Information processing device, information provision method, and program

Abstract

This system provides a mechanism for presenting patient treatment outcomes on a per-healthcare professional or per-medical institution basis. [Solution] An information processing device having a processor, the processor receives output of patient treatment results on a per-medical professional or per-medical institution basis, acquires health-related data linked to the medical professional or medical institution, and outputs treatment results on a per-medical professional or per-medical institution basis based on the quantitative information of the acquired health-related data.

Description

Technical Field

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

Background Art

[0002] Currently, for nicotine dependence and hypertension, prescriptions of application programs (hereinafter referred to as treatment apps) approved by the Pharmaceutical Affairs Law have been started. Treatment apps include a patient app operated by the patient himself / herself and a doctor app operated by a doctor or other medical staff. When using the doctor app, it is possible to view health-related data recorded through the patient app.

Prior Art Documents

Non-Patent Documents

[0003]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Doctors and other medical staff can check the progress of treatment of individual patients through the doctor app. On the other hand, there is no tool for checking the treatment results of all patients treated by the medical staff themselves or the entire medical institution to which they belong.

[0005] The present disclosure provides a mechanism for presenting the treatment results of patients in units of medical staff or medical institutions.

Means for Solving the Problems

[0006] The invention described in claim 1 is an information processing device having a processor, the processor receiving output of patient treatment results on a per-medical-person or per-medical-institution basis, acquiring health-related data associated with the medical-medical-person or per-medical-institution basis, and outputting the treatment results on a per-medical-person or per-medical-institution basis based on quantitative information of the acquired health-related data. The invention described in claim 2 is an information processing device according to claim 1, wherein the processor outputs the amount of change in the quantitative information within a predetermined period as the treatment result. The invention described in claim 3 is the information processing device according to claim 2, wherein the processor provides the predetermined period as the elapsed time from the prescription of a program that operates on the terminal operated by the patient. The invention described in claim 4 is the information processing apparatus described in claim 3, wherein the processor accepts the specification of the elapsed period length. The invention described in claim 5 is an information processing device according to claim 1, wherein the processor receives at least one designation of patient characteristics and information regarding the course of treatment, and outputs the treatment results based on health data that satisfies the received designation. The invention described in claim 6 is an information processing device according to claim 5, wherein the information relating to the patient's characteristics includes at least one of the severity level at the time of prescription, whether or not the patient was taking medication at the time of prescription, the patient's medical history at the time of prescription, the patient's age or age group at the time of prescription, and the patient's gender. The invention described in claim 7 is an information processing device according to claim 5, wherein the information relating to the course of treatment includes at least one of the severity level after prescription, the severity level during treatment, the status of hospital visits after prescription, the status of hospital visits during treatment, the status of app usage after prescription, the status of app usage during treatment, the status of medication adherence after prescription, and the status of medication adherence during treatment. The invention described in claim 8 is an information processing device according to claim 1, wherein the processor outputs a second treatment result based on quantitative information of second health data linked to other medical professionals or other medical institutions, in a manner that can be compared with the treatment result for each medical professional or medical institution. The invention described in claim 9 is an information processing device according to claim 8, wherein the processor accepts designations from other medical professionals or other medical institutions as a comparative example. The invention described in claim 10 is an information processing device according to claim 8, wherein the processor normalizes the second treatment results corresponding to other medical professionals or other medical institutions on a regional basis for comparison. The invention described in claim 11 is an information processing device according to claim 1, wherein the processor outputs a second treatment result predicted from the health data used to output the treatment result, in a manner that can be compared with the treatment result for each medical professional or each medical institution. The invention described in claim 12 is an information processing device according to claim 8 or 11, wherein the processor accepts the designation of at least one of patient characteristics information and treatment progress information as conditions defining the comparative treatment results and the second treatment results. The invention described in claim 13 is the information processing device according to claim 1, wherein the health data is recorded through a program that operates on a terminal operated by the patient who is working to change his behavior. The invention described in claim 14 is an information processing device according to claim 1, wherein the health data is recorded at a medical institution. The invention described in claim 15 is an information provision method in which a computer performs the following processes: receiving output of patient treatment results on a per-medical professional basis or on a per-medical institution basis; acquiring health-related data associated with the medical professional or the per-medical institution; and outputting the treatment results on a per-medical professional or per-medical institution basis based on quantitative information of the acquired health-related data. The invention described in claim 16 is a program for a computer that enables the following functions: a function to receive output of patient treatment results on a per-medical professional or per-medical institution basis; a function to acquire health-related data associated with the medical professional or the per-medical institution basis; and a function to output the treatment results on a per-medical professional or per-medical institution basis based on quantitative information of the acquired health-related data. [Effects of the Invention]

[0007] According to one form of this disclosure, patient treatment outcomes can be presented on a per-healthcare professional or per-healthcare institution basis. [Brief explanation of the drawing]

[0008] [Figure 1] This diagram illustrates an example of the overall configuration of an information processing system according to an embodiment. [Figure 2] This diagram illustrates an example hardware configuration for a PDT platform. [Figure 3] This diagram illustrates an example of status management data stored in the auxiliary storage device of the PDT platform. [Figure 4] This diagram illustrates an example hardware configuration for a PDT server. [Figure 5] This diagram illustrates an example of patient data stored in the auxiliary storage device of a PDT server. [Figure 6] This diagram illustrates an example of the hardware configuration for a physician's terminal and a patient's terminal. [Figure 7] This figure illustrates an example of a patient data viewing sequence in the embodiment. [Figure 8] This diagram illustrates an example of a management screen for managing the usage status of patient applications, which is displayed on the output device of a physician's terminal. [Figure 9] This is a diagram illustrating an example of a patient data viewing screen. [Figure 10] This figure illustrates an example of a display sequence for treatment results in an embodiment. [Figure 11] This is a diagram illustrating the patient app selection screen. [Figure 12] This diagram illustrates an example of a treatment results output screen. [Figure 13] This diagram illustrates an example of how treatment outcomes are displayed by individual physicians. [Figure 14] This diagram illustrates the output screen for treatment results related to a specific physician. [Figure 15]It is a diagram for explaining the filtering of treatment results. [Figure 16] It is a diagram for explaining an example of options assigned to the "Filtering" button. [Figure 17] It is a diagram for explaining an example of a screen when "Length of elapsed period from prescription" is selected. [Figure 18] It is a diagram for explaining an example of a screen when "Severity level at prescription" is selected. [Figure 19] It is a diagram for explaining an example of a screen when "Taking medicine or not" is selected. [Figure 20] It is a diagram for explaining an example of a screen when "Treatment history" is selected. [Figure 21] It is a diagram for explaining an example of a screen when "Age group" is selected. [Figure 22] It is a diagram for explaining an example of a screen when "Gender" is selected. [Figure 23] It is a diagram for explaining an example of an output screen of treatment results that can accept comparison targets. [Figure 24] It is a diagram for explaining an example of options assigned to the "Comparison target" button. [Figure 25] It is a diagram for explaining an example of an output screen of the treatment results of the hospital itself and the treatment results of the whole country (all medical institutions). [Figure 26] It is a diagram for explaining an example of an output screen of the treatment results of the hospital itself and the treatment results of the whole country (all medical institutions). [Figure 27] It is a diagram for explaining another display example of an output screen of treatment results that can accept comparison targets.

Modes for Carrying Out the Invention

[0009] <Terms> First, terms used in the embodiments described below will be explained. A "program designed to encourage behavioral change" refers to a program intended to encourage a change in a person's behavior. However, this program does not guarantee a change in a person's behavior. Whether or not a person's behavior actually changes depends on the user of the program. Therefore, a program designed to encourage behavioral change can also be described as a program that supports behavioral change.

[0010] This type of program includes programs classified as medical devices and programs classified as non-medical devices. Programs classified as medical devices are examples of programs that require a prescription, while programs classified as non-medical devices are examples of programs that do not require a prescription. Furthermore, programs classified as non-medical devices are not limited to programs that encourage behavioral change; they may also include programs that have the function of recording health-related data. Programs that encourage behavioral change and have functions to record health-related data include programs that can be downloaded from app stores, as well as programs used by private companies and public organizations for managing the health of their employees. Some programs classified as non-medical devices are used in medical institutions.

[0011] The "purpose of the app" refers to the effect that should be achieved by using a program that promotes behavioral change. The purpose of the app varies depending on the disease to which the program that promotes behavioral change corresponds. For example, the purpose of the app may include improving lifestyle habits, maintaining improved lifestyle habits, and maintaining improved numerical values.

[0012] "App goals" refer to the objectives that are aimed for by using a program that encourages behavioral change. Goals are defined from the perspective of achieving the objective. Goals are classified into qualitative and quantitative indicators. Furthermore, goals are classified into indicators defined by measurements or other numerical values ​​and indicators defined by the content of actions. For example, goals include improving or maintaining habits, behaviors, and numerical values. App goals can also be defined by one or more sub-goals. Sub-goals are smaller-grained goals set to achieve the corresponding goal.

[0013] A "therapeutic app" refers to a program that has received approval under the Pharmaceuticals and Medical Devices Act. A therapeutic app is classified as a medical device. Therapeutic apps are approved on a disease-by-disease basis. Diseases for which approval has already been obtained include, for example, hypertension, nicotine addiction, and insomnia. Diseases for which therapeutic apps are currently under development include, for example, NASH (non-alcoholic steatohepatitis), diabetes, dyslipidemia, kidney disease, and alcoholism.

[0014] A "user" refers to a person who uses a program that has the function of recording health-related data. A person who changes their behavior or works to change their behavior through the aforementioned behavioral change-promoting program is an example of a user. Users include both users before visiting a medical institution and users who have visited a medical institution. Users who have visited a medical institution include both users who are eligible for medical fee calculation and users who are not eligible for medical fee calculation. "Patient" refers to a user who is receiving treatment at a medical institution. In other words, a patient is a user who has started or is currently receiving treatment at a medical institution.

[0015] Treatment apps include patient apps and doctor apps. A "patient app" is a program that runs on a device operated by the patient (hereinafter also referred to as the "patient device") and is prescribed to the patient by a doctor. In this sense, patient apps are also called PDT (=Prescription Digital Therapeutic). The patient app can be downloaded, for example, from an app store. In the embodiment described later, the code required for activation (hereinafter referred to as the "prescription code") is issued by a doctor upon prescription. The patient app is used to record patient health data outside of medical facilities (hereinafter also referred to as "patient app data").

[0016] Patient apps have an expiration date set upon approval. This expiration date is determined based on, for example, the period during which the public health insurance system applies. The expiration date is also determined by the type of disease the patient app addresses. For example, the expiration date for a hypertension patient app is six months, starting from the month following the month in which the app was prescribed. However, six months is just an example; it could be nine months or twelve months, for example. The expiration date can also be set in days, such as 60 days or 180 days, or in weeks, such as eight weeks or 24 weeks. Needless to say, these numbers are just examples. Programs classified as non-medical devices generally do not have a set expiration date. However, it is permissible to set an expiration date even for programs classified as non-medical devices.

[0017] A "doctor app" refers to a program that can be used through a terminal operated by a doctor or other healthcare professional (hereinafter also referred to as a "doctor terminal"). In the embodiment described later, 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). Healthcare professionals are also referred to as medical personnel.

