JP2019159964A - Efficiency improvement support system and medical efficiency improvement support method - Google Patents

Abstract

To provide an efficiency improvement support system in which a person in charge of a medical institution can relatively easily understand a health condition of a subject, even if the data indicating the health condition of the subject is not possessed by the medical institution.SOLUTION: The system includes an information transmitting unit 104 (FIG. 18/STEP 1016) that receives a consent by a user from a terminal 41 and the information indicating the consent of the subject to disclose the personal information of the subject identified by the information to the medical related institution (FIG. 18/STEP 1002. YES) with at least 1 diseases of the subject to the terminal 50 of the medical related organization.SELECTED DRAWING: Figure 18

Description

  The present invention relates to an efficiency improvement support system and a medical efficiency improvement support method.

  2. Description of the Related Art Conventionally, a system is known that determines whether or not a subject needs to be examined based on a result of a health checkup of the subject and stores the result of the discrimination (see Patent Document 1).

  Patent Document 1 describes a system owned by the health insurance association, and each member of the health insurance association of the latest date among the diagnostic criteria for the names of wounds and diseases stored in the disease management index database and the health examination test data. Compare (insured) test data with the standard values of the test items for the health checkups that correspond to the diagnostic criteria for the name of the wound. A system is disclosed that records, as a discrimination result, that the insured needs to receive medical consultation for medical examination at a medical-related organization.

  The person in charge of the health insurance union can contact the insured person based on the determination result to prompt the insured person who has an abnormal inspection item to consult.

JP 2004-164173 A

  In order to make work more efficient, the person in charge of the medical institution can access the health checkup data of the insured person via the terminal of the medical institution and grasp the health condition of the insured person. It is preferable to do. However, in the technique described in Patent Document 1 described above, the health insurance examination data is stored in a system of a health insurance association, not a terminal of a medical related organization. From the viewpoint of protecting personal information, the person in charge of the medical-related organization used by the insured is not necessarily given the right to access the examination data for the medical examination.

  It may be possible for a medical institution to ask the insured person to bring a sheet of paper with the results of the medical examination, but if the insured forgets to bring the sheet or loses the sheet In the event of a loss, the person in charge of the medical institution could not grasp the health status of the insured person, leading to inefficiency in medical care.

  Therefore, the present invention provides an efficiency support that enables a person in charge of a medical-related organization to grasp the health status of the target person relatively easily even when the medical-related institution does not have data indicating the health condition of the target person. It is an object to provide a system and a medical efficiency improvement support method.

The efficiency improvement support system of the present invention is
A health condition storage unit that stores health condition information including information on the health condition of one or more persons including the subject person;
An onset probability quantification unit for quantifying the onset probability of at least one disease of the subject based on the health state information stored in the health state storage unit;
A document for the subject that includes information indicating the likelihood of the onset of the at least one disease of the subject quantified by the susceptibility digitization unit and information capable of uniquely identifying the subject; A document generator to generate;
Information that can identify the subject from the terminal, and information that indicates the consent of the subject about disclosing personal information of the subject identified by the information that can identify the subject to a medical-related organization. As a necessary condition, one or both of the health condition information of the subject identified by the identifiable information and the information indicating the possibility of developing the at least one disease are transmitted to the terminal of the medical related organization. An information transmission unit to perform,
It is characterized by providing.

  According to the efficiency improvement support system of the present invention, the likelihood of developing at least one illness of the subject is quantified by the onset probability quantification unit based on the health condition information.

  And the said subject including the information which can identify the said subject and the information which shows the onset possibility of the said at least 1 illness of the said subject who was digitized by the said onset possibility digitization part by the document production | generation part A document for the target audience is created.

  If the subject recognizes the contents of the document by receiving the printed matter of this document or sending the electronic data of this document to the terminal used by the subject and outputting it at the terminal, etc. The person can be made aware of the possibility of developing at least one disease. With this information, it is possible to make the subject recognize the possibility of developing his / her own disease, and as a result, the subject can be encouraged to consult.

  The subject's terminal agrees with the subject about disclosing to the medical related organization information that can identify the subject and personal information of the subject identified by the information that can identify the subject. The information indicating the health status information of the subject identified by the identifiable information and the information indicating the possibility of developing the at least one disease by the information transmitting unit of the system One or both are transmitted to the terminal of the medical related institution.

  One or both of the health status information of the subject and the information indicating the likelihood of the onset of the at least one disease are output in a format that can be recognized by the person in charge of the medical related institution at the terminal of the medical institution receiving the information. This makes it possible for the person in charge of the medical-related organization to grasp the health status of the subject relatively easily.

In the efficiency improvement support system of the present invention,
The efficiency improvement support system includes a receipt analysis unit that extracts information indicating at least one of a wound name, prescription history, and outpatient history from the receipt data,
The health condition information includes receipt data,
The information transmission unit, when the necessary condition is satisfied, information indicating at least one of the wound name, prescription history, and outpatient history of the subject extracted by the receipt analysis unit to the terminal of the medical related institution It is preferably configured to transmit.

  According to the efficiency improvement support system of the said structure, the information which shows at least 1 sort (s) among a wound disease name, prescription history, and outpatient history is extracted from the receipt data contained in health status information by a receipt analysis part.

  Then, when the necessary condition is satisfied by the information transmitting unit, information indicating at least one of the injury / illness name, prescription history, and outpatient history of the subject extracted by the receipt analysis unit to the terminal of the medical related organization is provided. Sent.

  Since the receipt data is data acquired at the time of medical care in any medical related institution, at least one of the wound name, prescription history, and outpatient history included in the subject's receipt data is the health status of the subject. Is presumed to be accurately shown.

  As a result, information that accurately indicates the health status of the target person is provided to the person in charge of the medical related organization while protecting the personal information of the target person. It is possible to grasp the health status of the child.

In the efficiency improvement support system of the present invention,
A question item storage unit that stores a question item about the subject's health condition, and determines whether information corresponding to the question item stored in the question item storage unit is included in the subject's health state information A question item related determination unit;
One or more question items excluding the question items determined to contain information corresponding to the question items stored in the question item storage unit included in the health status information of the subject are transmitted to the terminal of the subject It is preferable to provide a question item transmission unit.

  As described above, the health status information of the subject is transmitted to the terminal of the medical related institution when the necessary condition is satisfied. Here, it is not necessary to ask the subject again about the question items related to the information already transmitted to the medical institutions.

  According to the efficiency support system of the said structure comprised in view of this point, the information corresponding to the said question item memorize | stored in the said question item memory | storage part by the question item relevant determination part is the said subject's health state information Whether it is included.

  Then, the question item transmission unit asks the subject a question item that is determined that the information associated with the question item stored in the question item storage unit is included in the health status information of the subject, that is, the subject again. One or a plurality of question items excluding question items with low necessity are transmitted to the terminal of the target person.

  Therefore, according to the efficiency improvement support system of the said structure, since the question item is narrowed down using the information already transmitted to the medical related organization, work efficiency can be achieved.

In the efficiency improvement support system of the present invention,
A processing date recognition unit that recognizes the processing date is provided.
The health condition information includes an acquisition date of the information,
The question item transmission unit includes the information on which the processing date recognized by the processing date recognition unit is the information of the question items determined to include information corresponding to the question item in the health condition information of the subject. It is preferable that the question item is excluded on the condition that a predetermined period has not elapsed since the acquisition date.

  According to the efficiency improvement support system of the said structure, the said process date recognition part is the said question item determined that the information corresponding to a question item is contained in the said subject's health condition information by the question item transmission part. The question item is excluded on the condition that the recognized processing date has not passed a predetermined period from the acquisition date of the information.

  Therefore, according to the efficiency improvement support system having the configuration, the question items are narrowed down according to the freshness of the information corresponding to the question items, so that the work efficiency can be improved.

In the efficiency improvement support system of the present invention,
A processing date recognition unit that recognizes the processing date is provided.
The health condition information includes an acquisition date of the information,
The question item transmission unit includes the information on which the processing date recognized by the processing date recognition unit is the information of the question items determined to include information corresponding to the question item in the health condition information of the subject. It is preferable that the question item is excluded on the condition that the period corresponding to the question item has not elapsed since the acquisition date.

  The question items are, for example, items that are difficult to estimate the current state from past information, such as question items related to current symptoms, and the current state based on past information even after a certain period of time, such as question items related to lifestyle habits. There is a question item that is easy to estimate accurately.

  According to the efficiency improvement support system of the said structure, the said process date recognition part is the said question item determined that the information corresponding to a question item is contained in the said subject's health condition information by the question item transmission part. The question item is excluded on the condition that the recognized processing date has not passed the period corresponding to the question item from the acquisition date of the information.

  Regarding the excluded question items, it can be expected that the current state is accurately estimated from the information included in the health state information.

  Therefore, according to the efficiency improvement support system having the configuration, the question items are narrowed down according to the freshness of the information corresponding to the question items, so that the work efficiency can be improved.

In the efficiency improvement support system of the present invention,
It is preferable that the question item transmitting unit is configured to exclude a question item whose possibility of developing a disease corresponding to the question item is equal to or less than a predetermined value.

  According to the efficiency improvement support system having this configuration, the question item transmitting unit excludes question items whose disease corresponding to the question item is less than or equal to a predetermined value.

