Medical information estimation device and medical information estimation program

Abstract

To provide a medical information estimation device for estimating medical information for the entire group.SOLUTION: A medical information estimation device comprises: medical data acquisition means that acquires first medical data related to medical treatment on a first group that is part of the entire group, and second medical data related to medical treatment on a second group that is part of the entire group and different from the first group; condition acquisition means that acquires a specified period and an outcome condition; and estimation means that, when the specified period includes a specific period during which the first medical data is present but the second medical data is not present, determines a relation between the statistics of the first group and the statistics of the second group matching the outcome condition in a period during which the first medical data and the second medical data are present, and estimates the statistics of the second group matching the outcome condition in the specific period on the basis of the relation.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] The present invention relates to a medical information estimation device and a medical information estimation program. [Background technology]

[0002] For patients who have been examined, prescribed medicine, or otherwise treated at a medical institution, a medical fee statement, or so-called receipt data, is prepared to bill insurers (health insurance associations, local governments, etc.) for the costs of insured treatment (Non-Patent Document 1). [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] https: / / ja.wikipedia.org / wiki / Reception Summary of the Invention [Problem to be solved by the invention]

[0004] Meanwhile, there is a demand for technology to estimate the number of patients with specific diseases in order to investigate the demand for consultations, medicines, treatments, etc. Such estimations can be made based on medical claims data held by health insurance associations and local governments. However, not all of the medical claims data required for the estimation is necessarily available at the time the estimation is desired, and there is an issue that accurate estimation is difficult during periods when medical claims data is missing. [Means for solving the problem]

[0005] One aspect of the present invention is a medical information estimation device comprising: a medical data acquisition means for acquiring first medical data relating to the medical care of a first population, which is part of an overall population, and second medical data relating to the medical care of a second population, which is part of the overall population and different from the first population; a condition acquisition means for acquiring a designated period and outcome conditions; and, if the designated period includes a specific period during which the first medical data exists but the second medical data does not exist, an estimation means for determining a relationship between statistics of the first population and statistics of the second population that meet the outcome condition during the period during which the first medical data and the second medical data exist, and estimating the statistics of the second population that meet the outcome condition during the specific period based on the relationship.

[0006] Another aspect of the present invention is a medical information estimation program that causes a computer to function as: a medical data acquisition means for acquiring first medical data regarding the medical care of a first population, which is part of an overall population, and second medical data regarding the medical care of a second population, which is part of the overall population and different from the first population; a condition acquisition means for acquiring a designated period and outcome conditions; and, if the designated period includes a specific period during which the first medical data exists but the second medical data does not exist, an estimation means for determining a relationship between statistics of the first population that meet the outcome condition and statistics of the second population that meet the outcome condition during a period during which the first medical data and the second medical data exist, and estimating the statistics of the second population that meet the outcome condition during the specific period based on the relationship.

[0007] Here, it is preferable that the estimation means further estimates statistics of the entire population that meet the outcome condition in at least a partial period of the specified period based on statistics of the first population and the second population that meet the outcome condition in the partial period.

[0008] It is also preferable to estimate the statistics of the entire population that meets the outcome condition in the partial period by correcting the statistics of the first and second populations that meet the outcome condition in the partial period depending on the proportion of the first and second populations in the entire population.

[0009] In addition, it is preferable that the first medical data and the second medical data are medical receipt data including information on the name of the disease, prescribed medicines, and medical treatment.

[0010] In addition, the statistical amount is preferably the number of patients, the number of cases, the amount of use, or the amount of money that meets the outcome condition.

[0011] Furthermore, when estimating age-specific statistics for the entire population, it is preferable to use different medical data for estimating a first age range and a second age range different from the first age range.

[0012] Furthermore, when estimating age-specific statistics in the entire population, it is preferable to use the first medical data and the second medical data in estimating a first age range, to use the first medical data, the second medical data, and third medical data related to the medical care of a third population that is part of the entire population and different from the first population and the second population in estimating a second age range different from the first age range, and to use the third medical data in estimating a third age range different from the first age range and the second age range.

[0013] Furthermore, when the outcome condition includes a new drug or a new medical procedure, it is preferable to specify a drug or a medical procedure that is to be substituted for the new drug or the new medical procedure. Effect of the Invention

[0014] According to the present invention, it is possible to provide a medical information estimation device and a medical information estimation program for estimating medical information for an entire population. Other objects of the embodiments of the present invention will become apparent by reference to the entire specification. [Brief description of the drawings]

[0015] [Figure 1] 1 is a diagram showing a configuration of a medical information estimation system according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a diagram showing a configuration of a server according to an embodiment of the present invention. [Diagram 3] FIG. 2 is a diagram showing a configuration of a client according to an embodiment of the present invention. [Figure 4] FIG. 11 is a diagram showing an example of an available period for medical data according to an embodiment of the present invention. [Diagram 5] 1 is a flowchart showing a medical information estimation process according to an embodiment of the present invention. [Figure 6] FIG. 2 is a diagram illustrating the estimation process of medical information of logic 0 in an embodiment of the present invention. [Figure 7] FIG. 2 is a diagram illustrating the medical information estimation process of Logic 1 in the embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] [System Configuration] As shown in Fig. 1, a medical information estimating system 100 according to an embodiment of the present invention includes a server 102 and a client 104. The client 104 may be a single client or a plurality of clients. The server 102 and the client 104 are connected to each other via an information and communication network 106 such as the Internet so as to be able to exchange information with each other.

