level is determined based on two points, oxidized LDL and CKD, by the composite analysis process. A composite abnormality level can be determined from a different perspective from oxidized LDL and CKD by a composite analysis of other health checkup items and test items. Note that the determination using the analysis data obtained in the analysis process of medical checkup items and test items according to the above embodiment is just an example, and may be different from the above embodiment.

The above description is an explanation of the embodiments of the present invention, and does not limit the apparatus and method of the present invention, and various modifications can be easily implemented. Furthermore, the present invention also includes devices or methods configured by appropriately combining components, functions, features, or method steps in each embodiment.

By applying the present invention, it is possible to support the health management of individuals so that as many individuals as possible can improve their own health management and improve their physical and mental conditions by effectively utilizing data related to their health conditions. INDUSTRIAL APPLICATION This invention can be utilized in the field of a health care support device and a health care support program.

DESCRIPTION OF SYMBOLS 1... Health management support system, 10,210,310... Health management support server, 11, 31... CPU, 30... User terminal device, 80... Medical examination/examination data analysis processing section, 81... Individual analysis processing section, 82... Composite analysis processing section, 90, 190, 290... Recommendation presentation processing section, 91, 191, 291... Recommendation processing section, 92... List creation section, 93, 193, 293... Explanation image search processing section, 94, 194, 294... Consultation reservation processing unit, 95... Presentation processing unit, 103... Medical examination/examination data storage DB, 104... Analysis data storage DB, 110... Judgment standard value table, 112... Recommended product determination table, 114... Recommended product data file, 120 ...Secular change analysis processing section, 130...Predicted change analysis processing section.

Claims (12)

Data that shows an individual's health condition, including health checkup data that shows the measurement results of multiple health checkup items such as blood pressure, blood lipids, liver function, and kidney function, as well as specimens such as urine, stool, and saliva. Using at least one of the pre-symptomatic test data showing the test results of each of a plurality of pre-symptomatic items, which are the test items related to pre-symptomatic diseases used, determine whether the measurement results of each of the health checkup items show an abnormality or not. an analysis processing means that performs an analysis process to determine the degree of abnormality of the test result of each non-symptomatic item, and generates analytical data that includes an abnormality level indicating the result of the analysis process for each of the health checkup items and non-symptomatic items; ,
An explanatory image storage that stores an explanatory image that explains the measurement results of each of the medical checkup items or the test results of each of the pre-symptomatic items, and includes an explanation of the contents of the medical checkup items and the pre-symptomatic items. means and
image search means for searching for a corresponding explanatory image according to the analysis data generated by the analysis processing means from among the explanatory images stored in the explanatory image storage means;
presenting means for presenting explanation image data that can specify the corresponding explanation image searched by the image search means or the recording position of the corresponding explanation image;
The analysis processing means includes an individual analysis processing unit that generates individual analysis data by performing the analysis processing on each of the health checkup data and pre-symptomatic test data, and a measurement result of any of the plurality of health checkup items. A composite analysis process is performed to determine the degree of abnormality of the health condition using the abnormal level of the test result for any of the plurality of pre-symptomatic items, and the result of the composite analysis process is a composite analysis processing unit that generates the analysis data so as to include a composite abnormality level according to the
The composite analysis processing unit is a health management support device that uses data, of the individual analysis data, on which the analysis processing has been performed on both the health checkup data and the pre-symptomatic test data, and which has been subjected to the composite analysis processing. . Data that shows an individual's health condition, including health checkup data that shows the measurement results of multiple health checkup items such as blood pressure, blood lipids, liver function, and kidney function, as well as specimens such as urine, stool, and saliva. Using at least one of the pre-symptomatic test data showing the test results of each of a plurality of pre-symptomatic items, which are the test items related to pre-symptomatic diseases used, determine whether the measurement results of each of the health checkup items show an abnormality or not. an analysis processing means that performs an analysis process to determine the degree of abnormality of the test result of each non-symptomatic item, and generates analytical data that includes an abnormality level indicating the result of the analysis process for each of the health checkup items and non-symptomatic items; ,
The analysis data generated by the analysis processing means has a different year and month when the health checkup or test from which the health checkup data or pre-symptomatic test data was created, and the individual Using a plurality of equal data, the abnormality level continues to be abnormal based on the secular change in the abnormality level of the measurement results of each of the plurality of health checkup items or the test results of each of the plurality of pre-symptomatic items. A secular change analysis means that performs a secular change analysis process to extract a secular abnormality or a secular deterioration in which the abnormality level has worsened, and generates secular change analysis data indicating the analysis result;
An explanatory image storage that stores an explanatory image that explains the measurement results of each of the medical checkup items or the test results of each of the pre-symptomatic items, and includes an explanation of the contents of the medical checkup items and the pre-symptomatic items. means and
