Information processing system

Abstract

To provide an information processing system that can improve communication between patients and healthcare professionals. [Solution] The information processing system comprises: a pre-information acquisition means for acquiring patient information based on patient input; a prescription pattern determination means for determining one of a plurality of prescription patterns based on the pre-information acquisition means; a questionnaire generation means for generating a questionnaire corresponding to the prescription pattern determined by the prescription pattern determination means; a questionnaire result acquisition means for acquiring the patient's response results to the questionnaire generated by the questionnaire generation means; a supplementary explanation generation means for generating supplementary explanations for the patient based on the response results acquired by the questionnaire result acquisition means; and a feedback generation means for generating feedback for healthcare professionals based on the response results acquired by the questionnaire result acquisition means.

Description

Technical Field

[0001] The present invention relates to an information processing system.

Background Art

[0002] Conventionally, regarding medical diagnosis, it has been studied to simultaneously improve both the reliability of diagnosis and the speed to reach a diagnosis using an information processing device (computer). For example, Patent Document 1 discloses a technique for improving an inquiry diagnosis system.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, the technique described in Patent Document 1 above is only aimed at improving the speed and reliability of diagnosis, and nothing is considered regarding the communication between patients and medical staff. Specifically, even if a diagnosis result from a medical staff is presented based on a questionnaire from a patient, it is not known whether the patient can correctly understand the diagnosis result, and it is also not known whether the explanation from the medical staff at the time of presenting the diagnosis result was appropriate (that is, whether the explanation could be made so that the patient could understand). In such a case, there is a risk that there is a gap in the understanding between the patient and the medical staff regarding the diagnosis result. Therefore, a mechanism that allows communication between the patient and the medical staff is required so that there is no gap in the understanding between the patient and the medical staff regarding the diagnosis result.

[0005] This invention was made in view of the above circumstances, and aims to provide an information processing system that can improve communication between patients and healthcare professionals by enabling healthcare professionals to ascertain whether or not patients have understood the prescription (diagnosis) given by them. [Means for solving the problem]

[0006] To achieve the above objective, an information processing system according to an example of the present invention comprises: a pre-information acquisition means for acquiring patient information based on patient input; a prescription pattern determination means for determining one of a plurality of prescription patterns based on the pre-information acquisition means; a questionnaire generation means for generating a questionnaire corresponding to the prescription pattern determined by the prescription pattern determination means; a questionnaire result acquisition means for acquiring the patient's response results to the questionnaire generated by the questionnaire generation means; a supplementary explanation generation means for generating supplementary explanations for the patient based on the response results acquired by the questionnaire result acquisition means; and a feedback generation means for generating feedback for healthcare professionals based on the response results acquired by the questionnaire result acquisition means. [Effects of the Invention]

[0007] According to an example of the present invention, feedback is generated for healthcare professionals based on the patient's responses to a questionnaire corresponding to the prescription (diagnosis) from the healthcare professional. This allows healthcare professionals to understand whether the patient understood the prescription, which can be used to improve communication with the patient in the future and thus improve communication between patients and healthcare professionals. Furthermore, if the patient does not understand the prescription (diagnosis) from the healthcare professional, supplementary explanations are generated for the patient based on the patient's responses to the questionnaire corresponding to the prescription. This helps the patient understand the prescription without having to wait for further explanations from the healthcare professional in the future. [Brief explanation of the drawing]

[0008] [Figure 1] This figure shows an example configuration of an information processing system according to an embodiment of the present invention. [Figure 2] This figure illustrates an overview of a medical-related communication improvement service according to an embodiment of the present invention. [Figure 3] This figure shows an example of a server hardware configuration according to an embodiment of the present invention. [Figure 4] This figure shows an example of the functional configuration of a server, a patient terminal, and a medical professional terminal according to an embodiment of the present invention. [Modes for carrying out the invention]

[0009] Embodiments of the present invention will be described below with reference to the drawings.

[0010] First, an information processing system according to an embodiment of the present invention will be described with reference to Figure 1. The information processing system shown in Figure 1 is configured to include a server 1, a patient terminal 2, and a medical professional terminal 3.