[0018] "Patient data" includes, for example, patient attributes, measurements, activity records, mood records, physical condition records, medical history, patient app usage history, biological characteristics, psychological characteristics, social characteristics, habits, and goal achievement status. Patient data is an example of health data recorded by users working on behavioral change. However, the information recorded as patient data varies depending on the disease, and it does not need to include all of the information exemplified; it may include only some of it, or other information. Furthermore, all the information recorded as patient data is also an example of health-related data.

[0019] "Patient attributes" include, for example, the patient's name, gender, and date of birth. This information is just one example of basic patient information. "Measured values" refer to numerical values ​​measured using measuring instruments. The items of measured values ​​recorded as patient app data are defined for each disease. For example, if the disease is hypertension, blood pressure values ​​will be recorded as measured 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 measured values ​​would be carbon monoxide (CO) in exhaled breath and nicotine concentration in saliva.

[0020] "Activity records" include, for example, patient app operation history, medication records, meal records, smoking records, alcohol consumption records, and exercise records. These records also represent the user's behavior. Activity records are just one example of information related to activities. A "mood record" is, for example, a record of the user's perceived mood. A mood record is just one example of information related to mood. "Health record" refers to a record of physical condition or symptoms as perceived by the user. A health record is just one example of information related to health. Records of activities, moods, and physical condition can also serve as examples of a reference diary.

[0021] "Medical history" includes, for example, the start date of treatment, the date of the consultation, the content of the treatment, agreements between the doctor and the patient, and advice given by the doctor to the patient. Medical history is just one example of information related to a medical consultation. "Patient app operation history" refers to, for example, the history of operations related to launching the patient app, inputting measurement values ​​and reflections, etc. "Biological characteristics" include, for example, the presence or absence of other diseases, injuries currently being treated, the presence or absence of knee or foot pain, experience with disease treatment, and the number of years since the disease was diagnosed.

[0022] "Psychological characteristics" include, for example, expectations for app-based treatment, willingness to acquire knowledge about disease treatment, whether one finds reducing salt intake difficult, whether one believes one cannot change their taste preferences, and psychological resistance to leaving food on one's plate. "Social characteristics" include, for example, the type of work (e.g., shift work, day shift, night shift), the days of the week worked, the start time of work, the time of return home, regular days off, and the presence or absence of heating equipment in the changing room.

[0023] "Habits" include, for example, exercise habits, weight measurement habits, habits of checking calorie information on food labels, habits of choosing low-fat foods, habits of not consuming caffeine after 4 PM, eating habits after 10 PM, skipping breakfast habits, snacking habits, bathing habits one hour before bedtime, habits of stretching or massaging before bedtime, habits of getting more than 6 hours of sleep, wake-up and bedtime, the intensity of seasoning at home, and the amount of food consumed. "Goal achievement status" refers to information indicating the progress toward goals set by a doctor for each patient. It could also refer to the progress toward goals set by the patient themselves. Furthermore, it could refer to the progress toward goals presented by a patient app for each patient. Furthermore, the information mentioned above can be classified into subjective information and objective information.

[0024] Patient data is recorded using various formats, such as text, images (video and still images), audio, numerical data, and codes. Images include pictures of the affected area (e.g., inflamed areas) taken by the patient. Video and audio recordings are useful, for example, in the examination of mental illnesses. "Health-related data" refers to data owned by or related to a user, and includes personal information. Health-related data may include data recorded through programs that encourage behavioral change, as well as data that has been processed. Furthermore, health-related data may include data measured at medical institutions, data recorded by doctors, etc., during consultations, and data that has been processed from these. Health-related data includes both qualitatively identifiable information and quantitatively identifiable information. Hereafter, qualitatively identifiable information will be referred to as "qualitative information," and quantitatively identifiable information as "quantitative information."

[0025] The processed data includes, for example, processed data, data obtained through statistical processing, and summaries of patient data generated by artificial intelligence (AI). The processed data may also include, for example, the mean, the maximum and minimum values ​​within a given period, the data distribution for each given period, and the difference from the baseline. Health-related data may include data related to programs other than those that have the functionality to record health data or programs that encourage behavioral change. For example, it may include user accounts used to access various services and privacy-related information.

[0026] A "PDT server" is a server that manages patient data entered through patient applications, etc. A PDT server is also an example of a cloud server. A PDT server is typically set up for each patient application. Therefore, in order for a doctor to view patient data, they must log in to the PDT server running the doctor's application that is paired with the patient's application.

[0027] For example, if a patient is using a hypertension patient app provided by service provider A, the doctor needs to log in to the PDT server operated by service provider A for hypertension. Furthermore, if a patient is using a hypertension patient app provided by service provider B, the doctor needs to log in to the PDT server operated by service provider B for hypertension.

[0028] Furthermore, if a patient is using 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. Furthermore, a single PDT server may be shared among multiple patient applications targeting different diseases. Furthermore, multiple patient apps provided by different service providers may share a single PDT server.

[0029] The "PDT platform" is a server that manages the prescription of patient apps and the usage status of those 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, as it is a server that provides prescription services for patient apps. Usage statuses include, for example, "Not yet started," "Start date expired," "In use," "Scheduled to end," "Ended," and "Period expired."

[0030] "Before use begins" refers to a state where the prescription for the patient app has been issued, but the patient has not yet started using it on their device. For example, it refers to a state where the patient has not yet entered the prescription code into their device. "Expired start date" refers to a situation where the prescription code was not entered within the period during which the prescription code is valid (for example, within 4 days including the prescription date).

[0031] "In Use" refers to the state where the patient app installed on the patient's device has been activated and is available for use. Note that activating the patient app requires entering an activation code, such as the prescription code mentioned above. "Scheduled to end" refers to a state in which a medical institution has designated a patient as no longer subject to management within the validity period. For example, this state is set for patients who do not receive follow-up appointments.

[0032] "Termination" refers to a state where the patient app becomes unusable, for example, due to the expiration of its validity period. "Expiration of the period" indicates that a specified period has elapsed since the prescription date. The specified period is set to be longer than the validity period. For example, if the validity period is 6 months, the specified period is set to 8 months. "Selective Medical Treatment" is displayed when the validity period has expired but the treatment is still eligible for selective medical treatment. Selective medical treatment refers to a medical service that allows patients enrolled in social insurance to receive treatment not covered by insurance in conjunction with treatment covered by insurance, by bearing the additional cost.

[0033] The PDT platform supports 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 functions as a platform for multiple patient applications. Apps running on the PDT platform manage the usage status of multiple patient apps, each with different diseases and service providers, on a patient-by-patient basis.

[0034] In the embodiment described later, "medical institution" refers to a health insurance medical institution. More specifically, a medical institution refers to a health insurance medical institution to which the doctor who issues the prescription code necessary to activate the patient app belongs. However, if deregulation allows pharmacists, public health nurses, nurses, dietitians, hospital staff, and other healthcare professionals (hereinafter also referred to as "physicians, etc.") to issue prescription codes, then the term "healthcare institution" will also include facilities and organizations where these healthcare professionals are located.

[0035] Furthermore, prescription codes may be issued not only through medical consultations, but also through uninsured medical services (i.e., private medical services) or mixed medical services. Incidentally, medical consultations include not only in-person consultations but also online consultations. "Consultation" refers to receiving a medical examination at a medical institution. In the embodiments described below, the examination may include not only consultations by doctors but also interviews by other medical professionals. For example, it may include interviews by nurses or pharmacists.

[0036] <Embodiment> <Overall System> Figure 1 is a diagram illustrating an example of the overall configuration of an information processing system 1 according to an embodiment. The information processing system 1 shown in Figure 1 consists of a PDT platform 10, PDT servers 20 (20A, 20B, 20C...20F), a physician terminal 30, and a patient terminal 40.

[0037] Figure 1 shows only one PDT platform 10. However, multiple PDT platforms 10 may exist. The PDT platform 10 may consist of multiple servers connected via a network. In this case, the multiple servers cooperate to provide the services of the PDT platform 10. The PDT platform 10 and the PDT server 20 are connected via a network (not shown) that enables communication. For reference, the network could include, for example, a LAN (Local Area Network), the Internet, or a mobile communication system (4G, 5G, etc.).

[0038] In Figure 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 tasks such as patient authentication, management of patient application data entered through the patient application, and provision of patient data to physician terminals (not shown). The PDT server 20 is an example of an information processing device. In Figure 1, the PDT server 20A is a server that manages patient data for a patient application (hereinafter referred to as "Patient Application A") provided by Company A for patients with alcohol dependence. PDT Server 20B is a server that manages patient data for a patient application (hereinafter referred to as "Patient Application B") provided by Company B for patients with hypertension.

[0039] PDT Server 20C is a server that manages patient data for a patient application (hereinafter referred to as "Patient Application C") provided by Company A for patients with diabetes. PDT Server 20D is a server that manages patient data for a patient application (hereinafter referred to as "Patient Application D") provided by Company B for patients with diabetes. PDT Server 20E is a server that manages patient data for a patient application (hereinafter referred to as "Patient Application E") provided by Company C for patients with dyslipidemia. PDT Server 20F is a server that manages patient data for the patient application (hereinafter referred to as "Patient Application F") provided by Company D for patients with NASH.

[0040] As shown in Figure 1, a PDT server 20 is prepared for each combination of the disease targeted by the patient application and the service provider that provides the patient application. Therefore, even if the service provider that provides the patient application is the same, different PDT servers 20 will be prepared if the targeted diseases are different. Furthermore, even if the target disease is the same, different PDT servers 20 will be provided if the service provider offering the patient app is different.

[0041] However, it is also possible to provide a single PDT server 20 for multiple patient applications with different combinations. The PDT platform 10 and the PDT servers 20 are not limited to being operated by the same operator; they may be operated by different operators. For example, some of the operators of the multiple PDT servers 20 may be the same operator as the operator of the PDT platform 10.

[0042] The physician terminal 30 is a terminal operated by physicians and other medical professionals who use the services provided by the PDT platform 10 and the PDT server 20. Figure 1 shows only one physician terminal 30 as a representative example. For example, physicians can view the usage status of patient applications by logging into the PDT platform 10. They can also view patient data by logging into the PDT server 20. The physician's terminal 30 can be, for example, a desktop computer, a laptop computer, a tablet computer, a smartphone, smart glasses, or a server.

[0043] The patient terminal 40 is a terminal operated by the patient. The patient terminal 40 uploads patient application data recorded through the patient application to the corresponding PDT server 20. Figure 1 shows only one patient terminal 40 as a representative example. The patient terminal 40 is an example of a user terminal. The patient terminal 40 is also an example of an information processing device. The patient terminal 40 may be, for example, a smartphone, smart glasses, a desktop computer, a laptop computer, or a tablet computer.

[0044] <Terminal Hardware Configuration> <PDTプラットフォーム> Figure 2 illustrates 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 Figure 2 includes a processor 11, semiconductor memory 12, auxiliary storage device 13, and communication interface 14. Each device is connected via a bus or other signal lines.