image search means for searching for a corresponding explanatory image according to the analysis data generated by the analysis processing means from among the explanatory images stored in the explanatory image storage means;
presenting means for presenting explanation image data that can specify the corresponding explanation image searched by the image search means or the recording position of the corresponding explanation image;
The analysis processing means includes an individual analysis processing unit that generates individual analysis data by performing the analysis processing on each of the health checkup data and pre-symptomatic test data, and a measurement result of any of the plurality of health checkup items. A composite analysis process is performed to determine the degree of abnormality of the health condition using the abnormal level of the test result for any of the plurality of pre-symptomatic items, and the result of the composite analysis process is a composite analysis processing unit that generates the analysis data so as to include a composite abnormality level according to the
The composite analysis processing unit is a health management support device that uses data, of the individual analysis data, on which the analysis processing has been performed on both the health checkup data and the pre-symptomatic test data, and which has been subjected to the composite analysis processing. . Data that shows an individual's health condition, including health checkup data that shows the measurement results of multiple health checkup items such as blood pressure, blood lipids, liver function, and kidney function, as well as specimens such as urine, stool, and saliva. Using at least one of the pre-symptomatic test data showing the test results of each of a plurality of pre-symptomatic items, which are the test items related to pre-symptomatic diseases used, determine whether the measurement results of each of the health checkup items show an abnormality or not. an analysis processing means that performs an analysis process to determine the degree of abnormality of the test result of each non-symptomatic item, and generates analytical data that includes an abnormality level indicating the result of the analysis process for each of the health checkup items and non-symptomatic items; ,
The analysis data generated by the analysis processing means has a different year and month when the health checkup or test from which the health checkup data or pre-symptomatic test data was created, and the individual Using a plurality of equal data, the abnormality level continues to be abnormal based on the secular change in the abnormality level of the measurement results of each of the plurality of health checkup items or the test results of each of the plurality of pre-symptomatic items. A secular change analysis means that performs a secular change analysis process to extract a secular abnormality or a secular deterioration in which the abnormality level has worsened, and generates secular change analysis data indicating the analysis result;
The analysis data generated by the analysis processing means, the data of different years and months and the same individual are used to calculate the measurement results of each of the plurality of health checkup items or each of the plurality of pre-symptomatic items. A predictive change analysis process is performed to predict a future abnormality level indicating the degree of abnormality in the future of each of the measurement results or test results based on the secular abnormality or the secular deterioration of the abnormality level of the test results, and Predicted change analysis means for generating predicted change analysis data indicating a prediction result;
An explanatory image storage that stores an explanatory image that explains the measurement results of each of the medical checkup items or the test results of each of the pre-symptomatic items, and includes an explanation of the contents of the medical checkup items and the pre-symptomatic items. means and
image search means for searching for a corresponding explanatory image according to the analysis data generated by the analysis processing means from among the explanatory images stored in the explanatory image storage means;
presenting means for presenting explanation image data that can specify the corresponding explanation image searched by the image search means or the recording position of the corresponding explanation image;
The analysis processing means includes an individual analysis processing unit that generates individual analysis data by performing the analysis processing on each of the health checkup data and pre-symptomatic test data, and a measurement result of any of the plurality of health checkup items. A composite analysis process is performed to determine the degree of abnormality of the health condition using the abnormal level of the test result for any of the plurality of pre-symptomatic items, and the result of the composite analysis process is a composite analysis processing unit that generates the analysis data so as to include a composite abnormality level according to the
The composite analysis processing unit is a health management support device that uses data, of the individual analysis data, on which the analysis processing has been performed on both the health checkup data and the pre-symptomatic test data, and which has been subjected to the composite analysis processing. . Used to identify products or services that are useful for improving the abnormality in the measurement results of each of the health checkup items or the abnormality in the test results for each of the pre-symptomatic items, or for changing behavior to improve the abnormality. Recommended product registration means in which a product service code as data and the medical examination item or the pre-symptomatic item are registered in association with each other;
is associated with the medical examination item or the pre-symptomatic item according to the analysis data generated by the analysis processing means among the medical examination item or pre-symptomatic item registered in the recommended product registration means. The health management support device according to any one of claims 1 to 3, further comprising recommended product determining means for determining a product or service corresponding to the product or service code as a recommended product. Used to identify products or services that are useful for improving the abnormality in the measurement results of each of the health checkup items or the abnormality in the test results for each of the pre-symptomatic items, or for changing behavior to improve the abnormality. Recommended product registration means in which a product service code as data and the medical examination item or the pre-symptomatic item are registered in association with each other;