  Therefore, according to the efficiency improvement support system of the said structure, since a question item is narrowed down according to the onset possibility, work efficiency can be achieved.

In the efficiency improvement support system of the present invention,
An application accepting unit that accepts an application for the second instruction different from the first instruction or the first instruction based on the information received from the terminal;
A question history storage unit for storing question items made in the first instruction and the second instruction;
The question item transmitting unit is one or a plurality of question items for one of the first instruction and the second instruction, and the other of the first instruction and the second instruction stored in the question history storage unit. It is preferable to be configured to exclude the question items asked in

  In order to improve work efficiency, it is preferable to ask questions in advance before guidance. However, when multiple types of instruction are given, the same thing may be asked in each instruction.

  In this regard, according to the efficiency support system of the configuration, the question item transmission unit causes the question history storage unit to store one or more question items for one of the first instruction and the second instruction. Question items made in the other of the stored first instruction and second instruction are excluded.

  Thereby, since a question item is narrowed down according to the question item made in the other guidance, efficiency in guidance is achieved.

The figure which shows an example of the disease attention information printed on the paper medium. The whole block diagram of an example of an efficiency improvement support system. The figure which shows an example of the information memorize | stored in the health condition memory | storage part. The figure which shows an example of the information memorize | stored in the onset possibility exchange item memory | storage part. The figure which shows an example of the information memorize | stored in the change correlation item memory | storage part. The figure which shows an example of the information memorize | stored in the predicted value information storage part. The figure which shows an example of the information memorize | stored in the onset possibility numerical value information storage part. The figure which shows an example of the information memorize | stored in the log | history memory | storage part. 4A is a flowchart showing an outline of processing of the efficiency support system, FIG. 4A is a flowchart of processing from extraction of health state information to preparation processing, and FIG. 4B is a flowchart from health state prediction processing to output of disease attention information. It is a flowchart of a process. The flowchart of a preparation process. FIG. 6A is a flowchart of an onset probability correlation item extraction process, and FIG. 6B is a flowchart of an onset probability calculation model derivation process. FIG. 7A is a flowchart of change correlation item extraction processing, and FIG. 7B is a flowchart of second time information acquisition model derivation processing. The flowchart of a health condition prediction process. The figure which shows an example of the processing content which acquires the information which shows the health state of the person of the object in the 2nd time point by a prediction part. The flowchart of the onset possibility numerical conversion process. The figure which shows an example of the processing content which digitizes the onset possibility for every disease of each person by the onset possibility digitization part. The figure which shows an example of the processing content which digitizes the onset possibility in the 2nd time for every illness of each person by the onset possibility digitization part. The flowchart of attention information generation processing. The figure which shows the example of the conversion table of the numerical value of the onset possibility of a disease, and the classification of onset possibility. The figure which shows the example of the conversion table of the classification | category of the onset possibility of a disease, and the generation frequency of disease attention information. The figure which shows the example of the conversion table of the classification of the onset possibility of each disease, and improvement advice. The figure which shows the example of the conversion table of a disease and a complication. FIG. 15A is a diagram illustrating an example of question item information, and FIG. 15B is a diagram illustrating an example of question history information. The flowchart of an input information transmission process. FIG. 17A is a diagram showing an example of an identification number input screen, and FIG. 17B is a diagram showing an example of a screen for requesting consent to information sharing. The flowchart of an information sharing process. FIG. 19A is a diagram illustrating an example of a guidance selection screen, and FIG. 19B is a diagram illustrating an example of a screen including each question item and an input form for receiving an answer. The flowchart of a question selection process. The flowchart of a question exclusion process.

  The efficiency improvement support system of the present invention will be described with reference to FIGS.

(Disease attention information)
The disease attention information includes information on the possibility of developing the disease according to the numerical value of the possibility of developing the disease of each person 40 and the numerical value of the possibility of developing the disease, which are generated in the attention information generation process (STEP 80) described later. Information.

  For example, as shown in FIG. 1, the disease attention information is printed on a paper medium 32 by the terminal 30 and provided to each person 40.

  Alternatively, the disease warning information may be provided by being output by the terminal 30 as a document file including the contents shown in FIG. 1 and downloaded to the terminal 41 such as a personal computer, tablet, or smartphone used by each person 40. Good.

  As shown in FIG. 1, the disease attention information includes, for example, the past and most recently acquired health state information 321 of each person 40, improvement advice information 322 that is information on how to improve the possibility of developing each disease, disease Information 323 relating to complications that may occur due to a disease that each person may develop according to the numerical value of the possibility of onset, a graph 324 that represents the numerical value of the probability of occurrence for each disease, It includes information 325 relating to the ranking of the probability of occurrence of each person's disease in a group composed of all or part of the person, and a graph 326 including numerical values of the probability of occurrence at the second time point for each disease.

  The disease attention information further includes information 331 that prompts the user to download the guidance application to the terminal, information 332 that prompts the user to input identification information to the guidance application, and a QR code (registered trademark) of identification information that identifies each person. 333 and character information 334 of the identification information.

  Note that the disease attention information does not necessarily include all of the information, and may include a part. In addition, a specific method for generating disease attention information will be described later.

(Efficiency support system)
As shown in FIG. 2, the efficiency support system includes an efficiency support server 10, one or a plurality of terminals 30, and one or a plurality of terminals 50 of medical related organizations.

  The efficiency support server 10, the one or more terminals 30, and the one or more medical-related institution terminals 50 are configured to be able to communicate with each other via an information communication network 20 such as a LAN or the Internet. FIG. 2 shows one terminal 30 and one medical-related institution terminal 50.

  Alternatively, the efficiency improvement support server 10 may operate as one or both of the terminal 30 and the terminal 50 of the medical related institution.

(Disease attention information provision support server)
The efficiency support server 10 includes a server control unit 100 and a server storage unit 110. Note that a part or all of the computer constituting the efficiency improvement support server 10 may be constituted by a computer constituting one or both of the terminal 30 and the terminal 50 of the medical related institution.

  The server control unit 100 includes an arithmetic processing device such as a CPU (Central Processing Unit), a main storage device, and an input / output device. The server control unit 100 is configured by one or a plurality of processors.

  The server control unit 100 reads and executes a predetermined program to thereby execute an arithmetic processing described later, a symptom probability quantification unit 101, a prediction unit 102, a document generation unit 103, an information transmission unit 104, and a receipt analysis unit 105. , Functioning as question item relation determination unit 106, question item transmission unit 107, processing date recognition unit 108, and application reception unit 109.

  The server storage unit 110 includes a storage device such as a ROM (Read Only Memory), a RAM (Random Access Memory), and an HDD (Hard Disk Drive).

  The server storage unit 110 is configured to store the calculation result of the server control unit 100 or the health condition information captured from the terminal 30.

  The server storage unit 110 includes a health state storage unit 111, an onset probability correlation item storage unit 112, a calculation model storage unit 113, a change correlation item storage unit 114, a prediction model storage unit 115, a predicted value information storage unit 116, and an onset possibility. A numerical information storage unit 117, a history storage unit 118, a conversion table storage unit 119, a question item storage unit 120, and a question history storage unit 121 are provided.

  The health status storage unit 111 stores health status information that is information including information on the health status of one or more people.

  For example, as shown in FIG. 3A, the health state storage unit 111 includes an ID for identifying each person 40, acquisition date of health state information, age, sex, height, weight, waist circumference, total cholesterol, γ-GT, One or more sets of information on items related to the health status of each person 40 and information associated therewith composed of uric acid levels, blood glucose levels, HbA1c, blood pressure, smoking habits, exercise habits, drinking habits, and disease names being treated Storing. The health condition information includes data on the result of each person's health check (hereinafter referred to as health check data) and each person's receipt data. For example, the server control unit 100 can read health condition information from a storage medium such as a CD provided by a health insurance association to which each person joins, or store the health condition information in the server storage unit 110. The server control unit 100 can receive health status information from the terminal of the health insurance association and store the received information in the server storage unit 110.

  The health state storage unit 111 can store a plurality of sets of information regarding different acquisition dates for the same ID.

  Note that the health state storage unit 111 can store an information set having a certain interval (for example, one year) when the acquisition date is obtained. This one year is not limited to one year in a strict sense, but may be a period having a certain range, for example, 10 to 14 months, or an information set having a different acquisition year or acquisition year. It may be.

  Hereinafter, the information set acquired on the most recent acquisition date among the set of the information for the same ID is referred to as “latest health status information”, and the information acquired on the second new acquisition date. An information set that is included in a set period before the acquisition date of the latest health condition information (for example, 10 months to 14 months before) of the latest health condition information is set as “health condition information one year ago” It is an information set acquired on the third most recent acquisition date, and the acquisition date is one year before the predetermined period (for example, 10 to 14 months before). The information set included in the range is referred to as “health state information two years ago”.

  In the following, an example of using the above-mentioned latest, 1-year-old and 2-year-old health condition information will be described. However, the present invention is not limited thereto, and any information set may be used as long as the acquisition date has a certain interval. . The number of information sets with different acquisition times is not limited to three, and may be two or four or more. In addition, for each person 40, the acquisition time of the information set may be the same or different. In addition, information sets with different acquisition times for a single person are the latest, 1 year ago, 2 years ago health status information, 1 year ago, 2 years ago and 3 years ago health status information, A plurality may be used.

