Health data utilization support device and health data utilization support method

Abstract

To enable the collection and utilization of a wide range of health data to be continued in an efficient and economically meaningful manner among stakeholders.SOLUTION: A health data utilization support device 100 includes: a storage device 101 that holds demand information on biological observation data from data users who wish to use the data; and a computing device 104 that executes processing for acquiring observation data on a predetermined data provider from each of data providers' terminals, processing for determining the supply and demand balance of the observation data based on the amount of the acquired observation data and the demand information on the observation data, and processing for specifying an incentive to be given to the data provider for the observation data according to the supply and demand balance.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a health data utilization support device and a health data utilization support method.

Background Art

[0002] The number of patients with various diseases for which treatment methods have not yet been established is increasing year by year. The causes and onset mechanisms of such diseases involve various factors, and in many cases, they cannot be identified. Therefore, it is necessary to monitor health-related data including an individual's lifestyle over a long period and comprehensively analyze the relevance to the disease. As prior art related to data collection and analysis regarding medical care and health, for example, a medical data analysis system and program (see Patent Document 1) that obtain effective information for formulating a medical function expansion plan of one's own hospital have been proposed.

[0003] This technology is a medical data analysis system that analyzes target acute care hospitals based on medical data submitted and compiled from acute care hospitals, comprising: an acute care hospital database that stores acute care hospital data; a medical database that stores medical data of acute care hospitals; a target acute care hospital setting unit that sets the target acute care hospitals; an area setting unit that sets a predetermined area including the target acute care hospitals; a target medical data acquisition unit that acquires medical data of the target acute care hospitals set by the target acute care hospital setting unit from the acute care hospital database and the medical database; and a unit that acquires medical data of the target acute care hospitals included in the set area from the acute care hospital database and the medical database. The present invention relates to a medical data analysis system characterized by comprising: a total medical data acquisition unit that acquires medical data from any acute care hospital (including the aforementioned target acute care hospital); a classified target medical data acquisition unit that acquires classified target medical data by classifying the target medical data acquired by the aforementioned target medical data acquisition unit according to major diagnostic groups; a classified all medical data acquisition unit that acquires classified all medical data by classifying the total medical data acquired by the aforementioned total medical data acquisition unit according to major diagnostic groups; and a share calculation unit that calculates the share of the aforementioned target acute care hospital within a set area for each major diagnostic group by dividing the classified target medical data for each major diagnostic group by the similarly classified all medical data for each major diagnostic group.

[0004] Furthermore, technologies have been proposed (see Patent Document 2) that enable both those associated with those in need of support and medical professionals to easily, quickly, and accurately assess the health status of those in need of support, thereby realizing a community-based integrated care system that appropriately coordinates care and medical services.

[0005] This technology relates to a medical support system that allows information about a person in need of support to be shared between a person in need of support, a person associated with that person, and a medical professional via a computer, wherein the computer comprises: a health information acquisition unit that acquires health information indicating the health status of the person in need of support; a score calculation unit that converts the health information into numerical values ​​and scores them according to predetermined evaluation items of a medical institution, and calculates a score indicating the health status of the person in need of support; a common language conversion unit that converts the score into a common language that is an indicator of health status and can be commonly understood by both the person in need of support and the medical professional; an admission necessity determination unit that determines whether hospitalization is necessary when the score does not meet predetermined requirements; and a result display unit that displays the score or the common language. [Prior art documents] [Patent Documents]

[0006] [Patent Document 1] Japanese Patent Publication No. 2017-4419 [Patent Document 2] WO2018 / 084139 [Overview of the project] [Problems that the invention aims to solve]

[0007] Regarding the collection and analysis of health-related data as described above, not only public institutions that aim to maintain and improve the health of the nation, but also financial institutions and insurance companies that develop and manage financial and insurance products are showing great interest. Of course, each of us citizens also has a desire to maintain and improve our own health, and to gain peace of mind by contracting appropriate medical insurance, and therefore we have high expectations for the utilization of health-related data. On the other hand, no platform has been proposed to efficiently collect and utilize such health-related data in a wide range of ways, nor has a mechanism been proposed to ensure the effective use of the data collected and analyzed there.

