Information processing device, information processing method, and program

Abstract

To provide a mechanism for assisting in grasping a relative position of a specific user.SOLUTION: An information processing device with one or more processors is provided, the one or more processors being configured to acquire a first statistical value computed from first data recorded through a program running on a terminal operated by a specific user changing his / her behavior, and present the first statistical value in a manner that allows comparison with a second statistical value related to a given user group.SELECTED DRAWING: Figure 12

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device, an information providing method, and a program. [Background technology]

[0002] Currently, medical treatments using programmable medical devices (hereinafter referred to as "treatment apps") approved under the Pharmaceuticals and Medical Devices Act have begun. Treatment apps are divided into doctor apps for doctors and patient apps for patients. Patients record measurements, behavior, etc. through the patient app. Meanwhile, doctors access the data recorded by the patient themselves through the doctor app and consider treatment plans and comments, etc. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] CureApp, Inc., "By being able to see blood pressure trends and behavioral changes, we can provide effective guidance to each patient," [online], [Retrieved April 26, 2024], Internet<URL:https: / / cureapp.co.jp / productsite / ht / > Summary of the Invention [Problem to be solved by the invention]

[0004] With the current system, doctors can check changes in the data recorded by the patient, item by item. However, the comparison is limited to the patient's own data. As a result, comments tend to be limited to whether or not the most recent values ​​have improved compared to previous years.

[0005] As one embodiment of the present disclosure, a mechanism for assisting in grasping the relative position of a specific user is provided. [Means for solving the problem]

[0006] The invention described in claim 1 is an information processing device having one or more processors, wherein the one or more processors acquire a first statistical value calculated from first data recorded through a program executed on a terminal operated by a specific user whose behavior is changing, and present the first statistical value in a manner that can be compared with a second statistical value related to a specified set of users. The invention described in claim 2 is an information processing device described in claim 1, wherein the one or more processors calculate the second statistical value based on second data recorded by each user belonging to the specified user set, or acquire a statistical value calculated based on second data recorded by each user belonging to the specified user set as the second statistical value. The invention described in claim 3 is an information processing device described in claim 2, wherein the first statistical value and the second statistical value are calculated in predetermined period units determined according to the number of days elapsed since the start of recording of the first data and the second data. An invention according to claim 4 is the information processing device according to claim 2 or 3, wherein the first statistical value and the second statistical value are calculated as frequency distributions of the first data and the second data. The invention of claim 5 is the information processing device of claim 2, wherein the second data is recorded through a program used to record the first data. The invention of claim 6 is the information processing device of claim 2, wherein the second data is recorded through another program associated with the program used to record the first data. The invention described in claim 7 is an information processing device described in claim 1, wherein the one or more processors acquire publicly available statistical information regarding a disease associated with the program used to record the first data as the second statistical value. The invention described in claim 8 is an information processing device described in claim 1, wherein the one or more processors accept designation of the specified user set by the viewing user and present the second statistical value according to the accepted user set. The invention described in claim 9 is the information processing device described in claim 8, wherein the one or more processors accept designation of the predetermined user set through selection of one or more attributes by a viewing user. The invention described in claim 10 is an information processing device described in claim 9, wherein the attributes are at least one of age, gender, region, home hospital, other medical institution, whether or not the patient is employed, whether or not the patient cooks at home, BMI before the start of treatment, habits before the start of treatment, quality or quantity of sleep, whether or not the patient has renal dysfunction, and a score that may be correlated with the effectiveness of treatment. An eleventh aspect of the present invention is the information processing device according to the first aspect, wherein the one or more processors accept one or more users designated by a viewing user as the specific users. The invention described in claim 12 is an information processing device described in claim 1, wherein the one or more processors selectively or identifiably present the first statistical value that meets a predetermined criterion with respect to the second statistical value. The invention described in claim 13 is an information processing device described in claim 1, wherein the one or more processors calculate the first statistical value using both data recorded through a program that requires a prescription and data recorded through another program that does not require a prescription, or obtain as the first statistical value a statistical value calculated using both data recorded through a program that requires a prescription and data recorded through another program that does not require a prescription. The invention described in claim 14 is an information processing device described in claim 1, wherein the one or more processors calculate the second statistical value using both data recorded through a program that requires a prescription and data recorded through another program that does not require a prescription, or obtain as the second statistical value a statistical value calculated using both data recorded through a program that requires a prescription and data recorded through another program that does not require a prescription. The invention described in claim 15 is an information provision method in which a computer executes a process of acquiring a first statistical value calculated from first data recorded through a program executed on a terminal operated by a specific user whose behavior is changing, and a process of presenting the first statistical value in a manner that allows it to be compared with a second statistical value related to a specified set of users. The invention described in claim 16 is a program for enabling a computer to perform the following functions: obtain a first statistical value calculated from first data recorded through a program executed on a terminal operated by a specific user whose behavior is changing; and present the first statistical value in a manner that allows it to be compared with a second statistical value related to a specified set of users. [Effects of the Invention]

[0007] According to one embodiment of the present disclosure, a mechanism for assisting in grasping the relative position of a specific user can be provided. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating an overall configuration of an information processing system. [Figure 2] FIG. 1 is a diagram illustrating an example of the hardware configuration of a PDT platform. [Figure 3] FIG. 2 is a diagram illustrating an example of status management data stored in an auxiliary storage device of the PDT platform. [Figure 4] FIG. 2 is a diagram illustrating an example of the hardware configuration of a PDT server. [Figure 5] FIG. 10 is a diagram illustrating an example of patient data stored in an auxiliary storage device of the PDT server. [Figure 6] FIG. 2 is a diagram illustrating an example of the hardware configuration of a doctor terminal and a patient terminal. [Figure 7] FIG. 2 is a diagram illustrating an example of a processing sequence executed in the information processing system. [Figure 8] FIG. 10 is a diagram illustrating an example of an operation screen in a patient application. [Figure 9] FIG. 1 is a diagram illustrating a treatment program. [Figure 10] FIG. 10 is a diagram illustrating an example of a management screen for the usage status of a patient application displayed on the output device of the doctor terminal. [Figure 11] FIG. 10 is a diagram illustrating an example of a patient data viewing screen. [Figure 12] FIG. 10 is a diagram illustrating an example of a distribution table screen used to display statistical values ​​for individual patients and statistical values ​​for all patients. [Figure 13] 10 is a diagram illustrating an example of a display of a distribution table screen that allows selective specification of a set to be compared with statistical values ​​of patients. FIG. [Figure 14] FIG. 10 is a diagram illustrating an example of a display on a distribution table screen after "gender" is selected in a set selection field. [Figure 15] FIG. 10 is a diagram illustrating a display example of a distribution table screen when a group is designated by "gender." [Figure 16] FIG. 10 is a diagram illustrating how the distribution table screen changes when a group is specified by "age group." [Figure 17] 10A and 10B are diagrams illustrating changes in the distribution table screen when a group is designated by "place of residence." [Figure 18] FIG. 10 is a diagram illustrating another example of the display of the distribution table screen. [Figure 19] FIG. 10 is a diagram illustrating another example of the display of the distribution table screen. [Figure 20] FIG. 10 is a diagram illustrating a distribution table screen when the set to be compared is all patients at one's own hospital. [Figure 21] FIG. 10 is a diagram illustrating the distribution table screen when the set to be compared is switched from one hospital to another hospital. [Figure 22] FIG. 10 is a diagram illustrating an example of a distribution table screen on which multiple users can be designated as specific users. [Figure 23] FIG. 10 is a diagram illustrating another example of the display of the distribution table screen. [Figure 24] FIG. 10 is a diagram illustrating an embodiment in which measurements recorded through a non-patient app and measurements recorded through a patient app are used in combination. [Figure 25]FIG. 10 is a diagram illustrating another embodiment in which measurements recorded through a non-patient app and measurements recorded through a patient app are used in combination. [Figure 26] FIG. 10 is a diagram illustrating another example of a processing sequence executed in the information processing system. [Figure 27] FIG. 10 is a diagram illustrating another example of a processing sequence executed in the information processing system. [Figure 28] FIG. 10 is a diagram illustrating another example of a processing sequence executed in the information processing system. [Figure 29] FIG. 10 is a diagram illustrating an example of a distribution table screen display when the disease is diabetes. DETAILED DESCRIPTION OF THE INVENTION

[0009] <Terminology> First, terms used in the embodiments described below will be explained. A "program that encourages behavioral change" is a program that is provided with the intention of encouraging a change in a person's behavior. However, this program does not guarantee that a person's behavior will change. Whether or not a person's behavior actually changes depends on the user who uses the program. Therefore, a program that encourages behavioral change can also be said to be a program that supports behavioral change. This type of program can be classified as a medical device or a non-medical device. A medical device program is an example of a program that requires a prescription, while a non-medical device program is an example of a program that does not require a prescription.

[0010] "Program objectives" refer to the effects to be achieved by using a program to promote behavioral change. Program objectives vary depending on the disease for which the program promotes behavioral change is targeted. For example, program objectives may include improving lifestyle habits, maintaining improved lifestyle habits, and maintaining improved numerical values. "Program goal" refers to the goal to be achieved through the use of a program to promote behavioral change. Goals are defined from the perspective of achieving the objective. Goals are classified into qualitative indicators and quantitative indicators. Goals are also classified into indicators defined by measurements or other numerical values ​​and indicators defined by the content of actions. For example, goals can include improving or maintaining habits, behaviors, or numerical values. Program goals can also be further defined by one or more subgoals. Subgoals are smaller-scale targets set to achieve the corresponding goal.

[0011] "Therapeutic App" refers to an application program that has been approved under the Pharmaceuticals and Medical Devices Act. Therapeutic Apps are programs that are classified as medical devices. Therapeutic apps are approved for each disease. Diseases for which approval has already been obtained include hypertension, nicotine addiction, and insomnia. Diseases for which therapeutic apps are currently in development include NASH (non-alcoholic steatohepatitis), diabetes, dyslipidemia, kidney disease, and alcoholism.

[0012] "User" refers to a person who changes their behavior through a program that encourages behavioral change. Users include users who have not yet visited a medical institution and users who have visited a medical institution. Users who have visited a medical institution include users who are subject to medical fee calculations and users who are not subject to medical fee calculations. A "patient" refers to a user who is currently receiving treatment at a medical institution. In other words, a patient refers to a user who has started or is currently undergoing treatment at a medical institution.

[0013] Treatment apps include patient apps and doctor apps. A "Patient App" is an application program that runs on a device operated by a patient (hereinafter also referred to as a "Patient Terminal") and is prescribed to the patient by a doctor. In this sense, the Patient App is also called a PDT (Prescription Digital Therapeutic). The patient app can be downloaded from, for example, an app store. In the embodiment described below, a code required for activation (hereinafter referred to as a "prescription code") is issued by a doctor's prescription. The patient app is used to record patient data outside of medical institutions (hereinafter also referred to as "patient app data").

[0014] A validity period is set for a patient app at the time of approval. This validity period is determined based on, for example, the period during which the public medical insurance system applies. The validity period is determined depending on the type of disease that the patient app covers. For example, the validity period for a patient app for hypertension is six months, counting from the month following the month in which the patient app was prescribed. However, six months is just an example, and it could be, for example, nine months or 12 months. The validity period may also be determined in days, such as 60 days or 180 days, or in weeks, such as eight weeks or 24 weeks. Needless to say, these values ​​are just examples.

[0015] A "doctor app" refers to an application program that can be used through a terminal operated by a doctor or other medical professional (hereinafter also referred to as a "doctor terminal"). In the embodiment described below, the doctor app runs on a cloud server that can be operated from the doctor terminal. The doctor app is used to view patient data (including patient app data). Note that medical professionals are also referred to as medical workers.

[0016] "Patient data" includes, for example, patient attributes, measurement values, activity records, mood records, physical condition records, medical examination history, patient app operation history, biological characteristics, psychological characteristics, social characteristics, habits, and goal achievement status. However, the items recorded as patient data vary depending on the disease, and do not necessarily have to be all of the exemplified information, but may be some of it or may include other information. Note that the items recorded as patient data are all examples of health-related data.

[0017] The "patient attributes" include, for example, the patient's name, gender, and date of birth. These pieces of information are examples of basic patient information. "Measurement value" refers to a numerical value measured using a measuring device. The measurement value items recorded as patient app data are defined for each disease. For example, if the disease is hypertension, blood pressure values ​​are recorded as measurement values. Blood pressure values ​​are defined, for example, as systolic blood pressure (i.e., maximum blood pressure) and diastolic blood pressure (i.e., minimum blood pressure). For example, if the disease is nicotine addiction, the measurements recorded would be carbon monoxide (CO) in the breath and nicotine concentration in the saliva.

[0018] "Activity records" include, for example, operation history of the patient app, medication records, meal records, smoking records, drinking records, and exercise records. These records are also records of the user's behavior. Activity records are an example of information related to activities. The "mood record" is, for example, a record of the mood perceived by the user. The mood record is an example of information related to mood. The "physical condition record" is, for example, a record of the physical condition or symptoms that the user feels. The physical condition record is an example of information related to the physical condition.