[0011] Server 1 is managed by the provider of the "Medical Communication Improvement Service." The "Medical Communication Improvement Service" is a service that improves medical-related communication via the Web, application programs, etc., and is the service to which the information processing system according to this embodiment applies. Details of the "Medical Communication Improvement Service" will be described later, but one of the features of this service is that it allows medical professionals to know whether the patient understood the prescription (diagnosis result) from the medical professional, which can be used to improve communication with the patient in the future.

[0012] The patient terminal 2 and the healthcare professional terminal 3 are portable devices such as smartphones, and are equipped with at least an input unit (e.g., a touch panel) and a display unit (e.g., an LCD display). The patient terminal 2 is used by the patient, and the healthcare professional terminal 3 is used by the healthcare professional. As will be described in detail later, the patient terminal 2 is used when the patient enters preliminary information, enters answers to questionnaires, and checks supplementary explanations, while the healthcare professional terminal 3 is used when the healthcare professional checks candidate prescription patterns and decides which one to use, and when checking feedback based on the patient's questionnaire responses.

[0013] Furthermore, Server 1, Patient Terminal 2, and Medical Personnel Terminal 3 are interconnected via a predetermined network N, such as the Internet.

[0014] (Medical communication improvement service) Next, we will explain the overview of the "Medical Communication Improvement Service" using Figure 2. In the following explanation, the "Medical Communication Improvement Service" will be provided via application programs installed on patient terminals 2 and medical professional terminals 3, but it may also be provided via a website managed by server 1.

[0015] The "Medical Communication Improvement Service" according to this embodiment consists of the following steps (1) to (8). Strictly speaking, step (4) below is not implemented by an information processing system, but is included in the description as part of the service flow.

[0016] (1) The patient inputs preliminary information via the patient terminal 2. This preliminary information is sent to the server 1. The preliminary information includes the patient's current symptoms, medical history, etc. What items to include in the preliminary information is determined by the server 1 and displayed on the patient terminal 2, but items for which the same patient has already provided answers may be omitted. Also, the server 1 may determine different items according to the medical department the patient will visit.

[0017] (2) Based on the preliminary information input by the patient, the server 1 generates candidate prescription patterns. The server 1 creates a database based on the preliminary information input by multiple past patients and generates candidate optimal prescriptions based on machine learning such as the database and prescription guidelines for each symptom, etc., but is not limited to this. The candidate prescription patterns thus generated are sent to the medical staff terminal 3 and displayed on the display unit (e.g., liquid crystal display) of the medical staff terminal 3.

[0018] (3) The medical staff refers to the candidate prescription patterns displayed on the medical staff terminal 3 and determines one of the prescription patterns based on the patient's interview and their own rules of thumb, etc. The medical staff inputs the prescription pattern determined using the medical staff terminal 3, and thereby, the information on the determined prescription pattern is sent to the server 1.

[0019] (4) The medical staff explains the determined prescription pattern to the patient, and the patient asks questions of the medical staff, etc., and the medical staff and the patient communicate with each other.

[0020] (5) Server 1 generates a questionnaire corresponding to the prescription pattern determined by medical staff. As a method for generating the questionnaire, questionnaires may be pre-stored corresponding to each of a plurality of prescription patterns, and Server 1 may extract the one corresponding to the determined prescription pattern from the stored questionnaires, or may generate it each time by machine learning guidelines such as points to note for the prescription pattern. There are a plurality of questionnaire items, for example, items such as whether the prescription explanation was easy to understand, the side effects of the medicine, and questions about the degree of understanding of contents such as the timing of taking the medicine. The information of this questionnaire is transmitted to the patient terminal 2 and displayed on the display unit (for example, a liquid crystal display) of the patient terminal 2.

[0021] (6) The patient who has finished communicating with the medical staff checks the questionnaire displayed on the patient terminal 2 and inputs answers to each item of the questionnaire. The information of this answer result is transmitted to Server 1.

[0022] (7) Server 1 generates a supplementary explanation based on the patient's answer result to the questionnaire. Specifically, for example, when the degree of understanding of contents such as the side effects of the medicine and the timing of taking it is low, a supplementary explanation of that content is generated. As a method for generating the supplementary explanation, a supplementary explanation in the case where all the questionnaire results are assumed to have a low degree of understanding is pre-stored corresponding to each questionnaire, and it may be generated by deleting those with a low degree of understanding from it, or may be generated each time by machine learning guidelines such as points to note for the prescription pattern. The information of the supplementary explanation is transmitted to the patient terminal 2 and displayed on the display unit (for example, a liquid crystal display) of the patient terminal 2.