[0017] The information and communication network 106 is not limited to the Internet, but may be anything that can communicatively connect the server 102 and the client 104 to each other. For example, it may be a dedicated line, a public line (telephone line, mobile communication line, etc.), a wired LAN (Local Area Network), a wireless LAN, etc., or it may be a combination of the Internet and these.

[0018] As shown in FIG. 2, the server 102 includes a processing unit 10, a storage unit 12, an input unit 14, an output unit 16, and a communication unit 18. The processing unit 10 includes a means for performing arithmetic processing, such as a CPU. The processing unit 10 executes a medical information estimation server program stored in the storage unit 12, thereby realizing a function of performing medical information estimation in the medical information estimation system 100 in this embodiment. The storage unit 12 includes a storage means, such as a semiconductor memory or a memory card. The storage unit 12 is connected to the processing unit 10 so as to be accessible, and stores information necessary for medical information estimation processing, such as a medical information estimation server program and prescription data. The input unit 14 includes a means for inputting information. The input unit 14 includes, for example, a keyboard, a touch panel, a button, etc., for receiving input from an administrator. The output unit 16 includes a means for outputting a processing result in the server 102, such as a user interface screen (UI) for receiving input information from the administrator. The output unit 16 includes, for example, a display for presenting an image to the administrator. The communication unit 18 is configured to include an interface for communicating information with the client 104 via the information communication network 106. The communication by the communication unit 18 may be wired or wireless. The communication unit 18 may also receive information necessary for estimating medical information, such as prescription data, from computers installed in medical institutions, national institutions, local governments, health insurance associations, information providers, etc.

[0019] The server 102 accepts access via the information and communications network 106 from users' clients 104 who wish to receive information on medical information estimation, estimates the medical information requested by each user, and transmits the results to the users' clients 104.

[0020] As shown in FIG. 3, the client 104 includes a processing unit 20, a storage unit 22, an input unit 24, an output unit 26, and a communication unit 28. The client 104 is also called a communication terminal. The processing unit 20 includes a means for performing arithmetic processing such as a CPU. The processing unit 20 realizes the function of a client terminal in the medical information estimation system 100 in this embodiment by executing a medical information estimation client program stored in the storage unit 22. The storage unit 22 includes a storage means such as a semiconductor memory or a memory card. The storage unit 22 is connected to the processing unit 20 so as to be accessible, and stores the medical information estimation client program and information required for the processing. The input unit 24 includes a means for inputting information. The input unit 24 includes, for example, a keyboard, a touch panel, a button, etc. for receiving input from a user. The output unit 26 includes a means for outputting information required for processing in the client 104, such as a screen for receiving input information from a user and a display for displaying an image when presenting an estimation result of medical information. The communication unit 28 includes an interface for communicating information with the server 102 via the information and communication network 106. The communication by the communication unit 28 may be wired or wireless.

[0021] Various information processing devices capable of executing a client program for providing medical information estimation can be applied to the client 104. For example, the client 104 can be a stationary or portable medical information providing terminal installed in a medical institution or the like, a personal computer (PC), a tablet computer, a smartphone, a mobile phone terminal, a PHS (Personal Handy-phone System) terminal, a portable information terminal (PDA: Personal Digital Assistant), a multi-function television receiver equipped with an information processing function (so-called smart TV), and the like.

[0022] [Medical information estimation processing] The medical information estimation process is performed by the medical information estimation system 100. The statistics as medical information estimated by the medical information estimation system 100 can be the number of patients, the number of cases of medical examinations, treatments, and medications, and the amount of use, and financial information (amount) related to medical care. However, it is not limited to these, and any information related to medical care can be used.

[0023] The medical information estimation process is performed using first medical data related to medical care of a first group that is a part of the overall population, and second medical data related to medical care of a second group that is a part of the overall population and different from the first group. The medical information estimation process may also be performed using third medical data related to medical care of a third group that is a part of the overall population and different from the first and second groups, in addition to the first and second groups. Furthermore, medical data of a fourth or more groups may be processed.

[0024] The entire group may be, for example, a country. However, the entire group is not limited to this, and may be, for example, a local administrative division (such as a prefecture, city, town, village, etc.).

[0025] The first group may be, for example, a group whose elements are members of a health insurance association. In this case, the first medical data is health insurance receipt data submitted by a medical institution to a health insurance association and ledger information of members of the health insurance association. The first medical data may also include health checkup data that is a collection of health checkup results of members of a health insurance association. The first medical data may be health insurance receipt data related to a portion of members who are members of a health insurance association. The second group may be, for example, a group whose elements are members who are members of national health insurance. In this case, the second medical data is municipal receipt data submitted by a medical institution to a local government and ledger information of members of national health insurance. The second medical data may be municipal receipt data related to a portion of members who are members of national health insurance. The second medical data may also include health checkup data that is a collection of health checkup results of members of national health insurance. The third group may be a group whose elements are persons who are members of the medical care system for the elderly. The Medical Care System for the Elderly is a system in which elderly people who are 75 years of age or older, or 65 years of age or older and have disabilities, join. In this case, the tertiary medical data is municipal receipt data submitted by medical institutions to local governments and ledger information on subscribers of the Medical Care System for the Elderly. The tertiary medical data may also include health checkup data that collects the results of health checkups of subscribers of the Medical Care System for the Elderly. The tertiary medical data may be municipal receipt data on some of the subscribers who are enrolled in the Medical Care System for the Elderly.