[0008] Therefore, the objective of the present invention is to provide a technology that enables the collection and utilization of a wide range of health data to be continued in an efficient, economically meaningful manner among stakeholders. [Means for solving the problem]

[0009] The present invention, which solves the above problems, is characterized by comprising: a storage device that holds demand information regarding observation data for data users who wish to use biological observation data; a processing unit that performs a process to acquire observation data relating to a data provider from the terminal of each predetermined data provider; a processing unit that performs a process to determine the supply and demand balance of the observation data based on the amount of the acquired observation data and the demand information relating to the observation data; and a processing unit that performs a process to identify an incentive to be given to the data provider with respect to the observation data according to the supply and demand balance. Furthermore, the present invention provides a method for supporting the utilization of health data, characterized in that the information processing device includes a storage device that holds demand information regarding observation data of data users who wish to use biological observation data, and performs the following processes: acquiring observation data relating to a data provider from the terminal of each predetermined data provider; determining the supply and demand balance of the observation data based on the amount of the acquired observation data and the demand information relating to the observation data; and identifying an incentive to be given to the data provider with respect to the observation data in accordance with the supply and demand balance. [Effects of the Invention]

[0010] According to the present invention, the collection and utilization of a wide range of health data can be continued in an efficient, economically meaningful manner among stakeholders. [Brief explanation of the drawing]

[0011] [Figure 1] This is a network configuration diagram including the health data utilization support device of this embodiment. [Figure 2] This figure shows an example of the hardware configuration of the health data utilization support device in this embodiment. [Figure 3] It is a diagram showing a configuration example of the health data DB in this embodiment. [Figure 4] It is a diagram showing a configuration example of the customer DB in this embodiment. [Figure 5] It is a diagram showing a configuration example of the demand DB in this embodiment. [Figure 6] It is a diagram showing a configuration example of the condition table in this embodiment. [Figure 7] It is a diagram showing a configuration example of the usage fee DB in this embodiment. [Figure 8] It is a diagram showing a flow example of the health data utilization support method in this embodiment. [Figure 9] It is a diagram showing a flow example of the health data utilization support method in this embodiment. [Figure 10] It is a diagram showing a flow example of the health data utilization support method in this embodiment. [Figure 11] It is a diagram showing a screen example in this embodiment. [Figure 12] It is a diagram showing a screen example in this embodiment. [Figure 13] It is a diagram showing a screen example in this embodiment.

Mode for Carrying Out the Invention

[0012] <Network Configuration> Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. FIG. 1 is a network configuration diagram including a health data utilization support apparatus 100 according to this embodiment. The health data utilization support apparatus 100 shown in FIG. 1 is a computer that enables the efficient and economically meaningful continuous collection and utilization of a wide range of health data among stakeholders.

[0013] As shown in FIG. 1, the health data utilization support device 100 of the present embodiment is communicably connected to a customer terminal 200, a data utilization infrastructure 300, an insurance company system 400, etc. via a network 1. Therefore, these may be collectively referred to as a health data utilization support system 10.

[0014] The health data utilization support device 100 of the present embodiment is, for example, operated by a regional financial institution having a customer who has purchased an insurance product as a customer, obtains observation data regarding the health of the customer from the customer, and charges various public institutions such as the Ministry of Health, Labour and Welfare, and insurance companies that sell and develop insurance products. It can be said that it is a device that provides a series of services that provides the data and returns the consideration for providing the observation data to the customer in the form of various incentives such as insurance premium reduction.

[0015] On the other hand, the customer terminal 200 is a terminal used when the above-mentioned customer obtains an incentive as a consideration by distributing observation data to the health data utilization support device 100 (and further providing data to an insurance company or the like through it).

[0016] Specifically, a smartphone, a tablet terminal, a personal computer, etc. can be assumed. Such a customer terminal 200 may itself be various wearable terminals such as a smartwatch, or various observation units such as a sphygmomanometer and an electrocardiogram sensor. Alternatively, a smartphone, a tablet terminal, or a personal computer may be configured to be connectable to such an observation unit by appropriate communication means (e.g., a short-range wireless communication unit or a LAN (Local Area Network)).

[0017] The data utilization infrastructure 300 is an infrastructure operated by an organization or group that utilizes the above-mentioned observation data regarding the health of the customer. Specifically, an information management infrastructure for the health and diseases of citizens and residents in the Ministry of Health, Labour and Welfare and various local governments can be assumed.

[0018] This data utilization platform 300 receives observational data from the health data utilization support device 100 via network 1, and becomes a system that performs or supports various statistical analyses and policy considerations by the organization or group concerned.

[0019] Furthermore, the insurance company system 400 is a system used by insurance companies to sell insurance products to the customers of the aforementioned regional financial institutions. Insurance companies intend to secure the customers of regional financial institutions as purchasers of insurance products, i.e., policyholders, and also wish to efficiently develop and propose insurance products that are suitable for such customers.

