WO2024162032A1 - ヘルスケア情報ネットワーク - Google Patents

ヘルスケア情報ネットワーク Download PDF

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WO2024162032A1
WO2024162032A1 PCT/JP2024/001403 JP2024001403W WO2024162032A1 WO 2024162032 A1 WO2024162032 A1 WO 2024162032A1 JP 2024001403 W JP2024001403 W JP 2024001403W WO 2024162032 A1 WO2024162032 A1 WO 2024162032A1
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data
disease
disease risk
basic data
subject
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PCT/JP2024/001403
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English (en)
French (fr)
Japanese (ja)
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武 岡上
浩司 白木
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株式会社シンクメディカル
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Priority to JP2024574431A priority Critical patent/JPWO2024162032A1/ja
Publication of WO2024162032A1 publication Critical patent/WO2024162032A1/ja

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

  • the present invention relates to a healthcare information network, etc.
  • Patent Document 1 discloses a method for determining the risk of liver diseases such as fatty liver, cirrhosis, and liver cancer by using attribute data such as gender and age, physical examination data such as height and weight, and blood test data such as AST and ALT.
  • Patent Document 1 makes it possible to use machine learning to estimate the risk of liver disease and other conditions with high accuracy and ease.
  • Such assessment methods using machine learning are being expanded to assess not only the risk of liver disease, but also the risk of various other diseases such as brain disease and diabetes. It is hoped that a network environment that allows easy access to such information will be established soon in order to predict and prevent various diseases and ultimately promote people's health.
  • the present invention was made in consideration of the above problems, and aims to provide a network that allows easy access to the results of estimations of various disease risks.
  • the present invention relates to a basic information table in which basic data including a personal ID of a subject and health care data of the subject is recorded; an analysis engine having a plurality of estimation models optimized for each disease type by machine learning, the analysis engine estimating a disease risk for each disease type by inputting the basic data into the estimation models; a master database in which the individual ID, the basic data, and the disease risk level for each disease type are registered;
  • An information providing system comprising: a user terminal through which a user who wishes to use the disease risk level can access the master database of the information providing system; It provides a healthcare information network, including:
  • the present invention makes it possible to build a network that allows various institutions to easily access the results of risk estimates for various diseases, making it possible to provide high-quality healthcare services.
  • FIG. 1 is a schematic diagram showing the overall configuration of a healthcare information network according to an embodiment of the present invention.
  • FIG. 1 is a schematic diagram illustrating the main functions of a healthcare information network according to an embodiment of the present invention.
  • FIG. 1 is a schematic diagram for explaining an outline of Application Example 1.
  • FIG. 1 is a flow chart for explaining an outline of application example 1.
  • FIG. 1 is a schematic diagram for explaining an outline of application example 2.
  • FIG. 1 is a flow chart for explaining an outline of application example 2.
  • FIG. 3 is a schematic diagram for explaining an outline of application example 3.
  • FIG. 13 is a schematic diagram for explaining an outline of application example 4.
  • FIG. 13 is a schematic diagram for explaining an outline of application example 5.
  • the healthcare information network 10 includes an information provider-side information processing device (hereinafter sometimes referred to as the information provider system 20), which is made up of institutions, corporations, individuals, etc. that provide risk level information related to the risk levels of various diseases, and a user-side information processing device (hereinafter sometimes referred to as the user terminal 100), which is made up of institutions, corporations, individuals, etc. that wish to use the risk level information related to the risk levels.
  • the information provider system 20 an information provider-side information processing device
  • the user terminal 100 hereinafter sometimes referred to as the user terminal 100
  • the information providing system 20 comprises a basic information table 30, an analysis engine 40, and a master database 50.
  • the basic information table 30 is a table in which basic data including the subject's personal ID and the subject's health care data is recorded.
  • the analysis engine 40 has a risk estimation model for each of a plurality of diseases optimized for each disease type by machine learning, and estimates the disease risk for each disease type by inputting the basic data into the estimation model.
  • the master database 50 is where the personal ID, the basic data, and disease risk data relating to the disease risk for each disease type are registered.