[0026] Medical data includes information necessary to estimate medical information such as prescribed medications, examination results (disease name, etc.), and medical procedures (types and results of tests performed, details of treatment (procedures) and surgery, etc.) In addition, ledger information includes the subscriber's gender, age, and subscription period.

[0027] In addition, there is a difference in the period during which the data can be used between health insurance claim data, which is the first medical data, and local government claim data, which is the second and third medical data. There is a delay between the time of submission of health insurance claim data and the time it becomes available due to processing such as anonymization of the data. For example, only data older than about 7 months can be used for health insurance claim data, and only data older than about 9 months can be used for local government claim data.

[0028] In the medical information estimating system 100, the first medical data, the second medical data, and the third medical data are used to perform a process of estimating medical information. Hereinafter, the process of estimating medical information by the medical information estimating system 100 will be described with reference to the flowchart shown in FIG. 5. In this embodiment, a mode of estimating medical information for each combination of gender and age will be described. However, this is not limited to this, and the gender and age for estimating medical information may be set in the outcome condition described later.

[0029] In step S10, an initial setting process is performed, and the medical information estimating system 100 functions as an initial setting means by the process in this step.

[0030] The server 102 acquires health insurance receipt data, municipal receipt data related to national health insurance, and municipal receipt data related to the Long-Term Care Insurance System from computers located in health insurance associations and local governments, and stores them in the memory unit 12 as first medical data, second medical data, and third medical data, respectively.

[0031] Also, a login process is performed from the client 104 used by the user requesting an estimation of medical information. The user uses the input unit 24 of the client 104 to input information required for logging in to the medical information provision service, such as a user ID and a password. The client 104 transmits the information required for logging in to the server 102 via the communication unit 28 and the information communication network 106. When the server 102 receives the information required for logging in via the communication unit 18, it permits the client 104 to log in if the information satisfies the conditions for permitting logging in.

[0032] In step S12, a process for acquiring a designated period for estimating medical information is performed. Through the process in this step, the medical information estimation system 100 functions as a part of a condition acquisition means. In step S10, a screen for prompting input of a period for estimating medical information is displayed on the output unit 26 of the logged-in client 104. The user inputs the period for estimating medical information using the input unit 24 of the client 104. The client 104 transmits the period for estimating medical information inputted to the server 102 through the information communication network 106 via the communication unit 28. When the server 102 receives the period for estimating medical information via the communication unit 18, it stores the period in the memory unit 12 as a designated period.

[0033] In addition, it is preferable that the specified period is at least a period during which the first medical data is available. If the first medical data is health insurance receipt data, it is preferable that the specified period is a period during which the first medical data is available that is more than seven months prior to the present.

[0034] In step S14, a process of acquiring an outcome condition is performed. Through the process in this step, the medical information estimation system 100 functions as a part of a condition acquisition means. The outcome condition is a condition for performing an extraction process from the first medical data, the second medical data, and the third medical data. The outcome condition can be information that can be extracted from the first medical data, the second medical data, and the third medical data, such as the name of a disease, a prescribed drug, a type of medical treatment, sex, age, etc. The outcome condition may be a single condition, or a combination of multiple conditions.

[0035] For example, if one wishes to estimate the number of patients diagnosed with "diabetes" as medical information, the name of the disease, "diabetes," is set as the outcome condition. If one wishes to estimate the number of patients prescribed a specific drug as medical information, the name of the drug is set as the outcome condition. Similarly, other conditions such as the type of medical procedure, gender, age, etc. may be set. Furthermore, multiple conditions may be combined to set an outcome condition, such as combining the name of the disease with the prescribed drug.

[0036] In step S10, a screen prompting the user to input outcome conditions is displayed on the output unit 26 of the client 104 that has logged in. The user inputs the outcome conditions using the input unit 24 of the client 104. The client 104 transmits the input outcome conditions to the server 102 via the communication unit 28 and the information and communication network 106. When the server 102 receives the outcome conditions via the communication unit 18, it stores them in the memory unit 12 as outcome conditions.

[0037] In addition, if you want to estimate medical information related to a new drug or a new medical treatment or treatment, you can set the existing drug that replaces the new drug or the existing medical treatment or treatment that replaces the new medical treatment or treatment as the outcome condition. This makes it possible to estimate the number of patients to whom the new drug or new medical treatment or treatment can be applied, the number of medical treatments, treatments, and medications, and financial information (amount).

[0038] In step S16, an estimated period is set. Through the processing in this step, the medical information estimation system 100 functions as an estimated period setting means. The server 102 divides the designated period designated in step S12 into an estimated period, which is a predetermined time unit. The time unit can be one month, three months, six months, one year, etc.

