Display control method, program, and information processing device

Abstract

It visualizes the concerns patients have and provides information that appropriately conveys what to communicate to medical professionals based on those concerns. [Solution] The display control method involves a computer causing a display device to display a screen for a user who has logged in to a patient-oriented system using specified user identification information, acquiring information on the name of the user's illness and the status of the user's treatment for the illness, displaying a plurality of different worry keywords on the display device according to the name of the illness and the status of the treatment for the illness, acquiring information on the worry keywords selected by the user from among the plurality of worry keywords, inputting the information on the worry keywords selected by the user and information on output conditions including conditions for generating a plurality of sentences with different perspectives that are to be conveyed by the user to a machine-learned model, and controlling the display of the display device so that information on the plurality of sentences output by the machine-learned model is displayed on the display device.

Description

[Technical Field]

[0001] The present invention relates to a display control method, a program, and an information processing device for supporting a user in expressing their intentions to a medical professional. [Background technology]

[0002] Communication between patients and doctors is important in treating illnesses, but patients often face challenges in communicating with doctors. For example, patients may lack specialized knowledge or be anxious, which can prevent them from asking doctors appropriate questions. Patent Document 1 discloses a medical support device that presents potential questions for patients to ask doctors in order to facilitate communication between patients and doctors. Specifically, the device discloses a function for creating questions for patients to ask doctors based on the patient's medical information, including an expression conversion table for converting input questions into appropriate expressions and an AI model for generating questions. Furthermore, questions are automatically created based on frequently occurring keywords from information categorized by patient characteristics. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 7467057 Summary of the Invention [Problem to be solved by the invention]

[0004] The technology described in Patent Document 1 creates questions based on the patient's medical information, but the questions created do not necessarily match the concerns or intentions that each patient has. As a result, important intentions may not be conveyed during the examination, and a gap in understanding may arise between the doctor and the patient.

[0005] In today's world, where outpatient cancer treatment is carried out alongside daily life, it is becoming increasingly difficult for patients to express what they want to say to their doctors in the most appropriate way. Furthermore, patients may be unable to express their worries and anxieties in the most appropriate way due to limited expressive ability, vocabulary, or mental stress.

[0006] Therefore, the present invention aims to visualize the concerns that patients have and provide information that appropriately represents what to convey to medical professionals based on those concerns. [Means for solving the problem]

[0007] A method according to one aspect of the present invention for solving the above problem is a display control method for controlling a display on a display device, the method comprising: a computer causing the display device to display a screen for a user who has logged in to a patient-oriented system using predetermined user identification information; acquiring information on the name of the user's illness and the status of treatment for the illness; and displaying the acquired name of the illness and the status of treatment for the illness. Responding to information Multiple worry keywords Information on the display device, and obtain information on a worry keyword selected by the user from the plurality of worry keywords, and Healthcare workers The content to be conveyed to the Pre-set The method is characterized in that information on output conditions is input into a machine-learned model to obtain information on the plurality of sentences output by the machine-learned model, and the display of the display device is controlled so that the information on the plurality of sentences output by the machine-learned model is displayed on the display device. [Effects of the Invention]

[0008] According to the present invention, it is possible to visualize the concerns of patients and provide information that appropriately conveys what to convey to medical professionals in accordance with those concerns. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating a configuration of an information processing system. [Figure 2] FIG. 2 is a diagram illustrating a configuration of an information processing terminal. [Figure 3] FIG. 10 is a diagram showing an example of a screen displaying keywords of worries. [Figure 4] FIG. 10 is a flowchart illustrating an information processing method. DETAILED DESCRIPTION OF THE INVENTION

[0010] <Details of the embodiment> In this embodiment, as an example, an information processing system for providing applications (software) intended for patients suffering from illnesses will be described. Illnesses include various diseases, symptoms, disorders, injuries, poisonings, and abnormalities, and in this embodiment, a system for cancer patients will be described in particular.

[0011] FIG. 1 shows a configuration diagram of an information processing system according to this embodiment. This system includes information processing terminals (information processing devices) 1 and 2, and a server (information processing device) 3. These are connected via a network 4. Information processing terminal 1 is a terminal used by a user A, and information processing terminal 2 is a terminal used by another user B. In addition to information processing terminals 1 and 2, multiple information processing terminals used by multiple other users may be connected to network 4. Hereinafter, matters common to each information processing terminal will be referred to simply as information processing terminal without specifying a reference number.