  • the user terminal 100 is provided by a user institution (user) that wishes to use disease risk data, such as a clinic, medical institution, insurance company, fitness gym, drug discovery company, research institute, etc., and can be used by the user to access the master database 50 of the information provider system 20.
  • a user institution user that wishes to use disease risk data, such as a clinic, medical institution, insurance company, fitness gym, drug discovery company, research institute, etc., and can be used by the user to access the master database 50 of the information provider system 20.
  • the healthcare information network 10 allows users at each user institution to easily access highly accurate estimates of various disease risks provided by information providers. Such highly accurate and easily accessible healthcare services lead to improved health for subjects and more effective use of medical resources.
  • the master database 50 has a first database 52 and a second database 54.
  • the first database 52 is a database in which the disease risk data and basic data corresponding to a personal ID are registered.
  • the second database 54 is a database in which the disease risk data and basic data corresponding to the disease type are registered.
  • the healthcare information network 10 can set access rights to either or both of the first database 52 and the second database 54 for each user.
  • the healthcare information network 10 can set access rights for those users to the first database 52 in which disease risk data and basic data linked to personal IDs are registered, and when users are drug discovery companies, research institutions, and the like, the healthcare information network 10 can set access rights to the second database 54 in which disease risk data and basic data linked to disease types, that is, data not linked to personal IDs.
  • the healthcare information network 10 can also limit the types of basic data that can be extracted from the first database 52 in response to requests from users such as user institutions.
  • the healthcare information network 10 can provide a high level of information safety and improved security.
  • the basic data includes the subject's health checkup/examination data or the subject's daily monitoring data.
  • Examples of health checkup and examination data include attribute data, physical examination data, specimen test data obtained from blood tests and urine tests, and physiological function test data such as ECG. These data are one-time monitoring data.
  • daily monitoring data is data obtained from general-purpose wearable devices such as smart watches, medical devices, and medical terminals, and includes continuous monitoring data such as blood glucose levels, ECG, SPO2 , blood pressure, and body temperature.
  • Attribute data includes gender, age, medical history, presence or absence of underlying diseases, race, genetic predisposition (such as PNPLA3), etc.
  • Physical findings data includes height, weight, blood pressure, waist circumference, vision, hearing, body fat percentage, sitting height, head circumference, chest circumference, etc.
  • Sample test data includes AST (also called GOT), ALT (also called GPT), ⁇ -GTP, PLT, T-Cho, TG, Alb, HDL, LDL, HbA1c, ALP, ChE, type 4 collagen (especially type IV collagen 7S), Total-AIM, Free-AIM, etc.
  • Physiological function test data includes hardness and elastography using ultrasound and magnetic resonance, X-ray images, CT images, MRI images, endoscopic images of various parts including the chest, interview results, etc. Other basic data may include interview data from remote interviews, etc.
  • the analysis engine 40 preferably includes a first engine that estimates a disease risk level based on basic data, and a second engine that estimates the disease risk level based on either the basic data or the analysis results by the first engine.
  • the first engine can perform primary screening to assist in the diagnosis of each disease
  • the second engine can perform secondary screening to predict the future of each disease.
  • the analysis engine 40 further includes a third engine that estimates the disease risk level based on at least one of the basic data, the analysis results by the first engine, and the analysis results by the second engine.
  • An information providing system 20 which is one embodiment of this system, comprises a basic information table 30 in which basic data including the subject's personal ID and the subject's health care data is recorded, an analysis engine 40 that has multiple estimation models optimized for each disease type by machine learning and estimates the disease risk level for each disease type by inputting the basic data into the estimation model, and a master database 50 in which the personal ID, the basic data, and disease risk level data relating to the disease risk level for each disease type are registered.
  • This system that is, the information provider system 20, which is an information processing device on the information provider side, can evaluate the risk of multiple diseases with high accuracy based on limited basic data.
  • the present invention is also directed to a method for acquiring disease risk data for various diseases.
  • This method for acquiring disease risk data can be realized, for example, in the above-mentioned healthcare information network 10 and information provider system 20.