[0045] The processor 11 is a device that realizes various functions through the execution of a program. The processor 11 may be composed of multiple CPU (=Central Processing Unit) cores. In that case, the processor 11 executes the program through the cooperation of the multiple CPU cores. The semiconductor memory 12 stores UEFI (Unified Extensible Firmware Interface) and the like. The semiconductor memory 12 is also used as an execution area for programs. The processor 11 and the semiconductor memory 12 function as a computer.

[0046] The auxiliary storage device 13 is composed of, for example, a hard disk device or a semiconductor storage. The auxiliary storage device 13 stores an operating system and other programs. Other programs include, for example, a program for displaying a list of usage statuses of patient apps. In addition, the auxiliary storage device 13 stores data for managing the status management data 130 by prescription code prescribed by a medical institution. The communication interface 14 is an interface for communicating with an external terminal such as a PDT server 20 (see FIG. 1) through a network. The communication interface 14 is compatible with communication standards such as Ethernet (registered trademark), Wi-Fi (registered trademark), and mobile communication systems.

[0047] <Management data of PDT platform> FIG. 3 is a diagram for explaining an example of the status management data 130 stored in the auxiliary storage device 13 (see FIG. 2) of the PDT platform 10 (see FIG. 1). The status management data 130 shown in FIG. 3 stores a prescription code 130A, a patient ID / patient name 130B, a prescription date / consultation date 130C, a medical institution ID / prescriber ID 130D, a patient app name 130E, and a usage status 130F.

[0048] However, these are just examples, and for example, a patient's medical record number, the patient's gender, the patient's date of birth, age, type of insurance card, insurance number, version of the patient app, severity level of the disease at the time of prescription, presence or absence of medication at the time of prescription, and treatment history at the time of prescription may be stored. The severity level of the disease at the time of prescription, whether or not the patient is taking medication at the time of prescription, and the treatment history at the time of prescription are entered as patient information by, for example, the doctor prescribing the patient app. The PDT platform 10 may also obtain the severity level of the disease at the time of prescription, whether or not the patient is taking medication at the time of prescription, and the treatment history at the time of prescription from the PDT server 20.

[0049] Prescription code 130A is issued from the physician's terminal 30 (see Figure 1) for each prescription notification. Patient ID / Patient Name 130B is the patient ID and patient name registered when the prescription was issued in the patient app. In Figure 3, the patient name for patient ID "12543" is "Mr. A", the patient name for patient ID "12544" is "Mr. B", and the patient name for patient ID "12545" is "Mr. C".

[0050] The prescription date / consultation date 130C is the date the doctor examined the patient. In this embodiment, the consultation date on which the doctor prescribed the patient app is indicated as the "prescription date" to distinguish it from other consultation dates. In Figure 3, only the prescription date is stored. In Figure 3, the prescription date for the patient app for "Person A" and "Person B" is "2024 / 5 / 28", and the prescription date for the patient app for "Person C" is "2024 / 5 / 24". The Medical Institution ID / Prescriber ID 130D is an ID that identifies the medical institution and prescriber that prescribed the patient app. In Figure 3, prescription codes "12345" and "23456" were prescribed by the same doctor or other medical institution at the same medical institution.

[0051] Patient app name 130E is the name of the patient app prescribed by the doctor or other medical professional. However, it is sufficient to remember the patient app's identification code, as long as the patient app can be identified. In Figure 3, "Patient A" and "Patient B" are prescribed patient app A. Patient C is prescribed patient app D. Usage status 130F indicates the usage status of the patient's application. The usage status can store one of the following: not yet started, start date expired, in use, scheduled to end, ended, or period expired. In Figure 3, all patient applications are in the "in use" status.

[0052] <PDT Server> FIG. 4 is a diagram for explaining an example of the hardware configuration of the PDT server 20. The PDT server 20 shown in FIG. 4 includes 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.

[0053] The processor 21 is a device that realizes various functions through program execution. The processor 21 may be composed of a plurality of CPU cores. In that case, the processor 21 executes a program through the cooperation of the plurality of CPU cores. UEFI or the like 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.

[0054] 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. Other programs include, for example, a doctor app. The doctor app is a program that generates a browsing screen for patient data corresponding to patients examined by doctors or the like.

[0055] 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 an external terminal 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.

[0056] <Management Data of PDT Server> FIG. 5 is a diagram for explaining an example of patient data 230 stored in the auxiliary storage device 23 (see FIG. 4) of the PDT server 20. The patient data 230 shown in Figure 5 stores measurements and other information recorded through the patient app. The content of the information recorded as patient data 230 varies depending on the disease the patient app supports. In Figure 5, patient data 230 stores prescription code 230A, patient ID / patient name 230B, and patient application data 230C.

[0057] In this embodiment, patient application data 230C means patient application management data and data recorded by the user through the patient application. Prescription code 230A records prescription code 130A (see Figure 3) issued by the PDT platform 10 (see Figure 1). Prescription code 130A is registered by the patient when they start using the patient app (i.e., when they register for the first time). The patient ID / patient name 230B is, for example, the patient ID and patient name at the medical institution that prescribed the patient app. The patient ID and patient name are obtained from the PDT platform 10, for example, when the prescription code is authenticated.

[0058] The patient app data 230C shown in Figure 5 includes measurement date / time 230C1, reflection / input date / time 230C2, current step of the treatment program 230C3, treatment program implementation history 230C4, behavioral goal 230C5, target value 230C6, and medication record 230C7. Note that the items exemplified do not need to be all of the patient app data 230C; they may be only a part of it, or other items may be included.

[0059] The measurement / measurement date and time field 230C1 records the health-related values ​​and date and time measured by the patient. For example, in a patient app for hypertension, the measured blood pressure value and the date and time the blood pressure was measured are recorded. The blood pressure value is given as systolic blood pressure and diastolic blood pressure. The "Reflection / Input Date & Time 230C2" field records a reflection on the day's activities along with the input date and time. The review process records things like physical condition level, stress level, sleep duration, weight, alcohol consumption, activities undertaken, and a diary (user's thoughts on the day's activities, etc.). For example, sleep duration, weight, and alcohol consumption are examples of results recorded as health-related data.

[0060] The content of the actions consists of the genre of the action that was performed and text input. Incidentally, other possible labels include, for example, "sleep," "stress," "moderate alcohol consumption," and "other." In this embodiment, the genre of the action that was performed is recorded by checking buttons labeled with "salt reduction," "weight loss," "exercise," etc. Text input allows users to freely enter content, emotions, etc., that cannot be recorded by checking buttons.

[0061] The current step 230C3 of the treatment program records the steps indicating the progress of the treatment program provided by the patient app. In this embodiment, the treatment program consists of three steps. These three steps consist of, for example, "acquiring knowledge," "implementing behavioral goals," and "habituating behavior." The treatment program implementation history 230C4 records the history of learning and behavior practiced in accordance with the treatment program. For example, in the case of a patient app for hypertension, "Step 1" records the learning history, including whether or not each learning item has been completed. "Step 2" records the behavior history, including the progress towards goals such as "salt reduction," "weight loss," "exercise," "sleep," "stress," "alcohol consumption," and "smoking cessation." "Step 3" records the progress towards behaviors set as goals by the patient.

[0062] Behavioral objective 230C5 records the behaviors that the patient has set as goals. These goals include one or more behavioral objectives selected by the patient from among the goals presented by the patient app as the treatment program progresses. For example, behavioral objective 230C5 records the behavioral objective set by the patient in step 2 or 3 of the treatment program. A behavioral objective refers to the action that must be taken in order to achieve the target. Furthermore, behavioral goals are not limited to those selected from the behaviors presented by the patient app; they may also include behaviors set individually by the patient.

[0063] The target value 230C6 is recorded as the value set by the doctor or other medical professional during the examination. Examples of quantitative targets include blood pressure target values, daily alcohol intake, number of alcohol-free days, and body weight, all of which are given numerically. Medication record 230C7 contains information about the taking of prescribed medications.

[0064] <Physician terminal / Patient terminal> Figure 6 illustrates an example of the hardware configuration of a physician terminal 30 and a patient terminal 40. The hardware configuration of the physician terminal 30 and the patient terminal 40 are basically the same. Therefore, in Figure 6, they are expressed in the format of "code of the elements constituting the physician terminal 30 / code of the elements constituting the patient terminal 40". The physician terminal 30 / patient terminal 40 shown in Figure 6 includes a processor 31 / 41, semiconductor memory 32 / 42, auxiliary storage device 33 / 43, input interface 34 / 44, input device 35 / 45, output interface 36 / 46, output device 37 / 47, and communication interface 38 / 48. Each device is connected via a bus or other signal lines.

[0065] The processors 31 / 41 are devices that perform various functions through program execution. The processors 31 / 41 may consist of multiple CPU (=Central Processing Unit) cores. In that case, the processor 11 executes the program through the cooperation of the multiple CPU cores. The semiconductor memory 32 / 42 stores UEFI and other information. 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 composed of, for example, a hard disk device or a semiconductor storage. The auxiliary storage device 33 / 43 stores an operating system and other programs.

[0066] 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, the auxiliary storage device 43 stores a client certificate for accessing the PDT server 20 (see FIG. 1). The auxiliary storage device 43 also stores a patient app and patient app data 230C (see FIG. 5) recorded through the same program.

[0067] For the input interface 34 / 44, for connection with the input device 35 / 45, for example, USB (= Universal Serial Bus), Bluetooth (registered trademark) is used. The input device 35 / 45 uses, for example, a keyboard, a mouse, or a touch panel. For the output interface 36 / 46, for connection with the output device 37 / 47, for example, HDMI (= High-Definition Multimedia Interface) (registered trademark), a LAN interface is used. The output device 37 / 47 uses, for example, a monitor or a printer. The communication interface 38 / 48 is an interface for communicating with an external terminal through a network. The communication interface 38 / 48 corresponds to communication standards such as Ethernet (registered trademark), Wi-Fi (registered trademark), and mobile communication systems.

[0068] <Patient data viewing sequence> FIG. 7 is a diagram for explaining an example of the patient data viewing sequence in the embodiment. The viewing sequence shown in FIG. 7 corresponds to the period from one medical examination to the next. The symbol S in FIG. 7 means step.

[0069] <Before consultation> <Step 101> The patient, following the instructions on the patient app's interface, inputs information such as their daily alcohol intake, daily reflections, behavioral goals, target values, agreements made during consultations, and medication records. The patient terminal 40 records the input information as patient app data. As mentioned above, the patient app data may also include data processed from this data. Incidentally, the processing of the patient app data is performed by the patient app.

[0070] <Step 102> At predetermined times, the patient terminal 40 uploads patient application data. One predetermined time is when new data is recorded or updated. This upload is performed when the patient application remains logged into the PDT server 20. Another predetermined time is when the patient logs into the PDT server 20. This upload is performed when the patient application is logged out of the PDT server 20 and then logs back into the PDT server 20.

[0071] <Step 103> The PDT server 20 stores the uploaded patient application data. This patient application data constitutes a part of the patient data 230 (see Figure 5). Steps 101 to 103, shown in Figure 7, are executed in response to the patient's input of measurement values ​​and other information into the patient application.