[0023] (8) Server 1 also generates feedback for healthcare professionals based on the patient's responses to the questionnaire. Specifically, if there is a response such as "the explanation of the prescription was difficult to understand," that information is generated as feedback information. It is preferable that the questionnaire items are set up so that it is possible to identify which explanation (specifically, the explanation of the medication, the explanation of side effects, the explanation of symptoms, etc.) was difficult to understand. The feedback information is transmitted to the healthcare professional terminal 3 and displayed on the display unit (e.g., liquid crystal display) of the healthcare professional terminal 3.

[0024] Thus, according to the "Medical Communication Improvement Service" of this embodiment, even if a patient finds the explanation from a healthcare professional difficult to understand during the communication in step (4) and is unable to adequately explain or express to the healthcare professional what they did not understand, they can still provide feedback to the healthcare professional via Server 1 about what they did not understand. Furthermore, the patient will be shown supplementary explanations of what they did not understand, which can help them understand what they did not understand without having to wait for a future opportunity to communicate with the healthcare professional. In addition, healthcare professionals will receive feedback on whether the communication in step (4) was appropriate, which can be used to improve future communication with patients.

[0025] Next, we will describe an example of the hardware configuration of Server 1 in the information processing system shown in Figure 1. Figure 3 shows an example of the hardware configuration of Server 1 in the information processing system shown in Figure 1.

[0026] (Hardware configuration of Server 1) Server 1 comprises a CPU (Central Processing Unit) 11, ROM (Read Only Memory) 12, RAM (Random Access Memory) 13, a bus 14, an input / output interface 15, a storage unit 16, and a communication unit 17.

[0027] The CPU 11 executes various processes according to the program recorded in the ROM 12 or the program loaded from the storage unit 16 into the RAM 13. The RAM 13 appropriately stores data necessary for the CPU 11 to execute various processes.

[0028] The CPU 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output interface 15 is also connected to this bus 14. A storage unit 16 and a communication unit 17 are connected to the input / output interface 15.

[0029] The memory unit 16 is composed of DRAM (Dynamic Random Access Memory) and stores various types of data. The communication unit 17 controls communication with other devices via a network N, including the Internet, mobile phone networks, and LANs (Local Area Networks).

[0030] Furthermore, the hardware configurations of patient terminal 2 and medical professional terminal 3 are basically the same as those of server 1, except that they have input units for inputting information and display units for displaying information; therefore, a description of their hardware configurations will be omitted.

[0031] Next, we will explain the functional configuration of Server 1, Patient Terminal 2, and Medical Professional Terminal 3 in executing the series of processes in the "Medical Communication Improvement Service" described above (excluding the process in step (4)). Figure 4 shows an example of the functional configuration required for the processes executed by Server 1, Patient Terminal 2, and Medical Professional Terminal 3 in Figure 3.

[0032] (Functional configuration of patient terminal 2) The functional configuration of patient terminal 2 will now be described. The CPU 21 of patient terminal 2 functions as an input control unit 211 and a display control unit 212.

[0033] The input control unit 211 receives input information from the patient via the input unit 23 (typically a touch panel) (specifically, the preliminary information for step (1) and the results of the questionnaire in step (6) shown in Figure 2), and performs control to transmit the input information to the server 1 via the communication unit 22.

[0034] The display control unit 212 performs control to display a presentation screen (specifically, a screen showing the questionnaire for step (5) in Figure 2 or a screen showing supplementary explanations for step (7)) on the display unit 24 (typically a liquid crystal display) based on the generated information transmitted from the server 1.

[0035] (Functional configuration of terminal 3 for medical professionals) The functional configuration of the medical professional terminal 3 will now be described. The CPU 31 of the medical professional terminal 3 functions as an input control unit 311 and a display control unit 312.

[0036] The input control unit 311 receives input information from a medical professional via the input unit 33 (typically a touch panel) (specifically, the prescription pattern determined in step (3) shown in Figure 2), and executes control to transmit the input information to the server 1 via the communication unit 32.