[0039] For example, if the specified period is from 18 months ago (the beginning of the month 18 months ago) to 7 months ago (the end of the month 7 months ago) and the time unit is set to 1 month, the estimated period will be set in order of 1 month from 18 months ago (the beginning to the end of the month 18 months ago), 1 month from 17 months ago (the beginning to the end of the month 17 months ago), and 1 month from 7 months ago (the beginning to the end of the month 7 months ago).

[0040] In this embodiment, an example will be described in which the designated period is set for each month prior to seven months. However, this is not limited to this, and even if another time unit is set, the estimation process can be performed in the set time unit in the same manner.

[0041] In step S18, it is determined whether the estimated period is a specific period. Through the processing in this step, the medical information estimation system 100 functions as a specific period determination means. The specific period is a period during which any of the first medical data, the second medical data, and the third medical data cannot be used. If the estimated period set in step S16 is not a specific period, the processing proceeds to step S20, and if the estimated period is a specific period, the processing proceeds to step S22.

[0042] When the first medical data is health insurance claim data, and the second and third medical data are local government claim data, the specific period is a period during which the health insurance claim data is available but the local government claim data is unavailable. For example, when only health insurance claim data from about 7 months ago can be used and only local government claim data from about 9 months ago can be used, the period from 7 months ago to 9 months ago is determined to be the specific period.

[0043] In step S20, a medical information estimation process is performed. Through the process in this step, the medical information estimation system 100 functions as a medical information estimation means by logic 0. In the medical information estimation process by logic 0, the medical information is estimated using all of the first medical data, the second medical data, and the third medical data.

[0044] When it is considered that the entire population can be divided into multiple sets, the divided sets are called SET. Furthermore, it is assumed that data for a representative subset set of each divided set SET is currently available. Here, in the medical data held for the set set, the number of people of sex sex and age age belonging to the estimation period t is denoted by L. set (sex,age,t) This is called the number of data subscribers of the set. set (sex,age,t) The total number of people who have data that meet outcome condition O (which may be the number of medications or the amount of money depending on the outcome condition) is N set (O,sex,age,t) Let us call this the "number of data outcomes in the population set." In this case, the number of data outcomes per person in the population set for the outcome condition O, with sex and age in the estimation period t, is P set (O,sex,age,t) is expressed by equation (1).

number

[0045] For the population SET into which the entire population is divided, population information by sex, age, and year and month is obtained. Let us call this the "number of national subscribers to the population SET" L SET (sex,age,t) In addition, the above L SET (sex,age,t) The total number of people who meet the outcome condition O (or the number of medications or the amount of money, depending on the outcome condition) is N SET (O,sex,age,t)Let us call this the "number of national outcomes in the population SET." In this case, the number of national outcomes per person in the population SET for the outcome condition O, with sex and age in the estimation period t, is P SET (O,sex,age,t) is expressed by equation (2).

number

[0046] Here, since the data set set is representative of the national population SET, the P obtained in equation (2) SET (O,sex,age,t) is the P obtained from equation (1). set (O,sex,age,t) It is thought that it can be approximated by

number

[0047] Therefore, the number of national outcomes for the population SET, N SET (O,sex,age,t) can be estimated using equation (4).

number

[0048] In this embodiment, as shown in Figure 6, a medical information estimation process of logic 0 is applied during a period when both the first medical data, which is health insurance prescription data, and the second and third medical data, which is local government prescription data, are available.

[0049] First, medical information is estimated for the first medical data. The server 102 refers to the first medical data stored in the storage unit 12 and obtains statistics of the first medical data that meets the outcome condition set in step S14 during the estimation period set in step S16.

[0050] For the first medical data, the number of data outcomes N of the first medical data that meet the outcome condition O for the combination of gender sex and age age in the estimation period t set1 (O,sex,age,t) In addition, the number of data subscribers of the first medical data, L set1 (sex,age,t) The number of data outcomes in the first medical data is N set1 (O,sex,age,t) Dividing by , we obtain the number of data outcomes per person that meet the outcome condition O for the combination of sex and age in the estimation period t, P set1 (O,sex,age,t) Furthermore, if the first medical data is data for a subset of the first population, the number of national subscribers L for the combination of sex and age in the estimation period t for the entire set of the first population is calculated. SET1 (sex,age,t) The number of data outcomes per person is P set1 (O,sex,age,t) By multiplying by , we obtain the number of national outcomes N that meet the outcome condition O for the combination of gender and age in the estimation period t for the entire set of the first population. SET1 (O,sex,age,t) Estimate.

[0051] For example, if the first medical data is health insurance claim data, the estimation period is one month from 18 months ago (the beginning to the end of the month 18 months ago), and the disease name "diabetes" is set as the outcome condition, the number of patients diagnosed with "diabetes" for each combination of sex and age in the health insurance claim data for one month from 18 months ago is the number of data outcomes N set1 (O,sex,age,t) In addition, the number of subscribers for each combination of sex and age, which are elements that make up the health insurance receipt data, for the month 18 months prior is extracted as the number of data subscribers L set1 (sex,age,t) As the number of data outcomes, N set1 (O,sex,age,t) The number of data subscribers L set1 (sex,age,t)Divide by P to get the number of data outcomes per person matching "diabetes" for the sex and age combination in the month prior to the 18 month period. set1 (O,sex,age,t) Then, the number of people who joined the health insurance association for each combination of sex and age in the month 18 months prior is calculated as the number of national members L SET1 (sex,age,t) As a result, the number of data outcomes per person is P set1 (O,sex,age,t) Number of national subscribers L SET1 (sex,age,t) By multiplying this by , we obtain the number of national outcomes N that match "diabetes" for the combination of sex and age for all members of the health insurance association in the month 18 months prior. SET1 (O,sex,age,t) can be estimated in an expansive manner.

[0052] Furthermore, medical information is similarly estimated for the second medical data and the third medical data.

[0053] For the second medical data, the number of data outcomes N of the second medical data that meet the outcome condition O for the combination of gender sex and age age in the estimation period t set2 (O,sex,age,t) In addition, the number of data subscribers of the second medical data, L set2 (sex,age,t) The number of data outcomes in the second medical data is N set2 (O,sex,age,t) Dividing by , we obtain the number of data outcomes per person that meet the outcome condition O for the combination of sex and age in the estimation period t, P set2 (O,sex,age,t) Furthermore, if the second medical data is data for a subset of the second population, the number of national subscribers L for the combination of sex and age in the estimation period t for the entire set of the second population is calculated. SET2 (sex,age,t) The number of data outcomes per person is P set2 (O,sex,age,t)By multiplying by , we obtain the number of national outcomes N that meet the outcome condition O for the combination of sex and age in the estimation period t for the entire set of the second population. SET2 (O,sex,age,t) Estimate.

[0054] For example, if the second medical data is local government receipt data related to national health insurance, the estimation period is one month from 18 months ago (the beginning to the end of the month 18 months ago), and the disease name "diabetes" is set as the outcome condition, the number of patients diagnosed with "diabetes" for each combination of sex and age in the local government receipt data for one month from 18 months ago is the number of data outcomes N set2 (O,sex,age,t) In addition, the number of subscribers for each combination of sex and age, which are elements that make up the local government receipt data, for the month 18 months prior is extracted as the data subscriber count L set2 (sex,age,t) As the number of data outcomes, N set2 (O,sex,age,t) The number of data subscribers L set2 (sex,age,t) Divide by P to get the number of data outcomes per person matching "diabetes" for the sex and age combination in the month prior to the 18 month period. set2 (O,sex,age,t) Then, the number of people who were enrolled in the National Health Insurance for each combination of sex and age in the month 18 months prior is calculated as the number of people enrolled in the National Health Insurance for each combination of sex and age in the month 18 months prior. SET2 (sex,age,t) As a result, the number of data outcomes per person is P set2 (O,sex,age,t) Number of national subscribers L SET2 (sex,age,t) By multiplying this by , we obtain the number of national outcomes N that match "diabetes" for the combination of sex and age in the month 18 months prior among all people enrolled in the National Health Insurance. SET2 (O,sex,age,t) can be estimated in an expansive manner.

[0055] For the third medical data, the number of data outcomes N of the third medical data that meet the outcome condition O for the combination of gender sex and age age in the estimation period t set3 (O,sex,age,t) In addition, the number of subscribers to the third medical data, L set3 (sex,age,t) The number of data outcomes of the third medical data is N set3 (O,sex,age,t) Dividing by , we obtain the number of data outcomes per person that meet the outcome condition O for the combination of sex and age in the estimation period t, P set3 (O,sex,age,t) Furthermore, if the third medical data is for a subset of the third population, the number of national subscribers L for the combination of sex and age in the estimation period t for the entire set of the third population is calculated. SET3 (sex,age,t) The number of data outcomes per person is P set3 (O,sex,age,t) By multiplying by , we obtain the number of national outcomes N that meet the outcome condition O for the combination of sex and age in the estimation period t for the entire set of the third population. SET3 (O,sex,age,t) Estimate.

[0056] For example, if the tertiary medical data is local government receipt data related to the medical care system for the elderly, the estimation period is one month 18 months ago, and the disease name "diabetes" is set as the outcome condition, the number of patients diagnosed with "diabetes" for each combination of sex and age in the local government receipt data for one month 18 months ago is the number of data outcomes N set3 (O,sex,age,t) In addition, the number of subscribers for each combination of sex and age, which are elements that make up the local government receipt data, for the month 18 months prior is extracted as the data subscriber count L set3 (sex,age,t) As the number of data outcomes, N set3 (O,sex,age,t) The number of data subscribers L set3 (sex,age,t)Divide by P to get the number of data outcomes per person matching "diabetes" for the sex and age combination in the month prior to the 18 month period. set3 (O,sex,age,t) Then, the number of people who were enrolled in the medical care system for the elderly for the month 18 months prior for each combination of sex and age is calculated as the number of national enrollees L SET3 (sex,age,t) As a result, the number of data outcomes per person is P set3 (O,sex,age,t) Number of national subscribers L SET3 (sex,age,t) By multiplying this by , we obtain the number of national outcomes N that match "diabetes" for the combination of sex and age in the month 18 months prior among all members of the medical care system for the elderly. SET3 (O,sex,age,t) can be estimated in an expansive manner.

[0057] As described above, in this step, as a medical information estimation process by logic 0, medical information is estimated for an estimation period in which all of the first medical data, the second medical data, and the third medical data are available. Also, different medical data are used depending on the age range. That is, in an estimation of an age range of 64 years or younger, health insurance receipt data as the first medical data and local government receipt data as the second medical data are used, in an estimation of an age range of 65 years or older and 74 years or older, health insurance receipt data as the first medical data, the second medical data, and local government receipt data as the third group are used, and in an estimation of an age range of 75 years or older, local government receipt data as the third medical data is used to estimate medical information.

[0058] In step S22, a medical information estimation process is performed. Through the process in this step, the medical information estimation system 100 functions as a medical information estimation means by Logic 1. In the medical information estimation process by Logic 1, medical information is estimated for a specific period in which at least one of the first medical data, the second medical data, and the third medical data is unavailable.

[0059] For example, as described above, in a case where only data from more than approximately 7 months ago can be used for the health insurance receipt data, which is the first medical data, and only data from more than approximately 9 months ago can be used for the local government receipt data, which is the second and third medical data, the medical information estimation process of logic 1 in this step is applied to the two-month estimation period, the one month from 8 months ago and the one month from 7 months ago.

[0060] In this step, for medical data whose estimated period corresponds to the available period, the medical information estimation process is performed in the same manner as in step S20 Logic 0. For example, for the health insurance receipt data, which is the first medical data, the medical information estimation process is performed for the two months, 8 months ago and 7 months ago, in the same manner as in step S20 Logic 0.

[0061] On the other hand, for medical data whose estimated period does not fall within the available period, the medical information is estimated by multiplying by a constant the estimated value of medical information estimated from medical data whose estimated period falls within the available period.

[0062] If the first medical data is available during the estimation period t1 and the second medical data is unavailable, then, as shown in formula (5), for the set of first medical data set1, the number of data outcomes per person that meets the outcome condition O for the combination of gender sex and age age during the estimation period t1 is P set1 (O,sex,age,t1) with a constant k (O,sex,age) Multiplying by , we obtain the number of data outcomes per person P that meet the outcome condition O for the combination of gender sex and age age in the estimation period t1 for the second medical data set set2. set2 (O,sex,age,t1) Calculate.

number

[0063] where the constant k (O,sex,age)is the number of data outcomes per person that meet outcome condition O for the combination of sex and age in time period t2 for which both primary and secondary medical data are available. set1 (O,sex,age,t2) and number of data outcomes P set2 (O,sex,age,t2) Therefore, the calculation can be performed, for example, by using the formula (6). However, the calculation method is not limited to this.

number

[0064] If the second set of medical data set2 is a subset of the population, the number of national subscribers in the population SET for the combination of gender sex and age age in the estimation period t1 is L SET2 (sex,age,t1) Therefore, the number of national outcomes for population SET2 for the combination of sex and age in the estimation period t1 is N SET2 (O,sex,age,t1) can be estimated by approximation using equation (7).

number

[0065] Here, using equations (5) and (6), equation (7) can be transformed into the following equation (8). Therefore, the number of national outcomes N for population SET2 in period t1, during which the second medical data is unavailable, is SET2 (O,sex,age,t1) can be estimated using equation (8).

number

[0066] In this embodiment, as shown in Fig. 7, for the health insurance receipt data, which is the first medical data, the medical information estimation process is performed in the same manner as Logic 0. For the second medical data and the third medical data, which are the local government receipt data, the medical information estimation process of Logic 1 is applied if the estimation period is not an available period.

[0067] For example, if the second medical data is municipal receipt data for National Health Insurance, the estimation period is one month from seven months ago (estimated period t1), and the disease name "diabetes" is set as the outcome condition, first calculate the number of patients diagnosed with "diabetes" for each combination of sex and age during the period for which the municipal receipt data is available, for example, one month from nine months ago (period t2), which is the number of data outcomes N set2 (O,sex,age,t2) In addition, the number of subscribers to the local government receipt data for the month 9 months prior (period t2) is extracted as L set2 (sex,age,t2) The number of data outcomes for the local government receipt data is N set2 (O,sex,age,t2) Dividing by , we obtain the number of data outcomes per person that meet the outcome condition O for the combination of sex and age in the one month period 9 months prior (period t2), P set2 (O,sex,age,t2) Then, for the first medical data, the health insurance receipt data, we calculate the number of data outcomes per person that match the outcome condition O for the combination of gender sex and age age in the one month period t2 nine months ago (period t2). set1 (O,sex,age,t2) The number of data outcomes for the local government receipt data is P set2 (O,sex,age,t2) Divide by the constant k 1(O,sex,age) Then, the constant k 1(O,sex,age) The number of data outcomes per person that meet the outcome condition O for the combination of sex and age in the one month period (estimation period t1) seven months prior to the date when the health insurance claim data is available is P set1 (O,sex,age,t1) Multiplying this by , we obtain the number of data outcomes per person P that meet the outcome condition O for the combination of sex and age in the one-month period (estimation period t1) seven months prior to the period when the local government receipt data is unavailable. set2 (O,sex,age,t1) Estimate.

[0068] If the local government receipt data is for a subset of the National Health Insurance, the number of national subscribers L for the combination of sex and age for the entire set SET2 of National Health Insurance in the one month period 9 months prior (period t2) is SET2 (sex,age,t2) The number of data outcomes per person for the month 7 months prior (estimated period t1) P set2 (O,sex,age,t1) By multiplying this by , we obtain the number of national outcomes N that meet the outcome condition O for the combination of sex and age in the one month period 7 months prior (estimation period t1) for the entire set SET2 of national health insurance. SET2 (O,sex,age,t1) can be estimated in an expansive manner.