  • the method for acquiring disease risk data of the present invention includes the steps of inputting basic data including the subject's personal ID and healthcare data, inputting the basic data into an analysis engine 40 having multiple estimation models optimized for each disease type by machine learning to estimate the disease risk for each disease type, registering the personal ID, the basic data, and disease risk data related to the disease risk for each disease type in a master database 50, and accessing the master database 50 to obtain the disease risk data.
  • the method for acquiring disease risk data including these steps can be executed by the above-mentioned healthcare information network 10 and information provider system 20.
  • This method of acquiring disease risk data can be performed in a virtual space (metaverse). That is, the subject visits the virtual space as an avatar, visits the desired information user institution (user) within the virtual space, provides the information users with healthcare information including the basic data, and can receive the necessary information from each user.
  • the user can provide basic data to an information provider institution outside the virtual space, acquire disease risk data from the information provider institution, and use this.
  • the above-mentioned method of acquiring disease risk data can also be performed in such a way that the subject provides basic data to an information provider institution established within the virtual space, and the information provider institution evaluates the risk of various diseases within the virtual space.
  • the present invention is also directed to a healthcare information network 10 including an information providing system 20 including a basic information table 30 in which basic data including the subject's personal ID and the subject's healthcare data is recorded, an analysis engine 40 having an estimation model optimized by machine learning and estimating a disease risk level for a specific disease type by inputting the basic data into the estimation model, and a master database 50 in which disease risk level data relating to the personal ID, the basic data, and the disease risk level is registered, and a user terminal 100 provided for a user who wishes to use the disease risk level data and capable of accessing the master database 50 of the information providing system 20.
  • each user can easily access the highly accurate disease risk level estimation results held by the information providing organization.
  • Such a highly accurate and easily accessible healthcare service leads to improved health of the subject and effective use of medical resources.
  • the present invention is also directed to an analysis engine 40 that has multiple estimation models optimized for each disease type by machine learning, and estimates the disease risk level for each disease type by inputting basic data including the subject's healthcare data into the estimation models.
  • this analysis engine 40 it is possible to accurately estimate the disease risk level for each disease type based on basic data including the subject's healthcare data.
  • the present invention will be described below in accordance with the above-described embodiment, with specific examples of the healthcare information network 10, the information providing system 20, and the method of acquiring disease risk data, but the present invention is not limited to these examples.
  • the healthcare information network, the information providing system, and the method of acquiring disease risk data of the present invention can be other than the examples exemplified below, as long as they do not deviate from the spirit of the present invention.
  • the healthcare information network 10 of this embodiment is an IT network system between an information processing device that provides information and users who use it.
  • the information processing device on the information provider side i.e., the information provider system 20, has an analysis database (analysis DB) and a master database 50 (master DB).
  • analysis DB analysis database
  • master DB master database 50
  • the analysis database includes a basic information table 30 in which the subject's personal ID and basic healthcare-related information is recorded, an analysis engine 40 with multiple estimation models optimized for each disease type using machine learning, and a result information table 60 in which disease risk data for each type of disease predicted and estimated by the analysis engine 40 is registered.
  • the master database 50 personal IDs, basic data (healthcare information data), and disease risk data for each disease type are registered.
  • the master database 50 includes a first database 52 (DB-1) and a second database 54 (DB-2).
  • DB-1 first database 52
  • DB-2 second database 54
  • first database 52 personal IDs, basic data corresponding to these personal IDs, and disease risk data for each disease type are registered.
  • second database 54 basic data corresponding to a disease type and the risk of that disease are registered.
  • the user institutions such as clinics (medical institutions), insurance companies, fitness gyms, drug discovery companies, research institutes, etc., each have an information processing device (user terminal 100).
  • This system can access the master database 50 of the information providing system 20.
  • the information providing system 20 provides the user terminal 100 with the necessary information, such as basic data on healthcare-related information and the disease risk level for each type of disease.
  • the basic information table 30 in the information providing system 20 receives basic data, i.e., healthcare-related information, such as one-time monitoring data obtained from health checkups and examinations, and continuous monitoring data obtained from wearable devices, medical equipment, medical terminals, etc.