[0037] The display control unit 312 performs control to display a presentation screen (specifically, a screen showing candidate prescription patterns for step (2) and a screen showing feedback for step (8) as shown in Figure 2) on the display unit 34 (typically a liquid crystal display) based on the generated information transmitted from the server 1.

[0038] (Functional configuration of Server 1) Next, the functional configuration of Server 1 will be described. The CPU 11 of Server 1 functions as an information acquisition unit 111, an information management unit 112, and an information generation unit 113. In addition, an information database 160 is provided in the storage unit 16 of Server 1.

[0039] The information acquisition unit 111 acquires information transmitted from the patient terminal 2 and the medical professional terminal 3 via the communication unit 17 and notifies the information management unit 112 of this information.

[0040] The information management unit 112 collects and manages the notified information. This information is stored in the information database 160 of server 1. This allows for the accumulation of patient pre-information, survey responses, and prescription patterns determined by healthcare professionals. The accumulated information can also be used for machine learning. Furthermore, the information management unit 112 instructs the generation unit 113 to generate a predetermined presentation screen based on the notified information.

[0041] The generation unit 113 generates a predetermined display screen based on instructions from the information management unit 112 (based on information stored in the information DB 160) and performs control to transmit it to the patient terminal 2 or the medical professional terminal 3 via the communication unit 17. Specifically, if the information management unit 112 is notified of the preliminary information for step (1) shown in Figure 2, the generation unit 113 generates a screen showing candidate prescription patterns for step (2) based on instructions from the information management unit 112. If the information management unit 112 is notified of the determined prescription pattern for step (3) shown in Figure 2, the generation unit 113 generates a screen showing a questionnaire for step (5) based on instructions from the information management unit 112. If the information management unit 112 is notified of the results of the questionnaire for step (6) shown in Figure 2, the generation unit 113 generates a screen showing supplementary explanations for step (7) and a screen showing feedback for step (8) based on instructions from the information management unit 112.

[0042] Through the collaboration of the various hardware and software components of Server 1, Patient Terminal 2, and Medical Professional Terminal 3 as described above, it becomes possible to execute the series of processes in the "Medical Communication Improvement Service" described above (excluding the process in step (4)).

[0043] As described above, embodiments of the present invention have been explained, and embodiments of the present invention include the following various configurations.

[0044] In other words, the information processing system (for example, an information processing system consisting of a server 1 shown in Figure 1, a patient terminal 2, and a medical professional terminal 3) includes: a pre-information acquisition means for acquiring patient information based on patient input (for example, a part in which an information acquisition unit 111 acquires pre-information based on step (1) shown in Figure 2); a prescription pattern determination means for determining one of a plurality of prescription patterns based on the pre-information acquired by the pre-information acquisition means (for example, a part in which the information acquisition unit 111 acquires the determined prescription pattern based on steps (2) and (3) shown in Figure 2); and a questionnaire generation means for generating a questionnaire corresponding to the prescription pattern determined by the prescription pattern determination means (for example, a generation unit 1 based on step (5) shown in Figure 2). The system comprises: 13 (a part that generates a questionnaire); questionnaire result acquisition means (for example, a part in which the information acquisition unit 111 acquires the answer results to the questionnaire based on step (6) shown in Figure 2); supplementary explanation generation means (for example, a part in which the generation unit 113 generates supplementary explanations based on step (7) shown in Figure 2) based on the answer results acquired by the questionnaire result acquisition means; and feedback generation means (for example, a part in which the generation unit 113 generates feedback based on step (8) shown in Figure 2) based on the answer results acquired by the questionnaire result acquisition means. With this configuration, healthcare professionals can understand whether the patient understood the prescription by checking the feedback, which can be used to improve communication with the patient in the future and improve communication between patients and healthcare professionals. Furthermore, if a patient does not understand a prescription from a healthcare professional, supplementary explanations will be generated based on the patient's responses to a questionnaire related to that prescription. This helps patients understand their prescriptions without having to wait for further explanations from healthcare professionals.