is associated with the medical examination item or the pre-symptomatic item according to the analysis data generated by the analysis processing means among the medical examination item or pre-symptomatic item registered in the recommended product registration means. A product or service corresponding to the product/service code is determined as a first recommended product, and is associated with the medical checkup item or the disease-free item according to the secular change analysis data generated by the secular change analysis means. 3. The health management support device according to claim 2, further comprising recommended product determining means for determining a product or service corresponding to the product/service code as the second recommended product. Used to identify products or services that are useful for improving the abnormality in the measurement results of each of the health checkup items or the abnormality in the test results for each of the pre-symptomatic items, or for changing behavior to improve the abnormality. Recommended product registration means in which a product service code as data and the medical examination item or the pre-symptomatic item are registered in association with each other;
is associated with the medical examination item or the pre-symptomatic item according to the analysis data generated by the analysis processing means among the medical examination item or pre-symptomatic item registered in the recommended product registration means. A product or service corresponding to the product/service code is determined as a first recommended product, and is associated with the medical checkup item or the disease-free item according to the secular change analysis data generated by the secular change analysis means. A product or service corresponding to the product/service code is determined as a second recommended product, and the product or service is associated with the medical examination item or the disease-free item according to the predicted change analysis data generated by the predicted change analysis means. 4. The health management support device according to claim 3, further comprising recommended product determining means for determining a product or service corresponding to the product/service code as a third recommended product. The image search means searches for a secular change explanatory image according to the secular change analysis data generated by the secular change analysis means from among the explanatory images stored in the explanatory image storage means,
3. The health management support device according to claim 2, wherein the presenting means presents the aging change explanatory image together with the corresponding explanatory image or the explanatory image data. The image retrieval means searches for a secular change explanatory image corresponding to the secular change analysis data generated by the secular change analysis means among the explanatory images stored in the explanatory image storage means and the predicted change analysis means. Searching for a predicted change analysis explanation image according to the generated predicted change analysis data,
4. The health management support device according to claim 3, wherein the presentation means presents the secular change explanation image and the predicted change analysis explanation image together with the corresponding explanation image or the explanation image data. 7. The presentation means presents the first recommended product, second recommended product, and third recommended product determined by the recommended product determination means together with the corresponding explanation image or the explanation image data. Health management support device. A health management support program for making a computer function as a health management support device, which uses the computer to collect data indicating the health status of an individual, such as blood pressure, blood lipids, liver function, kidney function, etc. Health checkup data that shows the measurement results of each health checkup item, and Mibyo test data that shows the test results of each of multiple Mibyo items that are test items related to Mibyou using specimens such as urine, stool, and saliva. Using one, analysis processing is performed to determine whether the measurement results of each of the health checkup items show an abnormality or the degree of abnormality of the test results of each of the pre-symptomatic items, and the abnormality level indicating the result of the analysis processing is performed. analysis processing means for generating analysis data included for each of the health checkup items and disease-free items;
An explanatory image storage that stores an explanatory image that explains the measurement results of each of the medical checkup items or the test results of each of the pre-symptomatic items, and includes an explanation of the contents of the medical checkup items and the pre-symptomatic items. image search means for searching for a corresponding explanatory image according to the analysis data generated by the analysis processing means from among the explanatory images stored in the means;
functioning as a presentation means for presenting explanatory image data capable of specifying the corresponding explanatory image searched by the image retrieval means or the recording position of the corresponding explanatory image;
an individual analysis processing unit that generates individual analysis data by executing the analysis processing on each of the health checkup data and pre-symptomatic test data; and a measurement result of any of the plurality of health checkup items. A composite analysis process is performed to determine the degree of abnormality of the health condition using the abnormal level of the test result for any of the plurality of pre-symptomatic items, and the result of the composite analysis process is function as a composite analysis processing unit that generates the analysis data so as to include the corresponding composite abnormality level,
When functioning as the composite analysis processing unit, data of the individual analysis data that has been subjected to the analysis processing on both the health checkup data and the pre-symptomatic test data and is subject to the composite analysis processing is used. Health management support program. A health management support program for making a computer function as a health management support device, which uses the computer to collect data indicating the health status of an individual, such as blood pressure, blood lipids, liver function, kidney function, etc. Health checkup data that shows the measurement results of each health checkup item, and Mibyo test data that shows the test results of each of multiple Mibyo items that are test items related to Mibyou using specimens such as urine, stool, and saliva. Using one, analysis processing is performed to determine whether the measurement results of each of the health checkup items show an abnormality or the degree of abnormality of the test results of each of the pre-symptomatic items, and the abnormality level indicating the result of the analysis processing is performed. analysis processing means for generating analysis data included for each of the health checkup items and disease-free items;
The analysis data generated by the analysis processing means has a different year and month when the health checkup or test from which the health checkup data or pre-symptomatic test data was created, and the individual Using a plurality of equal data, the abnormality level continues to be abnormal based on the secular change in the abnormality level of the measurement results of each of the plurality of health checkup items or the test results of each of the plurality of pre-symptomatic items. A secular change analysis means that performs a secular change analysis process to extract a secular abnormality or a secular deterioration in which the abnormality level has worsened, and generates secular change analysis data indicating the analysis result;
An explanatory image storage that stores an explanatory image that explains the measurement results of each of the medical checkup items or the test results of each of the pre-symptomatic items, and includes an explanation of the contents of the medical checkup items and the pre-symptomatic items. image search means for searching for a corresponding explanatory image according to the analysis data generated by the analysis processing means from among the explanatory images stored in the means;
functioning as a presentation means for presenting explanatory image data capable of specifying the corresponding explanatory image searched by the image retrieval means or the recording position of the corresponding explanatory image;
an individual analysis processing unit that generates individual analysis data by executing the analysis processing on each of the health checkup data and pre-symptomatic test data; and a measurement result of any of the plurality of health checkup items. A composite analysis process is performed to determine the degree of abnormality of the health condition using the abnormal level of the test result for any of the plurality of pre-symptomatic items, and the result of the composite analysis process is function as a composite analysis processing unit that generates the analysis data so as to include the corresponding composite abnormality level,
When functioning as the composite analysis processing unit, data of the individual analysis data that has been subjected to the analysis processing on both the health checkup data and the pre-symptomatic test data and is subject to the composite analysis processing is used. Health management support program. A health management support program for making a computer function as a health management support device, which uses the computer to collect data indicating the health status of an individual, such as blood pressure, blood lipids, liver function, kidney function, etc. Health checkup data that shows the measurement results of each health checkup item, and Mibyo test data that shows the test results of each of multiple Mibyo items that are test items related to Mibyou using specimens such as urine, stool, and saliva. Using one, analysis processing is performed to determine whether the measurement results of each of the health checkup items show an abnormality or the degree of abnormality of the test results of each of the pre-symptomatic items, and the abnormality level indicating the result of the analysis processing is performed. analysis processing means for generating analysis data included for each of the health checkup items and disease-free items;
The analysis data generated by the analysis processing means has a different year and month when the health checkup or test from which the health checkup data or pre-symptomatic test data was created, and the individual Using a plurality of equal data, the abnormality level continues to be abnormal based on the secular change in the abnormality level of the measurement results of each of the plurality of health checkup items or the test results of each of the plurality of pre-symptomatic items. A secular change analysis means that performs a secular change analysis process to extract a secular abnormality or a secular deterioration in which the abnormality level has worsened, and generates secular change analysis data indicating the analysis result;
The analysis data generated by the analysis processing means, the data of different years and months and the same individual are used to calculate the measurement results of each of the plurality of health checkup items or each of the plurality of pre-symptomatic items. A predictive change analysis process is performed to predict a future abnormality level indicating the degree of abnormality in the future of each of the measurement results or test results based on the secular abnormality or the secular deterioration of the abnormality level of the test results, and Predicted change analysis means for generating predicted change analysis data indicating a prediction result;
An explanatory image storage that stores an explanatory image that explains the measurement results of each of the medical checkup items or the test results of each of the pre-symptomatic items, and includes an explanation of the contents of the medical checkup items and the pre-symptomatic items. image search means for searching for a corresponding explanatory image according to the analysis data generated by the analysis processing means from among the explanatory images stored in the means;
functioning as a presentation means for presenting explanatory image data capable of specifying the corresponding explanatory image searched by the image retrieval means or the recording position of the corresponding explanatory image;
an individual analysis processing unit that generates individual analysis data by executing the analysis processing on each of the health checkup data and pre-symptomatic test data; and a measurement result of any of the plurality of health checkup items. A composite analysis process is performed to determine the degree of abnormality of the health condition using the abnormal level of the test result for any of the plurality of pre-symptomatic items, and the result of the composite analysis process is function as a composite analysis processing unit that generates the analysis data so as to include the corresponding composite abnormality level,
When functioning as the composite analysis processing unit, data of the individual analysis data that has been subjected to the analysis processing on both the health checkup data and the pre-symptomatic test data and is subject to the composite analysis processing is used. Health management support program.

Images