  • basic data i.e., healthcare-related information, such as one-time monitoring data obtained from health checkups and examinations, and continuous monitoring data obtained from wearable devices, medical equipment, medical terminals, etc.
  • the analysis engine 40 in the information providing system 20 may be, for example, a program that executes the disease risk assessment method disclosed in Japanese Patent No. 7170368 (Patent Document 1). As disclosed in Patent Document 1, this analysis engine 40 has a disease risk estimation model constructed by pre-processing of learning data, neural network, deep learning, and post-processing. This analysis engine 40 is equipped with a plurality of estimation models optimized for a plurality of diseases. The analysis engine 40 is described in detail below, but it is equipped with a diagnosis engine (first engine) that performs primary screening to assist in the diagnosis of each disease, a prediction engine (second engine) that performs secondary screening to predict the future of each disease, and a diagnosis engine (third engine) that assists in making more accurate diagnoses for each disease. These first, second, and third engines are equipped with each estimation model having an algorithm optimized for estimating the disease risk of each disease.
  • the estimated result data of each disease risk level calculated by the analysis engine 40 is stored as disease risk level data together with the personal ID.
  • this information processing device does not need to have all of the functional units described below within the information provider; for example, the analysis engine 40 and master database 50 may be provided outside the information provider.
  • the healthcare information network 10 of this embodiment uses data obtained by health checkups and medical examinations (one-time monitoring data) and continuous sensing data (continuous monitoring data) obtained from wearable devices, medical devices, medical terminals, etc. Then, using an analysis engine 40 including a diagnostic engine that diagnoses the current risk of various diseases and a prediction engine that predicts the future risk of various diseases, it predicts the risk of various diseases and even cancer and complications, and assists in their diagnosis and treatment. If no medical treatment is required, it assists in the creation and implementation (progress monitoring) of a recovery program at, for example, a fitness gym. This allows for early detection of abnormalities, i.e., disease risks, and assists in the subject's recovery to health.
  • abnormalities i.e., disease risks
  • Indicators that can be used in health checkups and medical examinations include physical findings, blood tests, chest X-rays, ultrasound echoes, and CT/MRI. In continuous sensing, non-invasive blood glucose meters, ECG, SPO2 , HbA1c, blood pressure, and remote interviews can be used. Hereinafter, these indicator data may be referred to as basic data.
  • the analysis engine 40 is used to first quantify the current health condition. That is, the diagnostic engine (first engine) executes a primary screening to assist in diagnosis. Specifically, the following conditions are quantified: liver disease conditions for diagnosing hepatitis or cirrhosis, brain disease conditions for diagnosing dementia, diabetes conditions for diagnosing diabetes, mental disease conditions for diagnosing manic depression, lung disease conditions for diagnosing pneumonia, heart disease conditions for diagnosing angina or heart failure, kidney disease conditions for diagnosing pyelitis, etc.
  • the diagnostic engine includes an estimation model A that extracts specific data (physical findings data, blood test data here) corresponding to each disease from a plurality of basic data and performs screening for hepatitis and cirrhosis based on a specific algorithm a1, an estimation model B that similarly extracts specific data (physical findings data, blood test data here) corresponding to each disease from a plurality of basic data and performs screening for dementia based on a specific algorithm b1, and an estimation model C that similarly extracts specific data (physical findings data, blood test data, and continuous sensing data such as blood glucose data and blood pressure data in addition to physical findings data and blood test data) corresponding to each disease from a plurality of basic data and performs screening for diabetes based on a specific algorithm c1, etc.
  • the health condition is visualized by quantifying the risk level of a desired disease (preferably the risk level of multiple diseases), which contributes to early detection of abnormalities.
  • the above-mentioned analysis engine 40 is used to predict the future disease risk level using the basic data and the analysis results of the diagnosis engine. That is, in the prediction engine (second engine), a secondary screening for predicting the future of various diseases is performed based on the basic data and/or the data calculated by the primary screening. Specifically, the disease risk level for liver disease, that is, the liver fibrosis level, is quantified using each basic data, the analysis results regarding the level of hepatitis or cirrhosis, the analysis results regarding the level of diabetes, etc. An estimation model A is provided that performs screening of the fibrosis level based on a specific algorithm a2. Note that this prediction engine also has estimation models for other diseases based on specific algorithms for each disease using the data shown in the figure.