  The health status storage unit 111 receives health status information from a terminal 30 such as a hospital or a health insurance association via an external storage medium 31 such as a CD-ROM, DVD-ROM, or USB memory, or via the information communication network 20. Capture.

  As shown in FIG. 3B, the onset probability correlation item storage unit 112 stores one or a plurality of sets of items relating to a disease name and a health condition correlated with the onset possibility of the disease.

  The calculation model storage unit 113 stores a model for calculating the onset probability for each disease derived by the onset probability digitizing unit 101 in the onset probability calculation model preparation process (STEP 402) described later.

  As shown in FIG. 3C, the change correlation item storage unit 114 stores a set of items of health state information correlated with a part or all of the item names of the health state information and the magnitude of change in the value of each item. One or more are stored.

  The prediction model storage unit 115 includes information including the health state information at the first time point, the health state information at the first time point, and the information derived by the prediction unit 102 in the second time point information acquisition model preparation process (STEP 404) described later. A model that outputs information that can specify a health state at a second time point after the first time point by inputting information including the magnitude of change between the health state information at a time point before the first time point and 1 Or a plurality are stored.

  In the present embodiment, the first time point is the date of acquisition of the latest health information of each person 40, and the second time point is, for example, one year after the first time point and before the first time point. Is the date of acquisition of the health status information of each person 40 one year ago.

  As shown in FIG. 3D, the predicted value information storage unit 116 is a numerical value indicating the health status of each person 40 at the second time point calculated by the prediction unit 102 in the health status prediction process (STEP 50) described later. And one or more sets of information associated therewith.

  Note that the predicted value information storage unit 116 stores a numerical value indicating the health status of the third time point that is one year after the second time point for the same ID, and the date of the fourth time point that is one year later. It is possible to store a plurality of numerical values indicating health state information in the future date after the second time point, such as a numerical value indicating the health state in.

  As shown in FIG. 3E, the onset possibility numerical value information storage unit 117 is calculated or set by the onset possibility number conversion unit 101 in the onset possibility numerical value calculation process (STEP 601), such as diabetes risk, dyslipidemia risk, Information that classifies the type according to the numerical value of the onset probability for each person's 40 disease, such as the numerical value of the onset possibility for each person's 40 disease, the diabetes risk type, the dyslipidemia risk type, and the like. A set of information.

  The onset possibility numerical value information storage unit 117 stores one or more sets of the information.

  The onset probability numerical information storage unit 117 can store a plurality of sets of the information for different acquisition dates for the same ID.

  As shown in FIG. 3F, the history storage unit 118 sets one or more sets of the caution information last generation date and the information associated therewith as the date of generation of the latest disease caution information for each person 40. Multiple items are stored.

  As illustrated in FIGS. 14A to 14D, the conversion table storage unit 119 stores a conversion table that is referred to when the efficiency support server 10 executes various processes.

  The question item storage unit 120 stores question item information. For example, as shown in FIG. 15A, the question item information includes a question ID for identifying each question item, the content of each question item, a guidance type corresponding to each question, and health checkup data corresponding to each question item. Items, receipt data items corresponding to each question item, disease items corresponding to each question item, threshold values of disease onset possibility corresponding to each question item, and corresponding to each question item It is information including a period.

  The question history storage unit 121 stores question history information. For example, as shown in FIG. 15B, the question history information includes a question ID for identifying each question item, an ID for identifying a person who has received the question, a guidance type indicating the type of guidance for which the question has been made, It is information including the question date indicating the date when the question was made.

(Terminal)
The terminal 30 is configured by a desktop computer, a tablet terminal, a smartphone, or the like. The terminal 30 is a terminal used by a person in charge of the health insurance association, for example.

  The terminal 30 includes storage devices such as a ROM (Read Only Memory), a RAM (Random Access Memory), and an HDD (Hard Disk Drive), for example, from one or a plurality of human health status information and the efficiency support server 10. The received disease attention information is stored.

  For example, as illustrated in FIG. 1, the terminal 30 prints on a paper medium 32 and provides disease attention information to each person 40.

  Alternatively, the terminal 30 outputs a document file including the contents shown in FIG. 1 and provides the disease warning information by causing the terminal 41 such as a personal computer, a tablet, or a smartphone used by each person 40 to download the document file. It is good as well.

  Alternatively, the efficiency support server 10 may print the disease attention information on the paper medium 32 or output the document file and provide it to each person.

(Medical related terminals)
The terminal 50 of a medical related organization is configured by a desktop computer, a tablet terminal, a smartphone, or the like. The terminal 50 of a medical related organization is a terminal used by a person in charge of a medical related organization such as a doctor or a registered dietitian.

  A terminal 50 of a medical related organization includes a storage device such as a ROM (Read Only Memory), a RAM (Random Access Memory), and an HDD (Hard Disk Drive).

  In the initial state, the medical facility-related terminal 50 is not given access authority to the health status information of each person 40 and information indicating the possibility of the onset of each disease.

(Outline of processing)
With reference to FIG. 4, the outline | summary of a process of the efficiency improvement assistance system is demonstrated.

  The efficiency support system includes the processes from the health condition information extraction (STEP 10) by the terminal 30 to the preparation process (STEP 40) by the efficiency support server 10 shown in FIG. 4A, and the efficiency support server shown in FIG. 4B. 10 from the health state prediction process (STEP 50) by 10 to the output of the disease attention information by the terminal 30 (STEP 120).

  In addition, when the efficiency improvement support server 10 or the terminal 30 performs processing on the health condition information or the item of the health condition information, the item of the health condition information or the health condition information to be processed by the efficiency improvement support server 10 or the terminal 30 is , Part or all of it.

  The process shown in FIG. 4A is a series of processes for accumulating the health condition information of each person 40 and preparing for the execution of the series of processes shown in FIG. 4B. The process shown in FIG. It is a series of processes for generating and transmitting attention information.

  The terminal 30 and the efficiency improvement support server 10 may perform the series of processes shown in FIG. 4A and the series of processes shown in FIG. 4B all at once as a series of processes or at different frequencies. Also good.

  First, the process from the health condition information extraction (STEP 10) by the terminal 30 to the preparation process (STEP 40) by the efficiency support server 10 shown in FIG. 4A will be described.

  First, the terminal 30 takes out health status information including information on the health status of one or more persons from the storage device (STEP 10). The retrieval may be performed by storing health state information in an external storage medium 31 such as a CD-ROM, DVD-ROM, or USB memory, or by specifying a transmission destination via the information communication network 20. It may be done by sending health status information.

  Next, the efficiency improvement support server 10 takes in the health condition information extracted by the terminal 30 (STEP 20) and stores it in the health condition storage unit 111 (STEP 30).

  After that, the efficiency improvement support server 10 executes a preparation process (STEP 40), and makes preparations necessary for predicting health condition information and quantifying the possibility of developing a disease. Details of the preparation process (STEP 40) will be described later.

  The terminal 30 and the efficiency improvement support server 10 may perform the series of processes shown in FIG. 4A all at once as a series of processes, or, for example, perform only STEPs 10 to 30 each time, and a certain amount of data. STEP 40 may be executed every time it is accumulated or every certain period.

  Next, the process from the health state prediction process (STEP 50) by the efficiency improvement support server 10 to the output of disease caution information (STEP 120) by the terminal 30 shown in FIG. 4B will be described.

  First, the prediction unit 102 performs a health state prediction process (STEP 50).

  Next, the onset probability numerical unit 101 executes the onset probability digitization process (STEP 60) to digitize the onset probability of the disease of each person 40, and the onset probability numerical information is stored as the onset probability numerical information storage unit. It memorize | stores in 117 (STEP70).

  Subsequently, the document generation unit 103 executes attention information generation processing (STEP 80) to generate disease attention information including information indicating a numerical value of the likelihood of developing at least one disease of each person 40, and the information transmission unit 104 The disease caution information including the onset probability numerical value information is transmitted to the terminal 30 (STEP 90).

  Note that the information indicating the numerical value of the probability of developing the disease is not only information clearly indicating the numerical value of the probability of developing the disease, but also information that roughly (schematically) indicates the numerical value of the probability of developing the disease. Also good. For example, the information indicating the numerical value of the probability of developing the disease includes not only the numerical value of the probability of developing the disease itself but also information such as a graph indicating the numerical value of the probability of developing the disease and the color indicating the numerical value of the probability of developing the disease. It may be.

  Details of the health state prediction process (STEP 50), the onset probability digitization process (STEP 60), and the attention information generation process (STEP 80) will be described later.

  The terminal 30 receives the disease attention information including the numerical value of the possibility of onset transmitted from the efficiency support server 10 (STEP 100), stores it (STEP 110), outputs it (STEP 120), and ends the process.

  The terminal 30 and the efficiency improvement support server 10 may perform the series of processes shown in FIG. 4B all at once as a series of processes, or, for example, perform steps 50 to 110 each time, and a certain amount of data STEP 120 may be executed every time it is accumulated or every certain period.

(Preparation process)
The preparation process (STEP 40) includes a change correlation item extraction process (STEP 401), a second time point information acquisition model preparation process (STEP 402), an onset possibility correlation item extraction process (STEP 403), and an onset possibility calculation model preparation process (STEP 404). Composed.