[0012] In this system, the server controls and manages data, and the application (client) side of the information processing terminal displays that data. The server side (backend) manages data in a database, provides API endpoints, updates data and performs calculations, sends data to clients, controls the display of the app screen, and performs security control. The client side (application) sends requests to the server's API, displays the data received from the server on the screen, provides a user interface (UI), and temporarily caches data.

[0013] An application program for the user is installed on the information processing terminal. The program is installed by downloading it from a server connected to the information processing terminal via network 4. The information processing terminal transmits information input by the user to server 3 via network 4. The information processing terminal also receives (acquires) information output by server 3 via network 4 and displays information based on the received information.

[0014] Next, the configuration of the information processing terminal will be described. The information processing terminal has a computer provided in a device such as a general-purpose computer, a personal computer, a smartphone, a tablet, etc. The information processing terminal displays the screens described below on a display device (display screen) using an installed application.

[0015] 2 shows a configuration diagram of an information processing terminal 1 as an example of an information processing terminal. The information processing terminal has a processing unit 11, a memory 12, a storage 13, a communication unit 14, an input unit 15, and a display unit 16. These are electrically connected to each other via a bus 17. The bus 17 is commonly connected to each of the above components, and transmits, for example, address signals, data signals, and various control signals.

[0016] The processing unit 11 is an arithmetic unit that controls the operation of the information processing terminal as a whole, controls the transmission and reception of data between each unit, and performs information processing necessary for program execution and authentication processing, etc. The processing unit 11 includes an arithmetic processing unit such as a processor such as a CPU, GPU, or FPGA, and is stored in the storage 13, executes programs, etc. deployed in the memory 12, and performs various information processing operations described below.

[0017] The memory 12 (storage unit) includes a main memory configured with a volatile storage device such as a DRAM, and an auxiliary memory configured with a nonvolatile storage device such as a flash memory or HDD. The memory 12 is used as a work area for the processing unit 11, and also stores the BIOS executed when the information processing terminal is started up, various setting information, and the like.

[0018] The storage 13 (storage medium) stores various application programs. A database that stores data used in each process is constructed in the storage 13.

[0019] The communication unit 14 connects the information processing terminal to the network 4. The communication unit 14 communicates with external devices directly or via a network access point, for example, by a method such as a wired LAN, a wireless LAN, Wi-Fi (Wireless Fidelity, registered trademark), infrared communication, Bluetooth (registered trademark), short-range or contactless communication, etc. The input unit 15 is an information input device such as a keyboard, a mouse, or a touch panel.

[0020] The display unit (display device) 16 includes a display that displays information obtained by calculations performed by the processing unit 11 or information received from the outside by the communication unit 14. An organic EL display, a liquid crystal display, or the like can be used as the display. The display unit 16 provides a graphical user interface (GUI) that displays various information on its display surface. The display unit 16 is not limited to being provided integrally with the information processing terminal, but may also be a display device that is provided separately from the information processing terminal and connected to the information processing terminal.

[0021] Next, the configuration of the server 3 will be described. The server 3 includes a computer having a processing unit, memory (storage unit), storage (storage medium), and communication unit. Each unit is electrically connected to each other via a bus. The server 3 executes installed software programs to perform information processing, which will be described later, and control the display on the display device.

[0022] The processing unit is a computing device that controls the overall operation of server 3, controls the transmission and reception of data between each unit, and performs information processing necessary for program execution and authentication processing. The processing unit includes a computing device such as a processor such as a CPU, GPU, or FPGA, and is stored in storage, executes programs and the like deployed in memory, and performs various information processing described below. The memory of server 3 includes a main memory configured as a volatile storage device such as DRAM, and an auxiliary memory configured as a non-volatile storage device such as flash memory or HDD. The memory is used as a work area for the processing unit, etc. The memory and storage of server 3 store various programs. In addition, a database storing data used for each process may be constructed in this storage, or may be configured to be connectable to a separate database server.

[0023] The communication unit of the server 3 connects the server 3 to the network 4. The communication unit communicates with external devices directly or via a network access point using various communication methods. This communication unit receives information transmitted from an information processing terminal via the network 4. The received information is stored in memory and used for information processing performed by the processing unit or stored in storage. The server 3 receives (acquires) information transmitted from the information processing terminal via the network 4, performs various information processing based on the received information, and stores the information in memory or storage. In addition, the server 3 controls the communication unit to transmit the results of information processing or information stored in memory or storage to the information processing terminal in response to a request from the information processing terminal.