[0020] Therefore, insurance companies efficiently and promptly acquire desired observational data from their customers (e.g., numerical values ​​of health checkup items that have been identified as having a causal relationship with specific diseases, aging, or long-term care) from the health data utilization support device 100, and utilize this data for analysis and consideration for product development and product proposals. The insurance company system 400 is a system that supports these activities. <Hardware Configuration> Furthermore, the hardware configuration of the health data utilization support device 100 in this embodiment is as shown in Figure 2 below.

[0021] In other words, the health data utilization support device 100 comprises a storage device 101, a memory 103, a computing device 104, and a communication device 105.

[0022] Of these, the storage device 101 is composed of an appropriate non-volatile storage element such as an SSD (Solid State Drive) or a hard disk drive.

[0023] Furthermore, memory 103 is composed of volatile memory elements such as RAM (Random Access Memory).

[0024] Furthermore, the arithmetic unit 104 is a CPU (Central Processing Unit) that executes programs 102 stored in the storage device 101 by reading them into the memory 103, and performs overall control of the device itself, as well as performing various judgments, calculations, and control processes.

[0025] Furthermore, the communication device 105 is envisioned to be a network interface card or the like that connects to an appropriate network 1 such as the Internet and is responsible for communication processing with user terminals 200, data utilization infrastructure 300, insurance company systems 400, etc.

[0026] Furthermore, it is preferable that the health data utilization support device 100 also includes input devices such as a keyboard or mouse that accept key input or voice input from the user, and output devices such as a display or speaker that output processed data.

[0027] Furthermore, in addition to the program 102 for implementing the functions necessary for the health data utilization support device of this embodiment, the storage device 101 also stores at least the health data DB 125, customer DB 126, demand DB 127, condition table 128, and usage fee DB 129. However, details of these databases and tables will be described later. <Example Data Structure> Next, we will describe the various types of information used by the health data utilization support device 100 of this embodiment. Figure 3 shows an example of the health data DB 125 in this embodiment.

[0028] The health data DB125 of this embodiment is, for example, a database that stores observational data obtained regarding customers of a regional financial institution, such as numerical values ​​observed regarding biological functions using wearable devices (which may include customer terminals 200), and the results of health checkups conducted at workplaces, etc.

[0029] This health data DB125 is a collection of records that link data such as observation date and time, and observation data (observed target and observed value) to a customer ID that uniquely identifies the customer in question.

[0030] The aforementioned observational data may include the customer's blood pressure, pulse rate, blood glucose level, sleep duration, exercise level, and health checkup results (e.g., age, sex, height, weight, body fat percentage, blood and excretory test results, physician's findings based on imaging results of the respiratory and digestive systems, interview results, family structure and medical history, etc.).

[0031] Figure 4 also shows an example of the customer DB 126 in this embodiment. The customer DB 126 in this embodiment is a database that stores various types of customer information managed by a regional financial institution.

[0032] This customer database 126 is a collection of records that link data such as the customer's name, address, contact information, gender, age, accounts held, medical history, occupation, and financial products purchased, using a customer ID that uniquely identifies the customer as the key.

[0033] Furthermore, the data mentioned above, specifically the data related to financial products, may include values ​​such as the insurance premiums paid by the customer to a regional financial institution (or to an insurance company, etc., through a regional financial institution) and the premium reduction rate that would apply if the customer provided observational data.

[0034] Figure 5 also shows an example of the demand DB 127 in this embodiment. The demand DB 127 in this embodiment is a database that stores data such as the desired type, content, and required amount of observational data, which is provided by public institutions such as the government that operate the data utilization platform 300, and insurance companies that want to use observational data for product development and product proposals.

[0035] This demand database 127 is a collection of records that, for example, uses the name of the aforementioned public institution or insurance company (or an ID assigned to them according to a predetermined rule) as a key to identify the person (user) who has submitted a request for observation data. This records are linked to data such as the case ID, the age of the observation data provider, medical history, occupation, height, weight, subject of the observation data, acquisition period, and required amount.

[0036] Figure 6 also shows an example of the condition table 128 in this embodiment. The condition table 128 in this embodiment is a table that defines the weights of the number of demands and the number of supplies of observational data that can be collected from regional financial institutions and their customers and provided to external parties.

[0037] These weight values ​​will be applied to the calculation of usage fees for observational data by the data utilization platform 300 and insurance companies, etc.