[0045] (Additional examples) Next, we will describe an additional example of the "Medical Communication Improvement Service." This additional example utilizes the basic configuration shown in Figures 1, 3, and 4 described in the above embodiment, while strengthening the education of patients on essential medical information and the understanding of patients' concerns and anxieties.

[0046] Traditionally, regarding communication between patients and healthcare professionals, patients have a strong desire to be listened to extensively, while healthcare professionals need to conduct consultations with many patients, making it difficult to dedicate sufficient time to each individual. As a result, patients' anxieties are not addressed within the consultation time, leading to the burden of repeating the same explanations at subsequent consultations. This has resulted in a lack of effective communication between patients and healthcare professionals. This additional implementation aims to improve this communication.

[0047] The "Medical Communication Improvement Service" in this additional embodiment is a service performed after a medical professional's consultation (including diagnosis) with a patient is completed and before the next consultation takes place. Instead of the steps shown in Figure 2, it consists of the steps shown in (1A) to (5A) below. Strictly speaking, the steps shown in (1A) and (5A) below are not implemented by an information processing system, but are included in the description as part of the service flow. Furthermore, the step shown in (2A) below includes steps (2A-a) to (2A-d), and the order in which steps (2A-a) to (2A-d) are executed does not matter.

[0048] (1A) After the interview is completed, the medical professional (or their associate) will notify the patient of their access ID and access information (e.g., URL or QR code) to a dedicated website managed by Server 1. This notification may be given directly to the patient in writing after the interview, or it may be sent via electronic means such as email.

[0049] (2A) The patient uses the notified access ID to access the notified dedicated website via the patient terminal 2, and the dedicated website is displayed on the patient terminal 2. This dedicated website has at least the following functions: (a) an educational content presentation function that allows the patient to select and view multiple types of educational content (articles, diagrams, videos, etc.) related to the disease and treatment policies; (b) a search support function that allows the patient to search for related information by entering search terms of interest; (c) a confirmation test function that checks the patient's understanding of what they have learned or the content related to the disease; and (d) a questionnaire function that allows the patient to self-evaluate their own interests, concerns, and understanding of the disease. The server 1 that manages this dedicated website can store at least the following information for later analysis: (a) the history of educational content viewed by the patient and the viewing time (time spent) for each piece of content; (b) the patient's search terms and search history; (c) the results of the confirmation test; and (d) the results of the questionnaire. The dedicated website is not limited to these functions and may have additional functions as appropriate to facilitate communication between the patient and healthcare professionals.

[0050] (2A-a) Patients can access various educational content related to their disease and treatment plans on a dedicated website. This allows patients to gain knowledge about their disease and treatment plans. Preferably, the educational content will differ depending on the patient's disease, but common educational content may be presented to all patients, or a combination of both may be used. If different educational content is provided for each patient, the content presented should differ according to each patient's access ID. Alternatively, if different educational content is presented for each patient, the healthcare professional may select which educational content to present during the consultation (and present an access ID corresponding to this selection), or the content may be automatically presented using machine learning based on the patient's disease and consultation content (an access ID corresponding to the presented content will be automatically generated). In addition, common educational content may be presented regardless of the patient (i.e., all patients can access common educational content), but the educational content presented as "recommended" may differ depending on the patient. Furthermore, based on the patient's access to the dedicated website, Server 1 stores the viewing time (time spent) and viewing history of various educational content that the patient has selected and viewed.

[0051] (2A-b) The dedicated website is equipped with a search box, allowing patients to freely search by entering keywords of interest. This search box may be for searches within the dedicated website or for searches on external websites. In either case, Server 1 stores the search keywords entered in the search box. Furthermore, if the search is within the dedicated website, Server 1 can also store the time spent viewing (time spent) on the pages displayed after the search and viewed by the patient.

[0052] (2A-c) Patients may take a test to assess their understanding of their disease, etc., either before or after viewing various educational content. Preferably, the content of this test should differ depending on the patient's disease, but it may also include common content for all patients (for example, content at a minimum level that all patients need to know), or a combination of both. If the content differs for each patient, the content of the test presented should differ according to each patient's access ID. If different tests are presented for each patient, the healthcare professional may choose which test to present during the interview (and present an access ID corresponding to this selection), or the test may be automatically presented using machine learning based on the patient's disease and interview content (an access ID corresponding to the presented content will be automatically generated). Furthermore, the educational content presented may differ based on the results of this test. In other words, Server 1 may analyze the results of the confirmation test conducted in step (2A-c) and change the content of the educational content presented in step (2A-a) (or change the educational content presented as "recommended"). In other words, by repeating steps (2A-c) and (2A-a), the patient's understanding of the disease may improve. Server 1 also stores the results of this confirmation test.