[0024] Next, user registration will be described. User A can use information processing terminal 1 to register as a user of this system. First, user A installs an application on information processing terminal 1 or launches it using a web browser. When user A launches the application for the first time, information processing terminal 1 displays a screen (page) for user registration on the display surface of its display unit. User A inputs information for identifying the user, such as an email address and an authentication code, on the user registration screen. User A can also input information (user information) that represents the user's characteristics and attributes, such as the name of the disease (type of disease), the status of treatment for the disease, and age. The processing unit of information processing terminal 1 acquires the user information input by user A and controls the communication unit to transmit the acquired information to server 3. The communication unit transmits the information to server 3 via network 4.

[0025] Server 3 receives the user information entered by User A via network 4. At that time, Server 3 can automatically create a user ID to identify the user. Server 3 checks the user information for errors, and if there are no errors, it saves the user information in memory or a database and registers the user. If there are errors, Server 3 notifies information processing terminal 1 of the error. When information processing terminal 1 receives the error notification, it displays an error message and prompts User A to correct the user information. Email addresses and user IDs are unique information used to identify individuals in various information processing and sending / receiving information, so they must be registered so that they are unique.

[0026] When the server 3 automatically creates the user ID, the server 3 stores the user information entered by user A in memory or storage, generates a unique ID number for user A, and registers the user. The server 3 notifies the information processing terminal 1 that user registration has been completed, and controls the communication unit to transmit the generated ID to the information processing terminal 1 used by user A. The information processing terminal 1 stores the user ID received from the server 3 in memory.

[0027] Next, the login process will be explained. After completing user registration, the user launches an application on the information processing terminal and logs in by entering user identification information such as a user ID and email address, and an authentication code on the login screen. In response to the user's operation, the information processing terminal transmits the input user identification information and authentication code, device information of the information processing terminal (device-specific identifier), application version information, and network information such as an IP address to the server 3. The server 3 compares the received identification information such as the user ID and authentication code with the user information stored in the database. If the information input by the user is correctly matched and authenticated, the server 3 transmits a response indicating successful authentication to the information processing terminal. If authentication is successful, the server 3 controls the display unit of the information processing terminal to display a predetermined screen and information.

[0028] Next, we will explain the application screen (graphical user interface (GUI)) displayed by a display device that displays information provided by an information processing terminal that executes an application. Here, a screen refers to a visually recognizable image displayed on the display surface (an area consisting of multiple pixels) of the display device, and the same screen includes an image that is displayed by scrolling or swiping and moves within the display surface, regardless of the size of the display surface of the display unit. In screen design, the display area is determined for each type and item of information stored in the database. Therefore, even if the content of information displayed in the same display area on the screen varies from day to day, for example, as long as the type of information is the same, the respective screens displayed each day can be said to be the same screen. On the other hand, different screens are created independently as separate screens in the screen design, and each screen displays a unique type of information.

[0029] Figure 3 shows an application screen 20 displayed by the display device. Screen 20 is a screen for patients to create questions for medical professionals. The medical professional is, for example, a doctor, typically the patient's primary care physician. Screen 20 can be used for preparation, for example, the day before an appointment. On screen 20, the information processing terminal displays worry keywords 21 along with the words "consider question" and "select from keywords." Worry keywords 21 are keywords related to the worries the patient is having, and are important words that express the worries. In Figure 3, as an example, the following multiple keywords are displayed on screen 20. I want to know the details of the test results 21A Treatment Option 21B When can I resume work in the 21st century? How much exercise can you do? 21D Medical Expenses Consultation Desk 21E Frequency of regular inspections 21F

[0030] A user who logs in to an information processing terminal can select one or more of the above keywords for their worries on screen 20. Selection can be made by tapping or clicking on the keyword. Also displayed on screen 20 is a button 22 for creating a question.

[0031] 4 shows a flowchart of an information processing method using the information processing system of this embodiment. First, the user starts an application on the information processing terminal and logs in using the login screen. Then, on the top screen after logging in, the user selects a linked block (image) for transitioning to the screen 20 for creating a question. The linked block for transitioning to the screen 20 for creating a question can be displayed in words such as, for example, preparing for a medical examination, something you would like to discuss with a doctor, or verbalizing a concern you would like to share.