[0072] <When visiting the doctor> Steps 104 to 106, shown in Figure 7, are performed as a result of the patient visiting a medical institution. <Step 104> When a doctor or other medical professional logs into the PDT platform 10 via the doctor's terminal 30, the PDT platform 10 displays a screen on the doctor's terminal 30 showing the status of the patient's application usage. Figure 8 illustrates an example of a patient application usage status management screen 300 displayed on the output device 37 (see Figure 6) of the physician terminal 30 (see Figure 1).

[0073] The management screen 300 shown in Figure 8 is an example of a management screen that displays a list of usage statuses of patient applications prescribed to patients by a physician or other person operating the physician terminal 30. Furthermore, the management screen 300 may display only the usage status of patient apps prescribed by the physician operating the physician terminal 30, or it may display the usage status of patient apps prescribed by the medical institution to which the physician operating the physician terminal 30 belongs. In other words, the management screen 300 may also include the usage status of patient apps prescribed by other physicians belonging to the same medical institution.

[0074] In addition, the management screen 300 may also 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 implemented, for example, by a doctor operating the doctor terminal 30 providing the PDT platform 10 with information that identifies the patient being treated (e.g., medical record number or patient name). Furthermore, technically, it is possible to include all patient information managed by the PDT platform 10 in the management screen 300. However, in that case, it is desirable to enable filtering and sorting of patients displayed on the management screen based on the ID of the physician operating the physician terminal 30.

[0075] The management screen 300 shown in Figure 8 consists of an information field 301, the current date and time 302, a "Details" button 303, a "New Prescription" button 304, a "Treatment Results" button 305, and a "Close" button 306. Information section 301 displays management information for the patient app prescribed to each patient. In Figure 8, information section 301 is displayed in a table format. Specifically, the rows display information for each patient, and the columns display management items for the patient app. Figure 8 shows examples of management items, including medical record number / patient name 301A, application name 301B, usage status 301C, prescription date 301D, and prescription code expiration date 301E.

[0076] The field "Medical Record Number / Patient Name 301A" displays the medical record number and patient name, which are examples of information used to identify a patient prescribed by the patient app. Note that the patient identification information may also include user ID, address, etc. The app name 301B displays the name of the patient app prescribed to the patient. In this embodiment, four apps are displayed: patient app A, patient app B, patient app C, and patient app D. The app name 301B may also include version information.

[0077] If multiple patient apps are approved for the same patient, the information will be displayed on different rows. In addition to the app name 301B, or separately, the name of the disease targeted by the patient app may be displayed. Usage status 301C is used to display the usage status of the patient's application. In Figure 10, usage status 301C displays three options: "Before use," "In use," and "Finished." However, as mentioned above, usage status 301C can also display "Start deadline expired," "Scheduled end," "Period expired," etc.

[0078] The prescription date 301D displays the date the patient app was prescribed by a doctor or other healthcare professional. The prescription code expiration date 301E displays the last day the prescription code is valid. In this embodiment, the prescription code expiration date 301E displays a date three days after the prescription date 301D. The current date and time 302 is the date and time when the patient app usage status management screen 300 was viewed.

[0079] The "Details" button 303 is used to view patient data. A "Details" button 303 is placed for each row corresponding to a patient. When the "Details" button 303 is pressed, 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 pressed, a screen appears for entering the app name, patient name, gender, date of birth, etc. The name of the prescribing physician is also registered at the same time.

[0080] The "Treatment Results" button 305 is used to display treatment results on a per-physician or per-medical-institution basis. When the "Treatment Results" button 305 is pressed, the treatment results on a per-physician or per-medical-institution basis, as specified by the physician or other medical institution, are displayed. In this embodiment, the treatment results are created based on quantitative information of health data linked to the physician or medical institution. The treatment results are an example of statistically processed information. The "Close" button 306 is used to close the administration screen 300 shown in Figure 8. Let's return to the explanation of Figure 7.

[0081] <Step 105> The physician terminal 30 accepts patient selection on the management screen 300 (see Figure 8). Note that patient selection also includes the selection of the patient application associated with that patient. Upon receiving the selection operation, the physician terminal 30 accesses the PDT server 20 of the patient app prescribed to the selected patient. Furthermore, physicians and other medical professionals are assumed to be logged in to the PDT server 20 via the physician terminal 30. This access also includes information such as the prescription code issued when a prescription is issued via the patient app.

[0082] Incidentally, the physician terminal 30 may request the PDT platform 10 to issue a one-time token necessary to access the corresponding PDT server 20. In this case, the physician terminal 30 uses the one-time token received in return from the PDT platform 10 to access the PDT server 20. When this method is adopted, physicians and others can access patient application data on the PDT server 20 even if they are not logged in.

[0083] <Step 106> Upon receiving notification of patient selection from the physician's terminal 30, the PDT server 20 outputs a consultation support screen. In this embodiment, the PDT server 20 outputs a consultation support screen specifically for the patient being examined, based on the patient data. The consultation support screen is prepared for each disease. For example, in the case of alcohol dependence, a dedicated consultation support screen is displayed for each consultation. For example, in the case of hypertension, the same layout of the consultation support screen is displayed every time. In other words, in the case of hypertension, the same layout of the consultation support screen is displayed regardless of the consultation.

[0084] Figure 9 illustrates an example of a patient data viewing screen 310. The viewing screen 310 shown in Figure 9 consists of a "Patient Information" section 311, a "Measurement Results Display" section 312, a "Blood Pressure Target Value" section 313, a "Review Information Display" section 314, a "Activity Record Display" section 315, a "Go to Consultation Date Registration Screen" button 316, and an "Exit" button 317.

[0085] Figure 9 shows that the "Patient Information Section" 311 indicates that the medical record number is "12754", the patient's name is "Mr. H", his age is "60 years old", and his gender is "male". The "Measurement Results Display Section" 312 shown in Figure 9 displays the average blood pressure measured at home as both a numerical value and a graph. Daily measurements are recorded in Measurement Value / Measurement Date and Time 230C1 (see Figure 5).

[0086] In Figure 9, the average values ​​for two intervals are shown: "8 weeks to 4 weeks ago" and "4 weeks ago to the previous day." Here, "the previous day" refers to the day before the current date and time when the viewing screen 310 was opened. Doctors and other medical professionals check the progress of patient H's treatment by viewing the values ​​displayed in the "measurement results display area" 312.

[0087] Figure 9 shows that the "Target Blood Pressure Value" column 313 indicates that the target value for systolic blood pressure at home is less than 125 mmHg, and the target value for diastolic blood pressure is less than 75 mmHg. Note that the target blood pressure may vary depending on gender, co-existing diseases, age, and other factors. In Figure 9, the "Target Blood Pressure Value" column 313 includes a "Change" button for the target value. Only physicians or other qualified medical professionals can change the target value. The "reflection information display area" 314 shown in Figure 9 displays records of mood and physical condition. These records of mood and physical condition are recorded in the reflection / input date and time 230C2 (see Figure 5).

[0088] The activity record display area 315 shown in Figure 9 displays activity records such as the number of days the patient app was used, the number of days salt reduction in meals was recorded, and the number of days weight loss was recorded. The "Go to Consultation Date Registration Screen" button 316 shown in Figure 9 is a button that opens the consultation date registration screen (not shown). The "Exit" button 317 shown in Figure 9 is used to close the patient data viewing screen 310.

[0089] <Sequence for displaying treatment results> Figure 10 is a diagram illustrating an example of a display sequence for treatment results in this embodiment. In this embodiment, treatment results are provided as the amount of change in predetermined quantitative information or the distribution of predetermined quantitative information within a predetermined period. The specified quantitative information is determined for each disease. For example, in the case of hypertension, the specified quantitative information used is systolic blood pressure and diastolic blood pressure. For example, in the case of nicotine dependence, the specified quantitative information used is the carbon monoxide concentration in exhaled breath. For example, in the case of alcohol dependence, the specified quantitative information used is the average weekly alcohol intake. This information is recorded, for example, on the PDT server 20 (see Figure 1) via a patient application. The quantitative information common to multiple diseases includes, for example, the usage rate of the patient app, the progress of the treatment program, and the input rate of diaries and other reflections.

[0090] In this embodiment, the predetermined period is given by the length of time elapsed since the prescription in the patient app. The length of time elapsed may be given in months, for example, or in weeks. When determining the elapsed period length in months, the prescribed period can be set in one-month increments, for example, the period from one month after the prescription until one month has passed, the period from one month after the prescription until two months have passed, and the period from two months after the prescription until three months have passed. In addition, when determining the elapsed period length in months, it is possible to specify a predetermined period for multiple durations starting from the prescription, such as the period until one month has passed since the prescription, the period until two months have passed since the prescription (2 months), and the period until three months have passed since the prescription (3 months).

[0091] Furthermore, when defining the elapsed period length in months, it is possible to set the predetermined period to go back in time from the current time when the treatment results output was received. Alternatively, a specific period (for example, one month from May to June) may be arbitrarily designated as the predetermined period. Furthermore, when determining the length of the elapsed period in weeks, the prescribed period may be set in 12-week increments, such as the period from the prescription to 12 weeks and the period from 12 weeks to 24 weeks. In addition, when determining the length of the elapsed period in weeks, the prescribed period may be set for multiple periods starting from the prescription, such as the period from the prescription to 12 weeks and the period from the prescription to 24 weeks.

[0092] Furthermore, even in the case of weekly data, it is possible to define a predetermined period that goes back in time from the current time when the treatment results output was received. Alternatively, a specific period (for example, the four weeks from the 4th to the 8th week after prescription) may be arbitrarily designated as the predetermined period. In this embodiment, the predetermined quantitative information and predetermined period are set in advance by the PDT server 20 (see Figure 1). However, the quantitative information and predetermined period used to display treatment results may be freely specified by the physician or other personnel.

[0093] For example, if the disease is hypertension, the treatment outcome for a single physician is displayed as the change in the average systolic blood pressure of all patients examined by that physician over a specified period, and the change in the average diastolic blood pressure. Similarly, in the case of hypertension, treatment outcomes at the medical institution level are displayed as the change in the average systolic blood pressure of all patients receiving treatment at the institution over a specified period, and the change in the average diastolic blood pressure.

[0094] <Step 201> The physician terminal 30 accepts instructions for outputting treatment results on a per-physician or per-medical-house basis. For example, the physician terminal 30 accepts output instructions outside of consultation hours. Specific output instructions are accepted as operations on the "Treatment Results" button 305 (see Figure 8). In this embodiment, the physician terminal 30 that receives the instruction to output treatment results displays a patient application selection screen 320 (see Figure 11) for calculating treatment results. Figure 11 is a diagram illustrating the patient app selection screen 320. The patient app selection screen 320 shown in Figure 11 includes a description 321, a checkbox-style selection field 322, a "back" button 323, and a "forward" button 324.

[0095] In the case of Figure 11, the explanatory text 321 states, "Please specify the patient app (disease) for which you want to display treatment results and press the 'Next' button." In Figure 11, five options are displayed in the selection field 322. Specifically, these are Patient App A, Patient App B, Patient App C, Patient App D, and "All". As explained for the management screen 300 (see Figure 8), the types of patient apps displayed as options may be limited to those prescribed by the physician operating the physician terminal 30 (see Figure 10), or they may be all patient apps prescribed by the medical institution to which the physician operating the physician terminal 30 belongs. Alternatively, all patient apps handled by the PDT server 20 (see Figure 1) may be included.

[0096] In Figure 11, since it is a checkbox format, it is possible to select multiple options simultaneously. However, it is also possible to select only one of the five options. Incidentally, if "All" is selected, four patient apps will be selected: Patient App A, Patient App B, Patient App C, and Patient App D. If the "Back" button 323 is pressed, the patient app selection screen 320 is closed and the display returns to the management screen 300. If the "Proceed" button 324 is pressed, the patient app selection is confirmed, and the process transitions to treatment results 330 (see Figure 12).

[0097] Furthermore, if a patient app is uniquely identified (for example, if there is one patient app prescribed by the doctor operating the doctor terminal 30, or if there is one patient app prescribed by the medical institution to which the doctor operating the doctor terminal 30 belongs), the system may proceed to step 202 (see Figure 10) without displaying the patient app selection screen 320. In this case, the doctor terminal 30 or the PDT platform 10 (see Figure 10) will consider that a specific patient app associated with the doctor or medical institution has been designated by the doctor. In addition, if a patient app for displaying treatment results has been identified through prior registration or default settings, the patient app selection screen 320 will not be displayed. In this case as well, the physician terminal 30 or the PDT platform 10 will assume that a specific patient app, as defined in prior registration or default settings, has been selected.

[0098] <Step 202> Returning to the explanation of Figure 10. When the PDT platform 10 receives an output instruction from the physician's terminal 30, it extracts the patient IDs of all patients in its hospital who have been prescribed the patient app specified in the output instruction. In this embodiment, the treatment results for all patients in the hospital who have been prescribed the patient app specified in the output instruction are displayed on the screen.

[0099] <Step 203> The PDT platform 10 instructs the corresponding PDT server 20 to create treatment outcomes for the entire patient group, which consists of the extracted patient IDs. If there are multiple patient applications specified in step 201, and each patient application manages a different PDT server 20, the PDT platform 10 instructs the corresponding PDT servers 20 to create treatment results. However, if the treatment results are to be the overall usage rate of multiple patient applications, the PDT platform 10 may instruct the corresponding PDT servers 20 to provide usage information, etc., corresponding to the patient ID extracted in step 202.

[0100] <Step 204> The PDT server 20 creates treatment outcomes for the entire patient group based on the patient data of the patients who are members of the patient group. These treatment outcomes are created, for example, as changes or distributions of predetermined quantitative information within a predetermined period. In this embodiment, the predetermined period and predetermined quantitative information are defined for each patient application or for each combination of patient applications. <Step 205> The PDT server 20 notifies the PDT platform 10 of the generated treatment results. The treatment results may include numerical values, such as the mean, or statistical values ​​representing the distribution.

[0101] <Step 206> The PDT platform 10 creates a treatment results output screen and notifies the physician terminal 30. The output format of the treatment results can be text, numerical data, a list, a distribution chart, a graph, or other formats. In other words, the output format of the treatment results should allow for confirmation of changes in predetermined quantitative information due to treatment.

[0102] <Step 207> The physician's terminal 30 displays the treatment results output screen 330 (see Figure 12) upon notification from the PDT platform 10. A specific example of the treatment results output screen 330 will be described later.

[0103] <Step 208> In this embodiment, the treatment results output screen 330 consists of treatment results for the medical institution to which the physician operating the physician terminal 30 belongs. The display of this treatment results output screen 330 also makes it possible to grasp the treatment results for all patients in the medical institution. On the other hand, there are times when it is necessary to know the treatment results on a physician-by-physician basis, or when it is necessary to understand the relative relationship between one's own (including one's own hospital's) treatment results and those of others (including other hospitals). In such cases, the physician terminal 30 accepts the designation of a physician or medical institution. Upon receiving a new designation, the physician terminal 30, in cooperation with the PDT platform 10 and the PDT server 20, executes steps 202 to 207 anew.

[0104] <Example of treatment results output screen> <Initial screen example> Figure 12 illustrates an example of a treatment results output screen 330. The treatment results output screen 330 shown in Figure 12 is an example of a treatment results output screen for a medical institution. The treatment results output screen 330 shown in Figure 12 includes a "prescription code" button 331, a "physician (entire hospital)" selection button 332, baseline blood pressure information 333 and 334, and mean blood pressure change information 335 and 336.

[0105] The "Prescription Code" button 331 is used to select the prescription code from the patient app prescribed to the patient. In Figure 12, the "Prescription Code" button 331 is in the form of a pull-down menu. It may also be possible to input the prescription code as text. A prescription code is a unique code issued for each prescription. Therefore, specifying a prescription code means specifying a specific patient who will use a specific patient app identified by that prescription code. When the "Prescription Code" button 331 is pressed, for example, the patient data viewing screen 310 (see Figure 9) is displayed.

[0106] The "Doctors (entire hospital)" selection button 332 is used to select doctors belonging to the hospital. In Figure 12, the "Doctors (entire hospital)" selection button 332 is in the form of a pull-down menu. The options in the pull-down menu may include, for example, the doctor's name and the doctor's ID.

[0107] Baseline blood pressure information 333 displays baseline blood pressure (systolic blood pressure) information for all patients who have received a prescription for the patient app from a physician affiliated with the hospital. The baseline blood pressure information 333 shown in Figure 12 displays three pieces of information.

[0108] One piece of information is the amount of systolic blood pressure reduction 12 weeks after the patient app was prescribed. In Figure 12, this is a positive (i.e., increase) of 1.56 mmHg. Another piece of information is that 24 weeks after prescribing the patient app, the reduction in systolic blood pressure was 3.08 mmHg. By comparing these two pieces of information, doctors and other medical professionals can determine the change in systolic blood pressure within a specified period.

[0109] Another piece of information is the distribution of mean baseline systolic blood pressure. In Figure 12, the vertical axis represents frequency (i.e., number of people), and the horizontal axis represents blood pressure. In Figure 12, the blood pressure increment is 5 mmHg. In Figure 12, the interval in which the frequency of mean baseline systolic blood pressure is highest is 130–135 mmHg.

[0110] Baseline blood pressure information 334 displays baseline blood pressure (diastolic blood pressure) information for all patients who have received a prescription for the patient app from a physician affiliated with the hospital. The baseline blood pressure information 334 shown in Figure 12 displays three pieces of information.

[0111] One piece of information is the reduction in diastolic blood pressure 12 weeks after the patient app was prescribed. In Figure 12, this is a positive (i.e., increase) of 0.88 mmHg. Another piece of information is that 24 weeks after prescribing the patient app, the reduction in diastolic blood pressure was 3 mmHg. By comparing these two pieces of information, doctors and other medical professionals can determine the change in diastolic blood pressure within a specified period.

[0112] Another piece of information is the distribution of mean baseline diastolic blood pressure. In Figure 12, the vertical axis represents frequency (i.e., number of people), and the horizontal axis represents blood pressure. In Figure 12, the blood pressure increment is 5 mmHg. In Figure 12, the interval in which the frequency of mean baseline diastolic blood pressure is highest is 85-90 mmHg.

[0113] Mean blood pressure change information 335 shows the changes in the distribution of the mean systolic blood pressure. In Figure 12, the vertical axis is given by seven intervals, and the horizontal axis is the percentage with the maximum value set to 100%. The seven intervals on the vertical axis are given in 4-week increments, with the week the patient app was prescribed being week 0. Specifically, these are weeks 0 to 4, weeks 4 to 8, weeks 8 to 12, weeks 12 to 16, weeks 16 to 20, weeks 20 to 24, weeks 24 to 28, and weeks 28 to 32. Mean blood pressure change information 335 shows the change in the distribution of the average systolic blood pressure over a 4-week period.

[0114] Mean blood pressure change information 336 shows the changes in the distribution of the mean diastolic blood pressure. In Figure 12, the vertical axis is given by seven intervals, and the horizontal axis is the percentage with the maximum value set to 100%. Each interval is the same as that of mean blood pressure change information 335. Mean blood pressure change information 336 shows the change in the distribution of the mean diastolic blood pressure over a 4-week period. By viewing the treatment results output screen 330 shown in Figure 12, physicians operating the physician terminal 30 (see Figure 1) can check the treatment results for all patients prescribed the patient app at their hospital, i.e., the change in blood pressure values ​​since the start of treatment.

[0115] <Treatment outcomes by physician> Figure 13 illustrates an example of how treatment outcomes are displayed by physician. Figure 13 includes corresponding reference numerals for parts that correspond to those in Figure 12. Figure 13 shows the "Physician (entire hospital)" selection button 332 expanded, indicating that "Physician B" has been selected. Once this selection is confirmed, the information displayed in the baseline blood pressure information 333, 334 and the mean blood pressure change information 335, 336 will be changed to information based on the population of patients prescribed by "Physician B".

[0116] Figure 14 is a diagram illustrating the output screen 340 of treatment results for a specific physician. In Figure 14, corresponding parts with reference numerals are indicated in relation to Figure 12. In the case of the treatment outcome output screen 340 shown in Figure 14, the change in systolic blood pressure and the change in diastolic blood pressure 24 weeks after prescription in the patient app are both greater than those in the treatment outcome output screen 330 (see Figure 12). Therefore, it can be seen that the treatment outcomes of patients treated by Dr. B are better than the treatment outcomes of patients in the entire medical institution to which Dr. B belongs.

[0117] <Filtering by additional conditions> <Initial screen example> Figure 15 illustrates the filtering of treatment outcomes. Figure 15 includes corresponding reference numerals for parts that correspond to those in Figure 12. The treatment results output screen 330 shown in Figure 15 differs from the treatment results output screen 330 shown in Figure 12 in that a "filtering" button 337 is added. In the case of the treatment results output screen 330 shown in Figure 15, doctors can display treatment results corresponding to the combination of conditions identified by the "Doctors (entire hospital)" selection button 332 and the "Filtering" button 337. The "Filtering" button 337 shown in Figure 15 is in the form of a pull-down menu. It may also be possible to allow the input of any string.

[0118] Figure 16 illustrates an example of the options assigned to the "filtering" button 337. Figure 16 includes corresponding reference numerals for parts that correspond to those in Figure 15. The "Filtering" button 337 shown in Figure 16 is assigned six options: "Length since prescription," "Severity level at time of prescription," "Medication status," "Treatment history," "Age group," and "Gender." Each option is presented as a pull-down menu. These six options appear, for example, when the "Filtering" button 337 is clicked.

[0119] <Filtering Example 1> Figure 17 illustrates an example screen when "Length of time elapsed since prescription" is selected. Figure 17 includes corresponding reference numerals for parts that correspond to those in Figure 16. The screen display shown in Figure 17 appears when the "Length since prescription" dropdown button is operated. In Figure 17, seven options are assigned to "Length since prescription": "1 month", "2 months", "3 months", "4 months", "5 months", "6 months", and "Other".

[0120] In Figure 17, "3 months" is selected, while the others are not. Once this selection is confirmed, treatment outcomes are generated based on patient data from the hospital's patients at the point when 3 months have passed since the prescription was made in the patient app. For example, the baseline blood pressure information 333, 334 and the mean blood pressure change information 335, 336 are all updated with information up to 3 months after the prescription. If "Other" is selected, you can specify, for example, any number of days or weeks that have passed. Furthermore, if a specific doctor is selected using the "Doctor (entire hospital)" selection button 332, the difference in the time elapsed since the prescription was issued in the patient app for patients under the care of the specified doctor will be reflected in the treatment outcomes.

[0121] <Filtering Example 2> Figure 18 illustrates an example screen when "Severity Level at Time of Prescription" is selected. Figure 18 includes corresponding reference numerals for parts that correspond to those in Figure 16. The screen display shown in Figure 18 appears when the "Severity Level at Time of Prescription" dropdown button is operated. In Figure 18, four options are assigned to "Severity Level at Time of Prescription": "Level 1 (Mild)", "Level 2", "Level 3", and "Level 4 (Severe)".

[0122] In Figure 18, there are four levels of severity, but the number of selectable levels may vary depending on the disease. Furthermore, as a prerequisite for filtering, the severity levels are assumed to be recorded, for example, in status management data 130 (see Figure 3). In Figure 18, "Level 2" is selected, while the others are not. Once this selection is confirmed, treatment outcomes are created based on patient data from patients in the hospital whose disease severity level was "Level 2" at the time of prescription in the patient app. For example, the baseline blood pressure information 333, 334 and the mean blood pressure change information 335, 336 are all updated with information from patients whose severity level was "Level 2".

[0123] Furthermore, physicians and other healthcare professionals can view treatment outcomes for each severity level in sequence and compare the displayed outcomes to confirm the relationship between the severity level at the time of prescription and the treatment outcome. For example, it becomes possible to identify that patients with a treatment level of "Level 4" show particularly high therapeutic effects from using the patient app. This information can serve as one of the basic pieces of information for prescribing the patient app.

[0124] Furthermore, physicians and other medical professionals can intentionally switch between specifying the severity level using the "filtering" button 337 and not specifying the severity level using the "filtering" button 337. In other words, physicians and other medical professionals can switch between output that distinguishes the severity level and output that does not distinguish the severity level (for example, the treatment results output screen 330 (see Figure 12)). As a result, doctors and other healthcare professionals will be able to understand the impact of the severity level of a patient's condition at the time of prescribing the patient app on treatment outcomes. Furthermore, if a specific doctor is selected using the "Doctor (entire hospital)" selection button 332, the difference in the severity level of the patients prescribed by that doctor will be reflected in the treatment outcomes.

[0125] <Filtering Example 3> Figure 19 illustrates an example screen when "Medication taken" is selected. Figure 19 includes corresponding symbols for parts that correspond to those in Figure 16. The screen display shown in Figure 19 appears when the "Medication Status" dropdown button is operated. In Figure 19, two options, "Yes" and "No," are assigned to "Medication Status." It should be assumed that information regarding medication is recorded in, for example, status management data 130 (see Figure 3) as a prerequisite for filtering.

[0126] In Figure 19, "Yes" indicates a selected state, while the other indicates a non-selected state. Once this selection is confirmed, treatment outcomes are created based on patient data from patients in the hospital who are taking the medication. For example, the baseline blood pressure information 333 and 334, and the mean blood pressure change information 335 and 336 are both updated with information from patients in the hospital who are taking the medication. If "None" is selected, treatment outcomes will be generated based on patient data from patients at the hospital who are not taking medication. Therefore, by switching between selecting "Yes" and "No" for medication, it becomes possible to understand the impact of medication on treatment outcomes.

[0127] In Figure 19, there are only two options for "medication status," but it may be possible to specify one or more prescribed medications depending on the disease. If medication can be specified, doctors and other medical professionals can understand the impact of differences in medications on treatment outcomes. For example, it becomes possible to determine if the combination of medication A and the patient app is more effective than the combination of medication B and the patient app. Furthermore, if a specific doctor is selected using the "Doctor (entire hospital)" selection button 332, the medication adherence status of patients under the designated doctor's care can be reflected in the treatment outcomes.

[0128] <Filtering Example 4> Figure 20 illustrates an example screen when "Treatment History" is selected. Figure 20 includes corresponding reference numerals for parts that correspond to those in Figure 16. The screen display shown in Figure 20 appears when the "Treatment History" dropdown button is operated. In Figure 20, four options are assigned to "Treatment History": "None," "Yes (Smoking Cessation)," "Yes (Alcohol)," and "Other."

[0129] In Figure 20, there are four options, but the number of options may vary depending on the disease. In Figure 20, "Yes (Alcohol)" is selected, while the others are not selected. Once this selection is confirmed, treatment outcomes are created based on patient data from patients at the hospital with a history of treatment for alcohol dependence. For example, if the disease being treated is hypertension, the baseline blood pressure information 333, 334 and the mean blood pressure change information 335, 336 are updated with information from a hypertensive patient with a history of treatment for alcohol dependence.

[0130] If "Other" is selected, a list of diseases not included in the four options may be displayed for selection, or the user may be able to enter any disease name. Incidentally, the diseases listed in the treatment history may include not only diseases for which treatment has been completed, but also diseases for which treatment is currently being performed. As shown in Figure 20, the ability to specify treatment history for other diseases allows physicians and other healthcare professionals to understand the differences in treatment outcomes when the disease being treated is combined with other diseases. Furthermore, if a specific doctor is selected using the "Doctor (entire hospital)" selection button 332, the treatment results for patients with a specific treatment history who are under the care of that doctor will be displayed.

[0131] <Filtering Example 5> Figure 21 illustrates an example screen when "Age Group" is selected. Figure 21 includes corresponding reference numerals for parts that correspond to those in Figure 16. The screen display shown in Figure 21 appears when the "Age Group" dropdown button is operated. In Figure 21, six options are assigned to "Age Group": "20s", "30s", "40s", "50s", "60s", and "70s and over".

[0132] In Figure 21, the ages are in 10-year increments, but 5-year increments are also acceptable. For example, 21-25 years old, 26-30 years old, or 31-35 years old would also work. In Figure 21, there are six age group options, but the number of options may vary depending on the disease. In Figure 21, "70 years and older" is selected, while the others are not selected.

[0133] Once this selection is confirmed, treatment outcomes will be generated based on patient data from patients aged 70 and over at the hospital. For example, the baseline blood pressure information (333, 334) and the mean blood pressure change information (335, 336) will be updated with information from patients aged 70 and over. Furthermore, it may be possible to specify a particular age range. As shown in Figure 21, the ability to specify age groups allows doctors and other medical professionals to understand the differences in treatment outcomes based on age. Furthermore, if a specific doctor is selected using the "Doctor (entire hospital)" selection button 332, the treatment results for patients of that specific age group treated by that doctor will be displayed.

[0134] <Filtering Example 6> Figure 22 illustrates an example screen when "Gender" is selected. Figure 22 includes corresponding reference numerals for parts that correspond to those in Figure 16. The screen display shown in Figure 22 appears when the "Gender" dropdown button is operated. In Figure 22, two options, "Male" and "Female," are assigned to "Gender."

[0135] In Figure 22, "Male" is selected, while the other is not selected. Once this selection is confirmed, treatment outcomes will be generated based on patient data from male patients at your hospital. For example, the baseline blood pressure information (333, 334) and the mean blood pressure change information (335, 336) will be updated with information from male patients. As shown in Figure 22, the ability to specify gender allows doctors and other medical professionals to understand differences in treatment outcomes based on gender. Furthermore, if a specific doctor is selected using the "Doctors (entire hospital)" selection button 332, the treatment results for patients of a specific gender treated by that doctor will be displayed.

[0136] <Comparison display of treatment results> <Initial screen example> The following section explains how to display treatment results from your own hospital and those of other hospitals on a single screen for comparison. Figure 23 illustrates an example of the output screen 350 for treatment results that can be used for comparison. In Figure 23, corresponding parts with those in Figure 12 are indicated with corresponding numerals. The treatment results output screen 350 shown in Figure 23 differs from the treatment results output screen 330 shown in Figure 12 in that a "Comparison Target" button 338 is added.

[0137] In the case of the treatment results output screen 350 shown in Figure 23, doctors can display the treatment results corresponding to the "Doctors (entire hospital)" selection button 332 and the treatment results corresponding to the "Comparison Target" button 338 side by side on the same screen. Note that the treatment results corresponding to the "Comparison Target" button 338 are an example of the second treatment results. Also, the patient app data 230C (see Figure 5) used to create the treatment results corresponding to the "Comparison Target" button 338 is an example of the second health data.

[0138] Figure 24 illustrates an example of the options assigned to the "Compare" button 338. Figure 24 includes corresponding reference numerals for parts that correspond to those in Figure 12. The "Comparison Target" button 338 shown in Figure 24 is assigned eight options: "Nationwide," "Hospital Name," "Region," "Size," "Medical Department," "Doctor's Name," "Time of Patient App Implementation," and "Expected Treatment Outcome." Each option is presented as a dropdown menu. The eight options appear, for example, when you click the "Compare" button 338.

[0139] If "Nationwide" is selected, the treatment outcomes of all medical institutions will be used for comparison. The treatment outcomes here are created based on all patient data (second data) recorded in the PDT server 20 (see Figure 1). This patient data is recorded through a patient app prescribed by any doctor nationwide. Therefore, the treatment outcomes of all medical institutions are equivalent to the treatment outcomes of all doctors. If "Hospital Name" is selected, the treatment results of the medical institution identified by the hospital name specified in the pull-down menu or entered manually will be used for comparison.

[0140] If "Region" is selected, the treatment results of medical institutions belonging to the region specified in the pull-down menu or entered manually will be used for comparison. The region can be specified by administrative divisions such as cities, towns, villages, prefectures, countries, regional divisions of multiple prefectures (e.g., Kanto block, Kansai block), or other regional divisions of multiple prefectures (e.g., Southern Kanto, Northern Kanto, Tokai). In the case of regional designation, the treatment results displayed represent the entire population of patients associated with medical institutions or physicians belonging to that region. The designated area may also be defined as a predetermined range centered around landmarks such as the nearest train station or bus stop. This predetermined range may be defined by, for example, straight-line distance (e.g., within 2 kilometers), walking distance, or driving distance.

[0141] If "Scale" is selected, the treatment outcomes of medical institutions corresponding to the size of the medical institution specified in the pull-down menu will be used for comparison. Scale is given, for example, by the number of hospital beds. For example, it is given by six options: "less than 100 beds," "100 beds or more but less than 200 beds," "200 beds or more but less than 300 beds," "300 beds or more but less than 400 beds," "400 beds or more but less than 500 beds," and "500 beds or more."

[0142] If "Medical Department" is selected, the treatment outcomes of medical institutions corresponding to the medical department specified in the pull-down menu will be used for comparison. In the case of medical practice, for example, there are 26 specialties. Also, if deregulation allows pharmacists to prescribe patient apps, pharmacies may be included as an option. If "Doctor's Name" is selected, the treatment results of the specific doctor specified in the pull-down menu or entered manually will be used for comparison. If "Patient App Implementation Date" is selected, the treatment outcomes of doctors and medical institutions that started treatment using the patient app at the time specified in the pull-down menu (for example, January 2024) will be used for comparison.

[0143] If "Expected Treatment Outcomes" is selected, the expected treatment outcomes for patients associated with the physician or medical institution selected using the "Physician" selection button 332 will be used for comparison. For predicting treatment outcomes, a machine learning model is used, for example. If the machine learning model is a general-purpose model, it learns the relationship between all patient data (including patient characteristics and treatment progress information) managed by the PDT server 20 (see Figure 1) and the PDT platform 10 (see Figure 1), and the blood pressure values ​​of each patient (or the distribution of blood pressure values ​​for all patients) at different stages of treatment (e.g., 4 weeks, 8 weeks, etc.) as measured by the patient application. In other words, in the case of a general-purpose model, patient data linked to one of the doctors or medical institutions being compared is also included in the learning target.

[0144] Needless to say, different diseases require different information to be learned. For example, in the case of alcohol dependence, the information to be learned is the amount of alcohol consumed by each patient over time (or the distribution of alcohol consumption by all patients), while in the case of NASH, the information to be learned is the weight or weight loss of each patient over time (or the weight or weight loss of all patients). The data used for training is typically closed at the end of each month and then used for machine learning. However, depending on the amount of data and the computing power of the available resources, it may be possible to update the training data on a daily basis.

[0145] Alternatively, dedicated machine learning models (hereinafter also referred to as "dedicated models") specific to individual physicians or medical institutions may be prepared. A dedicated model refers to a machine learning model specifically for the physician or medical institution selected by the "Physician" selection button 332. For training this machine learning model, data excluding patients associated with the selected physician or medical institution is used. In other words, in the case of a dedicated model, patient data associated with one of the physicians or medical institutions being compared is excluded from the training.

[0146] The patient characteristics and treatment progress information included in the data to be used for learning refer to the following information, respectively. This information can also be used as options assigned to the "filtering" button 337 (see Figure 15). Patient characteristics include, for example, "severity level at the time of prescription," "medication status at the time of prescription," "treatment history at the time of prescription," "age group at the time of prescription," and "gender." This information is just one example of patient characteristics. Patient characteristics are also referred to as patient background information.

[0147] The course of treatment includes, for example, "severity level after prescription," "severity level during treatment," "attendance status after prescription (e.g., perfect attendance, interruption)," "attendance status during treatment," "app usage status after prescription (e.g., progress rate, recording rate)," "app usage status during treatment," "medication adherence after prescription," and "medication adherence during treatment." This information is just one example of information regarding the course of treatment. The course of treatment is also referred to as the patient's treatment status.

[0148] In this embodiment, when "Expected Treatment Outcomes" is selected, patient data linked to the doctor or medical institution selected by the "Doctor" selection button 332 is input into a general-purpose model or a dedicated model, and the blood pressure values ​​of each patient (or the distribution of blood pressure values ​​for all patients) at different stages of time since the start of treatment via the patient app (e.g., 4 weeks later, 8 weeks later, etc.) are output. In this case, the machine learning model to be used is predetermined. Furthermore, physicians may be allowed to select either a general-purpose model or a dedicated model from the "Expected Treatment Outcomes" dropdown menu.

[0149] In addition, the "Comparison Target" button 338 may allow users to specify the age group, gender, and years of experience of the doctors, etc. By increasing the options available, doctors and other healthcare professionals can select other doctors or medical institutions similar to their own or their own hospital as points of comparison, and confirm the relative position of their own or their hospital's treatment outcomes.

[0150] In the case of Figure 24, the comparison target is identified by selecting one of the eight options presented by the "Comparison Target" button 338, but it may also be possible to specify a comparison target using a combination of these eight options (except for mutually exclusive combinations). For example, it is possible to specify the treatment outcomes of internal medicine departments in hospitals with 300 or more beds but less than 400 beds in the Kanto region as a comparison target.

[0151] <Example of comparison screen> Figure 25 illustrates an example of the output screen 360 showing treatment results for our hospital and treatment results for the entire nation (all medical institutions). The output screen 360 shown in Figure 25 is an example of a comparison screen of baseline blood pressure information. The treatment results for the entire nation (all medical institutions) shown here are an example of the second treatment results. In Figure 25, the upper section displays the treatment outcomes for all physicians in the hospital, while the lower section displays the national average treatment outcomes. The treatment outcomes given as the national average are an example of normalized treatment outcomes. In this embodiment, "nationwide" represents the largest regional unit.

[0152] Incidentally, baseline blood pressure information 333A and 333B display the amount of systolic blood pressure reduction 12 weeks after prescription in the patient app, the amount of systolic blood pressure reduction 24 weeks after prescription in the patient app, and the distribution of the mean baseline systolic blood pressure. Similarly, baseline blood pressure information 334A and 334B displays the amount of diastolic blood pressure reduction 12 weeks after prescription in the patient app, the amount of diastolic blood pressure reduction 24 weeks after prescription in the patient app, and the distribution of the mean baseline diastolic blood pressure. In Figure 25, it can be seen that both the amount of blood pressure reduction in systolic and diastolic pressure is better at our hospital than the national average.

[0153] Figure 26 illustrates an example of the output screen 360 showing treatment results for one's own hospital and treatment results for the entire nation (all medical institutions). The output screen 370 shown in Figure 26 is an example of a comparison screen for mean blood pressure change information. In Figure 26, the upper section displays the treatment outcomes for all physicians at the hospital, while the lower section displays the national average treatment outcomes. The national average treatment outcomes shown here are an example of the second-tier treatment outcomes. Incidentally, mean blood pressure change information 335A and 335B show changes in the distribution of the average systolic blood pressure, while mean blood pressure change information 336A and 336B show changes in the distribution of the average systolic blood pressure. By comparing corresponding graphs on the same screen, it becomes easier to determine whether the changes in the average blood pressure distribution of patients at your hospital over a given period are favorable compared to the national average.

[0154] <Summary> By adopting the aforementioned mechanism, physicians and other medical professionals will be able to grasp their own or their hospital's overall trends regarding treatment using patient apps. To grasp these trends, treatment outcomes created based on quantitative information from patient app data 230 (see Figure 5) recorded through the patient app running on the patient terminal 40 (see Figure 1) are used. As mentioned above, treatment outcomes are calculated by statistically processing the quantitative information from patient app data 230. Therefore, it is possible to extract overall trends at the physician or medical institution level, which would be difficult to grasp by individually viewing the patient app data 230 for each patient.

[0155] Furthermore, as mentioned above, treatment outcomes are presented as the amount of change in quantitative information within a predetermined period (for example, the amount of reduction in the average blood pressure of multiple patients). This makes it easier to grasp the amount of change at the physician or medical institution level, which is difficult to grasp from individual patient values. Furthermore, by allowing users to specify a predetermined period, it becomes possible to understand trends within a particular timeframe. This specific period can be defined as the elapsed time since the prescription was issued in the patient app, making it possible to understand trends that appear in patients with the same elapsed time.

[0156] Furthermore, as mentioned above, treatment outcomes can be filtered using the patient app based on the patient's severity level, medication use, treatment history, age group, gender, and any combination thereof at the time of prescription. This allows physicians and other healthcare professionals to understand their own treatment trends and those of their hospitals from various perspectives. Furthermore, as mentioned above, treatment results can be displayed in a manner that allows for comparison with treatment results from other medical institutions (for example, on the same screen). This makes it possible for doctors and other medical professionals to understand their own or their institution's relative position in terms of treatment results.

[0157] <Other Embodiments> (1) Although embodiments of the present disclosure have been described above, the technical scope of the present disclosure is not limited to the embodiments described above. It is clear from the claims that various modifications or improvements to the embodiments described above are also included in the technical scope of the present disclosure.

[0158] (2) The processor in the embodiments described above refers to a processor in a broad sense, and includes not only general-purpose processors (e.g., CPUs) but also specialized processors (e.g., GPUs (=Graphical Processing Units), ASICs (=Application Specific Integrated Circuits), FPGAs (=Field Programmable Gate Arrays), programmable logic devices, etc.). Furthermore, the processor operations in each of the embodiments described above are not limited to a single processor, but may be performed collaboratively by multiple processors or multiple CPU cores. Also, the order in which each operation is executed in the processor is not limited to the order described above, but may be changed individually.

[0159] (3) In the above-described embodiment, the PDT platform 10 (see Figure 1) provides treatment results on a per-medical-person or per-medical-institution basis to the physician terminal 30 (see Figure 1). However, the PDT server 20 (see Figure 1) may also provide treatment results on a per-medical-person or per-medical-institution basis to the physician terminal 30.

[0160] (4) In the above-described embodiment, quantitative information from patient app data 230C (see Figure 5) recorded by the user (including the patient) through the patient app is provided to the physician terminal 30 as treatment results. However, the quantitative information provided to the physician's terminal 30 as treatment outcomes may include information recorded at the medical institution. For example, the quantitative information may include γ-GTP in alcohol dependence and the results of histopathological diagnosis in NASH.

[0161] (5) In the above-described embodiment, the case in which the physician selectable by the "physician" selection button 332 (see Figure 12) is another physician belonging to the same medical institution as the physician operating the physician terminal 30 (see Figure 1) was explained. However, it may be possible to designate other physicians belonging to a different medical institution than the physician operating the physician terminal 30. The designation of other physicians here may be on an individual basis or on a regional basis.

[0162] Regional designations can be based on administrative divisions such as cities, towns, villages, prefectures, or national borders, as well as regional divisions based on groups of prefectures (e.g., Kanto block, Kansai block) or other regional divisions based on multiple prefectures (e.g., Southern Kanto, Northern Kanto, Tokai). When regional designations are made, treatment outcomes are displayed based on the entire population of patients associated with medical institutions or physicians belonging to each region. The designated area may also be defined as a predetermined range centered around landmarks such as the nearest train station or bus stop. This predetermined range may be defined by, for example, straight-line distance (e.g., within 2 kilometers), walking distance, or driving distance.

[0163] (6) In the above-described embodiment, the case in which the physician selectable by the "physician" selection button 332 (see Figure 12) is another physician belonging to the same medical institution as the physician operating the physician terminal 30 (see Figure 1) was explained. However, instead of the "Doctor" selection button 332, or in addition to the "Doctor" selection button 332, a button for selecting a medical institution may be provided. When a user operates the medical institution selection button, medical institutions located within the same administrative unit as the user's own institution may be displayed as selectable options. The administrative unit here may be defined as, for example, the national government, a prefecture, a city, town, or village. Furthermore, the medical institutions to be selected may be designated within a specified area centered around landmarks such as the nearest train station or bus stop. The specified area may be defined, for example, by straight-line distance (e.g., within 2 kilometers), walking distance, or driving distance.

[0164] (7) In the above-described embodiment, the case in which quantitative information from patient application data 230 (see Figure 5) is used to create treatment results was explained. However, the creation of treatment outcomes may also utilize patient health data obtained at medical institutions or data entered by physicians or other medical professionals. Examples of this type of data include γ-GTP levels in patients with alcohol dependence and histopathological diagnoses in patients with NASH.

[0165] (8) In the case of the treatment results output screen 350 shown in Figure 24, the comparison target can be specified by operating the "Comparison Target" button 338, but it is not possible to narrow down the patients using the "Filtering" button 337 (see Figure 15). Therefore, the treatment results output screen 350 also allows for filtering of patients. Figure 27 illustrates another example of the display of the output screen 350 for treatment results that can be used for comparison. In Figure 27, corresponding parts with those in Figure 24 are indicated with corresponding numerals. The treatment results output screen 350 shown in Figure 27 has an added "Indicators" button 339. This "Indicators" button 339 corresponds to the "Filtering" button 337 and is used to narrow down the list of patients to be compared.

[0166] The "Indicators" button 339 shown in Figure 27 is assigned to "Age," "Gender," "Severity," "Blood Pressure," "App Usage Rate," and "Blood Pressure Change." It is also possible to use "age group" instead of "age" here. Similarly, it is possible to use "app usage status" instead of "app usage rate." "App usage status" could include, for example, "app usage status after prescription" or "app usage status during treatment." "App usage rate" or "app usage status" could include, for example, learning or treatment progress rates, or the rate of recording reflections and measurements.

[0167] For "severity," options such as "severity level at the time of prescription," "severity level after prescription," and "severity level during treatment" may be assigned. For "changes in blood pressure," options such as "a decrease of less than 5 mmHg on average" or "a decrease of 5 to 10 mmHg on average" are assigned. Furthermore, the "Indicators" button 339 shown in Figure 27 can be assigned various types of information related to the patient's characteristics and treatment progress, as mentioned above. By providing the "Indicator" button 339 in this way, it becomes possible to specify at least one of the following as a comparison target: information about the patient's characteristics and information about the course of treatment.

[0168] (9) In the above-described embodiment, as shown in Figures 12 to 26, the output screen of the treatment results is represented by the frequency distribution of the average blood pressure value, etc. However, multiple indicators for comparison may be overlaid on a single graph with the horizontal axis representing time and the vertical axis representing the control indicator, as shown in the time-series transition graph of blood pressure values ​​in the "Display of Measurement Results" section 312 (see Figure 9). These multiple indicators may include, for example, the average blood pressure values ​​of the patient(s) of the patient(s) of the patient(s) of the patient(s) of the patient(s) of the patient(s) of the medical institution designated as the comparison target.

[0169] (10) In the above-described embodiment, a patient application for hypertension was explained. However, the patient app used to create treatment results can be a patient app for other diseases, or even a non-patient app. Incidentally, the other diseases can be lifestyle-related diseases or non-lifestyle-related diseases. Lifestyle-related diseases include, for example, alcohol dependence, dyslipidemia, chronic heart failure, hyperuricemia, diabetes, NASH, kidney disease, nicotine addiction, chronic bronchitis, cancer, periodontal disease, attention deficit hyperactivity disorder, depression, tinnitus, delayed grief disorder, opioid-induced constipation, post-mastectomy pain syndrome, nephrotic syndrome, and insomnia.

[0170] (11) In the embodiments described above, patient apps and healthcare apps are assumed to be used as apps for recording health-related data. However, the type of app used to record health-related data is not restricted. For example, it could be a diary app, a photo app, or an audio app.

[0171] <Summary> An example of the disclosure described in the above-mentioned embodiment is shown below. (((1))) An information processing device having a processor, the processor receiving output of patient treatment outcomes on a per-healthcare worker or per-medical-institution basis, acquiring health-related data linked to the healthcare worker or medical institution, and outputting treatment outcomes on a per-healthcare worker or per-medical-institution basis based on the quantitative information of the acquired health-related data. This information processing device allows for the presentation of patient treatment outcomes on a per-healthcare professional or per-medical institution basis.

[0172] (((2))) The processor is an information processing device according to (((1))) that outputs the amount of change in quantitative information within a predetermined period as a treatment result. This information processing device makes it possible to verify treatment results within a predetermined period.

[0173] (((3))) The information processing device according to (((2))), wherein the processor provides a predetermined period of time from the prescription of a program that operates on a terminal operated by the patient. This information processing device makes it possible to check the relationship between the length of time elapsed since prescription and treatment outcomes.

[0174] (((4))) The processor is an information processing device as described in (((3))) that accepts a specified elapsed time. This information processing device allows you to specify the elapsed time period for outputting treatment results.

[0175] (((5))) The information processing device according to any one of (((1))) to (((4))), wherein the processor accepts at least one designation of patient characteristics and information regarding the course of treatment, and outputs the treatment results based on health data that satisfies the accepted designation. This information processing device allows for the output of treatment outcomes by specifying at least one of the following: information regarding the patient's characteristics and information regarding the course of treatment.

[0176] (((6))) Information relating to the patient's characteristics includes at least one of the severity level at the time of prescription, whether or not the patient was taking medication at the time of prescription, the patient's medical history at the time of prescription, the patient's age or age group at the time of prescription, and the patient's gender, as described in (((5)))). This information processing device can output treatment outcomes tailored to information about the patient's characteristics.

[0177] (((7))) Information processing device according to (((5))) that provides information on the course of treatment, including at least one of the severity level after prescription, severity level during treatment, hospital visit status after prescription, hospital visit status during treatment, app usage status after prescription, app usage status during treatment, medication adherence status after prescription, and medication adherence status during treatment. This information processing device can output treatment results based on information regarding the progress of treatment.

[0178] (((8))) The processor is an information processing device according to any one of (((1))) to (((7))), which outputs a second treatment outcome based on quantitative information of second health data linked to other healthcare professionals or other healthcare institutions, in a manner that allows comparison with treatment outcomes on a healthcare professional basis or on a healthcare institution basis. According to this information processing device, the objectivity of treatment results can be enhanced.

[0179] (((9))) The processor accepts the designation of other medical staff or other medical institutions as a comparison target, for the information processing device described in (((8))). According to this information processing device, the comparison target can be specified.

[0180] (((10))) The processor normalizes the second treatment results corresponding to other medical staff or other medical institutions in units of regions as a comparison target, for the information processing device described in (((8))). According to this information processing device, the objectivity of treatment results in units of regions as a comparison target can be enhanced.

[0181] (((11))) The processor outputs a second treatment result predicted from health-related data used for outputting treatment results in a manner comparable to treatment results per medical staff or per medical institution, for the information processing device described in any one of (((1))) to (((10))). According to this information processing device, the predicted treatment result and the actual treatment result can be compared.

[0182] (((12))) The processor accepts at least one designation of information regarding patient characteristics and information regarding the progress of treatment as conditions for defining the treatment results to be compared and the second treatment results, for the information processing device described in (((8))) or (((11))). According to this information processing device, at least one of information regarding patient characteristics and information regarding the progress of treatment can be specified as a comparison target.

[0183] (((13))) The health-related data is recorded through a program that operates on a terminal operated by a patient who works on behavior modification, for the information processing device described in any one of (((1))) to (((12))). This information processing device can present treatment outcomes at the healthcare provider level or healthcare institution level, based on health data recorded by patients undergoing behavioral change.

[0184] (((14))) Health-related data is recorded in a medical institution using one of the following information processing devices: (((1))) to (((12))) This information processing device allows for the presentation of patient treatment outcomes on a per-healthcare professional or per-medical institution basis.

[0185] (((15))) An information provision method in which a computer performs the following processes: receiving output of patient treatment outcomes on a per-healthcare worker or per-medical-institution basis; acquiring health-related data linked to healthcare workers or medical institutions; and outputting treatment outcomes on a per-healthcare worker or per-medical-institution basis based on quantitative information of the acquired health-related data. This information provision method allows for the presentation of patient treatment outcomes on a per-healthcare professional or per-healthcare institution basis.

[0186] (((16))) A program to enable a computer to implement the following functions: a function to receive patient treatment outcome data on a per-healthcare professional or per-medical-institution basis; a function to acquire health-related data linked to healthcare professionals or medical institutions; and a function to output treatment outcomes on a per-healthcare professional or per-medical-institution basis based on the quantitative information of the acquired health-related data. This program allows for the presentation of patient treatment outcomes on a per-healthcare professional or per-healthcare institution basis. [Explanation of Symbols]

[0187] 1…Information processing system, 10…PDT platform, 20, 20A, 20B, 20C, 20D, 20E, 20F…PDT server, 30…Physician terminal, 40…Patient terminal

Claims

1. It has a processor, The aforementioned processor, We accept patient treatment outcome reports on a per-healthcare professional or per-medical institution basis. We obtain health data linked to the aforementioned healthcare professionals or the aforementioned medical institutions. Based on the quantitative information of the acquired health data, the treatment results are output on a per-healthcare worker or per-medical institution basis. Information processing device.

2. The processor outputs the amount of change in the quantitative information within a predetermined period as the treatment result. The information processing apparatus according to claim 1.

3. The processor provides the predetermined period as the length of time elapsed since the prescription of the program that runs on the terminal operated by the patient. The information processing apparatus according to claim 2.

4. The processor accepts the specification of the elapsed period length. The information processing apparatus according to claim 3.

5. The processor receives at least one specification of patient characteristics and information regarding the course of treatment, and outputs the treatment results based on health data that satisfies the received specification. The information processing apparatus according to claim 1.

6. The information regarding the characteristics of the aforementioned patient includes at least one of the following: severity level at the time of prescription, whether or not the patient was taking medication at the time of prescription, treatment history at the time of prescription, age or age group at the time of prescription, and gender. The information processing apparatus according to claim 5.

7. The information regarding the course of the aforementioned treatment includes at least one of the following: severity level after prescription, severity level during treatment, attendance status after prescription, attendance status during treatment, app usage status after prescription, app usage status during treatment, medication adherence status after prescription, and medication adherence status during treatment. The information processing apparatus according to claim 5.

8. The processor outputs a second treatment outcome based on quantitative information of second health data linked to other healthcare professionals or other healthcare institutions, in a manner that allows comparison with the treatment outcome for each healthcare professional or healthcare institution. The information processing apparatus according to claim 1.

9. The aforementioned processor accepts designations from other healthcare professionals or other medical institutions as a point of comparison. The information processing apparatus according to claim 8.

10. The processor normalizes the second treatment outcome, corresponding to other healthcare professionals or other healthcare institutions, on a regional basis for comparison. The information processing apparatus according to claim 8.

11. The processor outputs a second treatment result predicted from the health data used to output the treatment result, in a manner that allows comparison with the treatment result for each medical professional or each medical institution. The information processing apparatus according to claim 1.

12. The processor accepts the designation of at least one of the following as conditions for defining the comparative treatment outcome and the second treatment outcome: information regarding the patient's characteristics and information regarding the course of treatment. The information processing apparatus according to claim 8 or 11.

13. The aforementioned health data is recorded through a program that runs on a terminal operated by the patient who is working to change their behavior. The information processing apparatus according to claim 1.

14. The aforementioned health data is recorded at the medical institution. The information processing apparatus according to claim 1.

15. Computers A process for receiving patient treatment outcome data on a per-healthcare worker or per-medical-institution basis, A process for acquiring health-related data linked to the aforementioned medical professional or the aforementioned medical institution, A process to output the treatment results on a per-healthcare worker or per-medical institution basis, based on the quantitative information of the acquired health data, A method for providing information to carry out the task.

16. On the computer, A function to receive patient treatment outcome data on a per-healthcare professional or per-medical institution basis, A function to acquire health-related data linked to the aforementioned medical professionals or medical institutions, A function to output the treatment results on a per-healthcare worker or per-medical institution basis based on the quantitative information of the acquired health data, A program to achieve this.

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