  • the prediction engine (second engine) that calculates the future risk level of each disease predicts the risk level of each disease, and a recovery program to a healthy state is formulated based on this predicted value, which contributes to the diagnosis of whether the patient is in a healthy state or a pre-disease state and the recovery to a healthy state.
  • the disease level is quantified using the above-mentioned basic data, the analysis results by the first engine, the analysis results by the second engine, etc. That is, in the second diagnostic engine (third engine), in addition to the data calculated by the second screening, a tertiary screening is performed to assist in highly accurate diagnosis of each disease based on the basic data and the data calculated by the first screening (data by the first diagnostic engine) as necessary.
  • the disease level related to liver cancer is predicted using the analysis results related to the fibrosis level and the analysis results related to the diabetes prediction.
  • this diagnostic engine uses the data calculated by the second screening of the fibrosis level, the data calculated by the second screening of dementia, the data calculated by the second screening of diabetes, the data calculated by the second screening of manic depression, and the data calculated by the second screening of heart disease, and further uses the basic data and the data calculated by each primary screening as necessary, and is provided with an estimation model A that predicts liver cancer based on a specific algorithm a3.
  • this diagnostic engine also has estimation models based on specific algorithms for predicting each disease and complication using the illustrated data for other diseases. These steps not only contribute to highly accurate diagnosis of each disease and complication, but also contribute to the recovery of health.
  • the analysis engine 40 has multiple estimation models optimized for each disease type. For example, it has a liver disease risk estimation model (estimation model A) with a calculation algorithm (algorithm a) for estimating the risk of liver disease (disease A), a brain disease risk estimation model (estimation model B) with a calculation algorithm (algorithm b) for estimating the risk of brain disease (disease B), and a diabetes risk estimation model (estimation model C) with a calculation algorithm (algorithm c) for estimating the risk of diabetes (disease C).
  • a liver disease risk estimation model (estimation model A) with a calculation algorithm (algorithm a) for estimating the risk of liver disease (disease A)
  • a brain disease risk estimation model (estimation model B) with a calculation algorithm (algorithm b) for estimating the risk of brain disease (disease B)
  • a diabetes risk estimation model (estimation model C) with a calculation algorithm (algorithm c
  • this table shows an example of the types of basic data used in the first screening, the risk of each disease is also estimated in the second and third screenings using a specific table.
  • the master database 50 registers personal IDs, basic data, and disease risk data relating to the disease risk for each disease type output from the analysis engine 40.
  • This master database 50 includes a first database 52 (DB-1) and a second database 54 (DB-2).
  • the first database 52 registers personal IDs, basic data corresponding to the personal IDs, and disease risk data for each disease type.
  • the second database 54 registers basic data corresponding to a disease type and the risk of the disease.
  • This application example relates to an application example in which the health care information network, information provider system (system), and method for acquiring disease risk data according to the present invention are applied to various collaboration models in collaboration models with clinics and medical institutions, as shown in Figures 5 and 6 .
  • the subject inputs basic data A into the subject's device.
  • Basic data A includes attribute data such as age and sex, physical findings such as height and weight, and blood test data such as AST (GOT), ALT (GPT), ⁇ GTP, PLT, T-Cho, TG, type 4 collagen (especially type IV collagen 7S), and AIM.
  • This basic data may be information that the subject inputs into their own device from the results of a health check, or it may be information collected from a wearable device such as a smartwatch.
  • the subject also selects the disease type for which a risk assessment is required.
  • a risk assessment may be required for multiple types of diseases, but in this example, a risk assessment is required for a specific disease A (for example, liver disease).
  • the information providing system 20 is a system provided on the information providing side, which is composed of an information providing institution, corporation, individual, etc., that provides information.
  • the information providing system 20 is composed of, for example, one or more computers, one or more storage devices, and other devices.
  • the information providing system 20 is equipped with the analysis database and master database 50 as described above, and calculates the risk level (disease risk level ⁇ 1) for disease A based on the basic data registered in the analysis database, and registers information related to the risk level calculation result (risk level data) together with the basic data in the master database 50.