[0019] The "medical examination history" includes, for example, the treatment start date, the consultation date, the content of the treatment, and advice. The medical examination history is an example of information related to medical examinations. The "patient application operation history" is, for example, a history of operations related to the patient application startup operation, and the input operation of measurement values ​​and reviews. "Biological characteristics" include, for example, whether or not the patient has other illnesses, whether or not they have had injuries undergoing treatment, whether or not they have pain in their knees or feet, whether or not they have had treatment for an illness, the number of years since the illness was diagnosed, how strongly seasoned their food is at home, and the amount of food they eat.

[0020] "Psychological characteristics" include, for example, expectations regarding app treatment, willingness to acquire knowledge about disease treatment, whether reducing salt intake is difficult, whether one believes one's sense of taste cannot be changed, and psychological resistance to leaving food on one's plate. "Social characteristics" include, for example, wake-up time, bedtime, type of work (e.g., shift work, day shift, night shift), days of the week worked, start time of work, time home from work, regular days off, and whether or not there is a heater in the changing room.

[0021] "Habits" include, for example, exercise habits, weighing oneself, checking the calorie count on food labels, choosing low-fat foods, not consuming caffeine after 4 p.m., eating and drinking after 10 p.m., skipping breakfast, snacking, taking a bath one hour before bedtime, stretching or massaging before bedtime, and sleeping for six hours or more. The "goal achievement status" is, for example, information indicating the progress status toward a goal set by a doctor for each patient. The "goal achievement status" may be, for example, information indicating the progress status toward a goal set by the patient himself / herself. The "goal achievement status" may be, for example, information indicating the progress status toward a goal presented by a patient app for each patient. The above-mentioned information can be classified into subjective information and objective information. Patient data is recorded, for example, as text, images (moving images, still images), audio, numbers, and codes.

[0022] The "PDT server" is a server that manages patient data entered through a patient app, etc. The PDT server is also an example of a cloud server. A PDT server is basically provided for each patient app. Therefore, in order for a doctor to view patient data, he / she must log in to the PDT server running the doctor app that is paired with the patient app used by the patient.

[0023] For example, if a patient uses a patient app for hypertension provided by service provider A, the doctor needs to log in to the PDT server operated by service provider A for hypertension. Also, if a patient uses a patient app for hypertension provided by service provider B, the doctor needs to log in to the PDT server operated by service provider B for hypertension.

[0024] In addition, if a patient uses a patient app for nicotine addiction provided by service provider A, the doctor needs to log in to the PDT server operated by service provider A for nicotine addiction. It should be noted that multiple patient apps targeting different diseases may share one PDT server. Furthermore, multiple patient apps provided by different service providers may share one PDT server.

[0025] The PDT platform is a server that manages patient app prescriptions and the usage status of patient apps after prescription. The PDT platform is also an example of a cloud server. The PDT platform is also called an APS (Application Prescription Service) server, meaning that it is a server that provides prescription services for patient apps. The usage status may be, for example, "before use begins," "expired," "in use," "scheduled to end," "ended," or "expired."

[0026] "Before use begins" refers to the state in which the prescription for the patient app has been completed but use on the patient's device has not yet begun. For example, this refers to the state in which the patient has not yet entered the prescription code into the device. "Start expired" refers to a state in which the prescription code has not been confirmed as entered within the period in which the prescription code is valid (for example, within four days including the prescription date).

[0027] "In use" refers to the state in which the patient app installed on the patient device has been activated and is ready for use. Activating the patient app requires the entry of an activation code, such as the prescription code mentioned above. "Scheduled to end" refers to a state in which a medical institution has set the patient to be outside the scope of management within the validity period. For example, this state is set for a patient who will not receive a follow-up examination.

[0028] "Terminated" refers to a state in which the patient app cannot be used due to, for example, the expiration of the validity period. "Expired" is a state that indicates that a predetermined period has passed since the prescription date. The predetermined period is set to be longer than the validity period. For example, if the validity period is 6 months, the predetermined period is set to 8 months. "Selected medical treatment" is displayed when the validity period has expired but the patient is still eligible for selected medical treatment. Selected medical treatment refers to medical services that patients enrolled in social insurance can receive in addition to insured treatment, treatment not covered by insurance, by paying an additional fee.

[0029] The PDT platform can support multiple patient apps that target the same disease but are provided by different service providers, as well as multiple patient apps that are provided by the same service provider but target different diseases. In this sense, the PDT platform acts as a platform for multiple patient apps. The app, which runs on the PDT platform, manages the usage status of multiple patient apps for different diseases and service providers on a patient-by-patient basis.

[0030] In the embodiments described below, the "medical institution" is assumed to be a medical institution that is covered by health insurance. More specifically, the medical institution refers to a medical institution that is covered by health insurance and to which a doctor who issues a prescription code required to activate a patient app belongs. However, if deregulation allows pharmacists, public health nurses, nurses, nutritionists, hospital staff, and other medical professionals to issue prescription codes, the term "medical institution" also includes facilities and organizations that employ these medical professionals. In the following, these medical professionals will be collectively referred to as "doctors, etc." The issuance of prescription codes is not limited to medical treatment, but may also be for non-insurance treatment (i.e., private treatment) or mixed treatment. Incidentally, medical treatment is not limited to face-to-face treatment, but also includes online treatment.

[0031] <Overall system> Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. 1 is a diagram illustrating the overall configuration of an information processing system 1. The information processing system 1 shown in FIG. 1 is composed of a PDT platform 10, PDT servers 20 (20A, 20B, 20C, . . . 20F), a doctor terminal 30, and a patient terminal 40.

[0032] 1 depicts only one PDT platform 10, although multiple PDT platforms 10 may exist. The PDT platform 10 may be configured with multiple servers connected via a network. In this case, the multiple servers cooperate to provide the services of the PDT platform. The PDT platform 10 and the PDT server 20 are communicatively connected via a network (not shown). The network may be, for example, a LAN (Local Area Network), the Internet, or a mobile communication system (4G, 5G, etc.).

[0033] 1, six PDT servers 20 are connected to one PDT platform 10. However, the number of PDT servers 20 connected to one PDT platform 10 is arbitrary. The PDT server 20 is a server that performs patient authentication, manages patient application data input through the patient application, provides patient data to the doctor terminal 30, and the like. In the case of FIG. 1, the PDT server 20A is a server that manages patient data of a patient application (hereinafter referred to as "patient application A") provided by company A for patients with hypertension. The PDT server 20B is a server that manages patient data of a patient application (hereinafter referred to as "patient application B") provided by Company B for patients with hypertension.

[0034] The PDT server 20C is a server that manages patient data of a patient application (hereinafter referred to as "patient application C") provided by Company A for diabetes patients. The PDT server 20D is a server that manages patient data of a patient application (hereinafter referred to as "patient application D") provided by Company B for diabetes patients. The PDT server 20E is a server that manages patient data of a patient application (hereinafter referred to as "patient application E") provided by Company C for patients with dyslipidemia. The PDT server 20F is a server that manages patient data of a patient application (hereinafter referred to as "patient application F") provided by Company D for patients with NASH.

[0035] 1, a PDT server 20 is prepared for each combination of a disease targeted by a patient app and a service provider that provides the patient app. Therefore, even if the service provider that provides the patient app is the same, if the targeted diseases are different, a different PDT server 20 is prepared. Furthermore, even if the target disease is the same, if the service provider that provides the patient app is different, a different PDT server 20 will be prepared.

[0036] However, it is also possible to provide one PDT server 20 for a plurality of patient applications with different combinations. The PDT platform 10 and the PDT server 20 may be operated by different operators, not necessarily by the same operator. For example, some of the PDT servers 20 may be operated by the same operator as the PDT platform 10.

[0037] The doctor terminal 30 is a terminal operated by a doctor or the like who uses the services provided by the PDT platform 10 and the PDT server 20. In Figure 1, only one representative doctor terminal 30 is depicted. For example, a doctor can view the usage status of the patient app by logging in to the PDT platform 10. Also, a doctor can view patient data by logging in to the PDT server 20. The patient terminal 40 is a terminal operated by a patient. The patient terminal 40 uploads patient application data recorded through the patient application to the corresponding PDT server 20. In Fig. 1, only one patient terminal 40 is depicted as a representative example.

[0038] <Device hardware configuration> <PDTプラットフォーム> Figure 2 is a diagram for explaining an example of the hardware configuration of the PDT platform 10. The PDT platform 10 is a so-called server. The PDT platform 10 shown in FIG. 2 has a processor 11, a semiconductor memory 12, an auxiliary storage device 13, and a communication interface 14. Each device is connected through a bus or other signal lines.

[0039] The processor 11 is a device that realizes various functions through program execution. The semiconductor memory 12 stores UEFI (Unified Extensible Firmware Interface) or the like. The semiconductor memory 12 is also used as a program execution area. The processor 11 and the semiconductor memory 12 function as a computer. <​​​​​​​​​​​The status management data 130 shown in FIG. 3 stores prescription code 130A, patient ID / patient name 130B, prescription date / treatment date 130C, medical institution ID / prescriber ID 130D, patient application name 130E, and usage status 130F. However, these are just examples, and other information that may be stored include the patient's medical record number, the patient's gender, the patient's date of birth, the patient's age, the type of health insurance card, the insurer number, and the version of the patient app.

[0042] The prescription code 130A is issued from the doctor terminal 30 for each notification of a prescription. The patient ID / patient name 130B is the patient ID and patient name registered when the prescription was made in the patient app. In the case of Figure 3, the patient name of patient ID "12543" is "Mr. A," the patient name of patient ID "12544" is "Mr. B," and the patient name of patient ID "12545" is "Mr. C."

[0043] The prescription date / treatment date 130C is the date on which the doctor examined the patient. In this embodiment, the treatment date on which the doctor prescribed the patient app is referred to as the "prescription date" to distinguish it from other treatment dates. In the case of FIG. 3, only the prescription date is stored. In the case of FIG. 3, the prescription date of the patient app for "Mr. A" and "Mr. B" is "2024 / 5 / 28", and the prescription date of the patient app for "Mr. C" is "2024 / 2 / 24". The medical institution ID / prescriber ID 130D is an ID that identifies the medical institution and prescriber that prescribed the patient app. In the case of Figure 3, the prescription codes "12345" and "23456" were prescribed by the same doctor at the same medical institution.

[0044] The patient app name 130E is the name of the patient app prescribed by a doctor or the like. However, as long as it is possible to identify the patient app, an identification code of the patient app may be stored. In the case of FIG. 3, patient app A has been prescribed to "Mr. A" and "Mr. B." Patient app D has been prescribed to "Mr. C." The usage status 130F indicates information on the usage status of the patient app. Among the usage statuses, one of "before usage start", "expired start deadline", "in use", "scheduled end", "ended", or "expired period" is stored. In the case of Figure 3, all patient apps are "in use".

[0045] <PDT Server> Figure 4 is a diagram for explaining an example of the hardware configuration of the PDT server 20. The PDT server 20 shown in Figure 4 has a processor 21, a semiconductor memory 22, an auxiliary storage device 23, and a communication interface 24. Each device is connected through a bus or other signal lines. The processor 21 is a device that realizes various functions through program execution. UEFI etc. is stored in the semiconductor memory 22. The semiconductor memory 22 is also used as a program execution area. The processor 21 and the semiconductor memory 22 function as a computer. [[ID=一三]]

[0046] The auxiliary storage device 23 is composed of, for example, a hard disk device or a semiconductor storage. An operating system and other programs are stored in the auxiliary storage device 23. Among the other programs, there is, for example, a doctor app. The doctor app is an application program that generates a browsing screen for patient data corresponding to patients treated by doctors etc.

[0047] The other programs also include an application program that generates a distribution table of measurement values. In addition, patient data 230 recorded through the patient app is also recorded in the auxiliary storage device 23. The communication interface 24 is an interface for communicating with external terminals such as the PDT platform 10 through a network. The communication interface 24 corresponds to communication standards such as Ethernet (registered trademark), Wi-Fi (registered trademark), and mobile communication systems. <PDT Server Management Data>

[0048] Fig. 5 is a diagram illustrating an example of patient data 230 stored in the auxiliary storage device 23 (see Fig. 4) of the PDT server 20. Note that the patient data 230 shown in Fig. 5 is assumed to be data of a hypertensive patient. Patient data 230 shown in FIG. 5 stores measurements and other information recorded through the patient app for hypertension.

[0049] 5, patient data 230 stores prescription code 230A, patient ID / patient name 230B, and patient application data 230C. In the present embodiment, patient application data 230C refers to management data of the patient application and data recorded by the patient through the patient application. The prescription code 130A (see FIG. 3) issued by the PDT platform 10 (see FIG. 1) is recorded in the prescription code 230A. The prescription code 130A is registered by the patient when the patient starts using the patient app (i.e., when the patient first registers). The patient ID / patient name 230B is, for example, the patient ID and the patient name of the medical institution that prescribed the patient app. The patient ID and the patient name are acquired from the PDT platform 10, for example, when authenticating the prescription code.

[0050] 5 includes a measurement value / measurement date and time 230C1, a review / input date and time 230C2, a current step of a treatment program 230C3, a treatment program practice history 230C4, a behavioral goal 230C5, a target value 230C6, an outpatient record 230C7, and a medication record 230C8. Note that the illustrated items do not need to be all of the patient application data 230C, and may be only a part of the patient application data 230C, or may include other items. The measured blood pressure value and the date and time when the blood pressure was measured are recorded in the measurement value / measurement date and time 230C1. The blood pressure value is given as a systolic blood pressure and a diastolic blood pressure.