[0038] This condition table 128 is generated and managed for each regional financial institution, for example, and is a collection of records that link data such as the number of demands and the number of supplies, along with their respective weight values, from various perspectives, including the medical history and occupation of customers who provide observation data to the regional financial institution, the period for which the observation data was acquired, and the target data.

[0039] In the example in Figure 6, under "medical history," the number of items in demand, the number of items provided, and their respective weight values ​​are defined for each item, such as stroke, cancer, arrhythmia, myocardial infarction, bronchial asthma, angina pectoris, and diabetes. Furthermore, for example, in "Bank A," a weight value of "35" is defined for the item "stroke," obtained by multiplying the weight value of the number of items in demand ("5") by the weight value of the number of items provided ("7").

[0040] Furthermore, the "occupation" category is a collection of records that link data such as the number of people in demand and supply, along with their respective weight values, for each category, including company employees, civil servants, self-employed / sole proprietors, company executives, freelancers, full-time housewives / househusbands, students, part-time workers, and unemployed. Additionally, for example, in "Bank A," the "company employee" category is defined as having a weight value of "8" which is obtained by multiplying the weight value of the number of people in demand ("8") by the weight value of the number of people supplied ("1").

[0041] Furthermore, the "acquisition period" is a collection of records that link data such as the number of items in demand and supply, along with their respective weight values, for each perspective: less than six months, six months to one year, one to two years, two to three years, three to four years, four to five years, and five years or more. In addition, for the "less than six months" item in "Bank A," a weight value of "1" is defined, which is obtained by multiplying the weight value of the number of items in demand ("1") by the weight value of the number of items supplied ("1").

[0042] Furthermore, the "target data" is a collection of records that link data such as the number of items in demand and supply for each aspect, including blood pressure, body temperature, sleep depth, heart rate, electrocardiogram, sweat volume, blood glucose level, and stress index, along with their respective weight values. In addition, for the "blood pressure" item at "Bank A," a weight value of "18" is defined, which is obtained by multiplying the weight value of the number of items in demand ("6") by the weight value of the number of items supplied ("3").

[0043] Figure 7 also shows an example of the usage fee DB129 in this embodiment. The usage fee DB129 in this embodiment is a database that stores data on usage fees to be billed to public institutions, insurance companies, etc. that have received observation data.

[0044] This usage fee database, DB129, is a collection of records that link data such as case ID, usage date, usage details, and usage fee, using, for example, the user identification information of the observation data as the key. The usage fees for this observation data serve as the source of incentives for the customers who provide the observation data.

[0045] The above-mentioned usage fee is calculated based on the amount of data utilized, by applying the weight values ​​defined in Condition Table 128 to a predetermined fee calculation formula.

[0046] One example of a fee calculation formula is one in which the sum of the basic fee (unit price) per observation data and the designated fee (unit price) per observation data when specific items are specified is multiplied by the number of observation data.

[0047] For example, if a public institution receives observation data from "Bank A" with the following specifications: the data provider is a person with a medical history of "cancer" and an occupation of "company employee," the data to be acquired is "stress index," and the acquisition period is "5 years or more," then the usage fee can be calculated by multiplying the basic fee set by "Bank A" ("100 yen / data") + the specified fee ("127 yen / data") by the number of data points ("1000"), resulting in (100 + 127) × 1000 = 227,000 yen.

[0048] The aforementioned designated fee of "127 yen / item" can be assumed to be the sum of the weight values ​​defined in condition table 128 for the designated items.

[0049] For example, in the conditions table 128, the weight for "Bank A" is "35" for medical history "cancer", "8" for occupation "company employee", "42" for observed data "stress index", and "5 years or more" for acquisition period, so adding them up gives 35 + 8 + 42 + 42 = 127. <Example Flow: Observational Data Collection> The actual procedure of the health data utilization support method in this embodiment will be explained below with reference to the diagram. The various operations corresponding to the health data utilization support method described below are realized by a program that the health data utilization support device 100 reads into memory or the like and executes. This program consists of code for performing the various operations described below.

[0050] Figure 8 is a diagram showing an example of a flow chart for a health data utilization support method in this embodiment, and specifically, it is a flow chart related to the collection of observation data.

[0051] In this case, the health data utilization support device 100 acquires observation data sensed by the customer from a wearable terminal worn by the customer, for example, as a customer terminal 200, at regular intervals (s1). In addition to cases where the wearable terminal itself observes the values, the observation data can also include values ​​entered by the customer themselves on the customer terminal 200, or measured values ​​obtained from medical devices (e.g., various measuring devices such as blood pressure monitors) that are linked to the customer terminal 200.