[0053] (2A-d) Patients can access a dedicated website and answer a questionnaire regarding their level of understanding of various information related to their disease, such as their concerns and anxieties about their disease and their treatment methods. This questionnaire may be a self-assessment on a scale of 1 to 5, or it may include open-ended questions. This questionnaire may be different for each patient, common to all patients, or a combination of both. If different questionnaires are used for each patient, the questionnaire presented should be different depending on each patient's access ID. Alternatively, if different questionnaires are presented for each patient, the healthcare professional may choose which questionnaire to present during the interview (presenting an access ID corresponding to this selection), or the questionnaire may be automatically presented using machine learning based on the patient's disease and interview content (an access ID corresponding to the presented content will be automatically generated). Server 1 stores the results (answers) of this questionnaire.

[0054] (3A) Server 1 generates various reports based on the various information stored through the patient's steps (2A-a) to (2A-d). These reports include at least explicit reports and implicit reports. Explicit reports suggest concerns and anxieties that the patient is aware of, while implicit reports suggest concerns and anxieties that the patient is unaware of, or concerns and anxieties that are difficult for the patient to express to healthcare professionals.

[0055] The manifest report is generated based on the results of the patient's confirmation test and the patient's questionnaire. Specifically, Server 1 analyzes the results of the patient's confirmation test to understand the patient's current knowledge level regarding the disease, etc., and analyzes the patient's self-reported questionnaire results to understand the patient's concerns and anxieties. Based on these, it presents the patient's knowledge level and extracts the concerns and anxieties that the patient is particularly interested in or anxious about as important matters. Furthermore, Server 1 can also compare these concerns and anxieties with the current knowledge level and extract matters that the patient is interested in (or anxious about) but does not understand well as the most important matters. Server 1 generates the manifest report from the important matters extracted in this way. Note that these important matters may be extracted from multiple items based on an appropriate threshold (for example, the top 5 items according to importance).

[0056] The latent report is generated based on the patient's viewing time (time spent) and viewing history for various educational content, as well as their search terms. Specifically, Server 1 can analyze which information the patient was interested in and which information took time to understand by analyzing which educational content the patient viewed and for how long. Furthermore, by analyzing what search terms the patient entered into the search box, Server 1 can analyze matters that the patient is concerned about but finds difficult to express to healthcare professionals. It should be noted that the information the patient searches for themselves can be considered their explicit concerns, but it may also include unexpected information that differs from the information that healthcare professionals anticipate (i.e., the information contained in the educational content). Therefore, it is treated here as concerns and anxieties used in the latent report. However, even if the information entered into the search terms is information contained in the educational content, it may be treated as an explicit concern or anxiety. In this way, Server 1 analyzes the information viewed by the patient, the time spent viewing it, and information that the patient is concerned about but finds difficult to tell healthcare professionals. This allows Server 1 to generate a latent report that identifies concerns and anxieties that the patient may not be consciously aware of, or concerns that the patient may find difficult to directly tell healthcare professionals, even though these concerns differ from the important matters explicitly stated in the overt report.

[0057] Server 1 may also present predetermined evaluation items based on the manifest report and the latent report. That is, even if an item is analyzed in the manifest report as having low interest based on self-reporting, if there is information in the latent report that there has been a long browsing history related to that item, that item can be presented as an item that can be evaluated as potentially of interest. Similarly, even if an item is analyzed in the manifest report as not causing high anxiety based on self-reporting, if keywords related to that item have been entered as search terms, that item can be presented as an item that can be evaluated as potentially causing anxiety.

[0058] In the above, it was assumed that various reports would be generated based on primary information stored by Server 1 (results of confirmation tests, survey results, viewing time and viewing history for various educational content, and search terms). However, instead of the above-mentioned reports, or in addition to the above-mentioned reports (or parts thereof), the above-mentioned primary information may be output as is. Furthermore, when Server 1 generates the above-mentioned manifest and latent reports, the various analyses and report generation performed by Server 1 may be carried out using machine learning.