[0051] The review / input date and time 230C2 records a review of the day's actions and the input date and time. In the review, for example, the level of physical condition, stress level, length of sleep, weight, and details of the actions taken are recorded. The details of the actions are composed of the category of the actions taken and text input. Other label candidates include, for example, "sleep," "stress," "moderate drinking," and "other." In this embodiment, the category of the actions taken is recorded by checking buttons labeled "reduce salt," "weight loss," "exercise," etc. In the text input, it is possible to freely input details and emotions that cannot be recorded by checking buttons.

[0052] The current step 230C3 of the treatment program records a step indicating the progress of the treatment program provided by the patient app. In this embodiment, the treatment program is composed of three steps. The three steps are, for example, "acquiring knowledge," "practicing behavioral goals," and "making behavior a habit." The treatment program practice history 230C4 records the history of learning and behavior practiced in accordance with the treatment program. For example, in the case of "Step 1," the learning history records whether or not each learning item has been taken. For example, in the case of "Step 2," the behavior history records the practice status of each goal, such as "reducing salt intake," "reducing weight," "exercise," "sleep," "stress," "alcohol," and "quitting smoking." For example, in the case of "Step 3," the practice status of the behavior set by the patient as a goal is recorded.

[0053] The behavioral goal 230C5 records behaviors that the patient has set as goals. The goals here include one or more behavioral goals that the patient has selected from among the goals presented by the patient app according to the progress of the treatment program. For example, the behavioral goal 230C5 records a behavioral goal that the patient has set in step 2 or 3 of the treatment program. A behavioral goal refers to a behavioral goal that should be achieved in order to achieve a target. The behavioral goal is not limited to a behavioral goal selected from the behaviors presented by the patient app, and may include behaviors individually set by the patient.

[0054] In the target value 230C6, a value set by a doctor or the like in the "blood pressure reduction target value field" 313 (see FIG. 11) on the patient data viewing screen 310 (see FIG. 11) is recorded. The blood pressure reduction target value field 313 is an example of a quantitative target in that it is given as a numerical value. The outpatient record 230C7 records appointments with the doctor, the next appointment date, medication timing, etc. The appointments with the doctor record appointments that the patient should make or be aware of before the next medical examination. In this embodiment, appointments with the doctor can only be recorded through the patient app. Note that appointments with the doctor may also be recorded only from the doctor terminal 30 (see FIG. 1). The medication record 230C8 records information about taking prescribed medication.

[0055] <Doctor's terminal / Patient's terminal> Figure 6 is a diagram illustrating an example of the hardware configuration of the doctor terminal 30 and the patient terminal 40. The hardware configuration of the doctor terminal 30 and the hardware configuration of the patient terminal 40 are basically the same. For this reason, in Figure 6, they are expressed in the format of "symbols of elements constituting the doctor terminal 30 / symbols of elements constituting the patient terminal 40". The doctor terminal 30 / patient terminal 40 shown in Figure 6 includes a processor 31 / 41, a semiconductor memory 32 / 42, an auxiliary storage device 33 / 43, an input interface 34 / 44, an input device 35 / 45, an output interface 36 / 46, an output device 37 / 47, and a communication interface 38 / 48. Each device is connected via a bus or other signal lines.

[0056] The processor 31 / 41 is a device that realizes various functions through the execution of a program. The semiconductor memory 32 / 42 stores UEFI and the like. The semiconductor memory 32 / 42 is also used as an execution area for programs. The processor 31 / 41 and the semiconductor memory 32 / 42 function as a computer. The auxiliary storage device 33 / 43 is configured by, for example, a hard disk drive or semiconductor storage, and stores an operating system and other programs.

[0057] In the case of the doctor terminal 30, the auxiliary storage device 33 stores a client certificate for accessing the PDT platform 10 (see FIG. 1) and a client certificate for accessing the PDT server 20 (see FIG. 1). In the case of the patient terminal 40, a client certificate for accessing the PDT server 20 (see FIG. 1) is recorded in the auxiliary storage device 43. In addition, the auxiliary storage device 43 also records patient application data 230C (see FIG. 5). The input interface 34 / 44 uses, for example, USB (=Universal Serial Bus) or Bluetooth (registered trademark) for connection to the input device 35 / 45. The input device 35 / 45 may be, for example, a keyboard, a mouse, or a touch panel.

[0058] The output interfaces 36 / 46 may be, for example, HDMI (=High-Definition Multimedia Interface) (registered trademark) or a LAN interface for connection to the output devices 37 / 47. The output devices 37 / 47 may be, for example, a monitor or a printer. The communication interface 38 / 48 is an interface for communicating with an external terminal via a network, and is compatible with Ethernet (registered trademark), Wi-Fi (registered trademark), mobile communication systems, and other communication standards.

[0059] <Patient data viewing screen output> The output process of the patient data viewing screen in the information processing system 1 (see FIG. 1) will be described below with reference to FIGS. 7 is a diagram illustrating an example of a processing sequence executed by the information processing system 1 (see FIG. 1). The symbol S in FIG. 7 represents a step.

[0060] <Step 101> Patients record measurements and other data from the patient app's operation screen. Patients are an example of users who change their behavior. Patients record, for example, measurements, reflections, behavioral goals, target values, outpatient records, and medication records. These records are examples of health-related data. 8 is a diagram illustrating an example of an operation screen in the patient application. The operation screen shown in FIG. 8 can be transitioned from the home screen 401. The measurement value input screen 402 is, for example, a screen for inputting blood pressure values. Measurement values ​​can be input manually or through data linkage with a blood pressure monitor. However, data linkage with a blood pressure monitor is limited to cases where the blood pressure monitor can be linked with a patient app.

[0061] The review input screen 403 allows users to record their physical condition level, stress level, sleep duration, weight, activity details, mood, and physical condition. As mentioned above, the activity details include records of button operations corresponding to the category of activity that was successfully performed. The activity details also include text input. The start program screen 404 is the screen used in "Step 1" of the treatment program.

[0062] Fig. 9 is a diagram for explaining the treatment program. The treatment program shown in Fig. 9 is composed of "Step 1," "Step 2," and "Step 3." "Step 1" is a learning stage aimed at acquiring knowledge about diseases. "Step 1" is made up of multiple chapters. The chapters are sequential, and you cannot move on to the next chapter until you have completed studying one chapter. Once you have completed studying all the chapters, you can move on to "Step 2." In "Step 1," the patient is first asked to enter information about their employment status, preferences, etc. Next, the patient's characteristics are analyzed from the entered information.

[0063] Once the analysis results are obtained, knowledge regarding "reducing salt intake," "weight loss," "exercise," "sleep," "stress," "alcohol," "tobacco," "understanding the disease," and "blood pressure measurement" is presented according to the patient's characteristics. In addition, some of the information presented to patients will be adjusted depending on whether or not kidney dysfunction is indicated, their usual exercise habits, alcohol consumption, etc. The recommended duration for "Step 1" is two weeks.

[0064] "Step 2" is the stage where behavioral goals are put into practice. First, in "Step 2," behavioral goals appropriate for the patient are presented from among "reducing salt," "reducing weight," "exercise," "sleep," "stress," "alcohol," and "tobacco." The patient confirms the presented actions and records the results of the actions. Regarding the behavioral goals in this step, the displayed content of each item is partially adjusted based on the information entered by the patient themselves.

[0065] In "Step 2," specific behavioral goals are presented for multiple behavioral categories that represent lifestyle habits that will improve the disease. Examples of behavioral categories include "reducing salt intake," "weight loss," "exercise," "sleep," "stress," and "alcohol." For example, behavioral goals in the "reducing salt intake" category include "avoiding salty snacks such as snacks and rice crackers," "avoiding drinking the broth of udon or ramen noodles," "avoiding convenience stores or eating out for lunch," "avoiding eating two hours before bed," "walking for 20 minutes," and "getting at least six hours of sleep." Once behavioral goals for all behavioral categories have been achieved, the patient can move on to "Step 3." The target duration for "Step 3" is one month.

[0066] "Step 3" is the stage where behavior becomes a habit. The patient sets the blood pressure target themselves. The blood pressure target is an example of an achievement goal. The patient records the progress of achieving the blood pressure target. In "Step 3," behaviors that the patient himself evaluated as being highly effective in "Step 2" are presented as behavioral goals with priority. The patient sets the presented behavior as a behavioral goal and aims to make the behavior a habit.

[0067] Returning to the explanation of Figure 8. The behavioral goal list screen 405 is a screen that can be displayed after "Step 2." A list of behavioral categories is displayed on the behavioral goal list screen 405. The degree of adherence is also displayed for each category. The social support screen 406 is a screen that can be displayed after "Step 2". A list of social supports is displayed on the social support screen 406. The social support screen 406 displays treatment know-how that is useful in real life.

[0068] The My Data screen 407 is a screen used to check the progress of steps, measurement values, salt intake status, knowledge, and behavior. The measurement value screen 408 is a screen on which the measurement values ​​recorded up to now are displayed as a trend graph and numerical values. A distribution chart, which will be described later, is also displayed on the measurement value screen 408. The salt intake screen 409 is a screen that can be displayed after "Step 2". On the salt intake screen 409, it is possible to record salt intake, record dietary details, and view the recorded details. The recorded details can be displayed as a trend graph or numerical values. The outpatient record screen 410 is a screen on which the patient can record appointments made with the doctor at the time of the visit, record the next visit date, record the time of day when prescribed medicines will be taken, and view the recorded contents. The medication record input screen 411 is a screen on which medication records can be recorded and the recorded contents can be viewed.

[0069] <Step 102> Returning to the explanation of Figure 7. The patient terminal 40 uploads patient app data at predetermined times. One of the predetermined times is when new data is recorded or updated. This upload is performed when the patient app remains logged in to the PDT server 20. Another predetermined time is when the patient signs in to the PDT server 20. This upload is performed when the patient app logs out of the PDT server 20 and then logs in to the PDT server 20 again.

[0070] <Step 103> The PDT server 20 stores the uploaded patient application data, which constitutes part of the patient data 230 (see FIG. 5). Steps 101 to 103 shown in FIG. 7 are executed in response to a recording operation by a patient to the patient application.

[0071] <Step 104> When a doctor or the like logs in to the PDT platform 10 via the doctor terminal 30, the PDT platform 10 presents a confirmation screen for the usage status of the patient app to the doctor terminal 30. FIG. 10 is a diagram illustrating an example of a management screen 300 for the use status of patient applications displayed on the output device 37 (see FIG. 6) of the doctor terminal 30 (see FIG. 6).

[0072] The management screen 300 shown in FIG. 10 is an example of a management screen that displays, in a list, the usage status of patient apps prescribed to patients by a doctor or the like who operates the doctor terminal 30. The management screen 300 may display only the usage status of patient apps prescribed by the doctor or the like operating the doctor terminal 30, or may display the usage status of patient apps prescribed by the medical institution to which the doctor or the like operating the doctor terminal 30 belongs. In other words, the management screen 300 may include the usage status of patient apps prescribed by other doctors or the like who belong to the same medical institution.

[0073] In addition, the management screen 300 may be configured to display the usage status of patient apps prescribed by other doctors or other medical institutions. This display function is useful, for example, when a patient requests a second opinion. This display can be realized, for example, by a doctor operating the doctor terminal 30 providing information identifying the patient under treatment (for example, a medical record number or a patient name) to the PDT platform 10. Furthermore, technically, it is also possible to include information on all patients managed by the PDT platform 10 on the management screen 300. In that case, however, it is desirable to be able to narrow down or rearrange the patients displayed on the management screen using the ID of the doctor operating the doctor terminal 30, etc.

[0074] The management screen 300 shown in FIG. 10 is composed of an information field 301 , a current date and time 302 , a “Details” button 303 , a “New prescription” button 304 , and a “Close” button 305 . Management information of the patient app prescribed to each patient is displayed in the information field 301. In the case of Fig. 10, the information field 301 is displayed in a table format. Incidentally, information for each patient is displayed in the rows, and management items of the patient app are displayed in the columns. FIG. 10 shows, as management items, examples of medical record number / patient name 301A, application name 301B, usage status 301C, prescription date 301D, and prescription code expiration date 301E.

[0075] The medical record number / patient name 301A displays the medical record number and the patient name, which are examples of information for identifying a patient to whom the patient application has prescribed a medication. The information for identifying a patient may include a user ID, an address, etc. The application name 301B displays the name of the patient application prescribed to the patient. In this embodiment, four applications are displayed: patient application A, patient application B, patient application C, and patient application D. The application name 301B may include version information.

[0076] Note that if multiple patient apps are approved for a patient, the information will be displayed in different rows. In addition to or in addition to the application name 301B, the name of the disease targeted by the patient application may be displayed. The usage status 301C is used to display the usage status of the patient app. In the case of Fig. 10, the usage status 301C displays three options: "Before starting usage," "In use," and "Ended." However, as mentioned above, the usage status 301C also displays "Start deadline expired," "Scheduled to end," "Expired," etc.