[0032] When a user selects a linked block to transition to the screen 20 for creating a question, the information processing terminal notifies the server 3. Upon receiving the notification, the server 3 acquires information about the user logged in to the information processing terminal, including the name of the disease the user is currently suffering from or has previously suffered from, and the status of the user's treatment for the disease (S1). Examples of disease names include carcinomas such as lung cancer, stomach cancer, colon cancer, breast cancer, and liver cancer. However, the disease name may be a more specific name or a broad classification name. The status of the disease treatment may include treatment phases such as pre-treatment, treatment during treatment, and post-treatment. The disease name and the status of the disease treatment are stored as a database in the memory of the server 3 or in a storage unit of an external device based on information entered by the user during user registration. In the database, the name of the user's disease and the status of the disease treatment are associated and stored in association with user identification information, such as the user's ID. Therefore, the server 3 acquires the identification information of the user who is logged in to the information processing terminal, and acquires the name of the disease and the information on the status of treatment of the disease stored in correspondence with the identification information from a memory, etc. Alternatively, the name of the disease and the information on the status of treatment of the disease may be acquired from an external server device belonging to a medical institution, etc.

[0033] Then, the server 3 performs display control to display a plurality of mutually different worry keywords on the screen 20 of the information processing terminal according to the acquired disease name and disease treatment status (S2). Data on the plurality of mutually different worry keywords is stored in advance in a storage device, and worry keywords are stored as a database corresponding to each disease name and each disease treatment status.

[0034] For example, if a patient is suffering from breast cancer and is in the pre-treatment state, multiple worries related to breast cancer that the patient worries about before treatment are registered in the database as worry keywords, as shown below. Pain and limitations of surgery, breast preservation, reconstruction method and timing, hair loss and changes in appearance, discomfort during examinations, who to contact for advice on extending leave of absence, anxiety about returning to work, requesting someone to accompany you to medical appointments, and the extent to which you should disclose your illness

[0035] As another example, if a patient is suffering from breast cancer and is currently undergoing treatment, the database may register as worry keywords multiple worries related to breast cancer that the patient may be concerned about during treatment, as shown below. Dealing with hair loss and wigs, nausea and loss of appetite, changes in taste, concerns about numbness in the fingertips, how to tell your workplace, deciding whether to take a leave of absence or quit your job, how to tell your children, and concerns about medical costs

[0036] The server 3 acquires data on worry keywords corresponding to the acquired disease name and treatment status from a storage device storing data on worry keywords. Then, the server 3 performs display control to display worry keywords like the above example on the screen 20 of the information processing terminal. For example, if the disease the patient is suffering from is breast cancer and the treatment status is before treatment, the server 3 displays the following worry keywords in the same way as above: Pain and limitations of surgery, breast preservation, reconstruction method and timing, hair loss and changes in appearance, discomfort during examinations, who to contact for advice on extending leave of absence, anxiety about returning to work, requesting someone to accompany you to medical appointments, and the extent to which you should disclose your illness

[0037] In this way, by displaying multiple worry keywords related to the user's own illness on screen 20, the user can easily select content that matches their own worries. Also, by looking at the verbalized worries, the user can clarify and organize their own worries. Furthermore, by displaying worries that people with similar illnesses and treatment situations to the user's own often face and allowing the user to select at will, it leads to the efficient creation of questions that match the user's condition and intentions.

[0038] A user who logs in to an information processing terminal selects one or more worry keywords on a screen 20. When the user selects a button 22 for creating a question after selecting a worry keyword, information on the worry keyword selected by the user is transmitted from the information processing terminal to the server 3 together with a signal instructing the user to create a question. The server 3 acquires information on the worry keyword selected by the user from among the multiple worry keywords (S3).

[0039] Then, server 3 performs information processing to create questions to ask the doctor using the machine-learned model (S4). The machine-learned model is, for example, an artificial intelligence (AI) such as GPT or a large-scale language model, which has previously learned a large amount of text and can create natural-sounding sentences like a human. Alternatively, a model can be used that has been trained in advance using training data, with information on concern keywords as input data and questions to ask the doctor as output data. The machine-learned model is typically installed in an information processing device (AI server) separate from server 3, and server 3 can obtain the output data of the machine-learned model by data communication between server 3 and the AI ​​server. Server 3 then sends an instruction to the AI ​​server to input a prompt as an output condition into the machine-learned model.