[0059] (4A) Server 1 transmits the generated manifest and latent reports to the healthcare professional terminal 3. Server 1 may also transmit predetermined evaluation items (items that can be evaluated as potentially of interest or anxiety) to the healthcare professional terminal 3 based on the manifest and latent reports. This allows healthcare professionals to understand in advance the reports that show the patient's level of knowledge about the disease and the patient's tendencies for interest and anxiety.

[0060] Furthermore, Server 1 may transmit information on multiple high-priority items from among the items of interest or concern to the patient, based on the manifest and latent reports, to the patient terminal 2. From the multiple items displayed on the patient terminal 2, the patient may select one or more items that they actually want to ask the healthcare professional about. In this case, the items selected by the patient are transmitted to the healthcare professional terminal 3 via Server 1. This allows healthcare professionals to know in advance what items the patient wants to ask about. Also, in the medical field, patients often find it difficult to immediately think of questions when asked "Do you have any questions?" during a consultation with a healthcare professional. However, since Server 1 presents items that the patient is likely to want to ask about, the patient only needs to select the items they actually want to ask about. This brings to light things that were potentially bothering the patient, and allows the patient to clarify their own understanding.

[0061] (5A) As described above, healthcare professionals can obtain reports in advance that show the patient's level of knowledge, tendencies of interest, and tendencies of anxiety, or they can obtain in advance the questions the patient may have. In addition, patients can check their level of knowledge about their illness, etc. by taking a confirmation test and improve their level of knowledge by viewing educational content. Then, the patient and healthcare professional will conduct the next interview. This will enable healthcare professionals to provide efficient and accurate explanations, and because the patient's level of knowledge has improved, they will be able to understand the explanations from the healthcare professional more easily, so that appropriate communication can be conducted between the patient and healthcare professional even with limited interview time.

[0062] In this additional embodiment, the specialized website is described as a website managed by Server 1, but it may also be a website on an external server. In this case, information entered by the patient may be provided to Server 1 from the external server via an API or the like. Furthermore, this dedicated website may be provided as an application program installed on the patient terminal 2, and information entered by the patient on the application program may be transmitted to Server 1.

[0063] Furthermore, in this additional embodiment, explicit and implicit reports are sent to healthcare professionals. However, these reports may also be generated and categorized according to the healthcare professional's category and sent to the healthcare professional corresponding to each category. For example, if healthcare professionals are categorized as doctors, nurses, technicians, etc., the explicit and implicit reports are generated and categorized in advance into matters to be handled by doctors, matters to be handled by nurses, and matters to be handled by technicians. This classification may be performed by machine learning, or it may be classified according to predetermined rules based on keywords included in the matters of interest and concern. Then, each report is sent individually to the doctor, nurse, and technician. In this way, by sending reports categorized according to the matters that each healthcare professional should handle, the doctor does not have to handle all of the patient's multiple concerns and concerns at once, but rather they can be assigned to the appropriate person to address. This makes it possible for each person to utilize their expertise and for efficient communication to be achieved. It should be noted that the form is not limited to sending reports categorized according to the matters that healthcare professionals should handle. Instead, a single report may be sent as a whole, and the report may also include which healthcare professional is responsible for each matter. In this way, the responsibilities of each healthcare professional become clear, and similarly, more efficient communication becomes possible.

[0064] As described above, this additional implementation allows patients to receive supplementary education between consultations, enabling them to clarify their lack of understanding and identify their concerns and anxieties. Healthcare professionals can also understand the patient's level of knowledge and major concerns and anxieties before the next consultation. As a result, communication errors between patients and healthcare professionals can be reduced, and consultation times can be shortened while the quality of consultation content can be improved simultaneously.