[0077] The prescription date 301D displays the date on which the patient app was prescribed by a doctor or the like. The last date on which the prescription code is valid is displayed in prescription code expiration date 301E. In this embodiment, the date three days after prescription date 301D is displayed in prescription code expiration date 301E. The current date and time 302 is the date and time when the patient application usage status management screen 300 is viewed.

[0078] The "Details" button 303 is a button used to display a patient data viewing screen. The "Details" button 303 is arranged on each row corresponding to a patient. When the "Details" button 303 is operated, the patient data viewing screen for the patient corresponding to the "Details" button 303 is displayed. The "New Prescription" button 304 is used when prescribing a patient app to a patient. When the "New Prescription" button 304 is operated, a screen for inputting the app name, patient name, gender, date of birth, etc. is displayed. The name of the prescribing doctor is also registered at the same time. The "close" button 305 is a button for closing the management screen 300 shown in FIG.

[0079] <Step 105> Returning to the explanation of Figure 7. The doctor terminal 30 accepts the selection of a patient on the management screen 300 (see FIG. 10) described above. Specifically, the doctor terminal 30 accepts the operation of the "Details" button 303 (see FIG. 10). For example, the doctor terminal 30 accepts the operation of the "Details" button 303 corresponding to Mr. H, for whom more than five months have passed since the prescription date. "Mr. H" here is an example of a specific user. The doctor terminal 30 that has accepted the operation of the "Details" button 303 accesses the PDT server 20 of the patient app prescribed to the patient corresponding to the operated "Details" button 303.

[0080] It is assumed that the doctor or the like has already logged in to the PDT server 20 to be accessed via the doctor terminal 30. In this access, the prescription code of the patient app prescribed to the patient corresponding to the operated “Details” button 303 is notified. The doctor terminal 30 may request the PDT platform 10 to issue a one-time token required to access the corresponding PDT server 20. In this case, the doctor terminal 30 accesses the PDT server 20 using the one-time token returned from the PDT platform 10. When this method is adopted, doctors and other personnel can access patient app data of PDT servers 20 to which they are not logged in.

[0081] <Step 106> The PDT server 20, which is accessed by the doctor terminal 30, generates a patient data viewing screen for the selected patient. FIG. 11 is a diagram illustrating an example of a patient data viewing screen 310. The viewing screen 310 shown in FIG. 11 is composed of a “patient information field” 311, a “measurement result display field” 312, a “blood pressure reduction target value field” 313, a “review information display field” 314, an “activity record display field” 315, a “Go to examination date registration screen” button 316, and an “Exit” button 317.

[0082] The "patient information column" 311 shown in FIG. 11 indicates that the medical record number is "13002," the patient name is "Mr. H," the age is "60 years old," and the sex is "male." The average blood pressure measured at home is displayed numerically and graphically in the "measurement result display column" 312 shown in Figure 11. Daily measurement values ​​can be entered on the measurement value input screen 402 (see Figure 8).

[0083] In the case of FIG. 11, the average values ​​for two intervals, "8 weeks to 4 weeks ago" and "4 weeks ago to the day before," are shown. Here, the day before means the day before the current date and time when the viewing screen 310 is opened. Doctors and other medical professionals can check the progress of treatment by viewing the numerical values ​​and other information displayed in the "measurement result display column" 312. A "Statistics" button 312A is arranged in the "Measurement result display column" 312 shown in Fig. 11. The processing operations executed when the "Statistics" button 312A is operated and the corresponding screens will be described later.

[0084] 11 indicates that the target value for home systolic blood pressure (maximum blood pressure) is less than 125 mmHg, and the target value for diastolic blood pressure (minimum blood pressure) is less than 75 mmHg. Note that blood pressure targets may differ depending on gender, the presence of other diseases, age, and other factors. 11, a "change" button for the target value is provided in the "blood pressure reduction target value field" 313. Only a doctor or the like can change the target time. Records of mood and physical condition are displayed in the "retrospective information display field" 314 shown in Fig. 11. The retrospective information can be input from the above-mentioned retrospective input screen 403 (see Fig. 8).

[0085] The activity record display field 315 shown in Figure 11 displays activity records such as the number of days the patient app was used, the number of days a reduction in salt intake was recorded, and the number of days a weight loss was recorded. The "Go to consultation date registration screen" button 316 shown in FIG. 11 is a button for opening a consultation date registration screen (not shown). The "Exit" button 317 shown in FIG. 11 is a button for closing the patient data viewing screen 310.

[0086] <Step 107> Returning to the explanation of Figure 7. The doctor terminal 30 accepts the display of statistical information through the patient data viewing screen 310 (see FIG. 11). Specifically, it accepts the operation of the "Statistics" button 312A (see FIG. 11). Note that the processing operations from step 107 onwards are executed only when the "Statistics" button 312A is operated. The doctor terminal 30 notifies the PDT server 20 of the acceptance of the operation of the "Statistics" button 312A.

[0087] <Step 108> The PDT server 20 calculates a statistical value (first statistical value) of a predetermined item. In this embodiment, the "predetermined item" is assumed to be a default value. Default values ​​include, for example, the usage status of the patient app, the status of reflection entries, and the progress of treatment using the patient app. An example of a statistical value indicating the usage status of the patient app is the usage rate. The usage rate is calculated, for example, as the percentage of days during which the patient app is operated within a specified period. The operations here are assumed to include, for example, inputting measurement values ​​and reflections via an input screen. However, the operations may also include launching the patient app and viewing the various screens shown in Figure 8.

[0088] The statistical value indicating the status of reflection writing is, for example, a writing rate, which is calculated as the percentage of days during which reflection writing is performed within a predetermined period. Statistics showing the implementation status of behavioral goals managed through the patient app include, for example, the number of behavioral goals implemented and the number of goals achieved. The number of actions and the number of goals achieved are calculated, for example, as the number of actions performed by the patient within a specified period and the number of actions for which the goal was achieved. As with other items, it is also possible to calculate them as a percentage. The "predetermined period" that is the unit for calculating each statistical value is, for example, four weeks. Note that a patient selected by a doctor or the like is an example of a specific user. Also, data recorded by a patient through the patient app is an example of first data. Also, statistical values ​​of a patient selected by a doctor or the like are an example of first statistical values.

[0089] <Step 109> Next, the PDT server 20 calculates statistical values ​​(second statistical values) for predetermined items of all people who use the same patient app. The predetermined items here are the same as the predetermined items in step 108. In the case of this embodiment, all people who use the same patient app are assumed to be compared with a specific patient. The patient data of each patient constituting the set to be compared is an example of second data. The statistical value of all patients to be compared is an example of second statistical value. Furthermore, all patients are an example of a predetermined user set.

[0090] <Step 110> The PDT server 20 generates and presents a distribution table screen in which the two statistical values ​​are arranged in a comparative manner. Figure 12 is a diagram illustrating an example of a distribution table screen 320 used to display statistical values ​​for individual patients and statistical values ​​for all patients. In Figure 12, parts corresponding to those in Figure 11 are assigned the same reference numerals. Therefore, the distribution table screen 320 shown in Figure 12 is for examining a patient named "Mr. H." The distribution table screen 320 is displayed by operating the "Statistics" button 312A (see FIG. 11) described above.

[0091] The distribution table screen 320 shown in FIG. 12 is composed of a statistical information display field 321 and a “return to previous screen” button 322. In the statistical information display field 321, the title "Application Usage Status" is displayed. In the statistical information display field 321, three graphs corresponding to the predetermined items described above are displayed.

[0092] The first graph is graph 321A for "application usage rate." Graph 321A is an example of a frequency distribution. In the case of FIG. 12, the vertical axis of graph 321A is a percentage. The maximum value on the vertical axis is 100% and the minimum value is 0%. The horizontal axis of graph 321A represents the period. The horizontal axis in FIG. 12 is divided into three periods. The period on the far left is the period from two months ago to three months ago. In FIG. 12, the same period is labeled "3 months ago / 13 weeks ago to 9 weeks ago." For the same period, a bar graph showing the utilization rate of all patients for that period and a bar graph showing the utilization rate of "Mr. H" for that period are displayed side by side. In this embodiment, "3 months ago" is the date when recording of patient data began.

[0093] The middle period is from one month to two months ago. In Figure 12, the same period is labeled "2 months ago / 8 weeks ago to 4 weeks ago." For this period, a bar graph showing the utilization rate for all patients and a bar graph showing the utilization rate for "Mr. H" are displayed side by side. The period on the far right is the period one month ago. In Figure 12, this period is labeled "One month ago / Last prescription date to the day before."

[0094] In this embodiment, it is assumed that consultations are conducted approximately once every four weeks. The "prescription date" in Figure 12 does not mean the prescription in the patient app, but the "consultation date." In the case of the same period, a bar graph showing the utilization rate of all patients during the same period and a bar graph showing the utilization rate of "Mr. H" are displayed side by side. 12 is merely an example, and the data may be displayed numerically or as a line graph.

[0095] The second graph is graph 321B for "reflection entry rate." The vertical axis of graph 321B is a percentage. The maximum value of the vertical axis is 100% and the minimum value is 0%. The horizontal axis of graph 321B represents the period. The horizontal axis of graph 321B is the same as the horizontal axis of graph 321A. In other words, the horizontal axis of graph 321B is divided into three periods. In the case of graph 321B, a bar graph showing the retrospective entry rate for all patients and a bar graph showing the retrospective entry rate for "Mr. H" are also displayed side by side for each period.

[0096] The third graph is the "App Progress" graph 321C. The vertical axis of graph 321C is frequency. The horizontal axis has no meaning and is divided into an area for "Number of Implementations" and an area for "Number of Achievements." Incidentally, graph 321C indicates that the current step is "Step 2" of the treatment program. That is, graph 321C displays a bar graph showing the number of times that "Mr. H"'s behavioral goals belonging to Step 2 have been implemented and a bar graph showing the number of times that they have been achieved, in contrast to the corresponding values ​​for all patients. The number of patients who underwent the treatment and the number of patients who achieved the treatment were normalized to the value per patient. Specifically, the average value was used.

[0097] <Summary> In this embodiment, a doctor or the like can open a distribution table screen 320 (see FIG. 12) from the patient data viewing screen 310 (see FIG. 11) to confirm the statistical values ​​of an individual patient and the statistical values ​​of all patients in a comparative manner. Specifically, the doctor or the like can open the distribution table screen 320 (see FIG. 12). For example, in the case of the distribution table screen 320 shown in FIG. 12, the doctor or the like can grasp the patient app usage rate, reflection entry rate, and progress status of the treatment program managed by the patient app by the patient under examination by comparing them with the statistical values ​​of all patients.

[0098] For example, a doctor or other medical professional can confirm that the app usage rate and review completion rate of a patient currently undergoing treatment, "Mr. H," were higher than the overall patient population up until two months ago, but that the figures for the most recent month were lower than the overall patient population. As a result, the doctor or other medical professional can notice that "Mr. H"'s decline in interest in treatment is more pronounced than that of other patients. This finding, which is easily overlooked by the general public, is that patient interest tends to decline over the course of treatment. In other words, it is a finding that is easily overlooked from just the changes in "Mr. H"'s statistical values.

[0099] On the other hand, doctors and other medical professionals can confirm that the number of behavioral goals implemented and achieved by patient "Mr. H" during their consultation is higher than that of the overall patient population. A high number of behavioral goals implemented and achieved means that behaviors that are highly correlated with treatment effectiveness are becoming habitual. Therefore, doctors and other medical professionals can determine that although the utilization rate and completion rate are declining, the treatment itself is progressing smoothly. Incidentally, if the statistical data for the entire patient population is not displayed comparatively, doctors and other medical professionals may be able to see that the rate at which patients are using the app or completing their reflections is declining, or that the number of behavioral goals is being implemented or achieved, but it is difficult to judge these figures from the perspective of treatment effectiveness. At best, they are limited to vague diagnoses based on experience.

[0100] <Other embodiments> (1) Although the embodiments of the present disclosure have been described above, the technical scope of the present disclosure is not limited to the scope of the above-described embodiments. It is clear from the claims that various modifications or improvements to the above-described embodiments are also included in the technical scope of the present disclosure.

[0101] (2) The processor in the above-described embodiments refers to a processor in a broad sense, and includes general-purpose processors (e.g., CPUs (=Central Processing Units)) as well as dedicated processors (e.g., GPUs (=Graphical Processing Units), ASICs (=Application Specific Integrated Circuits), FPGAs (=Field Programmable Gate Arrays), programmable logic devices, etc.). Furthermore, the operations of the processors in each of the above-described embodiments may be performed by multiple processors working together, rather than by a single processor. The order in which the processors perform the operations is not limited to the order described above, and may be changed individually.

[0102] (3) In the above-described embodiment, the calculation requirements for medical fees under the public medical insurance system are assumed, but information regarding the applicability of medical fees under the private medical insurance system may also be displayed on the work screen of the doctor terminal 30 (see FIG. 1). For example, if the requirements for approval of calculation of medical fees under the private medical insurance system require the use of a specific app or medical treatment that refers to patient data entered through a specific app, the explanation of the above-described embodiment can be applied.

[0103] (4) In the above-described embodiment, three statistical values, "application usage rate," "review entry rate," and "application progress," are displayed. However, the number of displayed statistical values ​​is not limited to three. For example, the number of statistical values ​​may be one, two, four, or more.