[0052] As already mentioned, various types of observational data can be expected, such as blood pressure, heart rate, blood glucose levels, body temperature, and stress index. Of course, the types of observational data can be appropriately selected depending on the functionality of the observation unit, such as a wearable device.

[0053] The health data utilization support device 100 stores the observation data obtained in s1 into the health data DB 125 of the storage device 101 (s2), and then terminates processing.

[0054] Furthermore, when storing the data as described above, records will be generated and stored by linking the customer ID and observation date and time obtained from the customer terminal 200 to the observation data. The health data DB 125 will be formed by storing these records. <Flow example: Incentive determination> Next, the incentive determination process will be explained with reference to the diagram. Figure 9 is a diagram showing an example of the flow of the health data utilization support method in this embodiment, and specifically shows the processing flow for incentive determination.

[0055] Furthermore, the health data utilization support device 100 is assumed to have obtained information about the needs for observation data in advance from the data utilization platform 300 and the insurance company system 400 (see Figure 11) and to have generated the demand DB 127.

[0056] In this case, the health data utilization support device 100 refers to the data held by the demand DB 127 and counts the number of needs at public institutions, insurance companies, etc. for each type of customer attribute and the target of the observed data (s10).

[0057] In this case, the health data utilization support device 100 counts the number of records designated as needs for each of the following medical conditions in the demand DB 127: "none", "stroke", "cancer", "arrhythmia", "myocardial infarction", "bronchial asthma", "angina pectoris", and "diabetes".

[0058] Similarly, count the number of records designated as needs for each occupation: "Company employee," "Civil servant," "Self-employed / Freelancer," "Company executive," "Freelancer," "Homemaker," "Student," "Part-time worker," and "Unemployed."

[0059] Similarly, for each of the observational data items targeted, such as "blood pressure," "body temperature," "sleep depth," "heart rate," "electrocardiogram," "sweating volume," "blood glucose level," and "stress index," the number of records specified as needs is counted.

[0060] Similarly, the number of records specified as needs is counted for each acquisition period: "less than six months," "six months to one year," "one to two years," "two to three years," "three to four years," "four to five years," and "five years or more."

[0061] Furthermore, if a public institution or insurance company has predetermined the regions or regional financial institutions to be targeted for observation data acquisition as part of its needs designation, the health data utilization support device 100 shall perform the above-mentioned count for each designated regional financial institution, for example. In that case, the health data utilization support device 100 shall store the counted value as the numerical value in the "Demand Count" column of the target regional financial institution in the condition table 128 exemplified in Figure 6.

[0062] Next, the health data utilization support device 100 refers to the data held in the health data DB 125 and counts the number of records for each target of the observed data (s11).

[0063] In this case, the health data utilization support device 100 counts the number of records for each of the target observation data in the health data DB 125, namely "blood pressure," "body temperature," "sleep depth," "heart rate," "electrocardiogram," "sweating volume," "blood glucose level," and "stress index."

[0064] Furthermore, when performing these counts, the health data utilization support device 100 can also identify the customer's location and the regional financial institution they use in the customer database 126, using the customer ID value in the health data database 125 as a key, and perform the count for each region and regional financial institution.

[0065] In that case, the health data utilization support device 100 will store the counted value as the numerical value in the "Number Provided" column of the target regional financial institution in the condition table 128 exemplified in Figure 6.

[0066] Next, the health data utilization support device 100 determines weight values ​​for the needs and observed data related to various items obtained in counts s10 and s11, according to their concentration and rarity (s12).

[0067] In this case, the health data utilization support device 100 will determine the weight value for each item according to the rules predetermined for each item. For example, regarding the need, or concentration, for the medical history "stroke," the device will determine that a "demand quantity" of "1 to 10" corresponds to a weight value of "1," a "demand quantity" of "11 to 20" corresponds to a weight value of "2," a "demand quantity" of "21 to 30" corresponds to a weight value of "3," a "demand quantity" of "31 to 40" corresponds to a weight value of "4," a "demand quantity" of "41 to 50" corresponds to a weight value of "5," a "demand quantity" of "51 to 60" corresponds to a weight value of "61 to 70" corresponds to a weight value of "7," a "demand quantity" of "71 to 80" corresponds to a weight value of "8," a "demand quantity" of "81 to 90" corresponds to a weight value of "91 or more" corresponds to a weight value of "10."