  Hereinafter, the details of each process will be described with reference to FIGS. 6 and 7.

  In addition, the efficiency improvement support server 10 may perform all of these processes at once as a series of processes, or may perform them at different frequencies.

(Change correlation item extraction processing)
The change correlation item extraction process (STEP 401) is a process of extracting items of health state information correlated with the magnitude of change in the value of each item of health state information by analyzing the health state information. The processing content is as shown in FIG. 6A and is performed for each item (loop L1) included in the health condition information.

  Hereinafter, the case of extracting items of health condition information correlated with the magnitude of change in the value of each item of health condition information for one year will be described.

  First, the prediction unit 102 calculates the value of each item of the latest health state information of a plurality of persons stored in the health state storage unit 111, the value of each item of health state information one year ago, and two years ago. The value of each item of the health status information is acquired (STEP 4011).

  Next, the prediction unit 102 calculates the difference between the value of each item of the acquired latest health condition information and the value of each item of the health condition information one year ago for the plurality of persons (STEP 4012).

  That is, for example, the value of the latest health condition information of a person is weight 89, abdominal circumference 80, total cholesterol 150, blood sugar level 125, the value of one year ago is weight 86, abdominal circumference 78, If the total cholesterol is 155 and the blood sugar level is 124, the difference between the value of the latest health condition information and the value of the health condition information one year ago is: weight +3, waist circumference +2, total cholesterol -5, blood sugar The value is +1.

  Subsequently, the prediction unit 102 calculates the difference between the value of each item of the acquired health condition information one year ago and the value of each item of the health condition information two years ago for the plurality of persons (STEP 4013).

  The prediction unit 102 calculates the difference between the acquired value of the health condition information one year ago and the value of the health condition information two years ago by the same calculation as STEP 4012.

  Next, the prediction unit 102 selects each item of the health state information with respect to the magnitude of the change in the value of the item for the health state information that is the extraction target of the item of the health state information that is correlated with the magnitude of the change in the value. Is calculated (STEP 4014). The prediction unit 102 calculates the degree of correlation, for example, by linear regression analysis.

  That is, for example, in the case of calculating the degree of correlation of each item with the magnitude of the change in the abdominal circumference value, the prediction unit 102 calculates the health status information of one year ago that is the magnitude of the change in the circumference of the subject person. The difference in abdominal circumference between the latest health status is the objective variable, and the difference in the abdominal circumference between the health status information of the person two years ago and the health status of the previous year, The coefficient of each item is obtained by linear regression analysis using a model in which the value of each item contained in the abdominal circumference value and the value of each item included in the health status information of the person one year ago is an explanatory variable.

  Items with a large absolute value of the coefficient of each health condition information item obtained by the above analysis are items of health condition information having a high degree of correlation with the magnitude of change in the abdominal circumference value (for example, smoking habits, exercise habits, Drinking habits, age and gender).

  Subsequently, the prediction unit 102 extracts a certain number of items having a high degree of correlation from the items of the health condition information (STEP 4015). For example, the prediction unit 102 extracts items having a high degree of correlation by a method such as selecting 10 items from the items of the health condition information in the order of the degree of the correlation.

  Then, the prediction unit 102 correlates the extracted item name with the change in the value of the item of the health condition information, with the item name of the target health condition information as the prediction target, for example, in a format as shown in FIG. 3C. Each item is stored in the change correlation item storage unit 114 as a certain item (STEP 4016).

  When the prediction unit 102 finishes these processes (STEPs 4011 to 4016) for all items of the health condition information, the prediction unit 102 exits the loop L1 and ends the change correlation item extraction process.

(Second time point information acquisition model preparation process)
The second time point information acquisition model preparation process (STEP 402) is a model in which information including the health state information of an arbitrary person at an arbitrary time point is input and information indicating the health state of the person at a later time point is output. This is a process of deriving the constants used in the second time point information acquisition model and the coefficient of each item of the health condition information and generating or updating the model.

  The processing content is as shown in FIG. 6B and is performed for each item of health condition information (loop L2).

  First, the prediction unit 102 calculates the value of each item of the latest health state information of a plurality of persons stored in the health state storage unit 111, the value of each item of health state information one year ago, and two years ago. The value of each item of the health condition information is acquired (STEP 4021).

  Next, the prediction unit 102 calculates the difference between the value of each item of the acquired latest health condition information and the value of each item of the health condition information one year ago for the plurality of persons (STEP 4022).

  Subsequently, the prediction unit 102 calculates the difference between the value of each item of the acquired health condition information one year ago and the value of each item of the health condition information two years ago for the plurality of persons (STEP 4023).

  Next, the prediction unit 102 refers to the information stored in the change correlation item storage unit 114, and acquires the item name of the health condition information correlated with the magnitude of the change in the value of the target item (STEP 4024).

  That is, for example, when the target item is abdominal circumference, the prediction unit 102 determines that the item name of the health condition information stored in the change correlation item storage unit 114 as the item correlated with the change in the value of the health condition information item is smoking. Recognize habits, exercise habits, drinking habits, age and gender.

  Note that one device “recognizes” information means that one device receives the information from another device, and one device stores information stored in a storage medium connected to the one device. Reading, one device acquiring information based on a signal output from a sensor connected to the one device, one device acquiring from received information or information or sensor stored in a storage medium Based on the obtained information, a predetermined calculation process (calculation process or search process) is performed to derive the information, and one apparatus uses the information as a calculation process result of the other apparatus as the other apparatus. Means that any arithmetic processing for acquiring the information is executed, such as reading the information from the internal storage device or the external storage device in accordance with the received signal.

  Next, the prediction unit 102 derives a constant used for the second time point information acquisition model of the item of the target health condition information and a coefficient of each item of the health condition information (STEP 4025). This model is the same type as the model in the change correlation item extraction process (STEP 401), and is, for example, a linear regression model.

  That is, for example, in the case of generating or updating the second time point information acquisition model of the abdominal circumference, the prediction unit 102 determines whether the health status information of one year ago that is the magnitude of the change in the abdominal circumference of the target person and the latest health status. The difference in abdominal circumference between the health status information of the person 2 years ago and the health status of the previous year, the value of the abdominal circumference of the person 1 year ago, and the A constant and a coefficient of each item of a model in which the value of the item correlated with the magnitude of the change in the abdominal circumference among items included in the health condition information of one year ago is obtained by linear regression analysis.

  The predicting unit 102 generates the second time point information acquisition model of the item of the health condition information of the target by applying the constant obtained by such processing and the coefficient of each item to the linear regression analysis formula (STEP 4026).

  The prediction unit 102 stores the generated second time point information acquisition model of each item of the health state in the prediction model storage unit 115 (STEP 4027).

  When the second time point information acquisition model of the item in the health state is already stored in the prediction model storage unit 115, the prediction unit 102 overwrites and stores the generated second time point information acquisition model. Then, the second time point information acquisition model of the item of the health condition is updated.

  When the prediction unit 102 completes these processes (STEPs 4021 to 4027) for all items of the health condition information, the prediction unit 102 exits the loop L2 and ends the second time point information acquisition model preparation process.

(Potential correlation item extraction processing)
The onset probability correlation item extraction process (STEP 403) is a process of extracting items having a correlation with the onset possibility of each disease by analyzing health condition information. The processing content is as shown in FIG. 7A and is performed for each disease (loop L3) stored in the onset possibility correlation item storage unit 112.

  First, the onset probability quantification unit 101 extracts the latest health state information of a person who can know the onset of the target disease from the health state information of a plurality of people stored in the health state storage unit 111 (STEP 4031). ).

  The onset probability digitization unit 101 determines whether or not the onset of the disease is known with reference to, for example, the name of the disease under treatment included in the health condition information.

  That is, for example, if there is any disease name in the name of the disease being treated or if it is clearly stated that there is no disease name being treated, it is determined that the presence or absence of the disease is known, and the disease name being treated If no value is present, it is determined that the presence or absence of the disease is unknown.

  Next, the onset probability digitization unit 101 calculates the degree of correlation of each item of the health condition information with respect to the onset of the target disease (STEP 4032). The onset probability digitization unit 101 calculates the degree of correlation by, for example, logistic regression analysis.

  That is, for example, in the case of calculating the degree of correlation of each item with respect to the onset of diabetes, the onset probability quantification unit 101 uses the onset probability of diabetes of the target person as an objective variable and is included in the health status information of the person The partial regression coefficient of each item of the model using the value of each item as an explanatory variable is obtained by logistic regression analysis.

  Items having a large absolute value of the partial regression coefficient of each item of health condition information obtained by the above analysis are items having a high degree of correlation with the onset of diabetes.

  Subsequently, the onset probability digitization unit 101 extracts a certain number of items having a high degree of correlation from the items of the health condition information (STEP 4033). The onset probability quantification unit 101 extracts, for example, a method such as selecting 10 items in descending order of the degree of correlation.

  Then, the onset probability quantification unit 101 uses the extracted disease name as an item correlated with the onset probability of each disease in the format as shown in FIG. 3B, for example. Each is stored in the onset likelihood correlation item storage unit 112 (STEP 4034).

  When the onset probability quantification unit 101 finishes these processes (STEPs 4031 to 4034) for all the diseases stored in the onset probability correlation item storage unit 112, it exits the loop L3 and extracts the onset probability correlation item. The process ends.