[0104] (5) In the above-described embodiment, the displayed statistical values ​​were "app usage rate," "review entry rate," and "app progress." However, the statistical values ​​may be statistical values ​​related to blood pressure, weight, heart rate, blood glucose level, carbon monoxide concentration in breath, exercise time, sleep time, or other measured values. Examples of the statistical values ​​here include average values, frequency distributions, maximum values, and minimum values. Incidentally, measured values ​​refer to values ​​measured using measuring equipment. The statistical values ​​may also be statistical values ​​related to values ​​input by the patient. Examples of the input values ​​here include salt intake, exercise time, sleep time, and alcohol intake.

[0105] (6) In the above-described embodiment, the statistical values ​​displayed on the distribution table screen 320 (see FIG. 12) are predetermined. That is, the statistical values ​​displayed are "application usage rate," "review entry rate," and "application progress." However, the items displayed on the distribution table screen 320 may be specified by a doctor or the like. For example, one or more items may be freely set by selecting a radio button or a check box. Here, a doctor or the like is an example of a viewing user. Viewing users include not only doctors or the like, but also patients and other users.

[0106] (7) In the above-described embodiment, the statistical values ​​to be compared were all patients. However, a doctor or the like may be able to specify the set used to calculate the statistical values ​​to be compared. Here, a doctor or the like is an example of a viewing user. Also, the set used to calculate the statistical values ​​to be compared is an example of a predetermined user set. 13 is a diagram illustrating a display example of a distribution table screen 320A that allows selective designation of a set to be compared with the statistical values ​​of patients. In FIG. 13, parts corresponding to those in FIG. 12 are assigned the same reference numerals.

[0107] The distribution table screen 320A shown in FIG. 13 is composed of a patient information field 311, a statistical information display field 321, and a "return to previous screen" button 322. 13 is composed of distribution graphs 321D and 321E of blood pressure values ​​recorded by "Mr. H," a patient currently undergoing medical examination, via the patient app, and a set selection field 321F. The distribution graphs 321D and 321E are examples of frequency distribution. The distribution graphs 321D and 321E are also examples of distribution information. Incidentally, distribution graph 321D is a distribution graph for the systolic period, and distribution graph 321E is a distribution graph for the diastolic period.

[0108] In the case of FIG. 13, the common title for the systolic distribution graph 321D and the diastolic distribution graph 321E is "Changes in Measured Blood Pressure." In the case of Fig. 13, the vertical axis of each of systolic distribution graph 321D and diastolic distribution graph 321E represents time going back from the present. The vertical axes of distribution graphs 321D and 321E shown in Fig. 13 are divided into six rows.

[0109] The top row is the period "one month ago." Specifically, the top row is the period from the present to one month ago. The second row from the top is the period "2 months ago." Specifically, the second row from the top is the period from one month ago to two months ago. The third row from the top is the period "3 months ago." Specifically, the third row from the top is the period from 2 months ago to 3 months ago.

[0110] The fourth row from the top is the period "four months ago." Specifically, the fourth row from the top is the period from three months ago to four months ago. The fifth row from the top is the period "five months ago." Specifically, the fifth row from the top is the period from four months ago to five months ago. The sixth row from the top is the "six months ago" period. Specifically, the sixth row from the top is the period from five months ago to six months ago. The oldest measurement value included in the "six months ago" period is the measurement value on the start date of recording. These six sections correspond to predetermined time units determined according to the number of days that have passed since the start of recording.

[0111] In the case of Fig. 13, the left end of the horizontal axis of systolic distribution graph 321D and diastolic distribution graph 321E is 0% and the right end is 100%. The horizontal axes of distribution graphs 321D and 321E shown in Fig. 13 also display 25%, 50%, and 75% as a guide. In the case of FIG. 13, the systolic distribution graph 321D and the diastolic distribution graph 321E have the frequency ratio of the measured values ​​classified into three blood pressure intervals associated with each row expressed by the length of the bar graph.

[0112] In the case of Figure 13, the first blood pressure period in the systolic distribution table 321D is "125 or less," the second blood pressure period is "126-144," and the third blood pressure period is "145 or more." In addition, the fourth blood pressure period in the diastolic distribution table 321E is "75 or less," the fifth blood pressure period is "76-89," and the sixth blood pressure period is "90 or more." For example, in the case of the systolic distribution graph 321D, it can be seen that the first blood pressure section was 0% six months ago, approximately 5% five months ago, approximately 11% four months ago, approximately 13% three months ago, approximately 12% two months ago, and approximately 30% one month ago.

[0113] On the other hand, in the case of the diastolic distribution graph 321E, it can be seen that the fourth blood pressure zone was approximately 13% six months ago, approximately 30% five months ago, approximately 35% four months ago, approximately 38% three months ago, approximately 70% two months ago, and approximately 87% one month ago. In this way, using distribution graphs 321D and 321E makes it easy to understand changes in the distribution of blood pressure values ​​over a long period of time. However, it is not possible to understand the changes in the distribution of blood pressure values ​​of "Mr. H" in terms of his relative position or relationship among all patients.

[0114] In the actual distribution table screen 320A, each blood pressure zone is displayed in a different color. For example, the first and fourth blood pressure zones are displayed in green, the second and fifth blood pressure zones are displayed in orange, and the third and sixth blood pressure zones are displayed in red. The length of the green bar graph represents the distribution percentage of normal measurement values. The length of the orange bar graph represents the distribution percentage of measurement values ​​requiring caution. The length of the red bar graph represents the distribution percentage of measurement values ​​requiring even greater caution. These distribution percentages are an example of distribution information by period.

[0115] Incidentally, consultation dates are not necessarily one month apart from the prescription date. Therefore, the number of days in the oldest period may be less than 30 days. Also, the number of days in a month varies, and it is possible that measurements may be forgotten. In other words, the number of days for which measurements are recorded each month is expected to vary. However, in this embodiment, the distribution ratio of the frequency in three blood pressure intervals is displayed, and therefore the variation in the number of measurement days corresponding to each period is ignored.

[0116] 13, the systolic distribution graph 321D and the diastolic distribution graph 321E show a list of changes in the frequency distribution of measurement values ​​classified into each blood pressure interval over a six-month period. In other words, the systolic distribution graph 321D and the diastolic distribution graph 321E show changes in the frequency distribution of measurement values ​​in each blood pressure interval after the start date of recording. In this embodiment, the display of distribution graphs 321D and 321E starts six months ago, but the display period of distribution graphs 321D and 321E varies depending on the prescription date of the patient app. For example, if the prescription date of the patient app is three months ago, the frequency distribution for three periods is displayed in three rows.

[0117] Incidentally, in the chart shown in the upper part of the "measurement result display column" 312 (see FIG. 11), only the average blood pressure values ​​for two periods can be confirmed, and the frequency distribution for the three blood pressure intervals cannot be seen. In addition, the line graph shown in the lower part of the "Measurement result display column" 312 allows you to check the changes in measured blood pressure on a daily basis from the previous day to five weeks ago, but it also does not show the frequency percentages for the three blood pressure ranges. The "return to previous screen" button 323 is a button for returning to the patient data viewing screen 310 (see FIG. 11).

[0118] In the group selection field 321F shown in FIG. 13, three radio buttons are arranged after the inquiry statement "Please select the item you want to display." In the case of FIG. 13, "Gender," "Age," and "Place of Residence" are assigned to the three radio buttons. In the case of FIG. 13, none of the radio buttons in the group selection field 321F are selected. In addition, a "Select" button is provided in the group selection field 321F shown in FIG. 13. The selection of the radio button can be changed until the "Select" button is operated.

[0119] 14 is a diagram illustrating a display example of a distribution table screen 320A after "gender" is selected in the set selection field 321F. In FIG. 14, parts corresponding to those in FIG. 13 are assigned the same reference numerals. In the case of FIG. 14, a gender specification field 321G has been added to the right of the group selection field 321F. In the case of FIG. 14, the gender can be specified as either "male," "female," or "all." "All" means both males and females. In FIG. 14, the position of "male" is shown in a selected state. Note that a "display" button is provided in the group selection field 321F. The selected gender can be changed until the "display" button is operated.

[0120] 15 is a diagram for explaining a display example of a distribution table screen 320A when a set is specified by "gender." In FIG. 15, parts corresponding to those in FIG. 13 are assigned the same reference numerals. In the case of Figure 15, the distribution graph for "Mr. H" is displayed in the upper section of the statistical information display field 321, and the distribution graph for "male" is displayed in the lower section. "Male" here is an example of a user set designated by a doctor or the like as a viewing user. The distribution graph for "male" is also an example of a second statistical value. In the case of Figure 15, the title "The Patient in Question" is added to the top row. "The Patient in Question" refers to the patient shown in the patient information column 311, that is, "Mr. H."

[0121] Meanwhile, the bottom row has the title "Male." In other words, the distribution graph shown in the bottom row is a group of men who recorded their blood pressure values ​​through the same patient app as Mr. H. The distribution graph shows the frequency distribution of the average systolic blood pressure values ​​and the average diastolic blood pressure values ​​for each period. By displaying two distribution graphs, one above the other, doctors and other medical professionals can see the changes in the frequency distribution of Mr. H's blood pressure over each period of his consultation, as well as the difference with the changes in the frequency distribution of the average blood pressure for all men. As a result, they can objectively assess the progress of Mr. H's treatment.

[0122] 16 is a diagram for explaining the changes in the distribution table screen 320A when a set is designated by "age group." In FIG. 16, parts corresponding to those in FIG. 13 are assigned the same reference numerals. The upper part of Fig. 16 shows a distribution table screen 320A at the time when "era" is selected as a set. The lower part of Fig. 16 shows a distribution table screen 320A in which an era specification field 321H has been added to the right of the set selection field 321F.

[0123] In the case of FIG. 16, the age group can be specified as either "under 50s," "60s," "70s," or "over 80s." In Figure 16, "60s" - the same age group as "Mr. H" - is selected. In this state, when the "Display" button in the set selection field 321F is operated, a distribution graph for "Mr. H" and a distribution graph for patients in their "60s" are displayed in two rows, one above the other, just like in Figure 15. In this case, doctors and others can objectively judge the progress of "Mr. H"'s treatment by comparing the frequency distribution for "Mr. H" with the frequency distribution for patients in the same age group.

[0124] 17 is a diagram for explaining the change in the distribution table screen 320A when a group is designated by "place of residence." In FIG. 17, parts corresponding to those in FIG. 13 are assigned the same reference numerals. The upper part of Fig. 17 shows the distribution table screen 320A at the time when "residence" is selected as the set. The lower part of Fig. 17 shows the distribution table screen 320A in which a residence specification field 321I has been added to the right of the set selection field 321F.

[0125] In the case of Figure 17, the place of residence can be specified as either "Kanto," "Kansai," or "Chubu." Note that this classification is just an example, and it may also be possible to specify by prefecture or by city, town, or village. In Figure 17, all three regions, "Kanto," "Kansai," and "Chubu," are selected. In this state, when the "Display" button in the set selection field 321F is operated, a distribution graph for "Mr. H" and distribution graphs for patients residing in "Kanto," "Kansai," and "Chubu" are displayed in two rows, one above the other, just like in Figure 15.

[0126] In this case, doctors and other medical professionals can objectively assess the progress of "Mr. H's" treatment by comparing the frequency distribution of "Mr. H" with the frequency distribution of patients linked to the specified residence. In the selection field 321F shown in Figures 13 to 17, we have explained the case where only one of "gender," "age," and "place of residence" can be selected, but it may also be possible to specify a set that combines two or three of these.

[0127] Fig. 18 is a diagram for explaining another display example of the distribution table screen 320A. In Fig. 18, parts corresponding to those in Fig. 13 are assigned the same reference numerals. 18, the set selection field 321F lists "occupation," "cooking at home," "BMI before treatment started," "habits before treatment started," "renal dysfunction," and "score with possible correlation with blood pressure reduction" as examples of selectable attributes. In the case of Fig. 18, radio buttons, check boxes, or input fields are provided depending on the attribute.

[0128] For example, radio buttons corresponding to "Yes" and "No" are provided for "Occupation." Because these are radio buttons, only one option can be selected at a time. In this embodiment, it is also possible to select neither option. In this case, the "Occupation" information linked to the patient is ignored when identifying the set. In addition, three radio buttons are provided for "Cooking at home." A numerical value input field is provided for "BMI (=Body Mass Index) before treatment begins." However, radio buttons may also be provided for BMI, allowing the user to specify the BMI by selecting the radio button.

[0129] "Habits before starting treatment" has three checkboxes: "Drinking," "Smoking," and "Exercise." Because they are checkboxes, it is possible to check all three at once. In the case of FIG. 18 , only the presence or absence of a habit for each of "drinking," "smoking," and "exercise" can be input, but the frequency and intensity of each habit may also be input. Incidentally, the frequency of "drinking" may be, for example, input or selection of the number of drinks per week. Furthermore, the intensity of "drinking" may be, for example, input or selection of the amount of alcohol consumed per occasion. The frequency and intensity of "quitting smoking" may be, for example, input or selection of the number of cigarettes smoked per day or the number of cartridges consumed. The frequency of "exercise" may be, for example, input or selection of the number of hours of exercise per day or the number of hours of exercise per week. Furthermore, the intensity of "exercise" may be, for example, input or selection of the type of exercise. Incidentally, the intensity of "exercise" may be, for example, measured using METs (metabolic equivalents), heart rate, or ratings of perceived exertion (RPE).

[0130] For "renal dysfunction," radio buttons corresponding to "yes" and "no" are provided. Three check boxes are provided for "Scores that may be correlated with blood pressure reduction." These are "salt," "sleep time," and "weight loss rate." "Sleep time" is an indicator of quantity. It may also be possible to input or select information about the quality of sleep. The attribute candidates shown in Fig. 18 are just examples, and can be combined with other attributes. For example, combinations with the aforementioned "gender," "age," and "place of residence" are also possible. On the other hand, it is not necessary to display all of the attribute candidates shown in Fig. 18, and only some of the displayed candidates may be displayed.

[0131] Furthermore, the patient app used by the patient for treatment may be specified as an attribute. For example, as will be described later, in the case where statistical values ​​of all patients using multiple patient apps for the same disease can be displayed for comparison (in the case of modified example (22)), differences between the patient apps may be specified. In this case, it becomes possible to grasp the progress of treatment distinguishing between the patient apps used. All of the exemplified attributes are also examples of attributes that may be correlated with treatment effects.

[0132] Fig. 19 is a diagram for explaining another display example of the distribution table screen 320A. In Fig. 19, parts corresponding to those in Fig. 13 are assigned the same reference numerals. In the case of the set selection field 321F shown in Fig. 19, it is possible to specify "own clinic only" as the comparison target. Specifically, a radio button for "own clinic only" is provided following the explanation "Please select the items you want to display." By checking the "Only at my hospital" radio button, doctors and other medical professionals can understand the progress of treatment for their current patient, "Mr. H," by comparing it with the progress of treatment for other patients at their hospital.

[0133] 20 is a diagram for explaining a distribution table screen 320A when the set to be compared is all patients at the hospital. In FIG. 20, parts corresponding to those in FIG. 15 are assigned the same reference numerals. In the case of Figure 20, the distribution graph for "Mr. H" is displayed in the upper section of the statistical information display field 321, and the distribution graph for all patients at "our hospital" is displayed in the lower section. In the case of Figure 20, the distribution graph in the upper section is given the title "The Patient in Question," and the distribution graph in the lower section is given the title "our hospital."

[0134] In the case of Fig. 20, a set switching button 321J is located to the right of the distribution graph in the lower row. The set switching button 321J shown in Fig. 20 is a slide-type button. In the case of Fig. 20, the left end indicates the selection state of "own hospital," and the right end indicates the selection state of "other hospital." Depending on the button position of this set switching button 321J, it is possible to switch the set to be compared. A doctor or other medical professional viewing the distribution table screen 320A shown in Figure 20 can understand the progress of treatment for the patient "Mr. H" currently under examination by comparing it with a distribution graph showing the progress of treatment for all patients at their hospital.

[0135] Fig. 21 is a diagram illustrating the distribution table screen 320A when the set to be compared is switched from one's own hospital to another hospital. In Fig. 21, parts corresponding to those in Fig. 20 are indicated by the same reference numerals. In Fig. 21, the set switching button 321J is located at the right end. Therefore, the title "Another Hospital" is displayed in the distribution graph at the bottom. "Another Hospital" is an example of another medical institution. A doctor or other medical professional viewing the distribution table screen 302A shown in Figure 21 can understand the progress of treatment for the patient "Mr. H" currently under examination by comparing it with a distribution graph showing the progress of treatment for all patients not only at their own hospital but also at other hospitals.

[0136] (8) In the above-described embodiment, the statistical values ​​of the patient under examination are displayed in comparison with the statistical values ​​of a set of patients on the distribution table screen 320 (see FIG. 12) or the distribution table screen 320A (see FIG. 15, etc.). That is, it is assumed that the “specific user” designated by a doctor or the like is a single patient. However, the specific user may be multiple patients. Fig. 22 is a diagram illustrating an example of a distribution table screen 320B that allows multiple users to be specified as specific users. In Fig. 22, parts corresponding to those in Fig. 20 are assigned the same reference numerals.

[0137] In the case of the distribution table screen 320B shown in FIG. 22, the upper and lower rows also display a list of the changes in the frequency distribution ratio of the measured values ​​classified into each blood pressure range over a six-month period. In the case of Fig. 22, a patient switching button 321K is located to the right of the distribution graph in the upper row. This patient switching button 321K is a slide-type button used to switch between displaying a distribution graph of all patients at "your hospital" and displaying a distribution graph of the "patient" currently being examined. In the case of Fig. 22, the leftmost button means selecting "your hospital," and the rightmost button means selecting "Mr. H."

[0138] In the case of Fig. 22, the patient switching button 321K is located at the left end. Therefore, the upper part of Fig. 22 displays a distribution graph showing the progress of treatment for all patients at the hospital. Specifically, the distribution graph shows the blood pressure values ​​of all patients at the hospital. Meanwhile, the lower part displays the distribution graphs of blood pressure values ​​of all patients at other hospitals. Therefore, doctors and other medical professionals can check the treatment results of their own hospital in comparison with those of other hospitals, not just in comparison with a specific patient. In other words, the distribution table screen 320B shown in Fig. 22 can be used as an index for doctors and other medical professionals to review their own treatment results and those of their own hospital.

[0139] The patient switching button 321K is intended to switch between "Mr. H" currently being examined and all patients at "your hospital," but it may also be possible to identify a specific user by specifying or selecting attributes such as those shown in Figures 13 to 18. Furthermore, a specific user may be able to individually specify one or more patients. For example, the specific user may be able to specify each patient individually, such as "Mr. A" and "Mr. H."

[0140] (9) In the above-described embodiment, an example was described in which three blood pressure intervals were set using the blood pressure reduction target as the reference value, as shown in Figure 13, etc. However, multiple blood pressure intervals may also be set using the blood pressure value on the start date of recording, i.e., the initial value, as the reference value. 23 is a diagram for explaining another display example of the distribution table screen 320C. In FIG. 23, parts corresponding to those in FIG. 13 are assigned the same reference numerals.

[0141] In the distribution table screen 320C shown in FIG. 23, the reference value is set to the weight on the start date of the record. In the case of distribution table screen 320C shown in FIG. 23, statistical information display field 321 is made up of weight distribution graph 321L and set selection field 321F. In the weight distribution graph 321L shown in Fig. 23, the intervals into which the weight measurements are classified are set based on the weight on the start date of recording. Specifically, four weight loss intervals are set.

[0142] The first weight loss range is "3% weight loss or less," the second weight loss range is "3% to 5% weight loss," the third weight loss range is "5% to 7% weight loss," and the fourth weight loss range is "7% or more weight loss." In Figure 15, the second weight loss range is labeled "5% weight loss," the third weight loss range is labeled "7% weight loss," and the fourth weight loss range is labeled "10% weight loss."

[0143] The vertical axis of the distribution table screen 320C shown in Figure 23 is the number of days. Therefore, in the case of Figure 15, the maximum value on the vertical axis is 30 days. Note that since there are months with less than 30 days and months with more than 30 days, the maximum number of days in the relevant month is normalized to 30 days and the frequency of each weight loss interval is tallied. Note that since normalization is performed, the maximum value on the vertical axis may be displayed as 100% and the minimum value as 0%.

[0144] In the actual distribution table screen 320C, each weight loss section is displayed in a different color. For example, the first weight loss section is displayed in red. The second weight loss section is displayed in orange. The third weight loss section is displayed in light green. The fourth weight loss section is displayed in dark green. Therefore, the length of the red bar represents the frequency of weights classified into the first weight loss section. Similarly, the length of the orange bar represents the frequency of weights classified into the second weight loss section, the length of the light green bar represents the frequency of weights classified into the third weight loss section, and the length of the dark green bar represents the frequency of weights classified into the fourth weight loss section.

[0145] The left end of the horizontal axis of the distribution table screen 320C shown in Figure 23 is "1 month ago" and the right end is "6 months ago." In the case of Figure 23 as well, the weight measurement values ​​are classified into one of four weight loss zones in monthly units. In the case of the distribution table screen 320C shown in FIG. 23, in the period six months ago, "less than 3% weight loss" occurred for 30 days, that is, 100%.

[0146] However, the proportion of "5% weight loss" gradually increased from five months ago to four months ago, and three months ago there were several days when the weight was classified as "7% weight loss." Two months ago, no weight measurements were taken that were classified as "less than 3% weight loss," and weight measurements that were classified as "10% weight loss" appeared on several days.

[0147] One month ago, there were 5 days (about 16%) where the weight was classified as "10% weight loss," 10 days (about 33%) where the weight was classified as "7% weight loss," and 15 days (about 50%) where the weight was classified as "5% weight loss." By viewing this distribution table screen 320C, it is possible to confirm the magnitude of weight loss after the prescription date as a percentage change in the four weight loss intervals. Of course, a set selection field 321F is also provided on the distribution table screen 320C, allowing a doctor or the like to select a set.

[0148] (10) In the above-described embodiment, the viewing screen for blood pressure values ​​recorded through a patient app prescribed for hypertension treatment is assumed, but for other diseases, different target values ​​or initial values ​​(see FIG. 23) are set as reference values. For example, in the case of nicotine addiction, the measured values ​​of the amount of nicotine intake, the number of times a patient has taken a medium containing nicotine, the number of times a patient has taken a medium containing nicotine, and the CO concentration in the exhaled breath may be classified into multiple intervals according to the reference values ​​set for each patient, and a frequency distribution table of the frequencies classified into each interval may be displayed.

[0149] In the case of alcoholism, for example, the measured amount of alcohol intake can be classified into multiple intervals according to the reference values ​​set for each patient, and a frequency distribution table showing the frequency of each interval can be displayed. In the case of NASH or obesity, for example, the measured weight may be classified into multiple intervals according to the reference values ​​set for each patient, and a frequency distribution table showing the frequency of classification into each interval may be displayed.

[0150] In the case of insomnia, for example, records of the amount of sleeping pills taken and the total score of the Pittsburgh Sleep Quality Index (PSQI), which consists of 24 items, can be classified into multiple intervals according to the standard values ​​set for each patient, and a frequency distribution table of the frequency of classification into each interval can be displayed. Other diseases contemplated include, for example, kidney disease, cancer, chronic heart failure, attention deficit hyperactivity disorder, depression, tinnitus, delayed grief disorder, opioid-induced constipation, post-mastectomy pain syndrome, bronchial asthma, and nephrotic syndrome.

[0151] (11) In the above-described embodiment, a distribution table with the same measurement items for one disease is displayed, but multiple distribution tables with different measurement items for one disease may be displayed. For example, a distribution table for blood pressure and a distribution table for weight may be displayed simultaneously or selectively.

[0152] (12) In the above-described embodiment, it is assumed that all measurement values ​​after the start date of recording are recorded through the patient app, i.e., measurement values ​​are recorded by a program located in a medical device. However, in addition to the measurement values ​​recorded through a program located in a medical device, the measurement values ​​may also include measurement values ​​recorded through a program located in a non-medical device, which is a program whose main purpose is to record measurement values ​​such as blood pressure.

[0153] In the following, programs that are classified as non-medical devices will be referred to as "non-patient apps." FIG. 24 is a diagram illustrating an embodiment in which measurement values ​​recorded through a non-patient app and measurement values ​​recorded through a patient app are used in combination.

[0154] The horizontal axis in Figure 24 is the time axis. The right end of the time axis is the consultation date, and the left end is the past tense. Figure 24 shows up to 7 months ago. In the case of Figure 24, the prescription date for the patient app is between three months and four months ago. Before being prescribed the patient app, the patient (i.e., the user) records measurement values ​​via the non-patient app. Note that the measurement values ​​may be recorded only on the patient terminal 40, only on the management server 50, or on both the patient terminal 40 and the management server 50. The management server 50 is, for example, a server operated by a business operator that provides the non-patient app.

[0155] Non-patient apps in this context are sometimes called healthcare apps. Healthcare apps are programs whose primary purpose is to record health information and are not medical devices. In the case of Fig. 24, the measurement values ​​recorded through the non-patient app before the patient app is prescribed are imported from the patient terminal 40 or the management server 50 to the PDT server 20. Of course, when importing, the identity of the user is confirmed using a user account or the like.

[0156] Therefore, on the day of the consultation, not only are the measurement values ​​for about four months after the patient app is prescribed, but also the measurement values ​​for about three months before the patient app is prescribed are stored in the PDT server 20. In other words, the PDT server 20 stores the patient's measurement values ​​for about seven months. In this case, for each period from the consultation date up to three months prior, the measurement values ​​recorded through the patient app (i.e., the first measurement values) are classified into one of several intervals, and the frequency distribution rate for each interval is calculated.

[0157] For the period four months prior to the consultation date, both the measurements recorded through the patient app (i.e., the first measurement) and the measurements recorded through the non-patient app (i.e., the second measurement) are classified into one of several intervals, and the frequency distribution percentage for each interval is calculated. For each period of at least five months prior to the consultation date, the measurements recorded through the non-patient app (i.e., the second measurements) are classified into one of several intervals, and the frequency distribution percentage for each interval is calculated.

[0158] Then, the PDT server 20 displays a distribution table in which these distribution ratios are arranged on the time axis on the doctor terminal 30 (see FIG. 1). In the case of the embodiment described using Figures 1 to 12, the distribution table displayed on the doctor terminal 30 covers a maximum of four months from the prescription date. Also, the distribution ratio for each interval four months prior is normalized by the frequency. Of course, even with this display format, it is possible to check the change in the distribution ratio of the measurement value.

[0159] However, in the embodiment described with reference to Fig. 24, it is also possible to check the distribution ratio of the measurement values ​​before starting treatment using the patient app. As a result, it becomes possible to diagnose the progress of the patient's treatment from a longer-term perspective. The calculation of statistical values ​​using the measurements recorded through the patient app and the non-patient app shown in Figure 24 may be used not only for the patient "Mr. H" currently under examination, but also for calculating statistical values ​​for a group to be compared.

[0160] (13) In the above-described embodiment, it is assumed that the examination date falls within the insured medical treatment period. However, a distribution table may also be generated without distinguishing between measurements recorded through a program that is classified as a non-medical device even after the insured medical treatment period. FIG. 25 is a diagram illustrating another embodiment in which measurement values ​​recorded through a non-patient app and measurement values ​​recorded through a patient app are used in combination.

[0161] The horizontal axis in Figure 25 is the time axis. The right end of the time axis is the consultation date, and the left end is the past tense. Figure 25 shows up to 7 months ago. In the case of Figure 25, the prescription date in the patient app is 7 months ago, and the insured medical treatment period expired within the period 2 months before the consultation date. In the case of Figure 25, after the expiration of the insured medical treatment period, a medical treatment period not covered by insurance (i.e., outside the health insurance medical treatment period) begins.

[0162] 25 assumes that a patient app continues to be used after the validity period has expired to record measurement values ​​outside of the health insurance medical treatment period. In other words, it assumes that a patient app continues to be used as a non-patient app after the validity period has expired. For this reason, not only measurement values ​​during the health insurance medical treatment period but also measurement values ​​outside of the health insurance medical treatment period are recorded in the PDT server 20.

[0163] However, measurement values ​​may be recorded on the patient terminal 40 (see FIG. 24) or management server 50 (see FIG. 24) using a non-patient app different from the patient app used during the insured medical treatment period. If the measurement values ​​recorded through the non-patient app are not stored in the PDT server 20, the measurement values ​​are imported from the patient terminal 40 or management server 50 to the PDT server 20, and then the distribution table generation process described above is started.

[0164] In the case of Figure 25, for each period three months or more prior to the consultation date, the measurement values ​​recorded through the patient app (i.e., the first measurement values) are classified into one of multiple intervals. Then, the frequency distribution rate for each interval is calculated. Additionally, for the period two months prior to the consultation date, both the measurements recorded through the patient app (i.e., the first measurements) and the measurements recorded through, for example, the non-patient app (i.e., the second measurements) are classified into one of several intervals, and the frequency distribution rate for each interval is calculated.

[0165] For the period one month prior to the consultation date, the measurements recorded through the non-patient app (i.e., the second measurements) are classified into one of several intervals, and the frequency distribution percentage for each interval is calculated. Then, the PDT server 20 displays a distribution table in which these distribution ratios are arranged on the time axis on the doctor terminal 30 (see FIG. 1).

[0166] In the embodiment described with reference to Figure 25, it is possible to check changes in the distribution ratio of measurement values, including those measured during treatment by the patient app, even after the expiration of the insured medical treatment period. As a result, it becomes possible to diagnose the progress of a patient's treatment from a longer-term perspective. In addition, the calculation of statistical values ​​using the measurement values ​​recorded through the patient app and the non-patient app shown in Figure 25 may be used not only for the patient "Mr. H" currently being examined, but also for calculating statistical values ​​for a group to be compared.

[0167] (14) In the above embodiment, the distribution table of measurement values ​​is generated by the PDT server 20 (see FIG. 1), but it may also be generated by the doctor terminal 30 (see FIG. 1). Fig. 26 is a diagram illustrating another example of a processing sequence executed by the information processing system 1 (see Fig. 1). In Fig. 26, parts corresponding to those in Fig. 7 are assigned the same reference numerals. In the case of FIG. 26, the processing contents up to step 107 are the same as those in FIG.

[0168] One difference is that in step 121, the PDT server 20, having received notification from the doctor terminal 30 that it has accepted the display of statistical information in step 107, notifies the doctor terminal 30 of the patient data corresponding to the specified items. Note that in Figure 26, only the data necessary to generate the distribution table is notified to the doctor terminal 30, but it is also possible to notify the doctor terminal 30 of all the patient data of the patient being treated.

[0169] Another difference is that the processes of steps 108 and 110 are executed on the side of the doctor terminal 30. After executing step 110, the doctor terminal 30 also executes a process of displaying the generated distribution table on the display. Step 109 is executed by the PDT server 20. This is because the patient data handled in step 109 also includes data on patients receiving treatment at medical institutions other than the medical institution where the doctor terminal 30 is installed. For this reason, Fig. 26 employs a mechanism for notifying the doctor terminal 30 of only statistical values. In the embodiment described with reference to FIG. 26, the processing load on the PDT server 20 can be reduced.

[0170] (15) In the above-described embodiment, the PDT server 20 (see FIG. 1) starts the process of generating a distribution table of measurement values ​​based on a notification that the doctor terminal 30 (see FIG. 1) has accepted the display of the distribution table. However, the process of generating the distribution table may be started as a function of the PDT server 20 alone or in response to an instruction from the patient terminal 40 (see FIG. 1). Fig. 27 is a diagram illustrating another example of a processing sequence executed by the information processing system 1 (see Fig. 1). In Fig. 27, parts corresponding to those in Fig. 7 are assigned the same reference numerals.

[0171] One difference is that the PDT server 20 receives from the patient terminal 40 an instruction to provide statistical information or to display statistical information (step 131), which triggers the start of the processing operations from step 108 onwards. The provision timing here refers to, for example, the timing of notification of a regular report. The notification timing is assumed to be, for example, once every four weeks. Another difference is that the PDT server 20, which has executed steps 108 to 110, executes step 132 of providing the generated distribution table screen to the patient terminal 40. In the embodiment described with reference to Fig. 27, the distribution table is output to the patient terminal 40. As a result, it is possible to assist the patient in checking changes in the distribution of their own measurement values.

[0172] (16) In the above-described embodiment, the PDT server 20 (see Figure 1) starts the process of generating a distribution table of measurement values ​​based on a notification that the doctor terminal 30 (see Figure 1) has accepted the display of the distribution table. However, the process of generating the distribution table may also be executed as a function of the patient app. Fig. 28 is a diagram illustrating another example of a processing sequence executed by the information processing system 1 (see Fig. 1). In Fig. 28, parts corresponding to those in Fig. 7 are assigned the same reference numerals.

[0173] One difference is that the timing of providing statistical information or receiving an instruction to display statistical information (step 141) by the patient terminal 40 (specifically, the patient app) serves as a trigger to start the processing operations from step 108 onwards. The timing of providing statistical information here is also assumed to be, for example, the timing of notifying a regular report. Another difference is that the patient terminal 40 that has executed steps 108 to 110 executes step 142 of displaying the generated distribution table. In the embodiment described with reference to Fig. 28, the distribution table can be displayed as a standalone function of the patient application running on the patient terminal 40. As a result, it is possible to assist the patient in checking changes in the distribution of their own measurement values.

[0174] (17) In the above-described embodiment, the patient app is assumed to be a program located in a medical device, but it may be a program located in a non-medical device. In other words, it may be assumed that all measurement values ​​are recorded through a program located in a non-medical device. In this case, as described above, the server that manages the measurement values ​​may generate the distribution table, or the patient terminal 40 may generate the distribution table.

[0175] (18) In the above embodiment, the case where blood pressure values ​​as measurement values ​​are classified into three blood pressure intervals has been described, but the number of intervals to be classified is not limited to three. For example, it may be two, or four or more. The display example shown in FIG. 23 above is an example of four intervals. Of course, it may be five or more. The number of sections into which the data are classified may vary depending on the type of disease and the magnitude of the change in the measured values.

[0176] For example, if the number of intervals is too small compared to the amount of change in the measured values, it becomes difficult to grasp the change in the distribution of the measured values. Therefore, a reference value for increasing the number of intervals can be set, and the number of intervals can be increased if the maximum amount of change in the measured values ​​within a period exceeds this reference value. On the other hand, if the number of intervals is too large compared to the amount of change in the measured values, it becomes difficult to grasp the change in the distribution of the measured values. Therefore, a reference value for reducing the number of intervals can be set, and the number of intervals can be reduced if the maximum amount of change in the measured values ​​within a period exceeds this reference value.

[0177] (19) In the above-described embodiment, the distribution table is generated using measurement values ​​after the prescription date of the patient app or the start date of recording measurement values ​​by the non-patient app. However, the doctor, patient, etc. may be able to specify any date as the start date of the recording used to generate the distribution table. For example, it may be possible to specify six months or 12 months from the present. In other words, the distribution table may be generated for a range specified by the doctor, patient, etc. after the start date of the recording. By allowing this type of specification, it is possible to avoid a decrease in visibility due to an excessive increase in the number of periods that make up the distribution table.

[0178] However, if the target period is longer than the reference value, the unit of the specified period may be changed from one month to two months, etc., to suppress the increase in the number of periods displayed. In this case, for measurements taken more than six months ago, the frequency of each interval may be tallied in two-month increments, but for measurements taken within six months, the frequency of each interval may be tallied in one-month increments. In other words, the unit length of the specified period may be changed based on the results of comparing the number of days going back in time from the present with the reference value.

[0179] (20) In the above-described embodiment, a distribution table is generated that shows the distribution of measurement values ​​in units of a period going back in time, starting from the day when the instruction to display the distribution table was received. However, the starting date of the distribution table may be arbitrarily specified by a doctor, patient, etc. For example, the starting date may be set to the previous day or one week ago. For example, if it is clear that there are missing measurement values ​​due to travel or other reasons just before a medical examination, a distribution table may be generated that avoids those periods.

[0180] (21) In the above-described embodiment, a patient app for hypertension is exemplified as a patient app used to record measurement values ​​at home, but as mentioned above, the above-described technology can also be applied to patient apps developed for various diseases. Of course, the content of the measurement values ​​shown in the distribution table will change depending on the disease. 29 is a diagram for explaining a display example of a distribution table screen 320D when the disease is diabetes. In FIG. 29, parts corresponding to those in FIG. 13 are assigned the same reference numerals. A distribution table 321M of fasting blood glucose and a distribution table 321N of two-hour postprandial blood glucose are arranged on a distribution table screen 320D shown in FIG.

[0181] The vertical axes of the two distribution tables shown in Figure 29 also represent time going back from the present to the past. Similarly, the horizontal axes of the two distribution tables shown in Figure 29 have 0% at the left end and 100% at the right end. The blood glucose level intervals in the fasting blood glucose distribution table 321M shown in FIG. 29 are divided into three ranges: "110 or less," "111-125 or less," and "126 or more." On the other hand, the blood glucose level ranges in the distribution table 321N of blood glucose levels two hours after a meal shown in FIG. 29 are divided into three ranges: "140 or less," "141-199 or less," and "200 or more."

[0182] The two distribution tables shown in Figure 29 also display each interval in a different color. For example, the intervals "110 or less" and "140 or less" are displayed in blue, "111-125 or less" and "141-199 or less" are displayed in yellow, and "126 or more" and "200 or more" are displayed in red. In the case of the distribution table screen 320D shown in FIG. 29, it can also be seen that the rate of normal values ​​increases as the current date and time approaches compared to the time at the start of treatment.

[0183] (22) In the above-described embodiment, it is assumed that the PDT server 20 (see Figure 1), which handles the patient data of "Mr. H," a patient currently under examination, calculates statistical values ​​for the patients who make up the set to be compared. That is, the patients constituting the set to be compared are limited to those managed by one PDT server 20 that handles the patient data of "Mr. H," a patient currently under examination. However, statistical values ​​may also be calculated including patient data from another PDT server 20 that handles patient data of the same disease as "Mr. H," who is currently being examined, and displayed so as to be comparable with the statistical values ​​for "Mr. H."

[0184] For example, if "Mr. H" has hypertension, statistical values ​​to be compared may be calculated using patient data managed by Company A's PDT server 20A (see Figure 1) and patient data managed by Company B's PDT server 20B (see Figure 1). In this case, even if the number of patients using the patient app provided by Company A is small, the reliability of the statistics can be increased by including the number of patients using the patient app provided by Company B.

[0185] Incidentally, if the hypertension patient app used by "Mr. H" is a patient app from Company A, a patient app for hypertension from Company B is an example of another program associated with Company A's patient app. Furthermore, the relationship between the program used by "Mr. H" to record blood pressure values ​​and other related programs also exists between a therapeutic app and a non-therapeutic app. That is, one program may be a therapeutic app and the other a non-therapeutic app.

[0186] (23) In the above-described embodiment, the set of patients to be compared with the patient "Mr. H" currently being examined is premised on patients who use the same patient app as "Mr. H." Specifically, it is premised on the case where the patients constituting the set to be compared and "Mr. H" currently being examined both use a patient app for hypertension. However, the patient app used by the patients constituting the set to be compared may be different from the patient app used by "Mr. H," the patient currently being examined. For example, if the patient app used by "Mr. H" to record measurement values ​​is a patient app for hypertension, the patient app used by the patients constituting the set to be compared may be a patient app for nicotine addiction.

[0187] The number of other patient apps used by patients constituting a comparison target group is not limited to one, and may be multiple. For example, there may be a patient app for nicotine addiction and a patient app for alcohol addiction. The nicotine addiction patient app and the alcohol addiction patient app here are examples of other programs that are associated with the hypertension patient app.

[0188] Even if the illness is different, information such as "app usage rate," "reflection entry rate," and "app progress" are examples of indicators that can be commonly obtained across multiple patient apps. Incidentally, patients who are actively engaged in treatment tend to have a high rate of patient app usage, regardless of illness. Therefore, even when comparing app usage rates calculated for different patient apps, it is possible to understand the level of engagement of "Mr. H," a patient currently undergoing treatment, by comparing him with other patients.

[0189] In addition, the usage rates of other patient apps for comparison may be obtained directly by the PDT server 20 related to the disease being examined from other PDT servers 20 (see Figure 1) with which it can cooperate, or may be obtained from other PDT servers 20 via the PDT platform 10 (see Figure 1), or may be obtained from the PDT platform 10. The PDT platform 10 here may be provided with a function to periodically collect information such as the usage rate of the patient app from one or more PDT servers 20, and deliver the information in response to a request from the PDT server 20 or at a predetermined timing. The PDT platform 10 may also calculate the usage rate of the patient app, etc.

[0190] (24) In the above-described embodiment, the statistical values ​​used for comparison with the statistical values ​​of "Mr. H," a patient currently being examined, are calculated from the patient data of patients who use the same patient app as "Mr. H." However, the statistical values ​​used for comparison with the statistical values ​​of the patient "Mr. H" currently being examined may be publicly available statistical information about the disease. The statistical information here is an example of the second statistical value. If the disease is hypertension, for example, the average blood pressure values ​​of Japanese people by age group, as well as statistical values ​​and indicators published in guidelines, etc. may be displayed. If the disease is alcoholism, for example, the average amount of alcohol consumed by Japanese people, as well as statistical values ​​and indicators published in guidelines, etc. may be displayed.

[0191] The published statistical information may be stored in advance in the auxiliary storage device 23 (see FIG. 4) of the PDT server 20 (see FIG. 1), or may be obtained from an external server. The display of this comparison information may be realized as one form of attribute selection in the above-described embodiment. For example, a doctor or the like may be able to switch on the screen in the set selection field 321F (see FIG. 13, etc.) between displaying statistical values ​​specifying gender and displaying published statistical information. In this embodiment, doctors and other medical professionals can compare the statistical values ​​of "Mr. H," a patient currently under examination, with published statistical values ​​for the disease. In other words, they can understand the progress of "Mr. H's" treatment from various perspectives.

[0192] (25) In the above embodiment, the statistical values ​​of a patient currently being examined are displayed in a comparative manner with the statistical values ​​of patients constituting a comparison group. However, the statistical values ​​of "Mr. H" who meets a predetermined standard with respect to the statistical values ​​of patients constituting the set to be compared may be selectively or identifiably displayed. For example, when a specific button or the like is operated, only statistical values ​​that meet a predetermined criterion may be extracted and displayed, or may be displayed surrounded by a red frame or the like. A display surrounded by a red frame or the like is an example of a "distinguishable display." The predetermined criteria may be that the progress of treatment is better than that of a comparison subject, or that the progress of treatment is worse than that of a comparison subject. The adoption of this display function can support efficient medical treatment, etc.

[0193] (26) In the above-described embodiment, it is assumed that the "blood pressure reduction target value field" 313 (see FIG. 11) on the patient data viewing screen 310 (see FIG. 11) remains the same throughout the treatment period. However, the blood pressure reduction target may change during treatment. For example, there may be cases where the patient develops another disease during treatment, making the blood pressure reduction target change necessary, or where the patient is classified as elderly during treatment, making the blood pressure reduction target change necessary. In this way, when the blood pressure reduction target is changed during treatment, multiple blood pressure intervals may be reset according to the changed blood pressure reduction target. When the blood pressure intervals are reset, the blood pressure values ​​are reclassified into each blood pressure interval. Furthermore, the frequency of blood pressure values ​​belonging to the reclassified blood pressure intervals may be recounted, and the display on the distribution table screen 320A may be updated.

[0194] In this way, the display of the distribution table screen 320A is updated from the perspective of the updated blood pressure reduction target in response to a change in the blood pressure reduction target, making it possible to check changes in the distribution of past blood pressure values ​​from the perspective of the new blood pressure reduction target. If the blood pressure reduction target used to generate the distribution table is fixed to the blood pressure reduction target at the start of treatment, it may not be consistent with the current effect target, and the usefulness of the distribution table screen 320A may decrease.

[0195] Furthermore, if both the distribution table screen 320A before the blood pressure reduction target is changed and the distribution table screen 320A after the blood pressure reduction target is changed are displayed, the target period of the distribution table screen 320A is divided into two or more parts, which reduces the usefulness of the distribution table screen 320A for grasping changes in the distribution of past blood pressure values ​​from a single reference value at a bird's-eye view. However, different distribution tables may be displayed before and after the blood pressure reduction target is changed. Of course, this technique of updating the distribution table screen 320A etc. in response to changes in treatment goals can also be applied to diseases other than hypertension.

[0196] <Summary> The disclosed examples described in the above-mentioned embodiments are shown below. (((1))) An information processing device having one or more processors, the one or more processors acquiring a first statistical value calculated from first data recorded through a program executed on a terminal operated by a specific user whose behavior is changing, and presenting the first statistical value in a manner that can be compared with a second statistical value related to a specified set of users. This information processing device can assist in grasping the relative position of a specific user.

[0197] (((2))) The information processing device described in (((1))), wherein one or more processors calculate a second statistical value based on second data recorded by each user belonging to a specified user set, or obtain a statistical value calculated based on second data recorded by each user belonging to a specified user set as the second statistical value. According to this information processing device, it is possible to easily grasp the relative position of a specific user.

[0198] (((3))) The information processing device according to (((2))), wherein the first statistical value and the second statistical value are calculated in predetermined time units determined according to the number of days elapsed since the start of recording of the first data and the second data. According to this information processing device, the relative position of a specific user can be grasped with the number of days elapsed since the start of recording being the same.

[0199] (((4))) The information processing device according to (((2))) or (((3))), wherein the first statistical value and the second statistical value are calculated as a frequency distribution of the first data and the second data. According to this information processing device, the relative position of a specific user can be grasped from the viewpoint of the similarity or difference in frequency distribution.

[0200] (((5))) The information processing device according to (((2))), wherein the second data is recorded through a program used to record the first data. This information processing device can assist in grasping the relative position of a specific user by comparing with data recorded through the same program.

[0201] (((6))) The information processing device according to (((2))), wherein the second data is recorded through another program associated with the program used to record the first data. This information processing device can assist in understanding the relative position of a specific user by comparing with related data.

[0202] (((7))) The information processing device according to (((1))), wherein the one or more processors acquire, as the second statistical value, statistical information that is publicly available regarding a disease associated with the program. This information processing device can assist in understanding the relative position of a specific user by comparing the information with publicly available statistical information related to the specific user's illness.

[0203] (((8))) The information processing device according to (((1))), wherein the one or more processors accept a designation of a predetermined user set by a viewing user, and present a second statistical value according to the accepted user set. According to this information processing device, the viewing user can specify the comparison target.

[0204] (((9))) The information processing device according to (((8))), wherein the one or more processors accept designation of a predetermined user set through selection of one or more attributes by a viewing user. This information processing device can assist in understanding the relative position of a specific user by comparing with a user set identified by attributes.

[0205] (((10))) The information processing device described in (((9))), wherein the attributes are at least one of age, sex, region, home hospital, other medical institution, whether or not the patient is employed, whether or not the patient cooks at home, BMI before treatment begins, habits before treatment begins, quality or quantity of sleep, whether or not the patient has renal dysfunction, and a score that may be correlated with treatment effectiveness. This information processing device can assist in understanding the relative position of a specific user with respect to a specific attribute.

[0206] (((11))) The information processing device according to (((1))), wherein the one or more processors accept one or more users designated by the viewing user as the specific users. According to this information processing device, it is possible for a viewing user to designate a specific user.

[0207] (((12))) The information processing device according to (((1))), wherein the one or more processors selectively or identifiably present first statistical values ​​that satisfy a predetermined criterion relative to second statistical values. According to this information processing device, it is possible to easily grasp the difference from the second statistical value.

[0208] (((13))) The information processing device described in (((1))), wherein one or more processors calculate a first statistical value using both data recorded through a program requiring a prescription and data recorded through another program not requiring a prescription, or obtain a statistical value calculated using both data recorded through a program requiring a prescription and data recorded through another program not requiring a prescription as the first statistical value. According to this information processing device, the first statistical value can be calculated by integrating data requiring a prescription and data not requiring a prescription.

[0209] (((14))) The information processing device described in (((1))), wherein one or more processors calculate a second statistical value using both data recorded through a program requiring a prescription and data recorded through another program not requiring a prescription, or obtain a statistical value calculated using both data recorded through a program requiring a prescription and data recorded through another program not requiring a prescription as the second statistical value. According to this information processing device, the second statistical value can be calculated by integrating the data requiring a prescription and the data not requiring a prescription.

[0210] (((15))) An information provision method in which a computer executes a process of acquiring a first statistical value calculated from first data recorded through a program executed on a terminal operated by a specific user whose behavior changes, and a process of presenting the first statistical value in a manner that allows it to be compared with a second statistical value related to a specified set of users. This information providing method can provide a mechanism for assisting in grasping the relative position of a specific user.

[0211] (((16))) A program for enabling a computer to perform the following functions: obtain a first statistical value calculated from first data recorded through a program executed on a terminal operated by a specific user whose behavior changes; and present the first statistical value in a manner that allows it to be compared with a second statistical value related to a specified set of users. This program can provide a mechanism for assisting in grasping the relative position of a specific user. [Explanation of symbols]

[0212] 10...PDT platform, 20, 20A, 20B, 20C, 20D, 20E, 20F...PDT server, 30...doctor's terminal, 40...patient's terminal, 50...management server

Claims

1. having one or more processors, the one or more processors: Obtaining a first statistical value calculated from first data recorded on a terminal operated by a specific user whose behavior is changing; presenting the first statistical value in a manner that allows comparison with a second statistical value for a predetermined set of users; Information processing device.

2. the one or more processors: Calculating the second statistical value based on second data recorded by each user belonging to the predetermined user set, or acquiring the second statistical value calculated based on the second data recorded by each user belonging to the predetermined user set. The information processing device according to claim 1 .

3. the first statistical value and the second statistical value are calculated in units of a predetermined period determined according to the number of days elapsed since the start of recording of the first data and the second data; The information processing device according to claim 2 .

4. the first statistical value and the second statistical value are calculated as a frequency distribution of the first data and the second data.

4. The information processing device according to claim 2 or 3.

5. The second data is recorded through the program used to record the first data. The information processing device according to claim 2 .

6. the second data is recorded through another program associated with the program used to record the first data; The information processing device according to claim 2 .

7. the one or more processors: acquiring, as the second statistical value, publicly available statistical information regarding a disease associated with the program used to record the first data; The information processing device according to claim 1 .

8. the one or more processors: accepting a designation of the predetermined user set by a viewing user, and presenting the second statistical value corresponding to the accepted user set; The information processing device according to claim 1 .

9. the one or more processors: Accepting designation of the predetermined user set through selection of one or more attributes by a viewing user; The information processing device according to claim 8 .

10. The attributes are at least one of age, sex, region, home hospital, other medical institution, occupation, cooking at home, BMI before treatment initiation, habits before treatment initiation, quality or quantity of sleep, presence or absence of renal dysfunction, and a score that may be correlated with treatment effect. The information processing device according to claim 9 .

11. the one or more processors: Accepting one or more users designated by the viewing user as the specific users; The information processing device according to claim 1 .

12. the one or more processors: selectively or identifiably presenting the first statistical values ​​that meet a predetermined criterion relative to the second statistical values; The information processing device according to claim 1 .

13. the one or more processors: Calculating the first statistical value using both data recorded through the program requiring a prescription and data recorded through another program not requiring a prescription, or obtaining as the first statistical value a statistical value calculated using both data recorded through the program requiring a prescription and data recorded through another program not requiring a prescription. The information processing device according to claim 1 .

14. the one or more processors: Calculating the second statistical value using both data recorded through the program requiring a prescription and data recorded through another program not requiring a prescription, or obtaining as the second statistical value a statistical value calculated using both data recorded through the program requiring a prescription and data recorded through another program not requiring a prescription. The information processing device according to claim 1 .

15. The computer A process of acquiring a first statistical value calculated from first data recorded through a program executed on a terminal operated by a specific user whose behavior is changed; presenting the first statistical value in a manner that allows comparison with a second statistical value for a predetermined set of users; Information provision method to carry out.

16. On the computer, A function of acquiring a first statistical value calculated from first data recorded through a program executed on a terminal operated by a specific user who changes behavior; presenting the first statistical value in a manner that allows comparison with a second statistical value for a predetermined set of users; A program to achieve this.

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