[0068] Furthermore, regarding the number (quantity) of observational data provided for a medical history of "stroke," the following weights are assigned: a weight of "10" for "1-10" data points, a weight of "9" for "11-150" data points, a weight of "8" for "151-300" data points, a weight of "7" for "301-500" data points, a weight of "6" for "501-800" data points, a weight of "5" for "801-1200" data points, a weight of "4" for "1201-1800" data points, a weight of "3" for "1801-2600" data points, a weight of "2" for "2601-4000" data points, and a weight of "1" for "4001 or more" data points.

[0069] Next, the health data utilization support device 100 calculates an overall weight as the supply and demand balance for a given item, based on the needs and observed data weights for various items obtained in s12 (s13). In other words, it determines a value that identifies the supply and demand balance by calculating a value that takes into account both the level of need and the scarcity of the data.

[0070] In this case, the health data utilization support device 100 calculates an overall weight value by multiplying the weight value related to the needs of the item by the weight value related to the amount of observed data. For example, if the weight value related to the need for a medical history of "stroke" is "5" and the weight value related to the amount of observed data is "7", then the overall weight value related to the medical history of "stroke" can be calculated as 5 × 7 = 35.

[0071] Next, the health data utilization support device 100 calculates the usage fees associated with providing observation data to public institutions, insurance companies, etc., based on the supply and demand balance calculated in s13 (s14). Here, it is assumed that the health data utilization support device 100 provides observation data to the data utilization platform 300, insurance company system 400, etc., according to their needs.

[0072] In this case, as already described, the health data utilization support device 100 will calculate the usage fee by applying the overall weight value calculated up to s13 to the predetermined fee calculation formula, based on the amount of data utilized.

[0073] One example of a fee calculation formula is one in which the sum of the basic fee (unit price) per observation data and the designated fee (unit price) per observation data when specific items are specified is multiplied by the number of observation data.

[0074] For example, if a public institution receives observation data from "Bank A" with the following specifications: the data provider is a person with a medical history of "cancer" and an occupation of "company employee," the data to be acquired is "stress index," and the acquisition period is "5 years or more," then the usage fee can be calculated by multiplying the basic fee set by "Bank A" ("100 yen / data") + the specified fee ("127 yen / data") by the number of data points ("1000"), resulting in (100 + 127) × 1000 = 227,000 yen.

[0075] The aforementioned designated fee of "127 yen / item" can be assumed to be the sum of the weight values ​​defined in condition table 128 for the designated items.

[0076] For example, in the conditions table 128, the weight for "Bank A" is "35" for medical history "cancer", "8" for occupation "company employee", "42" for observed data "stress index", and "5 years or more" for acquisition period, so adding them up gives 35 + 8 + 42 + 42 = 127.

[0077] Next, the health data utilization support device 100 identifies a predetermined percentage of the usage fee calculated in s14 as an incentive to be given to the customer who is the data provider, and provides it (s15). For example, 5% of the usage fee of 227,000 yen, or 11,350 yen, is identified as the incentive. At this time, it is preferable for the health data utilization support device 100 to notify the customer's terminal 200 of the incentive information (see Figure 12).

[0078] Regarding the percentage of the usage fee allocated as an incentive, it is conceivable that the value set in the "Reduction Percentage" column of customer DB126 would be used. In this case, the incentive amount would not be transferred to the customer's account, but rather a procedure would be carried out to reduce the insurance premium of the insurance policy the customer has contracted by 11,350 yen, for example, by a regional financial institution or insurance company. <Example Flow: Heatmap Generation> Next, the process of generating the heatmap will be explained with reference to the diagram. Figure 10 is a diagram showing an example of the flow of the health data utilization support method in this embodiment, and specifically shows the processing flow of heatmap generation.

[0079] In this case, the health data utilization support device 100 determines the supply and demand balance for each area based on observational data obtained regarding data providers located in each area and supply and demand information related to said observational data (s20).

[0080] Here, by obtaining a comprehensive weight value calculated in s13 for each customer attribute, type of observational data, and regional financial institution, we can consider that the supply and demand balance for each area has been determined.

[0081] Furthermore, the health data utilization support device 100 generates a heat map (see screen 1200 in Figure 13) that represents the supply and demand balance for each of the aforementioned areas using at least one of the following elements: color, shade of color, and icons (s21).

[0082] In this case, the health data utilization support device 100 shall generate a heat map with respect to the area, customer attributes, and types of observed data specified by the data utilization platform 300 and the insurance company system 400.