(Probability calculation model preparation process)
The onset probability calculation model preparation process (STEP 404) is an onset probability calculation model which is a model that receives the health status information of an arbitrary person at an arbitrary time point and outputs a numerical value of the onset probability for each person's disease. This is a process for deriving coefficients for each item of constants and health condition information to be used. The processing content is as shown in FIG. 7B and is performed for each disease (loop L4) stored in the onset possibility correlation item storage unit 112.

  First, the onset probability quantification unit 101 refers to the onset probability correlation item storage unit 112 and acquires the item name of the health condition information correlated with the onset possibility of the target disease (STEP 4041).

  That is, for example, when the target disease is diabetes, the onset probability quantification unit 101 stores the blood sugar level, waist circumference, drinking habits, HbA1c, exercise habits, smoking, as items that are correlated with the onset possibility. Recognize habits, height, weight, age and gender.

  Next, the onset probability quantification unit 101 includes the possibility of onset of the target disease acquired in STEP 4041 among the items of the latest health state information of a plurality of persons stored in the health state storage unit 111. The value of each item of the health condition information correlated with is extracted (STEP 4042).

  Next, the onset probability quantification unit 101 derives coefficients for each item of constants and health state information used in the onset probability calculation model of the target disease (STEP 4043). This model is the same as the model in the onset possibility correlation item extraction process (STEP 403), and is, for example, a logistic regression model.

  That is, for example, in the case of generating or updating the onset probability calculation model for the onset possibility of diabetes, the onset possibility quantification unit 101 uses the target person's diabetes onset probability as an objective variable and is extracted in STEP 4042 A constant and a partial regression coefficient of each item of a model having the value of an item correlated with the possibility of developing the target disease as an explanatory variable are obtained by logistic regression analysis.

  The onset probability quantification unit 101 generates a model for calculating the onset probability of the target disease by applying the constants obtained by such processing and the partial regression coefficient of each item to the logistic regression analysis formula (STEP 4044). .

  The onset probability digitization unit 101 stores the generated or updated onset probability calculation model of each disease in the calculation model storage unit 113 (STEP 4045).

  In addition, when the onset probability calculation model of the disease is already stored in the calculation model storage unit 113, the onset probability digitizing unit 101 overwrites and stores the generated onset probability calculation model. Update the onset probability calculation model for the item of health status.

The onset probability quantification unit 101 exits the loop L4 when all the diseases (STEP 4041 to STEP 4045) are finished for all the diseases stored in the onset probability correlation item storage unit 112, and the onset probability quantification model processing is performed. finish.
(Health condition prediction process)
The health state prediction process (STEP 50) is a process for acquiring information indicating the predicted health state of each person 40 at the second time point. Details of this processing will be described with reference to FIGS.

  The processing content is as shown in FIG. 8 and is performed for each ID (loop L5) included in the health condition information stored in the health condition storage unit 111. Further, STEPs 503 to 506 are performed for each item of health condition information (loop L6).

  First, the prediction unit 102 acquires the health state information J1 and the latest health state information J2 of the target ID one year ago from the health state storage unit 111 (STEP 501 and FIG. 9).

  Next, the prediction unit 102 calculates a difference J11 between the value of each item of the acquired latest health condition information and the value of each item of the health condition information one year ago for the ID (STEP 502, FIG. 9).

  For items that are not measured numerically, such as exercise habits and smoking habits, for example, the prediction unit 102 calculates the difference J11 between the value of the latest health condition information and the value of the health condition information one year ago. It may be included in the processing target in the subsequent processing, or the difference J11 between the value of the latest health state information and the health state information one year ago is not calculated, and is not included in the processing target in the subsequent processing. It is good.

  When an item that is not measured numerically is included in the processing target, the prediction unit 102 sets the value of each item such as 1 for habit and 0 for no habit, for example, if it is a smoking habit. Replace with numeric values.

  Next, the prediction unit 102 refers to the change correlation item storage unit 114, and acquires items of health state information correlated with the magnitude of changes in the items of the target health state information (STEP 503).

  Next, the prediction unit 102 extracts an item J13 having a correlation with the magnitude of change in the item of the target health condition information from the latest health condition information of the target ID for the item of the target health condition information (STEP 504). , FIG. 9).

  That is, for example, when the item of health condition information of the subject is abdominal circumference, what is stored as an item correlated with a change in the value of the health condition information item is smoking habits, exercise habits, drinking habits, age and sex Therefore, the prediction unit 102 extracts the values of these items from the latest health condition information of the ID.

  Next, the prediction unit 102 refers to the prediction model storage unit 115 and acquires the second time point information acquisition model F1 of the item of the target health condition information (STEP 505, FIG. 9).

  Next, the prediction unit 102 correlates with the difference J11 between the latest value of the target item of the target ID and the value one year ago, the latest value J12 of the target item of the target ID, and the target item. Based on the value of each item J13, the value at the second time point of the target item of the target ID is calculated (STEP 506, FIG. 9).

  The prediction unit 102 adds the difference J11 between the latest value of the target item and the value one year ago, the latest value J12, and the value of each item J13 correlated with the target item to the model F1 acquired in STEP 505. By inputting, a predicted value V1 of a difference between the latest value of the item of the health condition information of the target and the value at the second time point is calculated (FIG. 9).

  Further, the prediction unit 102 adds the obtained V1 and the latest value J12 of the item to be predicted, and obtains the predicted value V2 of the item to be predicted one year later as the second time point (FIG. 9). .

  The prediction unit 102 may obtain, for example, a 95% confidence interval value of the predicted value V1 of the difference between the latest value of the item of the target health condition information and the value at the second time point.

  In this case, the prediction unit 102 has a certain range as the predicted value V1 of the difference between the latest value of the item of the target health condition information and the value at the second time point and the predicted value V2 of the target item at the second time point. The value can be acquired (FIG. 9).

  Then, the prediction unit 102 stores the value at the second time point of the target item of the target ID calculated in STEP 506 in the predicted value information storage unit 116 (STEP 507).

When the prediction unit 102 finishes the processing of STEPs 503 to 507 for all items of the health state information, the prediction unit 102 exits the loop L6, and STEPs 501 to 507 for all IDs included in the health state information stored in the health state storage unit 111. When the above process is finished, the process exits the loop L5 and ends the health state prediction process.
(Probability quantification process)
The onset possibility quantification process (STEP 60) is a process for quantifying the latest onset and second onset illness of each person 40. Details of the processing will be described with reference to FIGS.

  The onset probability quantification unit 101 repeatedly quantifies the onset probability for each disease of each person 40 by the onset probability quantification process at an arbitrary frequency.

  The processing content is as shown in FIG. 10 and is performed for each ID (loop L7) included in the health condition information stored in the health condition storage unit 111. Further, STEPs 602 to 609 are performed for each disease (loop L8) stored in the onset possibility correlation item storage unit 112.

  First, the symptom probability quantification unit 101 stores the latest health state information of the target ID stored in the health state storage unit 111 and the second time point of the target ID stored in the predicted value information storage unit 116. The onset probability numerical value information of the subject ID stored in the health state information and onset probability numerical information storage unit 117 one year ago is acquired (STEP 601).

  Next, the onset probability digitizing unit 101 refers to the onset probability correlation item storage unit, and acquires items of health state information correlated with the onset probability of the target disease (STEP 602).

  Subsequently, the onset probability digitization unit 101 extracts the value of the item correlated with the onset possibility for each disease from the latest health state information of the target ID (STEP 603).

  Hereinafter, this processing will be described by taking as an example the case where the target disease is diabetes.

  That is, since the items stored as items correlated with the possibility of developing the disease are blood glucose level, waist circumference, drinking habits, HbA1c, exercise habits, smoking habits, height, weight, age, and sex, The unit 101 extracts the values of these items from the latest health state information J3 of the target ID (FIG. 11).

  Next, the onset probability quantification unit 101 refers to the calculation model storage unit 113, and acquires the onset probability calculation model F2 of diabetes (STEP 604, FIG. 11).

  Next, the onset possibility quantification unit 101 numerically calculates the latest onset possibility of the target disease of the target ID based on the value of each item J31 correlated with the onset possibility of diabetes extracted in STEP 603. (STEP 605, FIG. 11).

  The onset possibility quantification unit 101 inputs the value of each item J31 correlated with the onset possibility of diabetes extracted in STEP 603 to the model F2 acquired in STEP 604, and the latest onset possibility of the target disease. The numerical value V3 is calculated (FIG. 11).

  For example, when the target disease is dyslipidemia, the onset probability quantification unit 101 calculates the onset probability calculation model for dyslipidemia based on the item J32 correlated with the onset possibility of dyslipidemia. The latest possibility of onset is quantified using F3 (FIG. 11).

  Next, the onset probability digitization unit 101 extracts the value of the item correlated with the onset possibility of the target disease from the information indicating the predicted health state at the second time point of the target ID (STEP 606).

  That is, since the items of health condition information stored as items correlated with the possibility of developing diabetes are blood glucose level, waist circumference, drinking habits, HbA1c, exercise habits, smoking habits, height, weight, age, sex, The possibility digitizing unit 101 extracts the values of these items from the health state information J4 at the second time point of the target ID acquired in STEP 601 (FIG. 12).