  • the subject accesses the master database 50 from the subject's terminal and obtains disease risk level data related to disease risk level ⁇ 1 (primary analysis result).
  • the subject If the subject requests a medical examination at a medical institution as a result of checking the disease risk level based on the disease risk level data, the subject selects the medical institution at which the subject wishes to be examined on the subject's terminal and allows data sharing with the medical institution. In response to this, the information providing system 20 allows the medical institution's computer (terminal) to access the basic data A of the subject having the ID and the disease risk level data related to the disease risk level ⁇ 1 for disease A.
  • the medical institution accesses the information providing system 20 from the medical institution's computer (terminal) and obtains basic data A linked to the personal ID and disease risk data related to disease risk level ⁇ 1 for disease A.
  • the medical institution then examines the subject while referring to basic data A and disease risk data related to disease risk level ⁇ 1. If necessary, a detailed examination (additional examination) such as a CT scan, MRI, specialized marker, or genetic test (such as SNP of PNPLA3) is performed.
  • the supplementary data (additional data) obtained from the detailed examination is sent to the information providing system 20, where it is again input to the analysis database as basic data A' in which the supplementary data is added to basic data A, and disease risk data related to disease risk level ⁇ 2 (secondary analysis result) is calculated in this analysis database.
  • the specialist makes a definitive diagnosis while referring to basic data A' and disease risk data related to disease risk level ⁇ 2, and performs the necessary treatment.
  • the provider institution system will calculate the disease risk level ⁇ 1 for disease A, as well as the disease risk level ⁇ 1 for disease B, the disease risk level ⁇ 1 for disease C, and so on.
  • a computer or the like constituting the information provider system 20 will assess high-risk and low-risk diseases and present this on the subject's terminal.
  • the subject may request a diagnosis from a medical institution if necessary, and additional tests may be conducted at the medical institution as necessary.
  • the provider institution will then use this data to calculate a more accurate disease risk level ( ⁇ 2, ⁇ 2, ...) for each disease, and based on the results of these secondary analyses, treatment will be started at the medical institution.
  • this application example relates to an application example in which the healthcare information network, information provider system (system), and method for acquiring disease risk data according to the present invention are applied to various collaboration models in collaboration models with drug discovery companies and research institutes.
  • the master database 50 of the information providing system 20 stores basic data of multiple subjects A, B, C, etc., as well as the disease risk levels for each disease A, B, C, etc. calculated by the analysis engine 40.
  • Each piece of basic data is attribute data such as age and sex, physical examination data such as height and weight, and blood test data such as AST (GOT), ALT (GPT), ⁇ GTP, PLT, T-Cho, TG, type 4 collagen (particularly type IV collagen 7S), and AIM.
  • AST GAT
  • ALT GTP
  • PLT ALT
  • T-Cho T-Cho
  • TG type 4 collagen
  • type 4 collagen particularly type IV collagen 7S
  • AIM AIM
  • the information providing system 20 is equipped with the analysis database and master database 50 as described above, and calculates the risk of various diseases based on the basic data of each subject in the analysis database, and registers the basic data and the calculation results in the master database 50. In addition, a data table by risk level for each disease is registered.
  • Drug discovery companies and research institutions develop new drugs based on basic data registered by disease type and risk level in the master database 50 of the information provider system 20, and disease risk level data related to disease risk level. Specifically, real drugs and placebos are prepared according to risk level, and these are administered to subjects according to their risk level, and basic data (secondary data) after administration is obtained. In other words, while providing subjects with drugs adjusted according to risk level, basic data is collected periodically and sent to the provider institution, which recalculates the risk level (secondary) in the analysis DB and provides the result to the user institution (user). In this way, by updating the analysis result index (disease risk level data) based on the drug administration process and observing the changes, it is possible to efficiently confirm drug efficacy and improve the efficiency of drug discovery.
  • analysis result index disease risk level data
  • medication guidelines (such as drug names and dosages) based on disease risk levels will be registered in a database at the provider institution, and subjects can use this database as a reference when purchasing the necessary medications at a pharmacy or drug store.