[0083] For example, if a public institution's data utilization platform 300 specifies the area as "within Prefecture A," the customer attribute as "company employee," and the observed data as "stroke," the health data utilization support device 100 will refer to the condition table 128 at Bank A to extract the overall weight value of "stroke" for medical history "stroke" and the overall weight value of "company employee" "8" from the observed data obtained from company employees among the customers located in Prefecture A, and determine that the supply and demand balance for "stroke" is "43."

[0084] The higher this supply-demand balance value, the higher the demand for the observed data, but also the higher its scarcity. In such cases, the area is assigned a color closer to black on the map of the target area.

[0085] The health data utilization support device 100 distributes the heatmap 1200 generated in s21 to the data utilization platform 300, the insurance company system 400, or the customer terminal 200 (s22), and then terminates processing.

[0086] Those who view these heatmaps (1200) can consider which locations have available observation data and where usage fees can be kept down.

[0087] Although the best mode for carrying out the present invention has been described in detail above, the present invention is not limited thereto and can be modified in various ways without departing from its essence.

[0088] According to this embodiment, research institutions and health workers are no longer required to recruit large numbers of people to collect their health data (observational data), while simultaneously being able to efficiently obtain continuous health data from a wide range of people. Furthermore, financial institutions and insurance companies can more easily improve sales performance by appealing to customers that they can reduce insurance premiums in exchange for providing the aforementioned health data when selling their insurance products. Of course, in such cases, the customers who are insured can also receive benefits such as appropriate rewards or premium reductions as an incentive for providing their health data. Ultimately, the collection and utilization of a wide range of health data can be continued in an efficient, economically meaningful way among stakeholders.

[0089] The description herein makes it clear at least the following: In the health data utilization support device of this embodiment, the storage device may further hold information about insurance products under contract with the data provider, and the computing device may, upon providing the observation data to the insurance company which is the data user, identify the amount of premium reduction for the insurance product as the incentive based on the information about the insurance product.

[0090] This would allow for the provision or presentation of an incentive—a reduction in insurance premiums—to data providers who are also insurance policyholders. This could lead to increased data provision and an increase in insurance contracts among data providers who have experienced the benefits of providing data. Ultimately, this would enable the collection and utilization of a wide range of health data to continue in a more efficient, economically meaningful way among stakeholders.

[0091] Furthermore, in the health data utilization support device of this embodiment, the computing device may, when identifying the incentive, use a determination algorithm that assigns a larger incentive the greater the degree to which demand exceeds supply in the supply-demand balance of the observed data.

[0092] According to this, in the observation data utilization ecosystem of this embodiment, it becomes possible to recognize the value of observation data that is rare and in high demand, and to appropriately expand the degree of incentives provided for it. In turn, the collection and utilization of a wide range of health data can be continued in a more efficient, economically meaningful way among stakeholders.

[0093] Furthermore, in the health data utilization support device of this embodiment, the computing device may, when identifying the incentive, identify the incentive using a determination algorithm that grants more incentives in accordance with at least one of the following aspects of the data provider: the length of the period for which the observation data is provided, the frequency of provision, the number of data provided, and the variety of data provided.

[0094] According to this, in the observation data utilization ecosystem of this embodiment, the value of the volume and variety of observation data can be recognized, and the degree of incentives provided based on this can be appropriately expanded. Consequently, the collection and utilization of a wide range of health data can be continued in a more efficient, economically meaningful way among stakeholders.

[0095] Furthermore, in the health data utilization support device of this embodiment, the computing device may further acquire location information of each data provider from the terminal of each data provider, and for each area indicated by the location information, it may further perform the following processes: determining the supply and demand balance for each area based on the observation data obtained with respect to the data provider located in that area and the supply and demand information related to the observation data; generating a heat map for each area that represents the supply and demand balance using at least one of the elements of color, color intensity, and icons; and distributing the heat map to at least the terminals of the data users.

[0096] According to this, when public institutions, insurance companies, etc., consider acquiring observational data, it is more efficient to identify areas where the necessary observational data can be easily obtained, and then select and negotiate with organizations that are compiling observational data in those areas (e.g., regional financial institutions that operate health data utilization support devices) as data sources. In turn, the collection and utilization of a wide range of health data can be continued in a more efficient, economically meaningful way among stakeholders.

[0097] Furthermore, in the health data utilization support method of this embodiment, the information processing device may also store in the storage device information about insurance products under contract with the data provider, and, upon providing the observation data to the insurance company which is the data user, it may identify the amount of premium reduction for the insurance product as the incentive based on the information about the insurance product.

[0098] Furthermore, in the health data utilization support method of this embodiment, the information processing device may, when identifying the incentive, identify the incentive using a determination algorithm that assigns a larger incentive the greater the degree to which demand exceeds supply in the supply-demand balance of the observed data.