[0065] (Functional configuration of additional examples) Regarding the additional embodiment, the functional configuration of the patient terminal 2 and the medical professional terminal 3 are the same as the basic configuration shown in Figures 3 and 4, but the information entered into the patient terminal 2 and the screens displayed in the medical professional terminal 3 differ, as shown in steps (2A) to (4A) above. In addition, the server 1 has the basic configuration shown in Figures 3 and 4, and the information management unit 112 further operates as the following functional units. • Information Analysis Department: Analyzes patient confirmation test results, questionnaire results, patient viewing time (time spent) and viewing history for various educational content, and search terms. • Interest and Anxiety Estimation Department: Based on the analysis results from the Information Analysis Department, this department estimates the degree (importance) of the patient's concerns and anxieties. • Display screen generation instruction unit: Instructs the generation unit 113 to generate a predetermined display screen. Specifically, it instructs the generation of various educational content, search windows, confirmation tests, and questionnaire screens for display on the patient terminal 2, and instructs the generation of manifest reports and latent reports for display on the medical professional terminal 3. Furthermore, the information DB160 of server 1 stores the various primary information described above.

[0066] In this additional embodiment, while utilizing the existing basic configuration, a function is added to analyze the patient's knowledge level, interests, and concerns in order to support the next consultation, thereby enabling more advanced communication support.

[0067] As described above, additional embodiments of the present invention have been explained, and these additional embodiments include the following various configurations.

[0068] In other words, the information processing system (for example, an information processing system consisting of a server 1, a patient terminal 2, and a medical professional terminal 3 as shown in Figure 1) includes a generation means (for example, the part in step (2A) where the server 1 generates the screen for the dedicated website) that generates a dedicated screen (for example, a dedicated website) capable of displaying information about the patient's disease (for example, educational content, information based on search words, confirmation tests, questionnaires) according to the access ID entered by the patient; a display means (for example, the part in step (2A) where the patient terminal 2 displays the dedicated screen) that displays the dedicated screen generated by the generation means; a reception means that accepts input operations from the patient on the dedicated screen (for example, the part in steps (2A-a) to (2A-d) where the patient terminal 2 accepts browsing operations, search operations, input operations for confirmation tests, and input operations for questionnaires) that the patient makes; and a system that stores predetermined information based on the input operations accepted by the reception means. The system includes a memory means (for example, in step (2A), a part in which Server 1 stores the history of educational content viewed by the patient and the viewing time for each piece of content, the patient's search words and search history, the results of the confirmation test, and the results of the questionnaire), an analysis means for analyzing predetermined information (for example, in step (3A), a part in which Server 1 analyzes the results of the patient's confirmation test and the patient's questionnaire results, and a part in which Server 1 analyzes the patient's viewing time (time spent), viewing history, and search words for various educational content), a presentation screen generation means for estimating the patient's interests and anxieties based on the analysis by the analysis means and generating the estimation results (for example, in step (3A), a part in which Server 1 generates manifest reports and latent reports), and a display means for displaying the estimation results generated by the presentation screen generation means (for example, in step (4A), a part in which the medical professional terminal 3 displays manifest reports and latent reports).With this configuration, the actions performed by the patient on a dedicated screen capable of displaying disease-related information (browsing, searching, taking quizzes, and answering questionnaires) are analyzed. Healthcare professionals can review these analysis results in advance, allowing them to understand the patient's interests and concerns regarding information and improve communication with the patient during subsequent consultations.

[0069] Furthermore, the present invention can also be applied to information processing methods and information processing programs.

[0070] Although the present invention has been described above, the above description is merely illustrative, and various improvements and modifications may be made. Furthermore, the effects described in this embodiment and additional embodiment are merely a list of preferred effects arising from the present invention, and the effects of the present invention are not limited thereto. [Explanation of Symbols]

[0071] 1 server 2. Patient terminals 3. Terminals for medical professionals

Claims

[Claim 1] A means for acquiring prior patient information based on patient input, A prescription pattern determination means that determines one of a plurality of prescription patterns based on the prior information obtained by the prior information acquisition means, Questionnaire generation means for generating a questionnaire corresponding to the prescription pattern determined by the prescription pattern determination means, A questionnaire result acquisition means for acquiring patient responses to the questionnaire generated by the questionnaire generation means, A supplementary explanation generation means generates supplementary explanations for patients based on the response results obtained by the aforementioned questionnaire result acquisition means, An information processing system comprising: a feedback generation means that generates feedback for healthcare professionals based on the response results obtained by the survey result acquisition means.

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