[0069] Similarly, when the tertiary medical data is local government receipt data for the medical care system for the elderly, estimation processing can be performed in the same way. If the estimation period is one month 7 months ago (estimation period t1) and the disease name "diabetes" is set as the outcome condition, first, the number of patients diagnosed with "diabetes" for each combination of sex and age during the period for which the local government receipt data is available, for example, one month 9 months ago (period t2), is calculated as the number of data outcomes N set3 (O,sex,age,t2) In addition, the number of subscribers to the local government receipt data for the month 9 months prior (period t2) is extracted as L set3 (sex,age,t2) The number of data outcomes for the local government receipt data is N set3 (O,sex,age,t2) Dividing by , we obtain the number of data outcomes per person that meet the outcome condition O for the combination of sex and age in the one month period 9 months prior (period t2), P set3 (O,sex,age,t2) Then, for the first medical data, the health insurance receipt data, we calculate the number of data outcomes per person that match the outcome condition O for the combination of gender sex and age age in the one month period t2 nine months ago (period t2). set1 (O,sex,age,t2) The number of data outcomes for the local government receipt data is P set3(O,sex,age,t2) Divide by the constant k 2(O,sex,age) Then, the constant k 2(O,sex,age) The number of data outcomes per person that meet the outcome condition O for the combination of sex and age in the one month period (estimation period t1) seven months prior to the date when the health insurance claim data is available is P set1 (O,sex,age,t1) Multiplying this by , we obtain the number of data outcomes per person P that meet the outcome condition O for the combination of sex and age in the one-month period (estimation period t1) seven months prior to the period when the local government receipt data is unavailable. set3 (O,sex,age,t1) Estimate.

[0070] If the local government receipt data is for a subset of the medical insurance system for the elderly, the number of national subscribers L for the combination of sex and age in the one month period 9 months prior (period t2) for the entire set SET3 of the medical insurance system for the elderly SET3 (sex,age,t2) The number of data outcomes per person for the month 7 months prior (estimated period t1) P set3 (O,sex,age,t1) By multiplying this by , we obtain the number of national outcomes N that meet the outcome condition O for the combination of sex and age in the one month period 7 months prior (estimation period t1) for the entire set SET3 of the Long-Term Care Insurance System for the Elderly. SET3 (O,sex,age,t1) can be estimated in an expansive manner.

[0071] In addition, k (O,sex,age) In calculating k, the length of the period t2 during which both the first medical data and the second medical data are available is set to one month, but it is preferable that the length of the period t2 is the same as the length of the period t1. In addition, the length of the period t2 is set to a period longer than one month to calculate k. (O,sex,age) may be calculated.

[0072] As described above, in this step, as the medical information estimation process by Logic 1, estimation of medical information is performed for an estimation period during which any of the first medical data, the second medical data, and the third medical data is unavailable.

[0073] In step S24, it is determined whether the medical information estimation process has been completed for the entire specified period. If the entire specified period specified in step S12 has been set as the estimated period in step S16, the server 102 proceeds to step S26, and if the entire period has not been set as the estimated period, the server 102 returns to step S16 to set the next estimated period and repeats the medical information estimation process of steps S18 to S22.

[0074] In step S26, the result of the medical information estimation process is output. Through the process in this step, the medical information estimation system 100 functions as an estimation result output means. The server 102 transmits the medical information estimation result obtained by the medical information estimation process through the information and communication network 106 via the communication unit 18 to the client 104 used by the user who requested the medical information estimation. The client 104 receives the medical information estimation result through the communication unit 28, and presents the medical information estimation result to the user using the output unit 26.

[0075] In the above embodiment, an example of a process for estimating the number of patients as medical information has been described, but if the number of medical examinations, treatments, or medications that match the outcome conditions and financial information (amounts) related to medical care are extracted as the number of outcomes, it is possible to similarly estimate the number of medical examinations, treatments, or medications and financial information (amounts) related to medical care.

[0076] As described above, according to the present embodiment, it is possible to provide a medical information estimation device and a medical information estimation program for estimating medical information for an entire population.

[0077] [Configuration of the invention] [Configuration 1] A medical data acquisition means for acquiring first medical data relating to medical care of a first group that is a part of an overall population, and second medical data relating to medical care of a second group that is a part of the overall population and different from the first group; A condition acquisition means for acquiring a specified period and an outcome condition; an estimation means for, when the specified period includes a specific period during which the first medical data exists but the second medical data does not exist, determining a relationship between a statistic of the first population and a statistic of the second population that meets the outcome condition during the period during which the first medical data and the second medical data exist, and estimating a statistic of the second population that meets the outcome condition during the specific period based on the relationship; A medical information estimation device comprising: [Configuration 2] A medical information estimation apparatus according to configuration 1, The medical information estimation device is further characterized in that the estimation means estimates statistics of the entire population that meet the outcome condition in at least a partial period of the specified period based on statistics of the first population and the second population that meet the outcome condition in the partial period. [Configuration 3] 3. A medical information estimation apparatus according to claim 2, A medical information estimation device characterized by correcting the statistics of the first population and the second population that meet the outcome condition in the partial period depending on the ratio of the first population and the second population to the entire population, and estimating the statistics of the entire population that meets the outcome condition in the partial period. [Configuration 4] A medical information estimation device according to any one of configurations 1 to 3, A medical information estimation device characterized in that the first medical data and the second medical data are prescription data including information on the name of a disease, prescribed medications, and medical treatments. [Configuration 5] A medical information estimation device according to any one of configurations 1 to 4, A medical information estimation device characterized in that the statistical quantity is the number of patients, number of cases, usage amount, or amount that meets the outcome condition. [Configuration 6] 6. A medical information estimation device according to any one of configurations 1 to 5, A medical information estimation device characterized in that, when estimating age-specific statistics for the entire population, different medical data are used to estimate a first age range and a second age range different from the first age range. [Configuration 7] 7. A medical information estimation apparatus according to claim 6, A medical information estimation device characterized in that, when estimating age-specific statistics in the entire population, the first age range is estimated using the first medical data and the second medical data, the second age range is estimated using the first medical data, the second medical data, and third medical data related to the medical care of a third population that is part of the entire population and different from the first population and the second population, and the third age range is estimated using the third medical data. [Configuration 8] A medical information estimation device according to any one of configurations 1 to 7, A medical information estimation device characterized in that, when the outcome condition includes a new drug or a new medical procedure, a drug or medical procedure to be substituted for the new drug or new medical procedure is specified. [Configuration 9] Computer, A medical data acquisition means for acquiring first medical data relating to medical care of a first group that is a part of an overall population, and second medical data relating to medical care of a second group that is a part of the overall population and different from the first group; A condition acquisition means for acquiring a specified period and an outcome condition; an estimation means for, when the specified period includes a specific period during which the first medical data exists but the second medical data does not exist, determining a relationship between a statistic of the first population and a statistic of the second population that meets the outcome condition during the period during which the first medical data and the second medical data exist, and estimating a statistic of the second population that meets the outcome condition during the specific period based on the relationship; A medical information estimation program characterized by functioning as follows. [Explanation of symbols]

[0078] 10 processing unit, 12 memory unit, 14 input unit, 16 output unit, 18 communication unit, 20 processing unit, 22 memory unit, 24 input unit, 26 output unit, 28 communication unit, 100 medical information estimation system, 102 server, 104 client, 106 information and communication network.

Claims

1. a medical data acquisition means for acquiring first medical data relating to medical care of a first group that is a part of an overall population, and second medical data relating to medical care of a second group that is a part of the overall population and different from the first group; A condition acquisition means for acquiring a specified period and an outcome condition; an estimation means for, when the specified period includes a specific period during which the first medical data exists but the second medical data does not exist, determining a relationship between a statistic of the first population and a statistic of the second population that meets the outcome condition during the period during which the first medical data and the second medical data exist, and estimating a statistic of the second population that meets the outcome condition during the specific period based on the relationship; A medical information estimation device comprising:

2. The medical information estimation device according to claim 1 , The medical information estimation device is further characterized in that the estimation means estimates statistics of the entire population that meet the outcome condition in at least a partial period of the specified period based on statistics of the first population and the second population that meet the outcome condition in the partial period.

3. The medical information estimation device according to claim 2, A medical information estimation device characterized by correcting the statistics of the first and second populations that meet the outcome condition in the partial period depending on the ratio of the first and second populations to the entire population, and estimating the statistics of the entire population that meets the outcome condition in the partial period.

4. The medical information estimation device according to claim 1 , A medical information estimation device characterized in that the first medical data and the second medical data are prescription data including information on the name of a disease, prescribed medications, and medical treatments.

5. The medical information estimation device according to claim 1 , A medical information estimation device characterized in that the statistical quantity is the number of patients, number of cases, usage amount, or amount that meets the outcome condition.

6. The medical information estimation device according to any one of claims 1 to 5, A medical information estimation device characterized in that, when estimating age-specific statistics in the entire population, different medical data are used to estimate a first age range and a second age range different from the first age range.

7. The medical information estimation device according to claim 6, A medical information estimation device characterized in that, when estimating age-specific statistics in the entire population, the first medical data and the second medical data are used to estimate a first age range, the first medical data, the second medical data, and third medical data related to the medical care of a third population that is part of the entire population and different from the first population and the second population are used to estimate a second age range different from the first age range, and the third medical data is used to estimate a third age range different from the first age range and the second age range.

8. The medical information estimation device according to claim 1 , A medical information estimation device characterized in that, when the outcome condition includes a new drug or a new medical procedure, a drug or medical procedure to be substituted for the new drug or new medical procedure is specified.

9. Computer, a medical data acquisition means for acquiring first medical data relating to medical care of a first group that is a part of an overall population, and second medical data relating to medical care of a second group that is a part of the overall population and different from the first group; A condition acquisition means for acquiring a specified period and an outcome condition; an estimation means for, when the specified period includes a specific period during which the first medical data exists but the second medical data does not exist, determining a relationship between a statistic of the first population and a statistic of the second population that meets the outcome condition during the period during which the first medical data and the second medical data exist, and estimating a statistic of the second population that meets the outcome condition during the specific period based on the relationship; A medical information estimation program characterized by functioning as follows.

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