[0040] An output condition can be set to generate multiple questions from different perspectives that the user wants to communicate to the doctor. Specifically, the output condition can be, for example, to generate questions about sharing the situation, questions about presenting hopes or values, and questions about investing in the future. A condition can also be set to output questions frequently asked by people whose disease names and treatment situations are similar to the user's. In this way, the AI ​​server inputs information about the concern keywords selected by the user and information about the output conditions into the machine-learned model and outputs multiple questions using the machine-learned model. Then, server 3 acquires information about the multiple questions output by the machine-learned model.

[0041] For example, if a user selects "dealing with changes in taste" as a concern keyword, the machine learning model will: As a question for sharing the situation, we generated the following: "I've been losing my sense of taste recently. Is this related to treatment or illness?" As a question about the presentation of hopes or values, we generated the following: "Should we take any measures or come up with any ideas to deal with the change in taste?" A question regarding investment in the end is generated: "If your sense of taste does not return, will this affect your future treatment or life?"

[0042] In this embodiment, the output conditions can be generated based on three different perspectives: (1) a statement about sharing the situation, (2) a statement about expressing hopes or values, and (3) a statement about investing in the outcome. This provides a means for communicating a wide range of patient intentions and emotions to medical professionals. Each perspective is based on effective medical communication techniques in the 4 Habits Model for building a good doctor-patient relationship. By providing a multi-layered structure to the questions, the perception gap between the doctor and the patient can be narrowed. By conveying the generated questions to the doctor, good communication with the patient can be maintained.

[0043] As described above, when the machine learning model outputs three questions, the number of options is appropriate, allowing the user to independently select the question that they think is best while still matching the user's intent. On the other hand, if there is only one question, some users may find that it does not match what they want to convey to the doctor, and they may have to repeatedly create the question. The number of questions output by the machine learning model is not limited to three, and can be two, four, five, or any number greater than three. However, if too many questions are generated, it becomes difficult to select the question that matches the user's intent.

[0044] Next, the server 3 performs display control to display the information on the multiple questions output by the machine-learned model on the screen of the display unit of the information processing terminal (S5). The screen on which the generated questions are displayed can be a screen different from screen 20. The user can view the multiple questions displayed on the screen of the information processing terminal and convey one or more questions selected by the user to the doctor. The information on the generated questions may also be stored in the memory of the user's own information processing terminal or sent externally by email to be stored in an external information processing device.

[0045] Although a question to a doctor has been given as an example of a sentence generated by the machine learning model, the sentence is not limited to a question and may be anything that the user wants to communicate to the doctor, such as a verbalized statement of a concern the user wants to discuss with the doctor or a shared concern. For example, sentences related to sharing the situation, statements related to expressing hopes or values, and statements related to investing in the end may be used. Even if the sentence is simply a statement of the situation, it can encourage communication, such as the doctor making a proposal to solve the problem in the situation.

[0046] According to this embodiment, the patient's concerns are visualized, and information that appropriately conveys what the patient wants to convey to the medical professional can be provided in accordance with the concerns. The technology described in Patent Document 1 creates questions directly based on the patient's medical information, which creates a problem in that questions do not necessarily match the concerns or intentions that each patient has. On the other hand, according to this embodiment, the patient's concerns are visualized, and questions are created after selecting concern keywords that match the user's intentions, so the above problem does not occur.

[0047] Although preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, the technical scope of the present disclosure is not limited to such examples. Furthermore, not all of the components shown in the embodiments are necessarily essential components of the present disclosure. Furthermore, features shown in each embodiment can also be applied to other embodiments as long as they are not mutually inconsistent.

[0048] The display control of the display unit described above can be performed by the information processing terminal 1 on which the program is installed, or by transmitting and receiving information to and from other information processing devices via a network or cable. Various information processes other than the display control can also be performed by one or more information processing devices on which the program is installed, such as a cloud service or a distributed computer. The machine-learned model that creates the question in S4 may also be installed on the server 3.

[0049] On the screen 20 displayed by S2, the multiple concern keywords may be arranged in order of frequency, starting with the most frequently selected keyword. For example, the server 3 associates and stores the value of the frequency of selection for each concern keyword, and controls the display based on that data. By arranging the keywords in order of frequency in this way, a user-friendly interface can be provided, allowing the user to select a button in an easy-to-understand and easily visible position without hesitation.

[0050] Additionally, on screen 20, multiple worry keywords can be displayed in a categorized order based on the classification of total pain (holistic pain). Total pain is the pain a patient feels as a result of the integration of four factors: physical pain, mental pain, social pain, and spiritual pain. Physical pain is the pain caused by the cancer itself or the side effects of cancer treatment. Mental pain is depression or anxiety. Social pain is the pain of being unable to work due to the pain, which leads to financial problems. Spiritual pain is the pain of wondering about the meaning of life, the meaning of suffering, and one's purpose in life. Server 3 controls the display so that multiple worry keywords are classified into the four aspects of pain and are displayed in a group on screen 20 by pain.

[0051] For example, as items of physical pain, the pain and limitations of surgery, how to preserve the breast, the method and timing of reconstruction, hair loss and changes in appearance, and the discomfort of examinations are displayed together on screen 20, and as items of social pain, the contact person for advice on extending leave of absence, anxiety about preparing to return to work, requesting someone to accompany the patient to medical appointments, and the extent to which the patient will disclose the illness are displayed together on screen 20. As another example, as items of physical pain, the treatment of hair loss and wigs, nausea and loss of appetite, treatment of changes in taste, and anxiety about numbness in the fingertips are displayed together on screen 20, and as items of social pain, how to tell the workplace, deciding whether to take leave or quit work, how to tell children, and anxiety about medical expenses are displayed together on screen 20, separate from the physical pain.

[0052] By arranging the worry keywords by pain type in this way, a user-friendly interface can be provided, allowing users to select buttons in easy-to-understand and visible locations. Furthermore, the prompts input to the machine learning model can be set according to the pain type to which the selected worry keyword belongs, based on the total pain classification, promoting more appropriate sentence generation.

[0053] Furthermore, in S3, in addition to the information on the concern keywords selected by the user, the name of the acquired disease and information on the status of treatment for the disease can also be input as input information to the machine-learned model. By inputting the name of the disease and information on the status of treatment for the disease into the machine-learned model, it is possible to create questions to the doctor that are more appropriate for communication in response to the disease and its treatment status. In other words, the generated questions will be more suited to the individual circumstances of the patient, making it possible to achieve individually optimized medical communication.

[0054] Although the example of a doctor is given as the medical professional to whom the patient (user) communicates the sentences generated by the machine learning model, other medical professionals may also be involved. For example, medical professionals include nurses, licensed practical nurses, public health nurses, midwives, pharmacists, physical therapists, occupational therapists, speech-language-hearing therapists, clinical laboratory technicians, radiological technologists, registered dietitians, dentists, dental hygienists, dental technicians, clinical engineers, orthoptists, prosthetists, mental health and welfare workers, social workers, and medical social workers. Furthermore, the entity communicating questions to medical professionals is not limited to the patient, but can also be the patient's family members.

[0055] In each of the following examples, examples of sentences generated by the machine-learned model are shown.

[0056] Example 1 In this example, the name of the user's illness is breast cancer, and the treatment status of the illness is currently underway. The worry keyword selected by the user from the multiple worry keywords displayed on screen 20 is "nausea and loss of appetite." This worry keyword corresponds to physical pain. The prompt to be input to the machine-learned model is as follows: -Please create a list of "three questions to ask your doctor" that cancer patients can prepare before their appointment. The purpose of this question is to make the dialogue between patient and doctor meaningful and lead to a decision within the limited consultation time. The output should consist of three lines, with one question each corresponding to 1. Sharing the situation, 2. Presenting hopes or values, and 3. Investing in the end. If the selected distress keyword is physical pain, the OPQRST method is applied to physical pain. OPQRST is a method used to evaluate pain and symptoms, and the initials stand for Onset, Provocation, Quality, Radiation, Severity, and Timing. The prompt can also specify the number of characters, the expression method, prohibited words, and example sentences. In S4, the machine learning model is input with the above prompt, the name of the user's illness, the status of the illness's treatment, and the worry keywords selected by the user, and the machine learning model creates the following sentences: 1. sentences about sharing the situation, 2. sentences about presenting hopes or values, and 3. sentences about investing in the end. 1. I am still experiencing nausea after treatment and it is difficult for me to eat. 2. I would like to find something I can eat without losing too much weight if possible. 3. Can you tell me if there are any tips I can use right now to combat nausea?