  • This application example relates to an application example in which the healthcare information network, information provider system (system), and method for acquiring disease risk data according to the present invention are applied to various collaboration models in a collaboration model with an insurance company, as shown in Figure 9.
  • the subject provides basic data about the subject to the information providing system 20.
  • This basic data includes attribute data such as age and sex, physical examination data such as height and weight, and blood test data such as AST (GOT), ALT (GPT), ⁇ GTP, PLT, T-Cho, TG, type 4 collagen (especially type IV collagen 7S), and AIM.
  • the subject also designates an insurance company.
  • the information providing system 20 is equipped with the analysis database and master database 50 described above, and calculates the risk of various diseases based on the subject's basic data in the analysis database, and registers the basic data and the calculation results in the master database 50.
  • the designated insurance company accesses the master database 50 of the provider from the insurance company's information terminal and obtains disease risk data related to the desired disease risk for the desired subject.
  • the insurance company determines whether or not to enroll in insurance, calculates the insurance premium, recommends the type of insurance, etc. for the subject based on the disease risk data related to the disease risk registered in the master database 50 of the information provider system and linked to the individual ID.
  • the insurance company can access the provider's database and obtain information on the target disease risk. Access restrictions can be imposed on the insurance company so that information on other disease risk levels cannot be viewed, and in some cases, basic data cannot be viewed either.
  • the insurance company can provide advice and guidance on improving lifestyle habits to the subject based on the specific disease risk level, and can also change the insurance premium depending on the progress of the advice and guidance. This can promote the health of the subject.
  • This application example relates to an application example in which the health care information network, information provider system (system), and method for acquiring disease risk data according to the present invention are applied to various types of collaboration models in a collaboration model with a fitness gym, as shown in Figure 10.
  • the subject provides basic data about the subject to the information providing system 20.
  • This basic data includes attribute data such as age and sex, physical examination data such as height and weight, and blood test data such as AST (GOT), ALT (GPT), ⁇ GTP, PLT, T-Cho, TG, type 4 collagen (especially type IV collagen 7S), and AIM.
  • a fitness gym is also designated.
  • the information providing system 20 is equipped with the analysis database and master database 50 described above, and calculates the risk of various diseases based on the subject's basic data in the analysis database, and registers the basic data and the calculation results in the master database 50.
  • the designated fitness gym is registered in the master database 50 of the information provider system, and an exercise menu is created based on basic data linked to the personal ID and disease risk data related to the disease risk level, and this is shared with the subject. By periodically checking their disease risk level, subjects can self-manage while monitoring their symptoms, for example by losing weight and then increasing muscle mass. It is also possible to register exercise programs (health habit improvement programs) according to disease risk levels in the master database 50, and allow subjects to access these.
  • this application example relates to an application example in which the healthcare information network, information provider system (system), and method for acquiring disease risk data relating to disease risk in a virtual space are applied to various collaboration models.
  • the method of acquiring disease risk data relating to the disease risk level described above may be performed in a virtual space (metaverse).
  • the subject can visit the virtual space as an avatar, visit a desired information using institution (user) within this virtual space, provide healthcare information to the information using institution, and receive necessary information from each information using institution.
  • Each information using institution cooperates with an information providing institution in real space and provides the risk level of each disease to the information using institution.
  • this virtual space reproduces streetscapes constructed in the same way as in real space.
  • This invention can be used in healthcare services aimed at promoting health and making effective use of medical resources.

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JP2019160273A (ja) * 2018-03-15 2019-09-19 株式会社トプコン 医療情報処理システム及び医療情報処理方法
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WO2019022085A1 (ja) * 2017-07-24 2019-01-31 アクシオンリサーチ株式会社 対象システムの内部状態を推定する支援システム
JP2019159964A (ja) * 2018-03-14 2019-09-19 メドケア株式会社 効率化支援システム及び医療効率化支援方法
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JP2022095099A (ja) * 2020-12-16 2022-06-28 キヤノン株式会社 情報処理装置、情報処理方法、情報処理システムおよびプログラム
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