  In addition, in the health state prediction process (STEP 50), when the prediction unit 102 does not include items that are not measured with numerical values such as exercise habits and smoking habits, the likelihood of occurrence quantification unit 101 These pieces of information are extracted from the latest health condition information J3 of the target ID (FIG. 12).

  Next, the onset possibility quantification unit 101 quantifies the onset possibility at the second time point of the target disease of the target ID based on the item J41 correlated with the extracted onset possibility of diabetes (STEP 607). ).

  When this process is executed, the onset possibility calculation model F2 acquired in STEP 604 is used again.

  The value of each item J41 correlated with the possibility of the onset of diabetes extracted in STEP 605 is input to the model, and the numerical value V4 of the onset probability at the second time point of the target disease of the target ID is calculated (FIG. 12).

In STEP 506 of the health state prediction process, when the prediction unit 102 acquires a value with a certain width as the predicted value V2 at the second time point of the target item and stores it in the predicted value information storage unit 116. The onset probability digitization unit 101 can calculate a wide value as the onset probability value V4 at the second time point of the target disease. (Fig. 12)
In this case, the onset probability quantification unit 101 acquires, for example, the maximum value, the median value, and the minimum value of each item J41 correlated with the onset probability of the disease, and the combination of only the maximum value, the minimum value Only the combination, the maximum value, the median, and any combination of the minimum value, etc., are input to the onset probability calculation model F2 to calculate the numerical value V4 of the onset probability at the second time point of the target disease, Among the obtained calculation result values, the maximum value is the maximum value of the disease onset probability value V4, and the obtained calculation result value is the minimum value of the disease onset probability value V4. Calculated as a value (FIG. 12).

  For example, when the target disease is dyslipidemia, the onset probability quantification unit 101 calculates the onset probability calculation model for dyslipidemia based on the item J42 correlated with the onset possibility of dyslipidemia. The latest possibility of onset is quantified using F3 (FIG. 12).

  Subsequently, the onset probability digitization unit 101 sets the type of onset probability according to the latest value of the target ID, the second time point, and the onset probability of the disease one year ago (STEP 608).

  The onset probability digitizing unit 101 determines and sets the type of onset possibility by referring to the conversion table as shown in FIG. 14A stored in the conversion table storage unit 119 in order from the top, for example.

  That is, for example, if the numerical value of the onset probability of a certain disease is 151 to 200, the onset probability digitizing unit 101 sets the type of the onset probability to 4.

  Or, for example, the numerical value of the probability of developing a certain disease does not correspond to the types 4 and 3 of the probability of developing, and the numerical value of the probability of developing is 100 or less, but a disease that has increased by 50 or more compared to the previous time is 1 If there is more than one, the onset probability quantification unit 101 sets the type of onset possibility to 2.

  The onset probability quantification unit 101 determines whether there is one or more diseases whose onset probability has increased by 50 or more compared to the previous time, for example, for each disease of the target ID calculated in STEP 605 Compare the latest numerical value of the likelihood of onset and the value of the numerical value of the probability of onset one year before the disease with the ID acquired in STEP601, and one or more diseases whose numerical value of the probability of onset has increased by 50 or more Depending on whether or not there is.

  Alternatively, for example, the onset probability quantification unit 101 does not correspond to any of the 4 to 2 types of onset possibility of the onset possibility of a certain disease, but the onset possibility value at the second time point is 100 or more. For example, the type of onset possibility is set to 1.

  Then, the onset probability digitizing unit 101 stores the latest onset probability numerical information on the target ID and the type of onset possibility in the onset probability numerical information storage unit 117, and the disease at the second time point of the target ID. Are stored in the predicted value information storage unit 116 (STEP 609).

  The onset probability quantification unit 101 exits from the loop L8 when all the diseases stored in the onset probability correlation item storage unit 112 have been processed in STEPs 602 to 609, and is stored in the health state storage unit 111. When the processing of STEPs 601 to 609 is completed for all IDs included in the health condition information, the onset probability quantification processing is terminated.

(Caution information generation process)
The attention information generation process (STEP 80) is a process for generating disease attention information including information on the possibility of the onset of the disease according to the numerical value of the onset of the disease of each person 40. The processing content is as shown in FIG. 13 and is performed for each ID (loop L9) included in the health condition information stored in the health condition storage unit 111.

  Note that the generation of disease caution information by the document generation unit 103 is repeatedly performed at an arbitrary frequency.

  First, the document generation unit 103 acquires the latest type of onset possibility of the target ID (STEP 801), and recognizes the generation frequency of disease attention information corresponding to the type of onset possibility (STEP 802).

  The document generation unit 103 refers to a conversion table as illustrated in FIG. 14B stored in the conversion table storage unit 119, for example, and generates disease attention information according to the type of the latest disease onset possibility of the target ID. Recognize frequency.

  That is, for example, when the maximum value of the type of disease occurrence possibility of the target ID is 4, the document generation unit 103 sets the generation frequency of the disease attention information of the target ID as monthly.

  Then, the document generation unit 103 determines whether or not a time equal to or greater than the generation frequency of the disease attention information has elapsed since the disease attention information was previously generated for the target ID (STEP 803).

  The document generation unit 103 refers to the last date information of attention information as shown in FIG. 3F for the determination, and the last date of attention information of each target ID and the date on which the attention information is processed. To determine whether or not a time longer than the generation frequency has passed.

  For example, if there is one or more illnesses that have a probability of onset of 100 or less but increased by 50 or more compared to the previous time, the symptom probability type is set to 2, so the frequency of occurrence of disease caution information Is once in half a year, the document generation unit 103 determines that the time more than the generation frequency has passed if half a year has passed since the last date of the caution information about the ID. If not, it is determined that the time exceeding the generation frequency has not elapsed.

  If the determination is negative (STEP 803: NO), the document generation unit 103 does not execute STEPs 804 to 805 without executing the steps 804 to 805 because the ID does not satisfy the condition of the frequency of generating disease attention information. The process subject to STEP 801 and subsequent steps is repeated after transferring the person to be processed.

  On the other hand, if the determination is affirmative (STEP 803: YES), since the condition for the frequency of generating the disease attention information is satisfied for the ID, the document generation unit 103 creates the disease attention information. The latest health information of the ID, health status information of one year ago, and health status information at the second time point are acquired (STEP 804).

  Then, the document generation unit 103 generates disease caution information according to the numerical value of the possibility of onset of one or both of the latest disease of the target ID and the second time point (STEP 805).

  For example, the document generation unit 103 refers to a conversion table as illustrated in FIG. 14C stored in the conversion table storage unit 119 and generates improvement advice information 322.

  For example, the document generation unit 103 refrains from excessive intake of animal fats and carbohydrates (especially soft drinks), “eliminating obesity, reducing the amount of meals” when the type of possibility of developing diabetes is 4. The improvement advice information 322 is generated by acquiring the improvement advice corresponding to the type of disease and onset possibility such as “...”.

  Note that the document generation unit 103 may be configured to generate the improvement advice information only for a disease for which the numerical value of the probability of occurrence of the disease is a certain value or more.

  The document generation unit 103 generates complication information 323 with reference to a conversion table as illustrated in FIG. 14D stored in the conversion table storage unit 119, for example.

  For example, the document generation unit 103 obtains information on complications by acquiring information on complications such as “myocardial infarction, stroke, renal failure” for those who have or may develop diabetes. 323 is generated.

  The document generation unit 103 may be configured to arrange the graph 324 representing the numerical value of the onset probability for each disease in descending order of the numerical value of the onset probability for each disease.

  For example, the document generation unit 103 creates a group composed of all or part of a plurality of people for each ID having the same gender and the same age, and ranks the probability of onset for each disease of each ID in the group. By calculating, information 325 regarding the ranking of the onset probability for each disease of each person 40 is generated.

  The document generation unit 103 may calculate the actual rank within the group, and may be expressed as, for example, 50th out of 1975, or may be calculated by replacing the rank within the group with the rank out of 100, If there are numbers below the decimal point, they may be rounded to the third place out of 100.

  Alternatively, for example, the efficiency support server 10 captures information such as the work name of each person 40 from the terminal 30, the type of business of the work place, and the job type, and the document generation unit 103 determines whether all of a plurality of persons are based on the information. It is good also as creating the group comprised by a part.

  Note that the document generation unit 103 includes information such as improvement advice information 322, information 323 regarding complications, information 325 regarding the ranking of the probability of occurrence for each disease of each ID in a group composed of all or part of a plurality of people. May be generated based on not only the latest numerical value of the onset probability for each disease of each ID but also the numerical value of the onset probability at the second time point for each disease.

  Next, the document generation unit 103 stores identification information for identifying the subject in the disease attention information in the history storage unit 118 (STEP 806). For example, the terminal 30 can create the QR code (registered trademark) 333 and the character information 334 in FIG. 1 based on the identification information.

  Next, the document generation unit 103 stores the generation date for each ID of the disease attention information in the history storage unit 118 (STEP 807).

  When all these IDs (STEPs 801 to 806) have been completed for all IDs included in the health condition information stored in the health condition storage unit 111, the document generation unit 103 exits the loop L9 and ends the attention information generation process. .

(Input information transmission process)
Next, input information transmission processing will be described with reference to FIGS.

