JP2023133626A - Medical/therapy system and method for executing the same - Google Patents

Abstract

To provide a medical system and a method using (executing) the system for performing diagnosis, treatment, prevention or the like by an information processing terminal and an information processing network system, applicable to doctors and medical institutions in all medical departments, and having a function for estimating disease and/or a biological psychosocial factor of a patient.SOLUTION: A method includes the steps of: selecting, in response to the input medical condition or chief complaint, cross-diagnostic indicators and/or disease-specific indicators tagged to the medical condition or chief complaint; presenting the selected indicator to the patient side; and determining the disease and/or a biopsychosocial factor corresponding to a medical condition, chief complaint, or the like of a patient from the selected index and an answer thereto.SELECTED DRAWING: Figure 2

Description

The present invention provides diagnosis, treatment, prevention, prevention of recurrence, early detection of comorbid diseases, and diagnosis, treatment, prevention, prevention of recurrence, and early detection of comorbid diseases through information processing terminals (e.g., smartphones) and information processing network systems (e.g., the Internet) without the need for direct meetings between doctors and patients. Concerning medical technology for discovering, etc.

In recent years, technologies (so-called "medical applications") that allow doctors and patients to perform treatment without face-to-face meetings have been developed via information processing terminals and information processing network systems (for example, patent document 1, patent Reference 2).
Such technology can treat patients living in sparsely populated or remote areas where access to medical facilities is difficult. In addition, it is possible to improve the consultation rate even among patients who wish to keep the fact that they are receiving treatment as confidential as possible due to privacy concerns and do not wish to receive treatment from a specialist doctor or specialist. be.

In systems related to conventional technology (for example, Patent Documents 1 and 2), the disease is identified and attention is paid to the presence or absence of symptoms characteristic of the disease, and it is basically necessary to provide the same treatment to all patients. However, there is a high possibility that there will be an impasse for patients for whom treatment does not work.
CBT (cognitive behavioral therapy) is highly versatile in treating physical and mental illnesses, and its therapeutic effects are attracting attention. CBT, especially process-based CBT (process-based cognitive behavioral therapy), focuses not only on the presence or absence of symptoms characteristic of the disease, but also on the common symptoms of various diseases, such as psychosocial problems, life adjustment, and stress. It is important to understand patients based on their factors. To this end, it is necessary to use data from daily life to determine the extent to which patients are able to act adaptively or maladaptively in response to psychosocial problems, life adjustment, stress, etc. It is also possible to assess the extent to which patients are behaving adaptively or maladaptively in response to the symptoms characteristic of the disease. It is essential. Based on the individual evaluations as described above, it becomes possible to implement the optimal treatment for the patient, but in the prior art (for example, Patent Documents 1 and 2), such evaluations are not performed.

In addition, diseases related to stress and anxiety are related to all medical departments (dermatology, dentistry, otorhinolaryngology, orthopedics, internal medicine, pain clinics, neurology, ophthalmology, etc.), and cognitive behavioral therapy There are many diseases for which treatment is effective.
However, conventional medical apps are limited in target diseases and the medical departments that handle them, and focus on identifying the disease and checking for the presence or absence of symptoms characteristic of the disease. Therefore, even though medical apps based on psychotherapy, especially cognitive behavioral therapy, are effective in treating psychogenic and psychosomatic diseases, such medical apps are not used by patients in clinical departments other than those involved in psychotherapy. There are few opportunities to do so.
Furthermore, although it is very important to estimate a patient's disease in treatment, existing medical systems do not have such a function.
Furthermore, no system has yet been proposed that has a general medical treatment block that can be used by all doctors and medical institutions, regardless of the clinical department, and has the function of estimating diseases using psychotherapy, especially cognitive behavioral therapy. .

Patent No. 6339298 Patent No. 6245781

The present invention was proposed in view of the problems of the prior art described above, and is a medical system that performs diagnosis, treatment, prevention, etc. using an information processing terminal and an information processing network system, and a method using the system, The objective is to provide a system that can be used by doctors and medical institutions in all clinical departments and has the function of estimating a patient's disease, as well as a method for implementing the system.

The system (100) of the present invention includes:
Including a block (20: general medical treatment block) that functions as a general medical reception (for patients),
Block (20) has the function of general medical treatment reception.
A block (10A1) that has the function of recording cross-diagnostic indicators regarding the extent to which patients are behaving adaptively or maladaptively in response to psychosocial problems, life adjustment, stress, etc. : cross-diagnostic indicators part) and
A block (10A2: Disease-specific assessment part),
The cross-diagnostic indicators and disease-specific indicators are tagged according to the (patient's) medical condition or chief complaint.
A function to select (as an initial indicator) a cross-diagnostic indicator and/or disease-specific indicator tagged (corresponding to) the entered medical condition or chief complaint, and present the selected indicator (initial indicator) to the patient. an index selection block (10B: index selection part) having the function of
A disease (candidate disease) corresponding to the patient's medical condition or chief complaint is determined (estimated) from the selected index (initial index) and the response (by the patient) to it, and the determined disease (candidate disease) is determined. The present invention is characterized by having a disease estimation block (10C: disease estimation part) having a function of selecting a treatment application corresponding to the above.

Further, the system (100) of the present invention includes:
a server (10);
an information processing terminal (3) used by the patient;
It has an information processing terminal (4) used in medical institutions,
The server (10) includes a block (20: general medical treatment block) that functions as a general medical reception (for patients) and a plurality of medical applications (30).
Block (20) has the function of general medical treatment reception.
A block (10A1) that has the function of recording cross-diagnostic indicators regarding the extent to which patients are behaving adaptively or maladaptively in response to psychosocial problems, life adjustment, stress, etc. : cross-diagnostic indicators part) and
A block (10A2: Disease-specific assessment part),
The cross-diagnostic indicators and disease-specific indicators are tagged according to the (patient's) medical condition or chief complaint.
A function to select (as an initial indicator) a cross-diagnostic indicator and/or disease-specific indicator tagged (corresponding to) the entered medical condition or chief complaint, and present the selected indicator (initial indicator) to the patient. an index selection block (10B: index selection part) having the function of
A disease (candidate disease) corresponding to the patient's medical condition or chief complaint is determined (estimated) from the selected index (initial index) and the response (by the patient) to it, and the determined disease (candidate disease) is determined. and/or a disease estimation block (10C: disease estimation part) having a function of selecting a treatment application corresponding to biopsychosocial factors.

In the present invention, the server (10) is preferably constituted by a computer or other information processing machine.
In the present invention,
The block (20) having the function of the general medical treatment reception desk is
A function to analyze or analyze the medical condition or chief complaint (complained by the patient) to the extent that it can be tagged with cross-diagnostic indicators and/or disease-specific indicators;
It has a function to analyze or analyze the patient's response to the selected index (initial index) presented by the index selection block (10B: index selection part) to the extent that the disease (candidate disease) corresponding to the medical condition and chief complaint can be identified. It is preferable to have an analysis or analysis block (10D: answer analysis part).
Further, in the present invention,
The block (20) having the function of the general medical treatment reception desk is
If the patient's medical condition and chief complaint have not been cured for a long time and the patient has a history of visiting multiple medical institutions, the diagnosis results at each of the multiple medical institutions and the type of medication administered ( It is preferable to have a function of acquiring information (treatment history information) of (and others) and transmitting the information (treatment history information) to the disease estimation block (10C: disease estimation part).

In the system (100) of the present invention, each of the medical applications (30) includes:
Cross-diagnostic evaluation of the extent to which patients behave adaptively or maladaptively in response to psychosocial problems, life adjustment, stress, etc. while utilizing data from daily life. A block (cross-diagnostic assessment part 30A in each of the medical applications 30) having the function of
A block (in each medical app 30 Disease-specific assessment part 30B);
Patients are divided into specific groups (e.g., based on indicators for cross-diagnostic assessment (indicators for cross-diagnostic assessment), indicators for disease-specific assessment (indicators for disease-specific assessment), and other classification information. It is preferable to have a block (treatment module determination part 30D) having the function of classifying into clusters, groups) and selecting a treatment module linked to the classified population.

In this case, the block (10A1) in the server (10) that has the function of cross-diagnostic evaluation transmits the evaluation result to the block (30A) that has the function of cross-diagnostic evaluation in the medical application (30). Has the ability to send
The block (10A2) in the server (10) having the function of disease-specific evaluation transmits the evaluation result to the block (30B) having the function of disease-specific evaluation in the medical application (30). It is preferable to have the following.

The method of the present invention is a method for executing the above-mentioned system (100: the system according to any one of claims 1 to 3), including:
In response to a medical condition or chief complaint input (from a patient), selecting (as an initial index) a cross-diagnostic indicator and/or a disease-specific indicator tagged with the medical condition or chief complaint (of the patient) (Step S2) and
a step of presenting the selected index (initial index) to the patient (step S3);
A step (step S5) of determining a disease (candidate disease) corresponding to the patient's medical condition, chief complaint, etc. from the selected index (initial index) and the response (by the patient) to it (estimate the disease);
a step of selecting a treatment application corresponding to the determined disease (candidate disease) (step S6);
It is characterized by having the following.

In the method of the invention,
Analyzing or analyzing the medical condition or chief complaint (complained by the patient) to the extent that it can be tagged with cross-diagnostic indicators and/or disease-specific indicators;
Analysis or analysis having the function of analyzing or analyzing the patient's response to the selected index (initial index) (presented by the index selection block 10B) to the extent that the disease (candidate disease) corresponding to the medical condition and chief complaint can be identified. It is preferable to have a step of doing so.

Furthermore, in the method of the present invention,
If the patient's medical condition and chief complaint have not been cured for a long time and the patient has a history of visiting multiple medical institutions, the diagnosis results at each of the multiple medical institutions and the type of medication administered ( It is preferable to have a step of acquiring information (treatment history information) on (and others) and using the information (treatment history information) in the step of estimating the disease (the step executed by the disease estimation part 10C).

In the present invention, in each of the selected medical applications (30),
Cross-diagnostic evaluation of the extent to which patients behave adaptively or maladaptively in response to psychosocial problems, life adjustment, stress, etc. while utilizing data from daily life. process (process of performing cross-diagnostic assessment),
A step of disease-specific assessment of the extent to which a patient is able to act adaptively or non-adaptively in response to symptoms characteristic of the disease (a step of performing a disease-specific assessment);
Patients are classified into specific groups (e.g. , clusters, groups) and selecting a treatment module linked to the classified population (step of selecting a treatment module).

In the present invention, the phrase "medical application" may refer not only to application software but also to a system that executes a medical application.

According to the present invention having the above-described configuration, cross-diagnostic indicators and/or disease-specific indicators tagged (corresponding) to the input medical condition, chief complaint, or mental disorder situation (as the initial indicator) ), the selected index (initial index) is presented to the patient, and the disease (candidate disease ) or the corresponding biopsychosocial factors can be determined (disease can be estimated). Here, by using cross-diagnostic indicators and/or disease-specific indicators tagged (corresponding) to the entered medical condition or chief complaint and the patient's answers, a diagnosis is made that first specifies the disease name, etc. Compared to cross-sectional assessment and/or disease-specific assessment, it is possible to estimate diseases through cross-diagnostic assessment and/or disease-specific assessment over a wide range of areas, and it is possible to estimate diseases with high accuracy. become.

According to the present invention, the disease can be estimated by using cross-diagnostic indicators and/or disease-specific indicators and the patient's answers to them even when accessing from departments other than those for mental illnesses. Therefore, it is possible to estimate diseases with high accuracy without being tied to the type of examination department.
Since cross-diagnostic and/or disease-specific assessments are performed using cross-diagnostic indicators and/or disease-specific indicators, and the patient's responses are used, it is possible that diseases overlap (combined). In some cases, it is possible to determine which diseases overlap and the percentage of overlap. In other words, it is especially effective because psychogenic diseases and psychosomatic disorders often coexist, such as irritability due to paramenstrual symptoms and depression. Furthermore, it is possible to achieve a certain level of effectiveness by customizing the contents of the medical application selected by the present invention for each patient or type of disease based on the overlap of diseases for each patient and their ratio. It is. That is, according to the present invention, it is possible to assess a plurality of comorbid disease symptoms/conditions, and it is possible to propose or provide a personalized and optimal treatment method. For example, if a patient with psychogenic ED has a tendency toward comorbid conditions such as depression, anxiety, and diabetes, an assessment may be performed to determine the types of overlapping diseases or biological, psychological, and social factors (and the rate of overlapping diseases). ), it is possible to provide the optimal medical app for each individual patient.
In each medical application, treatment can be personalized and optimized by performing cross-diagnostic evaluations and disease-specific evaluations using quantitative measurement indicators and performing cluster determination.

The medical application used in the present invention uses cross-diagnostic assessment to determine to what extent patients are able to act adaptively or non-adaptively in response to biopsychosocial problems, life adjustment, stress, etc. It becomes possible to evaluate whether the Furthermore, disease-specific assessment makes it possible to assess the extent to which patients are behaving adaptively or maladaptively in response to the symptoms characteristic of the disease in question. .
Based on these ratings, the optimal treatment module can be provided for the patient. In particular, it is important to contribute to extremely effective medical care (treatment, diagnosis, prevention/recurrence prevention, early detection, detection of coexisting diseases, etc.) for cases that cannot be improved by simply identifying and treating the disease. I can do it.

The present invention allows multiple patients and/or their supporters to be configured to use the same medical app together. If multiple patients, such as the partner of a patient with ED or infertility (male or female), or the family or support person of a patient with depression or anxiety disorder, can use the same medical app together, By sharing information and content necessary for treatment, etc. with patients, and performing exercises related to treatment modules together with patients, they can accompany patients and deepen their understanding and practice of the disease and how to provide support. You can deepen it.
Then, it is possible to establish that the patient actively participates in determining the treatment policy and receive the treatment in accordance with the decision (adherence), thereby improving the therapeutic effect.

1 is a functional block diagram showing an overview of a system according to an embodiment of the present invention. It is a functional block diagram of a server in an embodiment. 12 is a flowchart showing a procedure for estimating a disease and selecting a medical application in the general medical treatment block of the server. 3 is a functional block diagram of the medical application in FIG. 2. FIG. FIG. 2 is a functional block diagram of a patient-side information processing terminal used in the embodiment. FIG. 2 is a functional block diagram of an information processing terminal on the medical institution side used in the embodiment. FIG. 3 is a diagram showing an example of tagging used in the embodiment. FIG. 3 is a diagram illustrating an example of estimating diseases and the like used in the embodiment.

Embodiments of the present invention will be described below with reference to the accompanying drawings. Each of the functions used in this system consists of a software program that runs on a server or information processing terminal, and an input/output means. Alternatively, it may be configured as a combination thereof.
In FIG. 1, a system generally designated by the reference numeral 100 includes a server 10 (for example, a computer) including a general medical treatment block 20 and a medical application 30, an information processing terminal 3 used by a patient (an information processing terminal on the patient side), It has information processing terminals 4 (information processing terminals on the medical institution side) used by doctors or in medical institutions, and these are interconnected by, for example, an information processing network 60 (for example, a local area network or the Internet). ing. The server 10 will be described later with reference to FIG.
Here, the term "patient" refers to an individual who is aware that he or she may be suffering from a disease and who is willing to undergo treatment for the disease. In other words, the word "patient" is not limited to only those who are receiving the treatment under the guidance of doctors and other related parties at a medical institution, but also refers to those who are judged to be "healthy" by a third party. This may include healthy people who have been recommended to use this system by a third party or those who have been recommended to use this system by a third party.

Although not clearly shown in FIG. 1, the information processing terminal 3 on the patient side also has a function as a "device for acquiring daily life data", for example, information from a smartphone or a personal computer (PC, personal computer), etc. It can be configured by an electronic device having a processing function and a communication function. Alternatively, a wearable device or the like can be used as a device for acquiring daily life data. Daily life data includes psychological data collected by having people answer various questions in their daily life. In addition, for example, heart rate, blood pressure, blood oxygen concentration, number of steps, sleep time and quality of sleep (deep/light sleep, etc.), reflex speed, walking speed, amount of activity, range of activity (behavior history: measured by GPS, etc.) (obtained), physiological data such as blood sugar levels, and behavioral data.
As will be described later, daily life data is sent to the section 30A (Figure 3) that performs cross-diagnostic assessments in various medical applications. It is used for cross-diagnostic evaluation (cross-diagnostic assessment) to determine the extent to which a patient is able to act adaptively or maladaptively in response to adaptation, stress, etc.
In FIG. 1, the information processing terminal 4 on the medical institution side can be an electronic device (for example, a PC) having an information processing function and a communication function, but it is also possible to use a smartphone or a tablet terminal.

Here, the information processing terminal 3 (for example, a smartphone) on the patient's side is not only used as a tool to access the server 10 via the line 3L and the network 60, but also to be used as a tool for cross-diagnostic assessment. It also functions as a tool to measure "data" and send it to medical applications. The functions of measuring daily life data and sending the medical application 30 to the cross-diagnostic assessment part can be achieved by installing a dedicated application for performing such functions on the patient's information processing terminal 3 (for example, a smartphone). It becomes possible.
When installing such a dedicated application, the information transmission line 3L (which may be wired or wireless) that connects the patient's information processing terminal 3 and the network 60 is connected to the server 10. It is an access line and also an information transmission line for transmitting the patient's daily life data measured by the information processing terminal 3 to the medical application side.
Although not shown in FIG. 1, the information processing terminal 4 on the medical institution side has a communication block that has a function of accessing the server 10 via the network 60 and exchanging information.

Although not shown in FIG. 1, a plurality of patient-side information processing terminals 3 and a plurality of medical institution-side information processing terminals 4 can also be connected to the network 60.
Although not shown in FIG. 1, the server 10 has a built-in database 10F, but the database 10 (not shown) is configured separately from the server 10 and is connected to the server 10 via the network 60. good.

Although not shown, when the server 10 uses medical or healthcare big data or combines it with such big data, the results of assessment and treatment module selection in the server 10, treatment status, etc. can be utilized as part of big data, and knowledge obtained by utilizing big data can be used to improve the performance of the system 100 according to the illustrated embodiment. It is also possible to incorporate the knowledge and information obtained through the use of big data into the construction of AI (artificial intelligence) algorithms and develop medical applications using AI.

Next, referring to FIG. 2, the server 10 of the system 100 according to the illustrated embodiment will be described.
The server 10 has a general medical treatment block 20 and a plurality of medical applications 30.
The general medical treatment block 20 has a function similar to a portal site or general reception desk that guides patients to an appropriate medical department rather than a medical treatment block. The general medical treatment block 20 is an index used in cross-diagnostic assessment, and is tagged with the patient's medical condition or chief complaint (chief complaint, etc.: chief complaint, current medical history, past medical history, etc.) in a comprehensive diagnostic manner. A cross-diagnostic indicator part 10A1 that stores the information, and a disease-specific indicator that stores an indicator that is used in disease-specific assessment and that is disease-specifically tagged with the patient's medical condition or chief complaint (chief complaint, etc.). Part 10A2 is included. The cross-diagnostic indicator part 10A1 and the disease-specific indicator part 10A2 may be configured integrally or separately.
Here, it is also possible to provide only the cross-diagnostic indicator part 10A1 that stores cross-diagnostic indicators.
In FIG. 2, the server 10 includes a plurality of medical applications 30 (30, 30-1, 3-2, . . . ).
In the illustrated embodiment, the information transmission lines SL1, . . . may be not only wired but also wireless.

In Figure 2, the indicators recorded in the cross-diagnostic indicator part 10A1 indicate the extent to which the patient is able to behave adaptively in response to biopsychosocial problems, life adjustment, stress, etc. One or more indicators for assessing whether the patient is behaving in a cross-diagnostic manner (indicators for performing a cross-diagnostic assessment, such as the PFQ described in JP 2022-068362, A set of indicators for depression (BDI, SDS, etc.) and anxiety disorders (LSAS, GAD-7, etc.)). From among the indicators recorded in the cross-diagnostic indicator part 10A1, appropriate indicators are selected based on the input patient's medical condition, chief complaint, etc., and are presented to the patient. Specifically, the cross-diagnostic indicator part 10A1 stores a list of indicators (FIG. 7(A)) that are associated with disease names, medical conditions, and chief complaints (in the present invention, this association is referred to as " The disease name, condition, and chief complaint entered from the patient's information processing terminal are searched from the "Disease, Symptom, etc." column of this list (for example, if "anxiety" is entered, Since cross-diagnostic 2 is applicable, the index is GHQ), and the index that has a corresponding relationship with them is selected and presented to the patient. In addition, as an additional function, insomnia is recorded in the complication item, so you may search for insomnia again and add the corresponding index for use. Note that FIG. 7 is an example of correspondence (tagging), and it goes without saying that other diseases, symptoms, complications, index names, etc. may exist as appropriate. Additionally, information on diseases, symptoms, etc., or complications can be designed to be updated at any time using AI.
The indicators recorded in the disease-specific indicators part 10A2 are the extent to which patients (subjects) whose medical conditions and chief complaints have been input are able to act adaptively in response to characteristic symptoms, or non-specific indicators. It is an indicator for evaluating whether the person is behaving adaptively (performing a disease-specific assessment), and in order to respond to multiple different diseases, it is possible to record indicators corresponding to multiple different diseases. convenient.
In various clinical departments (not shown) of a medical institution, the chief complaint, medical history, past medical history, etc., and medical conditions of patients with various diseases are checked. The patient's medical conditions include, for example, ``I can't sleep,'' ``I have constant pain in ○○ (specific part),'' ``I have no appetite,'' and ``I think I may have ED.''
The patient's medical condition, chief complaint, etc. are input to the server 10 via the information processing terminal 4 (FIG. 1) on the medical institution side, and the cross-diagnostic assessment part 10A1 and the disease identification index part 10A2 integrally process these various information. The tagged indicators for each medical condition are identified (initial evaluation indicator selection). The identified (selected) indicators are then presented to the patient. A specific index selection method is performed in the same manner as the cross-diagnostic selection method using FIG. 7(B).
Cross-diagnostic is based on biopsychosocial factors that are recognized across psychogenic diseases, psychosomatic disorders, and stress-related diseases, and disease-specific is characteristics that appear only in a specific disease; It's a different concept.

In addition to the cross-diagnostic indicator part 10A1 and the disease-specific indicator part 10A2, the general medical treatment block 20 includes an indicator selection part 10B, a disease estimation block 10C, an answer analysis part 10D, and a treatment history part 10E.
The indicator selection part 10B is a cross-diagnostic indicator and/or tagged with the patient's medical condition, chief complaint, etc. transmitted from the cross-diagnostic indicator part 10A1 and the disease-specific indicator part 10A2, respectively, via the transmission latin SL2 and SL3. The disease-specific index (selected index) is used as the initial evaluation index, and information processing is performed on the side of the patient who complains about the disease condition or chief complaint, etc., that is, on the side of the medical institution where the patient is receiving a diagnosis, etc., via the transmission line SL11. It has a function of transmitting information to the terminal 4, or to the information processing terminal 3 on the patient's side if the patient is accessing from home or the like. The indicator selection part 10B has a function of transmitting the selected indicator to the disease estimation block 10C via the transmission line SL4 when the patient answers the selected indicator.
The disease estimation block 10C generates candidates corresponding to the patient's medical condition, chief complaint, etc. based on the initial evaluation index (selected index) and the patient's response to it (sent to the disease estimation block 10C via the transmission line SL9). It has the function of determining disease and/or biopsychosocial factors. Determination of such candidate diseases and/or biopsychosocial factors constitutes an estimate of the patient's disease and/or biopsychosocial factors. Based on the cross-diagnostic indicators and disease-specific indicators selected for the patient's medical condition and chief complaint, as well as the patient's responses, if the patient is suffering from multiple types of diseases, the overlap is determined. It is possible to estimate the disease caused by the disease. In other words, the disease estimation block 10C has a function of estimating overlapping diseases. Figure 8 shows an example of the estimation method. In FIG. 8, the patient is provided with index A and index B, and the respective answers are processed by the disease estimation block 10C based on the algorithm stored in the disease estimation block 10C, and the answer result of index A is given as a predetermined result. It is divided depending on whether the value is greater than or equal to point A, and if it is greater than or equal to point A, it is further branched depending on whether the result of index B is greater than or equal to a predetermined value, point B, and estimation is made. Here, it is possible to memorize an algorithm for the combination of evaluation results of all indicators and make estimations based on it, but it is also possible to use AI etc. to build relationships between indicators and diseases and biopsychosocial factors. The algorithm may be modified using network analysis, analysis using correlation coefficients, or AI. Furthermore, the estimation process is not limited to these statistical processes, and estimation may be performed using means well known to those skilled in the art. In addition, in this example, each disease is classified into one disease name or biopsychosocial factor, but in reality, it may be classified into multiple disease names or biopsychosocial factors. Alternatively, classification such as a category indicating a combination of each may be used.
The disease estimation block 10C also has a function of extracting necessary disease information (for example, pathological information, etc.) from the disease information database 10F via the transmission line SL5 when determining candidate diseases. .
Furthermore, the disease estimation block 10C has a function (medical application selection function) of selecting a medical application (see FIG. 4) corresponding to the candidate disease based on the candidate disease (determined by the disease estimation block 10C). It also has a function of transmitting information regarding the patient to the selected medical application 30 (30-1 to 30-4) via the transmission line SL10.
In the illustrated embodiment, the reference numeral 30 is used to comprehensively represent the medical application.

When tagging cross-diagnostic indicators and/or disease-specific indicators to the patient's medical condition or chief complaint in indicator selection part 10B, if the patient's medical condition or chief complaint is abstract or vague, It becomes difficult to tag cross-diagnostic and/or disease-specific indicators. In the illustrated embodiment, if a medical condition, chief complaint, etc. is too abstract or vague and it is difficult to tag it with cross-diagnostic indicators and/or disease-specific indicators, the medical condition, chief complaint, etc. are transferred to the transmission line SLD1. is sent to the answer analysis part 10D via. The answer analysis part 10D analyzes or analyzes the medical condition, chief complaint, etc. to the extent that cross-diagnostic indicators and/or disease-specific indicators can be tagged in the indicator selection part 10B, and is analyzed to the extent that it can be tagged via the transmission line SLD2. It has a function of transmitting the determined medical condition etc. to the index selection part 10B. (Analysis may be performed using AI or deep learning.)
The response analysis part 10D has a function as a chatbot, for example, and by communicating with the patient via the transmission line SLD1, it organizes the contents of the patient's medical condition, chief complaint, etc. to the extent that the above-mentioned tagging is possible. Be specific. In addition, other direct input means such as a voice response may be used.

If the patient's response to the selected index (initial index) is unclear, it becomes difficult for the disease estimation block 10C to determine a candidate disease corresponding to the patient's medical condition, chief complaint, etc. In such a case, the response analysis part 10D analyzes the patient's response to the extent that a candidate disease, etc. can be identified in the disease estimation block 10C, and then analyzes the patient's response to the extent that the candidate disease, etc. can be identified via the transmission line SLD3. It has a function of transmitting the patient's answers to the disease estimation block 10C.
The answer analysis part 10D can be configured with AI, for example, and can organize contents such as medical conditions and chief complaints, and can be made concrete to the extent that the above-mentioned tagging is possible. In addition, the response analysis part 10D is equipped with a function as a chatbot, for example, and communicates with the patient via the transmission line SLD1 to organize the contents of the patient's medical condition, chief complaint, etc., and to the extent that the above-mentioned tagging is possible. Be specific. Alternatively, the chatbot function communicates with the patient, organizes the patient's responses to the selected index (initial evaluation index), and specifies the candidate disease to the extent that it can be estimated.
In the response analysis part 10D, chief complaints and medical conditions can be categorized and listed, and multiple selections can be made using check boxes. In addition, if there is insufficient information to tag appropriate indicators, additional questions are sent to the patient and the patient answers, allowing for a "question-and-answer session" to break it down into tags. , it is possible to guide the user to an appropriate tag and determine an appropriate index.

Here, in the case of a patient whose medical condition or chief complaint (e.g. physical ailment) has not been cured for a long period of time and who has visited multiple medical institutions, treatment at the previous medical institution (e.g. There are cases where the prescribed drugs are not appropriate. Therefore, information that a patient has received from multiple medical institutions over a long period of time is often effective in estimating the disease of the patient.
In the illustrated embodiment, the general medical care block 20 comprises a treatment history part 10E, which communicates with the patient via a transmission line SL7 and communicates with the patient over a long period of time, including medical conditions, chief complaints (e.g. physical complaints). If the patient has not been cured and has visited multiple medical institutions, the patient can obtain the diagnosis results, types of drugs administered, and other information (treatment history information) at each of the multiple medical institutions. , has a function of transmitting treatment history information to the disease estimation block 10C via the transmission line SL8. As a result, information on ineffective treatments and drugs is transmitted to the disease estimation block 10C, thereby making it possible to exclude inappropriate candidate diseases.

In the illustrated embodiment, it is possible to select the optimal treatment module for the patient in the medical application 30 based on the patient's responses to the indicators presented to the patient from the indicator selection part 10B. In other words, cross-diagnostic indicators and disease-specific indicators selected for the patient's medical condition and chief complaint (complaint, current medical history, past medical history, etc.) are presented to the patient, and the patient responds. It is expected to have a similar effect as performing both an assessment and a disease-specific assessment, and it will be possible to select the most appropriate treatment module based on the patient's responses to the initial evaluation index. In particular, it is important to contribute to extremely effective medical care (treatment, diagnosis, prevention/recurrence prevention, early detection, detection of coexisting diseases, etc.) for cases that cannot be improved by simply identifying and treating the disease. I can do it.

Here, cross-diagnostic indicators and disease-specific indicators selected for the patient's medical condition and chief complaint are presented to the patient, and the patient responds to determine if the diseases overlap. It is possible to determine multiple diseases (for example, when depression and psychogenic ED coexist) and their overlapping ratio (for example, 70% for depression and 30% for psychogenic ED). Based on the overlap of diseases for each patient and their ratio, it is possible to achieve a certain effect by customizing the contents of the medical application selected by the present invention for each patient or type of disease. It is.

Referring mainly to FIG. 3, a procedure for selecting a medical application in the general medical treatment block 20 shown in FIG. 2 will be described.
In FIG. 3, in step S1, medical conditions (medical conditions, chief complaints, etc.) such as chief complaint, current medical history, past history, etc. are confirmed for patients with various diseases in various clinical departments (not shown) in a medical institution; The patient's medical condition, chief complaint, etc. are input into the server 10 via the information processing terminal 4 (FIG. 1) on the medical institution side.
Here, if the patient is visiting a medical institution, the patient's medical condition, chief complaint, etc. are transmitted from the information processing terminal 4 on the medical institution side. However, it is also assumed that the patient does not go to a medical institution and accesses the server 10 from home. Therefore, in step S1, medical conditions, chief complaints, etc. are input from the medical institution's information processing terminal 4 or the patient's processing terminal 3 to the general medical treatment block 20 of the server 10.

In the next step S2, cross-diagnostic indicators and/or disease-specific indicators corresponding to the medical condition, chief complaint, etc. input in step S1 are selected. That is, the cross-diagnostic index and/or disease-specific index (selected index: initial evaluation index) tagged with the medical condition, chief complaint, etc. input in step S1 is selected and sent to the index selection part 10B. .
Here, if the patient's medical condition or chief complaint is so abstract or vague that it is not possible to tag it with cross-diagnostic indicators and/or disease-specific indicators, the patient's medical condition or chief complaint, etc. etc. are sent to the response analysis part 10D, and the response analysis part 10D analyzes or parses the medical condition, chief complaint, etc. to the extent that cross-diagnostic indicators and/or disease-specific indicators can be tagged, and then the indicator selection part 10B Send to.
In step S3, the cross-diagnostic indicators and/or disease-specific indicators (selected indicators) sent to the indicator selection part 10B are used as initial evaluation indicators at the information processing terminal 4 on the medical institution side or on the patient side processing terminal. 3, and the selected index is presented to the patient complaining of the above-mentioned medical condition or chief complaint. Then, the process advances to step S4.

In step S4, the patient responds to the index presented in step S3 (selected index: initial index), and transmits the response to the disease estimation block 10C.
If the patient's response to the selected index (initial index) is unclear and it is difficult to determine a candidate disease, the unclear response is sent to the response analysis part 10D. Then, the disease estimation block 10C analyzes the patient's response to the extent that a candidate disease can be identified, and transmits the analyzed patient's response to the disease estimation block 10C.
In the next step S5, the disease estimation block 10C determines a candidate disease corresponding to the patient's medical condition, chief complaint, etc., based on the initial evaluation index (selected index) and the patient's response thereto. That is, the medical condition, chief complaint, etc. are input in step S1, and the disease of the patient who responded is estimated in step S4. When estimating a disease in step S5, necessary disease information (for example, pathological information, etc.) may be retrieved from the disease information database 10F and referred to. Here, cross-diagnostic indicators and disease-specific indicators are presented to the patient, and the patient responds to estimate the number of overlapping diseases if the patient is suffering from multiple types of diseases. Is possible. That is, in step S5, it is possible to estimate overlapping diseases that patients are suffering from.

When estimating the disease in step S5, if the patient's condition or chief complaint has not been cured for a long time and the patient has a history of visiting multiple medical institutions, the diagnosis results at each of the multiple medical institutions, The treatment history part 10E acquires the type of drug administered and other information (treatment history information), transmits the information (treatment history information) to the disease estimation block 10C, and uses the treatment history information for disease estimation. You can.
Such information (treatment history information) is effective in determining candidate diseases (disease estimation), and can greatly contribute to estimation of the patient's disease in the disease estimation block 10C. By estimating the disease using such information, if inappropriate treatment was given at the medical institution you previously visited (for example, treatment based on the assumption that the disease was inappropriate, or treatment where the prescribed drug was inappropriate), The possibility of estimating the same disease as the inappropriately assumed disease is reduced.
Once the disease is estimated in step S5, the process advances to step S6.

In step S6, based on the candidate disease determined in step S5, the medical applications 30 (30-1 to 30-4) for the candidate disease are selected. 30-4), information regarding the patient is transmitted.At the same time, information on the candidate disease (estimated disease) determined in step S5 and the treatment application is transmitted to the patient.
After step S6, the individual medical applications 30 (30-1 to 30-4) are executed.
Although not clearly shown in the figure, as a modification of the illustrated embodiment, it is possible to include only the general medical treatment block 20 having the function of a portal site or general reception, and to have the output of the general medical treatment block 20 (candidate diseases, diseases It is possible to configure the system to output duplicates (appropriate medical applications) to other institutions (for example, insurance companies, etc.).

Next, an overview of the various medical applications shown in FIG. 2 will be explained with reference to FIG. 4.
In FIG. 4 showing functional blocks in one of the various applications 30, 30-1, 30-2, etc. shown in FIG. 2, the medical application 30 (any one of the medical applications shown in FIG. 2) is , a part that performs a cross-diagnostic assessment (cross-diagnostic assessment part 30A in the medical application 30), a part that performs a disease-specific assessment (disease-specific assessment part 30B in the medical application 30), and Cluster analysis part 30C for classifying patients into corresponding clusters (groups, etc.) based on data, results of cross-diagnostic assessment, disease-specific assessment, and other classification information, and determining treatment modules for curing the disease. It has a treatment module determination part 30D.
Each of the medical applications 30, 30-1, 30-2, . . . shown in FIG. 2 basically has a configuration similar to that shown in FIG. 4.
In the illustrated embodiment, the phrase "medical application" refers not only to the application software but also to the system that executes the medical application.
Note that to prevent the illustration from becoming complicated, the signal transmission lines are not shown in FIG. 4.

The cross-diagnostic assessment part 30A of the medical application 30 improves the psychological flexibility of the accessing user (patient: target person) by evaluating adaptive behavior in response to psychosocial problems, life adjustment, stress, etc. Assess gender. The evaluation of adaptive behavior is based on Acceptance and Commitment Therapy (ACT), which is a type of CBT. If the target person shows adaptive behavior in response to psychosocial problems, life adjustment, stress, etc., they are healthy, have a high quality of life, have low stress, and have resistance to various diseases. It is rated as being in high condition.
The cross-diagnostic assessment part 30A does not perform an evaluation to identify a disease or evaluate symptoms characteristic of the disease. Cross-diagnostic assessment part 30A evaluates the extent to which the patient is able to behave adaptively or maladaptively in response to psychosocial problems, life adjustment, stress, etc. be.
In other words, in the cross-diagnostic assessment part 30A of the medical application 30, evaluation is performed using daily life data. The cross-diagnostic assessment part 30A of the medical application 30 utilizes daily life data to assess how adaptively the patient is able to behave in response to psychosocial problems, life adaptation, stress, etc. It has the function of cross-diagnostically assessing whether the patient is behaving in a non-adaptive manner (the process of performing cross-diagnostic assessment).
In addition, the disease-specific assessment part 30B in the medical application 30 evaluates to what extent the patient is able to act adaptively or non-adaptively in response to symptoms characteristic of the disease. It has the function of evaluating (the process of performing disease-specific assessment).

During the cluster analysis in the cluster analysis part 30C of the medical application 30, the cross-diagnostic indicators and/or disease-specific indicators tagged in the indicator selection part 10B in the general medical treatment block 20 (FIG. 2) and the patient's responses thereto may be available. Although not explicitly stated, in the illustrated embodiment, in order to deal with such a case, the initial evaluation index tagged with the patient's medical condition, chief complaint, etc. and the patient's response thereto are sent to the medical application 30, It is configured to be used for cluster analysis in the cluster analysis part 30C.

In FIG. 4, the cluster analysis part 30C shows the results (ratings) of the cross-diagnostic assessment in the cross-diagnostic assessment part 30A and/or the results (ratings) of the disease-specific assessment in the disease-specific assessment part 30B, and other classifications. Based on this information, patients are classified into clusters (groups) using statistical methods (e.g., cluster analysis) (categorizing patients into specific groups). Here, the results of the cross-diagnostic assessment in the cross-diagnostic assessment part 30A (rating of the cross-diagnostic assessment) and the results of the disease-specific assessment in the disease-specific assessment part 30B (rating of the disease-specific assessment) Cluster analysis can be performed on the diagnosis, or cluster analysis can be performed by integrating the ratings of cross-diagnostic assessment and disease-specific assessment.

In FIG. 4, in the treatment module determination part 30D, a treatment module (or work) linked to a cluster (group) into which patients are classified is selected (step of selecting a treatment module). The linked treatment modules are recorded in a recording device (database) for each medical application (not shown in FIG. 4).
It can be expected that implementing the treatment module will promote adaptive behavior in patients.
Although not shown, the treatment module determination part 30D in each medical application 30 transfers the selected treatment module to the patient's information processing terminal 3 (FIGS. 1 and 5) via an interface and information transmission line (not shown). It has the function of transmitting information. However, in the server 10, it is also possible to provide a treatment module execution part that executes the treatment module selected by the treatment module determination part 30D on the patient.

In this specification, the performance of psychotherapy may be expressed as an intervention for a patient.
In the medical application in the illustrated embodiment, both a treatment module based on the rating of the cross-diagnostic assessment part 30A and a treatment module based on the rating of the disease-specific assessment part 30B can be proposed. In other words, both cross-diagnostic and disease-specific interventions are possible.
For example, in cross-diagnostic interventions, psychological interventions are performed based on the evaluation results of "psychological flexibility" with the aim of learning/acquiring healthy new behavioral patterns. Disease-specific interventions involve psychological interventions aimed at learning/acquiring healthy new behavior patterns according to the disease, using techniques appropriate for each cluster, in situations and situations unique to each disease. conduct.
In the medical app in the illustrated embodiment, cross-diagnostic interventions take precedence over disease-specific interventions. However, there are cases where this is not the case.

In the medical application 30, it is possible to provide a treatment module using "virtual reality." For example, in the case of a patient suffering from anxiety, virtual reality can be used to give the patient the experience of ``riding a train'' (an experience in virtual reality).
For example, in the case of psychogenic ED, an example of work provided to the patient by a medical application is work using a known negative pressure erection aid (VCD). It is preferable to practice this work with a partner (spouse, etc.), and the fact that the patient is erect enough to have sexual intercourse and that the sexual intercourse is successful has a positive effect on restoring the patient's confidence.
Here, in the medical application 30, homework to be performed by the patient is prepared corresponding to the classified cluster. The homework has been edited in advance to have content that can be expected to improve the patient's symptoms and improve the patient's independence when carried out by the patient. For example, in the treatment module ``Attention Training,'' participants spend about 15 minutes a day training to pay attention to various sounds recorded in a sound source.

Although not shown, the medical application 30 may include an evaluation part (not shown) having a function of evaluating the effectiveness of treatment by the treatment module. The evaluation part has a function of evaluating the effectiveness of treatment according to a predetermined algorithm using existing evaluation scales and the like. The evaluation content sent to the patient includes, for example, advice for improving symptoms. As an example, in the case of psychogenic ED, evaluation is performed using the International Erectile Function Score (IIEF) as an evaluation scale (measurement index) of erectile function, which is a characteristic symptom of the disease. The International Erectile Function Score classifies severity based on scale scores, making it possible to evaluate the effectiveness of treatment.
Here, data on the patient's daily life can be acquired and recorded using the patient's terminal 3, wearable equipment, etc., and the results of effect measurement before and after treatment can be made into a report format. The evaluation part can have a function of providing such feedback to the patient (or doctor).

Although not shown, when inputting responses to evaluation scales and measurement indicators for treatment effects, there is a clocking means and recording means for recording the time until input, a measuring means for measuring heart rate and blood pressure, and a patient's pupil. A means for measuring opening/closing behavior, the direction of the line of sight, the number of blinks (for example, a camera if the patient terminal 3 is a smartphone or PC), a means for measuring the number of breathing, a means for measuring body temperature, or a means for measuring the complexion. It is preferable to provide a means of observation (for example, a camera on the patient-side terminal 3), to monitor objective information at the time of index input, and to provide a device to measure and observe the accuracy of answers and the condition of the respondent. .
By providing a device to measure and observe the accuracy of answers and the condition of respondents, it is possible to objectively monitor the accuracy of answers and the condition of respondents. Matters to be monitored regarding the condition of respondents include, for example, whether the patient who is the respondent is inputting measurement indicators carelessly, or whether they are inputting false information in order to obtain a result of ``improved.'' Inputting measurement indicators includes whether the patient is under stress, whether the patient is thinking deeply, the patient's reactivity, the patient's cognitive function, etc. Through such monitoring, it can be incorporated as a factor in cluster analysis.

When using a smartphone or PC camera as the patient terminal 3 as a device to measure and observe the accuracy of answers and the condition of the respondent, the above-mentioned monitoring can be carried out by attaching a special filter, lens, etc. to the camera. It can be done. Furthermore, it is possible to use measuring devices (not shown) and applications for measuring heart rate, blood pressure, etc.
Furthermore, it is possible to acquire objective information about respondents by wearing devices such as odor sensors and brain wave sensors. This makes it possible to measure the mental and physical state of respondents and use it as a factor in cluster analysis. For example, if a headset-like device that measures brain waves or the like is worn, the patient will feel more comfortable with the treatment, and the therapeutic effect will be further improved.

Although not shown, the medical application 30 may be provided with a device having a function of sending messages such as reminder notifications or encouragement to use the medical application regularly or irregularly at regularly determined times. I can do it. It is also possible to apply game-like elements. Furthermore, it is possible to provide some reward to the patient for accessing the medical app 30. Then, it is possible to provide a wearable device or the like having the above-mentioned functions so as to increase the motivation to access the medical application 30 and actively execute the medical application, and to continue the treatment (medical It is possible to strengthen the "positive" behavior of continuing to use the app.
By exhibiting these functions, the patient's desire to continue using the medical application 30 to continue executing the treatment module by accessing the medical application 30 is increased, and the patient no longer accesses the medical application 30 during treatment. The medical application 30 can improve the therapeutic effect (or the effect of the medical application).

Regarding the timing of reminder notifications, is the patient active or inactive? information and whether the patient is on the move? Are you at home? It is possible to manage information (status management) and make decisions.
Also, based on daily life data, reminder notifications can be sent when the heartbeat is stable, when the patient is free, etc.
Furthermore, since there is a high possibility that the patient is touching the smartphone after the patient's information processing terminal 3 (for example, smartphone) is activated (after recovery), a reminder notification can be sent at that time.

During cross-diagnostic assessments and disease-specific assessments in the medical application 30, patients use a smart speaker or the like to read out audio indicators (for example, questions), input instructions, guidance, etc. by voice, and record them. It is possible to configure the device to have a function of interacting with each other by voice (an interactive function) by making it possible to make a response and respond. Here, a smart speaker (AI speaker) is a speaker with an AI assistant function that supports interactive voice operations, and uses a built-in microphone to recognize voice and search for information and operate linked home appliances.

Next, referring to FIG. 5, the information processing terminal 3 on the patient side will be explained. The information processing terminal 3 on the patient side includes a control block 3A, a display block 3B, an input block 3C, a storage block 3D, and a communication block 3E.
The control block 3A has a function of controlling information processing and the like in the information processing terminal 3 on the patient side. The display block 3B has a function of displaying information to the user (patient) of the information processing terminal 3 on the patient side. The input block 3C has a function of receiving information input by the user (patient).
The storage block 3D is constituted by existing storage equipment, and stores a patient program indicated by reference numeral 3F for the information processing terminal 3 on the patient side. Although not explicitly shown, it is possible to use database 10F (FIG. 1) as storage block 3D. The communication block 3E has a function of connecting to the network 60 (FIG. 1) through wired or wireless communication.
The information processing terminal 3 on the patient side is not particularly limited as long as it is an electronic device having an information processing function and a communication function. However, considering the recent popularity, portable information terminals such as so-called smartphones are preferable.

Referring to FIG. 6, the information processing terminal 4 on the medical institution side will be described. The information processing terminal 4 on the medical institution side has a control block 4A, a display block 4B, an input block 4C, a storage block 4D, and a communication block 4E.
The control block 4A has a function of controlling information processing and the like in the information processing terminal 4 on the medical institution side. The display block 4B has a function of displaying information to users of the information processing terminal 4 on the medical institution side (doctors, personnel of the medical institution, etc.). The input block 4C has a function of receiving information input by a doctor, a person related to a medical institution, or the like.
The storage block 4D is constituted by existing storage equipment, and since it is the information processing terminal 4 on the medical institution side, a medical institution program indicated by reference numeral 4F is stored therein. It is also possible to use database 10F (FIG. 1) as storage block 4D. The communication block 4E has a function of connecting to the network 60 (FIG. 1) through wired or wireless communication.

Although not clearly shown in the diagram, it is possible to connect websites outside the system 100, such as anonymous patient groups, chat rooms, and BBSs, to share the usage status and trends of medical apps in a community of patients suffering from the same disease. , it is possible to view data regarding other users as statistical data. Here, a website external to the system 100 that can be connected needs to meet the requirements of a medical institution.
By connecting to websites outside the system, it is possible to obtain the same effect as participating in a self-help group for various diseases (for example, a self-help group for drug addicts), and to continue treatment. can motivate you.

The medical app used in the illustrated embodiment utilizes daily life data to visualize the degree of "psychological flexibility" and "mindfulness" in acceptance and commitment therapy (ACT). .
By acquiring and accumulating digital biomarkers, which are physiological data that can be acquired with wearable devices, as daily life data, it can be used to predict the onset of diseases, understand and manage the condition, and improve the accuracy of assessment. It is possible to improve. In addition, it is possible to propose/provide a more personalized and optimal treatment method. Furthermore, it can serve as big data for algorithm development using machine learning and AI.
By utilizing this big data, for example using AI, it is possible to predict diseases, and it is also possible to develop preventive measures based on the predictions. Interventions can then be made to prevent disease onset. Alternatively, big data can be analyzed using AI, and based on the analysis results, a medical app with content that matches each patient's personality, degree of disease, etc. (content that suits each patient), that is, a medical app for each patient It will be possible to provide customized apps.