[0099] Furthermore, in the health data utilization support method of this embodiment, the information processing device may, when identifying the incentive, identify the incentive using a determination algorithm that grants more incentives in accordance with at least one of the following aspects of the data provider: the length of the period for which the observation data is provided, the frequency of provision, the number of data provided, and the variety of data provided.

[0100] Furthermore, in the health data utilization support method of this embodiment, the information processing device may further acquire location information of each data provider from the terminal of each data provider, and for each area indicated by the location information, perform a process to determine the supply and demand balance for each area based on the observation data obtained with respect to the data provider located in that area and the supply and demand information related to the observation data, and generate a heat map for each area that represents the supply and demand balance using at least one of the elements of color, color intensity, and icons, and further perform a process to deliver the heat map to at least the terminal of the data user. [Explanation of symbols]

[0101] 1 Network 10. Health Data Utilization Support System 100 Health Data Utilization Support Device 101 Storage device 102 Programs 103 memory 104 Arithmetic unit 105 Communication equipment 125 Health Data Database 126 Customer DB 127 Demand DB 128 Condition Table 129 Usage fee DB 200 customer terminals 300 Data Utilization Platform 400 Insurance Company Systems

Claims

1. A storage device that holds demand information regarding biological observation data from data users who wish to use such observation data, A computing device that performs the following processes: acquiring observational data relating to each predetermined data provider from their respective terminals; determining the supply and demand balance of the observational data based on the amount of the acquired observational data and the demand information relating to the observational data; and identifying an incentive to be given to the data provider with respect to the observational data according to the supply and demand balance. Equipped with, The aforementioned computing device is The system further performs the following processes: obtaining location information of each data provider from their respective terminals; determining the supply and demand balance for each area indicated by the location information; generating a heatmap for each area that represents the supply and demand balance using at least one of the following elements: color, color intensity, and icons; and distributing the heatmap to at least the terminals of the data users. A health data utilization support device characterized by the following features.

2. The aforementioned storage device is The aforementioned data provider further retains information regarding the insurance products under contract, The aforementioned computing device is In connection with providing the aforementioned observational data to the insurance company, which is the data user, the details of the premium reduction for the insurance product are identified as the incentive based on the information of the insurance product. The health data utilization support device according to feature 1.

3. The aforementioned computing device is In identifying the aforementioned incentives, the algorithm used to determine the incentives is such that the greater the degree to which demand exceeds supply in the supply-demand balance of the observed data, the greater the incentive awarded. The health data utilization support device according to feature 2.

4. The aforementioned computing device is In identifying the aforementioned incentives, the determination algorithm is used to determine which incentives are given in proportion to at least one of the following aspects of the data provider: the length of the period for which the observation data is provided, the frequency of provision, the quantity of data provided, and the variety of data provided. The health data utilization support device according to feature 3.

5. The information processing device is A storage device is provided to hold demand information regarding biological observation data from data users who wish to use such observation data, A process for acquiring observational data relating to each predetermined data provider from their respective terminals; a process for determining the supply and demand balance of the observational data based on the amount of the acquired observational data and the demand information relating to the observational data; and a process for identifying an incentive to be given to the data provider with respect to the observational data, in accordance with the supply and demand balance. From each of the data providers' terminals, the system further acquires the location information of the data provider, performs a process to determine the supply and demand balance for each area indicated by the location information, generates a heatmap for each area that represents the supply and demand balance using at least one of the following elements: color, color intensity, and icons, and delivers the heatmap to at least the terminals of the data users. A method for supporting the utilization of health data, characterized by the following features.

6. The aforementioned information processing device The storage device further holds information regarding the insurance product under contract with the data provider, In connection with providing the aforementioned observational data to the insurance company that is the data user, the details of the premium reduction for the insurance product are identified as the incentive based on the information of the insurance product. The method for supporting the utilization of health data according to feature 5.

7. The aforementioned information processing device In identifying the aforementioned incentives, the algorithm used to determine the incentives is such that the greater the degree to which demand exceeds supply in the supply-demand balance of the observed data, the greater the incentive. The method for supporting the utilization of health data as described in feature 6.

8. The aforementioned information processing device In identifying the aforementioned incentives, the determination algorithm is used to determine which incentives are given in proportion to at least one of the following aspects of the data provider: the length of the period for which the observation data is provided, the frequency of provision, the quantity of data provided, and the variety of data provided. The method for supporting the utilization of health data according to feature 7.

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