  This process can be executed by, for example, the terminal 41 of the person who has received the paper medium 32 (hereinafter referred to as “subject” as appropriate). For example, when the target person installs an application on the terminal 41 used by the target person according to the information 331 that prompts the user to download the application on the paper medium 32 and starts the application, the terminal 41 executes this process according to the application.

  The terminal 41 outputs an identification number input screen to the image display unit (FIG. 16 / STEP 902). For example, as shown in FIG. 17A, the identification number input screen M1 includes a display M11 that prompts the user to input an identification number, a display M12 that prompts the user to read a QR code (registered trademark), and a button M13.

  The terminal 41 determines whether or not the pressing operation of the button M13 has been detected via an input device such as a touch panel (FIG. 16 / STEP904).

  If the determination result is negative (FIG. 16 / STEP904... NO), the terminal 41 executes the process of FIG. 16 / STEP904.

  When the determination result is affirmative (FIG. 16 / STEP904... YES), the terminal 41 recognizes information input via an input device such as a touch panel (FIG. 16 / STEP906).

  The terminal 41 outputs a screen requesting consent to information sharing to the image display unit (FIG. 16 / STEP 908). For example, as shown in FIG. 17B, the screen M2 asking for consent to information sharing says "By continuing, I understand and agree to the service terms of service and personal information protection policy" In addition, a display M21 indicating that the subject is requested to agree to share the health status information of the subject with the medical-related organization is included. The terminal 41 is configured to read the contents of the service use agreement and the personal information protection policy in accordance with the operation of the subject via the input device, and output the read contents to the read image display unit.

  The screen M2 for requesting consent to information sharing further includes a button M22 for continuing the registration operation, and a display M23 for prompting input of information indicating the identity of the person such as age and gender.

  The terminal 41 determines whether or not the pressing operation of the button M13 has been detected via an input device such as a touch panel (FIG. 16 / STEP 910).

  If the determination result is negative (FIG. 16 / STEP 910... NO), the terminal 41 executes the processing of FIG. 16 / STEP 910.

  If the determination result is affirmative (FIG. 16 / STEP 910... YES), the terminal 41 recognizes information input via an input device such as a touch panel (FIG. 16 / STEP 912).

  The terminal 41 receives the input information (identification number and information indicating the attributes of the target person) recognized in FIG. 16 / STEP 906 and FIG. 16 / STEP 912 through the communication device and the target person to the medical institution by the target person. The information indicating consent to the sharing of the health status information is transmitted to the efficiency improvement support server 10. (FIG. 16 / STEP914).

  Thus, this process ends.

(Information sharing process)
Next, details of the information sharing process will be described with reference to FIG. The server control unit 100 repeatedly executes the information sharing process regularly or irregularly.

  The information transmitting unit 104 determines whether information is received from the terminal (FIG. 18 / STEP 1002).

  When the determination result is negative (FIG. 18 / STEP 1002... NO), the information transmission unit 104 executes the processing of FIG. 18 / STEP 1002.

  If the determination result is affirmative (FIG. 18 / STEP 1002... YES), the information transmission unit 104 recognizes the identification number of the subject included in the information received in FIG. The identification number of the subject is information that can uniquely identify the subject, and may be an ID that identifies the health status information of the subject, for example. The information transmission unit 104 can recognize an ID for identifying the health condition information of the subject based on the identification number.

  Based on the identification number, the information transmission unit 104 refers to the health state storage unit 111 and recognizes the health state information of the subject shown in FIG. 3A, for example (FIG. 18 / STEP 1006). As described above, the health condition information may include information indicating the attributes of the subject such as the age and sex of the subject.

  The information transmitting unit 104 recognizes information indicating the attributes of the target person included in the information received in FIG. 18 / STEP 1002 (FIG. 18 / STEP 1008).

  The information transmission unit 104 compares the information indicating the attributes of the subject recognized in FIG. 18 / STEP 1008 with the health status information of the subject recognized in FIG. 18 / STEP 1006 (FIG. 18 / STEP 1010).

  The information transmission unit 104 determines whether the information indicating the attribute of the subject recognized in FIG. 18 / STEP 1008 and the health status information of the subject recognized in FIG. 18 / STEP 1006 match. 18 / STEP 1012). For example, when the information indicating the attribute of the subject and the corresponding health status information of the subject are the same, the information transmission unit 104 may evaluate the determination result as positive. Further, the information transmission unit 104 may determine that the age included in the information indicating the attribute of the subject is a difference within a predetermined range, such as one year older than the age included in the health status information corresponding to the subject. The determination result may be evaluated as positive.

  If the determination result is negative (FIG. 18 / STEP 1012... NO), the information transmission unit 104 ends this process.

  When the determination result is affirmative (FIG. 18 / STEP 1012... YES), the receipt analysis unit 105 recognizes the subject's injury / illness name, prescription history, and outpatient history from the receipt data included in the health condition information (FIG. 18). / STEP 1014). For example, the information transmission unit 104 may acquire the name of the subject's injury or illness from the injury / disease name record of the receipt data, or the medical action indicated by the medical record of the receipt data or the medicine indicated in the medicine record of the receipt data. Based on this, the name of the subject's injury or illness may be estimated. Moreover, the information transmission part 104 may recognize the prescription history with respect to a subject from the pharmaceutical record of receipt data. The information transmission unit 104 may recognize the outpatient history by referring to the creation date of the receipt data.

  The information transmission unit 104 includes the injury / illness name, prescription history, and outpatient history of the subject recognized in FIG. 18 / STEP 1014, the health status information of the subject of FIG. 3A, and information indicating the possibility of the onset of each disease shown in FIG. Is transmitted to the terminal 50 of the medical institution (FIG. 18 / STEP 1016).

  The application receiving unit 109 transmits a guidance selection screen to the terminal of the target person (FIG. 18 / STEP 1018). For example, as shown in FIG. 19A, the instruction selection screen M3 includes a button M31 that accepts a reservation application for the first instruction, and a button M31 that accepts a reservation application for the second instruction. The first instruction and the second instruction are instructions whose types of instruction are different from each other (for example, telemedicine by a doctor, face-to-face medical care by a doctor, or nutritional guidance by a registered dietitian).

  The server control unit 100 executes a question selection process (FIG. 18 / STEP 1100).

  Details of the question selection process will be described later.

  Thus, this process ends.

(Question selection process)
Next, the details of the question selection process of FIG. 18 / STEP 1100 will be described with reference to FIG.

  The application reception unit 109 determines whether information is received from the terminal (FIG. 20 / STEP 1102).

  If the determination result is negative (FIG. 20 / STEP 1102... NO), the application receiving unit 109 executes the processing of FIG. 20 / STEP 1102.

  If the determination result is affirmative (FIG. 20 / STEP 1102... YES), the application reception unit 109 recognizes information indicating the selected instruction included in the information received in FIG. 20 / STEP 1102 (FIG. 20 / STEP 1104). ).

  The question item transmission unit 107 refers to the question item storage unit 120 and recognizes each question item corresponding to the guidance recognized in FIG. 20 / STEP 1104 (FIG. 20 / STEP 1106). For example, it is assumed that the instruction recognized in FIG. 20 / STEP 1104 is the second instruction. In this case, the question item transmission unit 107 refers to the question item information (see FIG. 15A) stored in the question item storage unit 120, and the question ID corresponding to the instruction type of instruction recognized in FIG. 20 / STEP 1104 The question items “1”, “2”, “3”, and “4” are recognized.

  The processing date recognition unit 108 recognizes the date (processing date) at the time of processing (FIG. 20 / STEP 1108). The processing date recognizing unit 108 may recognize the processing date by referring to the clock unit in the efficiency improvement support server 10 or by recognizing the processing date by communicating with an NTP (Network Time Protocol) server. May be.

  The server control unit 100 loops for each question item and executes the question exclusion process (FIG. 20 / STEP 1200). Details of the question exclusion process will be described later.

  The question item transmission unit 107 transmits the question items that are not excluded in the question exclusion process among the question items recognized in FIG. 20 / STEP 1106 (FIG. 20 / STEP 1110).

  For example, as illustrated in FIG. 19B, the terminal that has received the question item outputs a screen M4 including a display M41 indicating each question item and an input form for receiving an answer to the image display unit. In this way, the question item corresponding to the selected instruction type can be transmitted to the terminal.

(Question exclusion process)
Next, the details of the question exclusion process of FIG. 20 / STEP 1200 will be described with reference to FIG.

  The question item relation determination unit 106 refers to the health state storage unit 111 and recognizes the latest health state information of the target person corresponding to each question item recognized in FIG. 20 / STEP 1106 and the acquisition date of the health state information. (FIG. 20 / STEP 1202). For example, the question item relation determination unit 106 recognizes the type of health condition information corresponding to each question item included in the question item information. For example, in the example shown in FIG. 15A, the question item relation determination unit 106 includes “history”, “smoking habit” of the health check data corresponding to the question IDs “1” and “2” recognized in FIG. Recognize items of health condition information such as “wound name record” corresponding to the receipt data. The question item relation determination unit 106 determines the health of the subject (for example, ID “A01”) illustrated in FIG. 3A based on the recognized items of “health history”, “smoking habit”, and “sickness name record” in the health condition information. Among the state information, information corresponding to the recognized item of health state information (for example, “smoking habit” is “having a smoking habit”) and its acquisition date (April 8, 2018) are recognized.