In the illustrated embodiment, a medical app can utilize daily life data to visualize the degree of "psychological flexibility" and "mindfulness" in acceptance and commitment therapy (ACT). be. As an example of psychological data in daily life data, for example, using the method of ecological momentary assessment (EMA), we ask people to answer their moods, emotions, and thoughts on the spot and at that moment every morning and evening. This can be mentioned.

In the illustrated embodiment, the treatment modules in various medical applications may have the same or similar contents. Therefore, if the diseases overlap and multiple medical applications are selected, the multiple medical applications may suggest the same or extremely similar treatment modules.
In such a case, if the patient independently executes the selected medical applications, the same or very similar treatment modules will be repeatedly proposed to the patient. Depending on the proportion of overlapping diseases, it may be effective to repeat the same treatment module, but in general, if the same or very similar treatment modules are repeatedly proposed to the patient, the patient's The motivation to carry out the treatment module decreases. Alternatively, the patient's confidence in the determined treatment module may be reduced.
Furthermore, conventional medical apps do not deal with overlapping diseases; in the case of overlapping diseases, multiple diseases are identified and medical apps corresponding to each are presented. Therefore, as described above, the same treatment module is repeatedly presented, and treatment modules with similar contents are likely to be presented one after another.

On the other hand, in the illustrated embodiment, for example, in the disease estimation block 10C, the condition that "the treatment modules in the plurality of selected medical applications are not the same" is added, so that the same treatment module in overlapping diseases is It is possible to prevent duplication. Alternatively, the disease estimation block 10C may have a function of selecting such that "the treatment modules in the plurality of selected medical applications are not the same."
Furthermore, by linking treatment modules with similar contents in advance, it is possible to add the condition that "there is no linked treatment module in the plurality of selected medical applications" in the disease estimation block 10C. . Therefore, it is possible to prevent patients from being presented with similar treatment modules for overlapping diseases. Alternatively, the disease estimation block 10C can have a function of selecting such that "there is no linked treatment module among the plurality of selected medical applications."
Even if an existing medical application is incorporated into the system as the illustrated embodiment as shown in Figs. 2 and 3, even in the case of overlapping diseases, the same treatment module or similar treatment module will be used for the patient. can be prevented from being presented.

It should be noted that the illustrated embodiments are merely examples, and are not intended to limit the technical scope of the present invention.
For example, in the illustrated embodiment, the server 10 can be customized to correspond to a disease, customized to correspond to a patient's application, etc. As for the customization method, known techniques can be applied.
In addition, the present invention can be used to treat relationship problems with a spouse or partner, erectile dysfunction, depression, obsessive-compulsive disorder, panic disorder, agoraphobia, generalized anxiety disorder, social anxiety disorder, speech anxiety, specific phobia, Post-traumatic stress disorder, complex anxiety disorder, insomnia, irritable bowel syndrome, eating disorders and obesity, obesity and other lifestyle-related diseases, bipolar disorder, borderline personality disorder, attention-deficit/hyperactivity disorder, chronic or persistent Sexual pain, sexual dysfunction, relationship problems with spouse or partner other than erectile dysfunction, alcohol dependence and other types of addiction, schizophrenia and other severe mental illnesses, generalized anxiety disorder, social anxiety disorder, and speech anxiety disorder. Other diseases mainly related to the patient's mental condition, such as anxiety disorders, extreme phobias, infertility (male and female), atopic dermatitis, overactive bladder (urinary incontinence), anti-aging, and menopausal disorders. (disease) or not. Then, it is possible to estimate whether or not the patient has some other disease.

In the above embodiment, the disease and biopsychosocial factors are estimated based on the patient's response to the indicators selected in the cross-diagnostic indicator part and/or the disease-specific indicator part and the indicator selection part, and treatment is performed accordingly. We have shown an example in which a treatment app is provided and re-assessment is performed within that treatment app. You may also omit the assessment using the application. It goes without saying that accuracy can be improved by performing the assessment again using a treatment app.

Furthermore, the present invention can contribute to preventing the spread of infectious diseases such as coronavirus.
Moreover, even higher therapeutic effects can be expected by combining regenerative medicine using stem cells or treatment using stem cell supernatant with various medical applications of the present invention.
In addition, the present invention makes it possible to use quantitative measurement indicators, objective monitoring methods, and cluster analysis, so it is possible to clearly explain the program's algorithm to a third party, and it is possible to see the actual results. It is easy to use this as evidence. Therefore, it is easy to prepare the data necessary for various approvals.

In the above example, psychogenic diseases, psychosomatic disorders, stress-related diseases, etc. are classified using cross-diagnostic indicators and disease-specific indicators that include at least items for evaluating the mental situation associated with diseases, etc. In addition, we have added interview items and indicators to evaluate organic diseases, and added algorithms to perform these evaluations. It may also be configured to evaluate psychogenic diseases, biopsychosocial factors, or a combination thereof.

Although the words "medical treatment," "medical care," and "treatment" are used in the present invention, these concepts include not only diagnosis, treatment, and prevention by doctors, but also health care and the maintenance and improvement of mental health. Needless to say.

1... Control device 3... Patient-side information processing terminal (e.g. smartphone)
4... Information processing terminal on the medical institution side (e.g. PC)
10...Server 10A1...Cross-diagnostic indicator part 10A2...Disease-specific indicator part 10B...Indicator selection part 10C...Disease estimation part 10D...Response analysis part 10E...Treatment history Part 20...General medical treatment block 30...Medical application 30A...Cross-diagnostic assessment in medical applications Part 30B...Disease-specific assessment in medical applications Part 30C...Clusters in medical applications Analysis part 30D...Treatment module determination in medical application Part 60...Network 100...System

Claims (4)

In response to the input medical condition or chief complaint, selecting cross-diagnostic indicators and/or disease-specific indicators tagged with respect to the medical condition or chief complaint;
a step of presenting the selected index to the patient;
a step of determining the disease and/or biopsychosocial factors corresponding to the patient's medical condition, chief complaint, etc. from the selected indicators and their answers;
A method characterized by having the following. selecting a therapeutic app corresponding to the determined disease and/or biopsychosocial factors;
The method according to claim 1, characterized in that it comprises: Analyzing or analyzing the medical condition or chief complaint to the extent that it can be tagged with cross-diagnostic indicators and/or disease-specific indicators;
2. The method according to claim 1, further comprising the step of analyzing or analyzing the patient's response to the selected index to the extent that a disease corresponding to the medical condition and chief complaint can be identified. If the patient's medical condition and chief complaint have not been cured for a long time and the patient has a history of visiting multiple medical institutions, the diagnosis results at each of the multiple medical institutions and the type of medication administered 4. The method according to claim 1, further comprising the step of obtaining information and utilizing the information in said step of estimating a disease.

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