[0057] <Example 2> In this example, the worry keyword selected by the user is different from that in Example 1. In this example, the name of the user's illness is breast cancer, and the treatment status of the illness is currently underway. The worry keyword selected by the user from the multiple worry keywords displayed on screen 20 is "hesitation about participating in school events." This worry keyword corresponds to social pain. The prompts input to the machine-learned model are set corresponding to the type of pain based on the total pain classification. Since the worry keyword in this example is social pain, output rules related to social pain are set, such as concisely stating social facts when sharing the situation and sharing objectively without emotion. In S4, the prompt, the name of the illness, the treatment status of the illness, and the worry keyword selected by the user are input to the machine-learned model, and the machine-learned model generates the following sentences: 1. sentences related to sharing the situation, 2. sentences related to expressing hopes or values, and 3. sentences related to investing in the end. 1. I am thinking about participating in a school event, but I am unsure whether to do so because of my physical condition during treatment. 2. If possible, I would like to attend my child's school events. 3. Regarding participation in events, could you please tell me if there is anything I should be careful about now, taking into account the effects of treatment?

[0058] Example 3 In this example, the name of the user's illness is colon cancer, and the treatment status of the illness is post-treatment. The worry keyword selected by the user from the multiple worry keywords displayed on screen 20 is "change in bowel habits." This worry keyword corresponds to physical pain. In S4, the name of the illness, the treatment status of the illness, and the worry keyword selected by the user are input into the machine-learned model, and the machine-learned model creates the following sentences: 1. sentences related to sharing the situation, 2. sentences related to presenting hopes or values, and 3. sentences related to investing in the end. 1. (Example: About a week after surgery) I have been having more loose stools (example: after breakfast). 2. Is there a way to prepare for bowel movements while out and about? 3. If there is a change in bowel movements, how should you determine when to return to the hospital?

Claims

1. A display control method for controlling a display of a display device, comprising: The computer displaying on the display device a screen for a user who has logged in to the patient-oriented system with predetermined user identification information; Acquire information on the name of the user's illness and the status of treatment for the illness of the user; displaying, on the display device, information on a plurality of worry keywords corresponding to the acquired information on the name of the disease and the status of treatment of the disease; acquiring information about a concern keyword selected by the user from among the plurality of concern keywords; inputting information on the concern keywords selected by the user and information on preset output conditions including conditions for generating a plurality of sentences from different perspectives that are to be conveyed to healthcare professionals into a machine-learned model, and acquiring information on the plurality of sentences output by the machine-learned model; A display control method characterized by controlling the display of a display device so that information about the plurality of sentences output by the machine-learned model is displayed on the display device.

2. The method described in claim 1, characterized in that the acquired information on the name of the disease and the status of treatment for the disease is further input into the machine-learned model to obtain information on the multiple sentences output by the machine-learned model.

3. The method described in claim 1, characterized in that the output condition includes generating a question from the user to the doctor as the sentence.

4. The method described in claim 1, characterized in that the output condition includes generating, as the sentence, a question sentence that is frequently asked by people whose name of the acquired disease and whose treatment status for the disease are similar to those of the user.

5. The method according to claim 1, wherein the plurality of worry keywords are displayed in order for each category based on the classification of total pain.

6. setting the output conditions to be input to the machine-learned model in accordance with the type of pain based on the classification of the total pain; The method according to claim 5, wherein the output condition is applied in accordance with the type of distress of the selected distress keyword.

7. The method of claim 1, wherein the output conditions input to the machine-learned model are to create statements regarding sharing a situation, statements regarding expressing hopes or values, and statements regarding investing in an end, respectively.

8. A program for causing a computer to execute the method according to any one of claims 1 to 7.

9. In the information processing device, a processing unit for controlling the display of a display device that displays a screen to a user who has logged in to the patient-oriented system with predetermined user identification information; The processing unit Acquire information on the name of the user's illness and the status of treatment for the illness of the user; displaying, on the display device, information on a plurality of worry keywords corresponding to the acquired information on the name of the disease and the status of treatment of the disease; acquiring information about a concern keyword selected by the user from among the plurality of concern keywords; inputting information on the concern keywords selected by the user and information on preset output conditions including conditions for generating a plurality of sentences from different perspectives that are to be conveyed to healthcare professionals into a machine-learned model, and acquiring information on the plurality of sentences output by the machine-learned model; An information processing device comprising: controlling a display of the display device so as to display information on the plurality of sentences output by the machine-learned model on the display device.

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