  The question item relation determination unit 106 determines whether or not there is information corresponding to the question item for the subject (FIG. 21 / STEP 1204). For example, in the above-described example, the question item relation determination unit 106 determines that there is corresponding information “smoking habit” for the ID “A01” for the health condition information item “smoking habit” corresponding to the question item. To do. Further, the question item relation determination unit 106 determines that there is no corresponding information for the items “history” and “sickness name record” of the health status information of the subject corresponding to the question item.

  When the determination result is affirmative (FIG. 21 / STEP 1204... YES), the question item transmission unit 107 determines whether the acquisition date of the information is within a predetermined period from the processing date (FIG. 20 / (STEP 1206). The predetermined period may be, for example, a uniformly defined period, or may be a period defined for each question item as shown in FIG. 15A.

  When the determination result is affirmative (FIG. 21 / STEP 1206... YES), the question item transmission unit 107 excludes the question item (FIG. 21 / STEP 1218).

  When the determination result of STEP 1204 or STEP 1206 is negative (FIG. 21 / STEP 1204... NO or FIG. 21 / STEP 1206... NO), the question item transmission unit 107 refers to the question history storage unit 121 and is included in the question history information. The question item and question date for the subject are recognized (FIG. 21 / STEP 1208). For example, in the example illustrated in FIG. 15B, the question item transmission unit 107 recognizes the question IDs “1” and “2” corresponding to the ID “A01” and the date of December 1, 2017.

  The question item transmitting unit 107 determines whether or not the question date of the question item included in the question history information is within a predetermined period from the processing date (FIG. 21 / STEP 1210).

  If the determination result is affirmative (FIG. 21 / STEP 1210... YES), the question item transmission unit 107 excludes the question item (FIG. 21 / STEP 1218).

  When the determination result is negative (FIG. 21 / STEP 1210... NO), the question item transmission unit 107 refers to the question item storage unit 120 and recognizes a disease corresponding to the question item and a threshold value for its onset possibility. (FIG. 21 / STEP 1212). For example, in the example illustrated in FIG. 15A, the question item transmission unit 107 recognizes the disease “diabetes” corresponding to the question ID “4” and its threshold value “100”.

  The question item transmission unit 107 refers to the onset probability numerical information storage unit 117 and recognizes a numerical value indicating the onset possibility of the subject for the disease recognized in FIG. 21 / STEP 1212 (FIG. 21 / STEP 1214). For example, a numerical value “156” indicating the possibility of the onset of the disease “diabetes” corresponding to the ID “A01” is recognized.

  The question item transmission unit 107 determines whether or not the possibility of the disease recognized in FIG. 21 / STEP 1214 is equal to or less than the threshold value recognized in FIG. 21 / STEP 1212 (FIG. 21 / STEP 1216).

  When the determination result is affirmative (FIG. 21 / STEP 1216... YES), the question item transmission unit 107 excludes the question item (FIG. 21 / STEP 1218).

  If the determination result is negative (FIG. 21 / STEP 1210... NO), the server control unit 100 ends this process.

(Other embodiments)
The example of the embodiment of the present invention has been described above, but the embodiment of the present invention is not limited to this.

  For example, the model or prediction unit 102 used when the onset probability quantification unit 101 quantifies the onset probability of each disease indicates the predicted health state of the target person at a second time point after the first time point. Instead of a model used when acquiring information, an identification machine such as deep learning or a support vector machine may be used.

DESCRIPTION OF SYMBOLS 10 ... Efficiency improvement support server, 100 ... Server control part, 101 ... Probability quantification part, 102 ... Prediction part, 103 ... Document generation part, 104 ... Information transmission part, 110 ... Server storage part, 111 ... Health condition storage , 112... Predictive likelihood correlation item storage unit, 113... Calculation model storage unit, 114... Change correlation item storage unit, 115... Prediction model storage unit, 116. 118, history storage unit, 119 ... conversion table storage unit, 20 ... information communication network, 30 ... terminal, 40 ... each person.

The efficiency improvement support system of the present invention is
A health condition storage unit that stores health condition information including information on the health condition of one or more persons including the subject person;
An onset probability quantification unit for quantifying the onset probability of at least one disease of the subject based on the health state information stored in the health state storage unit;
A document for the subject that includes information indicating the likelihood of the onset of the at least one disease of the subject quantified by the susceptibility digitization unit and information capable of uniquely identifying the subject; A document generator to generate;
Consent by the subject about disclosure to the medical related organization of information that can uniquely identify the subject included in the document and personal information of the subject identified by the information that can identify the subject as a prerequisite to receive the information from the terminal indicating the, a possibility of the onset of the health information and the at least one disease of the subject to be identified by a unique identifiable information the subject information An information transmission unit that transmits one or both of the information to a terminal of the medical-related organization,
It is characterized by providing.

Claims (8)

A health condition storage unit that stores health condition information including information on the health condition of one or more persons including the subject person;
An onset probability quantification unit for quantifying the onset probability of at least one disease of the subject based on the health state information stored in the health state storage unit;
A document for the subject that includes information indicating the likelihood of the onset of the at least one disease of the subject quantified by the susceptibility digitization unit and information capable of uniquely identifying the subject; A document generator to generate;
Information that can identify the subject from the terminal, and information that indicates the consent of the subject about disclosing personal information of the subject identified by the information that can identify the subject to a medical-related organization. As a necessary condition, one or both of the health condition information of the subject identified by the identifiable information and the information indicating the possibility of developing the at least one disease are transmitted to the terminal of the medical related organization. An information transmission unit to perform,
An efficiency improvement support system characterized by comprising: The efficiency improvement support system according to claim 1,
The efficiency improvement support system includes a receipt analysis unit that extracts information indicating at least one of a wound name, prescription history, and outpatient history from the receipt data,
The health condition information includes receipt data,
The information transmission unit, when the necessary condition is satisfied, information indicating at least one of the wound name, prescription history, and outpatient history of the subject extracted by the receipt analysis unit to the terminal of the medical related institution An efficiency support system configured to transmit. In the efficiency improvement support system according to claim 1 or 2,
A question item storage unit that stores one or more question items about the health status of the subject, and information corresponding to the one or more question items stored in the question item storage unit is health status information of the subject A question item related determination unit that determines whether or not included in,
One or more question items excluding the question items determined to contain information corresponding to the question items stored in the question item storage unit included in the health status information of the subject are transmitted to the terminal of the subject An efficiency improvement support system comprising: a question item transmission unit that performs In the efficiency improvement support system of any one of Claims 1-3,
A processing date recognition unit for recognizing the processing date;
A question item storage unit for storing question items about the health condition of the subject;
A question item transmission unit that transmits one or more question items stored in the question item storage unit to the terminal of the target person,
The health condition information includes an acquisition date of the information,
The question item transmission unit includes the processing date recognized by the processing date recognition unit among the one or more question items determined to include information corresponding to the question item in the health status information of the subject. Is configured to exclude question items on the condition that a predetermined period has not elapsed since the date of acquisition of the information. In the efficiency improvement support system of any one of Claims 1-4,
A processing date recognition unit that recognizes the processing date is provided.
A question item storage unit for storing question items about the health condition of the subject;
A question item transmission unit that transmits one or more question items stored in the question item storage unit to the terminal of the target person,
The health condition information includes an acquisition date of the information,
The question item transmission unit includes the processing date recognized by the processing date recognition unit among the one or more question items determined to include information corresponding to the question item in the health status information of the subject. Is configured to exclude question items on the condition that the period corresponding to the question item has not elapsed since the acquisition date of the information. In the efficiency improvement support system of any one of Claims 1-5,
A question item storage unit for storing question items about the health condition of the subject;
A question item transmission unit that transmits one or more question items stored in the question item storage unit to the terminal of the target person,
The efficiency improvement support system characterized by the said question item transmission part being comprised so that the onset possibility of the disease corresponding to a question item may exclude the question item which is below a predetermined value. In the efficiency improvement support system of any one of Claims 1-6,
An application accepting unit that accepts an application for the second instruction different from the first instruction or the first instruction based on the information received from the terminal;
A question history storage unit for storing question items made in the first instruction and the second instruction;
A question item storage unit for storing question items about the health condition of the subject;
A question item transmission unit that transmits one or more question items stored in the question item storage unit to the terminal of the target person,
The question item transmitting unit is one or a plurality of question items for one of the first instruction and the second instruction, and the other of the first instruction and the second instruction stored in the question history storage unit. An efficiency improvement support system, characterized in that the system is configured to exclude question items asked in A method executed by a computer including a health state storage unit that stores health state information including information on the health state of one or more persons including a subject,
Quantifying the probability of developing at least one disease of the subject based on the health condition information stored in the health condition storage unit;
Generating a document for the subject that includes the digitized information indicating the likelihood of the at least one disease onset of the subject and information capable of uniquely identifying the subject;
Information that can identify the subject from the terminal, and information that indicates the consent of the subject about disclosing personal information of the subject identified by the information that can identify the subject to a medical-related organization. Sending one or both of the health condition information of the subject identified by the identifiable information and the information indicating the possibility of developing the at least one disease to the terminal of the medical related institution as a necessary condition And steps to
An efficiency improvement supporting method characterized by including: