Clinical support system and clinical support device

Abstract

To support communication in medical care.SOLUTION: A clinical support system comprises a first acquisition unit, a first analysis unit, a second acquisition unit, a second analysis unit, and a display control unit. The first acquisition unit acquires patient information related to a first patient, inquiry information indicating the symptom of the first patient, and conversation information in which the conversation with the first patient is recorded. The first analysis unit performs analysis processing using the patient information, the inquiry information, and the conversation information to generate patient characteristic information indicating a psychological profile and a social profile of the patient. The second acquisition unit acquires the patient characteristic information on the first patient generated by the first analysis unit and the conversation information on a second patient similar in the patient characteristic information. The second analysis unit analyzes the characteristics of the conversation between the second patient and a medical worker based on the conversation information acquired by the second acquisition unit. The display control unit displays a result of the analysis conducted by the second analysis unit.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The embodiments disclosed in the present specification and drawings relate to a clinical support system and a clinical support device.

[0002] Traditionally, doctors and other medical professionals have been required to provide medical care based not only on biological aspects such as test results, but also on the social and psychological aspects of patients. Therefore, medical professionals collect information on psychological and social aspects through communication with patients.

[0003] However, it can be difficult to collect information on psychological and social aspects, and it is also difficult to utilize this information while keeping an overall overview. Therefore, there is a need for technology that supports communication during medical treatment. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Special Publication No. 2014-503894 Summary of the Invention [Problem to be solved by the invention]

[0005] One of the problems that the embodiments disclosed in this specification and drawings aim to solve is to support communication in medical treatment. However, the problems that the embodiments disclosed in this specification and drawings aim to solve are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described below can also be positioned as other problems. [Means for solving the problem]

[0006] A clinical support system according to an embodiment includes a first acquisition unit, a first analysis unit, a second acquisition unit, a second analysis unit, and a display control unit. The first acquisition unit acquires patient information related to a first patient, medical interview information indicating the symptoms of the first patient, and conversation information recording the conversation of the first patient. The first analysis unit generates patient characteristic information indicating psychological and social aspects of the patient through an analysis process using the patient information, the medical interview information, and the conversation information. The second acquisition unit acquires the patient characteristic information of the first patient generated by the first analysis unit and the conversation information of a second patient whose patient characteristic information is similar. The second analysis unit analyzes characteristics of the conversation between the second patient and a medical professional based on the conversation information acquired by the second acquisition unit. The display control unit displays the analysis results by the second analysis unit. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a block diagram showing an example of the configuration of a clinical support system according to the first embodiment. [Figure 2] FIG. 2 is a block diagram showing an example of the configuration of the clinical support device according to the first embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of the data configuration of the analysis target information. [Figure 4] FIG. 4 is an explanatory diagram illustrating an example of a conversation analysis screen according to the first embodiment. [Figure 5] FIG. 5 is a flowchart showing an example of a display process executed by the clinical support device according to the first embodiment. [Figure 6] FIG. 6 is a block diagram showing an example of the configuration of a clinical support device according to the second embodiment. [Figure 7] FIG. 7 is a flowchart showing an example of a display process executed by the clinical support apparatus according to the second embodiment. [Figure 8] FIG. 8 is a flowchart showing an example of a display process executed by the clinical support device according to the first modification of the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, a clinical support system and a clinical support device according to embodiments will be described with reference to the drawings. In the following embodiments, parts with the same reference numerals perform similar operations, and redundant description will be omitted as appropriate.

[0009] (First embodiment) Doctors and other medical professionals are expected to provide holistic medical care that takes into account not only biological aspects but also the psychological and social aspects of patients.More specifically, medical professionals are expected to determine treatment plans that take into account various aspects, rather than determining treatment plans based solely on biological aspects such as the results of diagnostic imaging and clinical tests.

[0010] For example, patients with chronic diseases such as lifestyle-related diseases may not be cured by simply administering medication. For example, in the case of lower back pain, it is preferable for medical professionals to not only prescribe a medicinal patch but also provide advice based on the patient's lifestyle, such as their work.

[0011] Medical professionals can expect greater improvement by implementing treatments that encourage lifestyle changes rather than simply administering medication. Furthermore, by determining treatment plans that take into account the patient's parental status and work status, patients can achieve better adherence (patients actively participating in treatment and medication and receiving treatment in accordance with their decisions) through treatment that fits their lifestyle both during and after treatment, leading to better quality of life and better outcomes. Furthermore, patients can concentrate on their treatment with peace of mind by receiving explanations and treatment tailored to their individual personalities.

[0012] In this way, medical professionals can provide better medical care by determining treatment plans based on biological, psychological, and social aspects. In other words, medical professionals are required to provide so-called holistic medical care that includes not only biological aspects but also psychological and social aspects. Medical professionals also collect information on psychological and social aspects through communication with patients.

[0013] However, medical professionals communicate with patients based on their own training and experience. They may find it difficult to gather information related to psychological and social aspects of patients. They must also decide which information to record in electronic medical records. Furthermore, medical professionals are required to utilize this information while maintaining a comprehensive overview.

[0014] On the other hand, experienced medical professionals can communicate smoothly with patients based on their own experience and knowledge.Therefore, there is a need for technology that supports medical practice by helping medical professionals learn from others by referring to the key points of communication between other medical professionals and patients.

[0015] FIG. 1 is a block diagram showing an example of the configuration of a clinical support system 1 according to the first embodiment. The clinical support system 1 includes a hospital information system (HIS) 10, a radiology information system (RIS) 20, a picture archiving and communication system (PACS) 30, a laboratory information system (LIS) 40, a patient terminal 50, a patient measurement terminal 60, a clinical support device 70, and a display device 80. Each system and device is connected to each other via a network 90 so as to be able to communicate with each other. The configuration shown in FIG. 1 is an example, and the number of each system and device may be changed as desired. Devices not shown in FIG. 1 may also be connected to the network 90.

[0016] The hospital information system 10, the radiology department information system 20, the medical image management system 30, and the clinical test information system 40 are realized by computer devices such as servers and workstations.

[0017] The hospital information system 10 stores electronic medical record information 11 and medical interview information 12. The electronic medical record information 11 is information that records the progress of a patient's medical treatment. For example, the electronic medical record information 11 includes information for identifying the patient, personal information such as the patient's age, sex, and family structure, the name of the disease the patient has, the name of the prescribed medication, and the treatment period. The medical interview information 12 is information that indicates the patient's responses to a medical interview. For example, the medical interview information 12 includes responses to the patient's current symptoms, responses to the patient's living environment, and responses to a personality assessment.

[0018] The radiology department information system 20 stores image interpretation report information 21. The image interpretation report information 21 is information containing findings by a doctor or the like who has interpreted image information 31 captured by a medical image diagnostic apparatus.

[0019] The medical image management system 30 stores image information 31. The image information 31 includes images captured by a medical image diagnostic device. The medical image diagnostic device is, for example, an X-ray CT (Computed Tomography) device, an MRI (Magnetic Resonance Imaging) device, an X-ray diagnostic device, an ultrasound diagnostic device, or the like. The image information 31 complies with the DICOM (Digital Imaging and Communications in Medicine) standard.

[0020] The clinical test information system 40 stores clinical test result information 41 and clinical test report information 42. The clinical test result information 41 is information showing the results of a clinical test. The clinical test report information 42 is information containing findings on the clinical test results.

[0021] The patient terminal 50 is a terminal used by the patient. The patient terminal 50 is realized by, for example, a smartphone, a tablet terminal, a wearable terminal, or a personal computer. The patient terminal 50 also stores conversation information 51 in which the patient's conversation is recorded. For example, the conversation information 51 is information about conversations with medical professionals such as doctors, nurses, and counselors. Specifically, the conversation information 51 has audio information in which the conversation is recorded. The conversation information 51 may also have video information such as videos and still images that include the conversation. If video information is included, the conversation information 51 has video that shows the patient's facial expressions.

[0022] The patient measurement terminal 60 is a terminal that measures the vital signs of a patient. The patient measurement terminal 60 is realized, for example, by a wearable terminal. The patient measurement terminal 60 measures vital signs such as blood pressure, pulse, body temperature, and respiration. The patient measurement terminal 60 may also record conversations. The patient measurement terminal 60 then stores personal health information 61 that includes the measurement results of the vital signs.

[0023] The patient measurement terminal 60 is not limited to a wearable terminal, but may be a body fat meter, an activity monitor, or other devices. The patient measurement terminal 60 may measure body fat, an activity level, or other items. Furthermore, the clinical support system 1 may be equipped with a plurality of patient measurement terminals 60. Furthermore, in the clinical support system 1, the measurement targets of each of the plurality of patient measurement terminals 60 may be different.

[0024] The clinical support device 70 is a device that supports medical professionals such as doctors in clinical practice. The clinical support device 70 is realized by computer equipment such as a server or a workstation. The clinical support device 70 analyzes biological, social, and psychological aspects of a patient based on information acquired from each system and device of the clinical support system 1. The clinical support device 70 also analyzes information that does not belong to the biological, social, or psychological aspects of the patient, i.e., other aspects. The clinical support device 70 also generates information indicating the biological, social, psychological, and other aspects of the patient. The clinical support device 70 may be installed in a facility such as a hospital, or may be a server on the Internet.

[0025] The display device 80 is a device capable of displaying various types of information. The display device 80 is realized by a computer device such as a personal computer or a tablet terminal. The display device 80 displays various types of information generated by the clinical support device 70. For example, the display device 80 displays a conversation analysis screen G1 (see FIG. 4). The display device 80 may also be a transmissive display that displays different information on the medical professional side and the patient side. The display device 80 is an example of a display control unit.

[0026] A medical professional such as a doctor can refer to the analysis results of the conversation between other medical professionals and patients during medical treatment by looking at the information displayed on the display device 80. Therefore, the medical professional can refer to the conversation between other medical professionals and patients during medical treatment.

[0027] Next, the clinical support device 70 will be described.

[0028] 2 is a block diagram showing an example of the configuration of a clinical support device 70 according to the first embodiment. The clinical support device 70 includes an NW (network) interface 710, an input interface 720, a display 730, a storage circuitry 740, and a processing circuitry 750.

[0029] The NW interface 710 is connected to the processing circuit 750, and controls the transmission and communication of various data between the processing circuit 750 and each device connected via the network 90. ​​For example, the NW interface 710 is realized by a network card, a network adapter, a NIC (Network Interface Controller), or the like.

[0030] The input interface 720 is connected to the processing circuitry 750 and converts input operations received from an operator (medical professional) into electrical signals and outputs the electrical signals to the processing circuitry 750. Specifically, the input interface 720 converts the input operations received from the operator into electrical signals and outputs the electrical signals to the processing circuitry 750. For example, the input interface 720 may be realized by a trackball, a switch button, a mouse, a keyboard, a touchpad that performs input operations by touching the operation surface, a touchscreen that integrates a display screen and a touchpad, a non-contact input circuit using an optical sensor, a voice input circuit, or the like. Note that in this specification, the input interface 720 is not limited to those that include physical operation components such as a mouse and a keyboard. For example, an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the device and outputs the electrical signal to a control circuit is also included as an example of the input interface 720.

[0031] The display 730 is connected to the processing circuit 750 and displays various types of information and image data output from the processing circuit 750. For example, the display 730 is realized by a liquid crystal display, a CRT (Cathode Ray Tube) display, an organic EL display, a plasma display, a touch panel, or the like.

[0032] The storage circuitry 740 is connected to the processing circuitry 750 and stores various data. The storage circuitry 740 also stores various programs that are read and executed by the processing circuitry 750 to realize various functions. For example, the storage circuitry 740 is realized by a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, a hard disk, an optical disk, or the like.

[0033] The memory circuit 740 stores analysis target information 741, reference conversation information 742, and standard treatment information 743.

[0034] 3 is a diagram showing an example of the data configuration of the analysis target information 741. The analysis target information 741 is information acquired by each system or device of the clinical support system 1, and is information that specifies information to be analyzed as biological aspects, social aspects, psychological aspects, and other aspects of the patient.

[0035] The analysis target information 741 is associated with a category, an analysis item, and an analysis target item. The category is a division such as biological aspect, psychological aspect, social aspect, and others. The biological aspect is a classification indicating the biological aspect of the patient. For example, the biological aspect is associated with biological analysis items such as the patient's various test results, diseases, and physical condition. Furthermore, the biological aspect is not limited to biological aspects, and analysis items related to physical aspects, medical aspects, and pathological aspects may also be associated with the biological aspect. The psychological aspect is a classification indicating the psychological aspect of the patient. For example, the psychological aspect is associated with analysis items related to psychological aspects such as the patient's intentions and personality. Furthermore, the psychological aspect may include the patient's emotional state. The emotional state may be feelings such as depression or anger. The social aspect is a classification indicating the social aspect of the patient. For example, the social aspect is associated with analysis items related to social aspects such as work, interpersonal relationships, hobbies, and educational background. Other is associated with analysis items that do not belong to the biological, psychological, or social aspects. The analysis items are items used to analyze the patient. The analysis target item is information that specifies whether or not to analyze the analysis item. That is, the clinical support device 70 analyzes the analysis item that is set as the analysis target in the analysis target item. The association between the classification of the analysis target information 741 and the analysis items shown in FIG. 3 is an example and may be changed as desired.

[0036] The reference conversation information 742 is information having a plurality of conversation information 51 to be referenced. The reference conversation information 742 has a plurality of pieces of information in which patient characteristic information, conversation information 51, and medical professional identification information are associated with each other. The patient characteristic information is the analysis result for each analysis item of the patient's biological aspect, psychological aspect, social aspect, or other aspect. The conversation information 51 is information in which a conversation between a patient in the patient characteristic information and a medical professional is recorded. The medical professional identification information is identification information for identifying a medical professional who conversed with a patient in the conversation information 51. Furthermore, the medical professional identification information may be the medical professional's years of experience, the medical department to which the medical professional belongs, the medical professional's classification such as specialist, a combination of these pieces of information, or other information.

[0037] The standard treatment information 743 is information in which a standard treatment policy for an injury or illness is registered. The standard treatment information 743 has injury or illness identification information for identifying the injury or illness, and treatment policy information indicating the treatment policy. The injury or illness identification information is, for example, the name of the injury or illness, or code information for identifying the injury or illness. The treatment policy information is information indicating the standard treatment for the injury or illness. For example, the treatment policy information is information indicating a treatment policy recommended by each facility such as a hospital, or a treatment policy recommended by an academic society.

[0038] The processing circuitry 750 controls the overall operation of the clinical support device 70. The processing circuitry 750 includes, for example, a patient designation function 751, an information acquisition function 752, an analysis target designation function 753, a feature analysis function 754, a similarity calculation function 755, a similar patient selection function 756, a conversation information acquisition function 757, a conversation analysis function 758, a standard of care acquisition function 759, and an information generation function 760. In the embodiment, each processing function performed by the components of the patient designation function 751, the information acquisition function 752, the analysis target designation function 753, the feature analysis function 754, the similarity calculation function 755, the similar patient selection function 756, the conversation information acquisition function 757, the conversation analysis function 758, the standard of care acquisition function 759, and the information generation function 760 is stored in the storage circuitry 740 in the form of a computer-executable program. The processing circuitry 750 is a processor that reads and executes the program from the storage circuitry 740 to realize the function corresponding to each program. In other words, the processing circuit 750 in the state in which each program has been read has each function shown in the processing circuit 750 of FIG.

[0039] 2 is described as realizing the patient designation function 751, information acquisition function 752, analysis target designation function 753, feature analysis function 754, similarity calculation function 755, similar patient selection function 756, conversation information acquisition function 757, conversation analysis function 758, standard treatment acquisition function 759, and information generation function 760 by a single processor, but it is also possible to combine multiple independent processors to configure the processing circuit 750 and have each processor execute a program to realize the function. Also, in FIG. 2, it is described as realizing the program corresponding to each processing function by a single storage circuit such as the storage circuit 740, but it is also possible to configure multiple storage circuits to be distributed and have the processing circuit 750 read out the corresponding program from each storage circuit.

[0040] The term "processor" used in the above description refers to a circuit such as a CPU (Central Processing Unit), a GPU (Graphical Processing Unit), an Application Specific Integrated Circuit (ASIC), a programmable logic device (e.g., a Simple Programmable Logic Device (SPLD), a Complex Programmable Logic Device (CPLD), and a Field Programmable Gate Array (FPGA)). The processor realizes its functions by reading and executing a program stored in the memory circuit 740. Note that instead of storing a program in the memory circuit 740, the processor may be configured so that the program is directly embedded in its circuitry. In this case, the processor realizes its functions by reading and executing the program embedded in its circuitry.

[0041] The patient designation function 751 accepts designation of a patient for whom a treatment plan is to be created. In other words, the patient designation function 751 accepts designation of a patient for whom physical, social, and psychological aspects of the patient are to be analyzed. The patient designation function 751 accepts designation of a patient from, for example, the display device 80. For example, the patient designation function 751 accepts information that can identify the patient, such as the patient's name or a patient code for identifying the patient. Alternatively, the patient designation function 751 accepts designation of a patient by selection from a list of patients.

[0042] The information acquisition function 752 acquires patient information related to the patient designated by the patient designation function 751, medical interview information 12 indicating the patient's responses to medical interviews, and conversation information 51 recording the patient's conversation. The information acquisition function 752 is an example of a first acquisition unit. Here, the patient information is information obtained by monitoring the patient, such as electronic medical record information 11, radiology report information 21, image information 31, clinical test result information 41, clinical test report information 42, and personal health information 61. For example, when a patient is designated by the patient designation function 751, the information acquisition function 752 acquires the patient information, medical interview information 12, and conversation information 51 of the designated patient.

[0043] The analysis target designation function 753 designates analysis target items, which are analysis items to be analyzed by the characteristic analysis function 754, from among the analysis items of the biological, psychological, and social aspects of the patient. In other words, the analysis target designation function 753 designates the analysis target items of the analysis target information 741.

[0044] More specifically, the analysis target designation function 753 accepts designation of an analysis target to be analyzed using the patient information, interview information 12, and conversation information 51 acquired by the information acquisition function 752. For example, the analysis target designation function 753 accepts designation of an analysis target by information received by the NW interface 710 or an operation accepted by the input interface 720. For example, the analysis target designation function 753 accepts designation of an analysis target from the display device 80 via the NW interface 710. Then, the analysis target designation function 753 designates the analysis target items of the designated analysis target information 741 as the analysis target.

[0045] The feature analysis function 754 generates patient feature information indicating biological, psychological, social, and other aspects of the patient through an analysis process using the patient information, the medical interview information 12, and the conversation information 51. The feature analysis function 754 is an example of a first analysis unit. The analysis process is a process of extracting, determining, analyzing, and generating new information from the patient information, the medical interview information 12, and the conversation information 51. The patient feature information is information indicating the patient's features in biological, psychological, social, or other aspects. The patient feature information is also the analysis result for each analysis item of the patient's biological, psychological, social, or other aspects.

[0046] More specifically, the feature analysis function 754 generates patient feature information for each of the analysis target items of the patient's biological aspects, psychological aspects, and social aspects. Of the information included in the patient information, the feature analysis function 754 generates patient feature information that corresponds to the analysis target items of the biological aspects specified by the analysis target information 741. Specifically, the feature analysis function 754 generates patient feature information that indicates comorbid diseases, physical conditions, etc. included in the patient information.

[0047] Furthermore, the feature analysis function 754 generates patient feature information for the analysis target items of psychological and social aspects specified by the analysis target information 741 from the patient information, the medical interview information 12, and the information included in the conversation information 51. For example, the feature analysis function 754 generates patient feature information from the medical interview information 12 that corresponds to the analysis target items of psychological and social aspects specified by the analysis target information 741. Specifically, the feature analysis function 754 extracts answers to questions about personality judgment, work, interpersonal relationships, etc., included in the medical interview information 12.

[0048] Furthermore, the feature analysis function 754 generates patient feature information including conversation content information and emotion information by analyzing the conversation information 51. The conversation content information is information indicating the content of the conversation. The emotion information is information indicating the emotion of the patient when each phrase in the conversation was uttered.

[0049] More specifically, the feature analysis function 754 generates patient feature information including conversation content information and emotional information corresponding to the analysis target items of the psychological and social aspects specified by the analysis target information 741. For example, the feature analysis function 754 generates conversation content information indicating the content of the conversation by performing natural language processing on the conversation information 51. For example, if the conversation information 51 includes the utterance "my lower back hurts," the feature analysis function 754 generates the sentence "my lower back hurts."

[0050] Furthermore, the feature analysis function 754 generates emotion information indicating the emotion of the patient who spoke. Here, when the patient's symptoms are worsening, the patient often speaks with negative emotion. On the other hand, when the patient's symptoms are improving, the patient often speaks with positive emotion. For example, the feature analysis function 754 generates emotion information indicating the emotion of the patient expressed by the tone of voice, hesitation, nuance, etc. when saying, "My lower back hurts." In this way, the feature analysis function 754 generates patient feature information in which emotion information is associated with conversation content information.

[0051] For example, the feature analysis function 754 may utilize artificial intelligence such as AI (Artificial Intelligence). For example, the feature analysis function 754 generates emotion information indicating the emotion of a speaker of a conversation included in the conversation information 51 using a trained model generated by machine learning. The trained model is generated by supervised learning in which the conversation information 51 is input as training data on the input side, and emotion information indicating the emotion of the speaker is input as training data on the output side. Note that the trained model is not limited to this type of machine learning and may be generated by other methods.

[0052] Furthermore, the feature analysis function 754 may generate information indicating the patient's emotional information and values ​​by comparing the conversation information 51 with other information such as the patient information and the medical interview information 12. For example, if the patient answers that their standing work is moderate in intensity in the medical interview information 12 and that their standing work is six hours in the conversation information 51, the feature analysis function 754 can generate information indicating the patient's values ​​indicating that the patient recognizes six hours as moderate intensity.

[0053] Furthermore, as time passes, the social and psychological conditions of the patient change. Therefore, the characteristic analysis function 754 may generate patient characteristic information at regular intervals, or may generate patient characteristic information for each medical event such as an examination or notification.

[0054] In addition, the characteristic analysis function 754 may generate patient characteristic information indicating psychological and social aspects, not limited to all biological, psychological, social, and other aspects of the patient.

[0055] The similarity calculation function 755 calculates the degree of similarity between the patient characteristic information generated by the characteristic analysis function 754 and each of the multiple pieces of patient characteristic information contained in the reference conversation information 742. More specifically, the similarity calculation function 755 calculates the similarity by comparing each analysis item contained in the patient characteristic information. For example, the similarity calculation function 755 calculates the similarity by counting the number of matching analysis items. Note that the similarity calculation function 755 may determine that the analysis items match not only when they are completely identical, but also when they can be determined to be approximately identical. The above-mentioned method of calculating the similarity is one example, and other methods may also be used for calculation.

[0056] The similar patient selection function 756 selects a similar patient, which is a patient similar to the patient specified by the patient specification function 751. The similar patient is an example of a second patient. More specifically, the similar patient selection function 756 selects a similar patient based on the similarity between the patient characteristic information of the patient generated by the characteristic analysis function 754 and each piece of patient characteristic information in the reference conversation information 742. For example, the similar patient selection function 756 selects as similar patients patients whose patient characteristic information has a similarity equal to or greater than a threshold. Note that the similar patient selection function 756 may select as similar patients patients patients whose patient characteristic information has the highest similarity up to a set number of similarities, without being limited to the threshold, or may select as similar patients by other methods.

[0057] The conversation information acquisition function 757 acquires the patient characteristic information of the patient generated by the characteristic analysis function 754 and the conversation information 51 of a similar patient whose patient characteristic information is similar to the patient characteristic information. The conversation information acquisition function 757 is an example of a second acquisition unit. More specifically, the conversation information acquisition function 757 acquires the conversation information 51 of a similar patient selected from one or more similar patients selected by the similar patient selection function 756 from the reference conversation information 742. Alternatively, when the similar patient selection function 756 selects one similar patient, the conversation information acquisition function 757 may acquire the conversation information 51 of the similar patient selected by the similar patient selection function 756 from the reference conversation information 742.

[0058] The conversation analysis function 758 analyzes the characteristics of a conversation between a patient and a healthcare professional based on the conversation information 51 acquired by the conversation information acquisition function 757. The conversation analysis function 758 is an example of a second analysis unit. More specifically, the conversation analysis function 758 analyzes the names of each process of a conversation based on the conversation information 51 acquired by the conversation information acquisition function 757. For example, the process of a conversation during a medical examination may include an introduction section in which the patient's recent condition is asked, an announcement section in which the patient is informed of the illness they are suffering from, an explanation section in which the illness they are suffering from is explained, and a conclusion section in which future plans are explained. The conversation analysis function 758 performs natural language processing on the conversation information 51 acquired by the conversation information acquisition function 757 to analyze which process the conversation falls into.

[0059] Furthermore, the conversation analysis function 758 analyzes the duration of each step in the conversation between the similar patient and the healthcare professional based on the conversation information 51 acquired by the conversation information acquisition function 757. That is, the conversation analysis function 758 analyzes how many minutes of conversation were spent in the introductory section, the announcement section, the explanation section, and the conclusion section. The conversation analysis function 758 also analyzes the total duration of the conversation between the similar patient and the healthcare professional, the duration of the speech by the healthcare professional, and the duration of the speech by the patient.

[0060] Furthermore, the conversation analysis function 758 analyzes the emotions of the similar patient at each stage of the conversation between the similar patient and the medical professional based on the conversation information 51 acquired by the conversation information acquisition function 757. For example, the conversation analysis function 758 analyzes emotional values ​​that quantify the positive, negative, and other emotions of the similar patient at each stage of the conversation. Note that the conversation analysis function 758 may analyze emotions such as joy, anger, sadness, and happiness, rather than being limited to positive and negative. Here, the feature analysis function 754 generates emotional information indicating the emotions of the patient uttered at each stage. For example, the conversation analysis function 758 analyzes the emotions of the similar patient at each stage by acquiring emotional information for each stage.

[0061] Furthermore, the conversation analysis function 758 analyzes keywords for each process in the conversation between similar patients and healthcare professionals based on the conversation information 51. For example, the conversation analysis function 758 analyzes keywords that indicate the theme of the conversation in each process. The conversation analysis function 758 analyzes the theme by performing natural language processing on the linguistic information of the conversation information 51 for each process. The conversation analysis function 758 then acquires the analyzed theme as a keyword for each process. Note that the conversation analysis function 758 is not limited to the theme of the conversation in each process, and may also acquire characteristic utterances as keywords.

[0062] Furthermore, the conversation analysis function 758 extracts treatments that were considered to be implemented in the conversation between the second patient and the healthcare professional based on the conversation information 51. For example, the conversation analysis function 758 extracts proposed treatment information indicating a treatment proposed by the healthcare professional to the patient using natural language processing. Furthermore, the conversation analysis function 758 extracts selected treatment information indicating a treatment selected to be implemented using natural language processing.

[0063] The standard treatment acquisition function 759 acquires standard treatment that is normally performed for the illness or injury of the patient designated by the patient designation function 751 from the standard treatment information 743. More specifically, the standard treatment acquisition function 759 acquires illness or injury identification information, such as the name of the illness or injury of the patient designated by the patient designation function 751, from the electronic medical record information 11. In addition, the standard treatment acquisition function 759 acquires treatment policy information indicating the standard treatment corresponding to the acquired illness or injury identification information from the standard treatment information 743.

[0064] The information generation function 760 generates information to be displayed on the display device 80. Then, the display device 80 displays the various pieces of information generated by the information generation function 760. More specifically, the information generation function 760 generates information indicating the analysis results by the conversation analysis function 758. The information generation function 760 is an example of an information generation unit. For example, the information generation function 760 generates a conversation analysis screen G1 indicating the analysis results of the conversation between a similar patient and a medical professional by the conversation analysis function 758.

[0065] 4 is an explanatory diagram showing an example of the conversation analysis screen G1 according to the first embodiment. The conversation analysis screen G1 includes patient summary information G11, treatment selection flow information G12, first conversation flow information G13a, and second conversation flow information G13b.

[0066] The patient summary information G11 is information that shows an overview of the similar patient. More specifically, the patient summary information G11 is information that shows the similarities between the similar patient, the medical professionals who conversed with the similar patient, and the current status of the similar patient. The information generation function 760 acquires patient characteristic information for analysis items determined to match by the similarity calculation function 755, medical professional identification information associated with the conversation information 51 in the reference conversation information 742, and electronic medical record information 11 of the similar patient selected by the similar patient selection function 756.

[0067] The information generation function 760 also generates patient summary information G11 based on the patient characteristic information, electronic medical record information 11, and medical professional identification information. That is, the information generation function 760 generates information indicating the similarities between similar patients based on the patient characteristic information for the analysis items determined to match by the similarity calculation function 755. By presenting the similarities in this way, medical professionals can inform patients of the similarities between similar patients and explain treatment options, etc. This often leads patients to feel that the medical professional is treating them with compassion. This allows medical professionals to communicate smoothly. The information generation function 760 also generates information indicating medical professionals who have conversed with the similar patient based on the medical professional identification information associated with the conversation information 51. The information generation function 760 also generates information indicating the current status of the similar patient based on the electronic medical record information 11 of the similar patient selected by the similar patient selection function 756.

[0068] The information generating function 760 may also present information indicating the patient's characteristics in the patient summary information G11. For example, the information generating function 760 may display whether the patient is one who will avoid risky treatment or one who will choose risky treatment if it is expected to have a favorable effect. This allows the medical professional to determine how to explain things to the patient appropriately. Furthermore, the information generating function 760 may present a list of paraphrases so that explanations can be paraphrased according to the patient type. The information generating function 760 may also present examples of failed communication with similar patients.

[0069] The treatment selection flow information G12 is information indicating a flow leading up to the selection of a treatment to be administered to a similar patient. For example, the treatment selection flow information G12 is information indicating the standard treatment typically administered for the illness or injury of a similar patient, the proposed treatment proposed by a healthcare professional to the similar patient, and the selected treatment. The information generation function 760 generates the treatment selection flow information G12 based on the treatment policy information acquired by the standard treatment acquisition function 759, the proposed treatment information extracted by the conversation analysis function 758, and the selected treatment information. In other words, the information generation function 760 generates information indicating the standard treatment typically administered for the illness or injury of a similar patient, the treatment proposed by a healthcare professional to the similar patient, and the selected treatment. By referring to the flow for narrowing down the selected treatments shown in the treatment selection flow information G12, healthcare professionals can refer to the conversation flow used to explain the treatment. Note that the treatment selection flow information G12 shown in FIG. 4 narrows down the selected treatments through three stages: standard treatment, proposed treatment, and selected treatment. However, the treatment selection flow information G12 may have four or more stages.

[0070] The first conversation flow information G13a and the second conversation flow information G13b are information indicating the flow of conversation between a similar patient and a medical professional during the medical professional's examination of the similar patient. When the first conversation flow information G13a and the second conversation flow information G13b are not distinguished, they are referred to as conversation flow information G13. In the conversation analysis screen G1 shown in FIG. 4, the information generation function 760 generates two pieces of conversation flow information G13, the first conversation flow information G13a and the second conversation flow information G13b, because the similar patient and the medical professional have examined each other twice. In other words, the information generation function 760 generates information indicating the number of conversation flow information G13 corresponding to the number of examinations. The conversation flow information G13 includes conversation time information G131, emotion value information G132, process information G133, and emotion distribution information G134.

[0071] The conversation time information G131 is information indicating the duration of a conversation between a similar patient and a healthcare professional. For example, the conversation time information G131 includes the total duration of conversation between the similar patient and the healthcare professional, the duration of speech by the healthcare professional, and the duration of speech by the patient. In this manner, the information generation function 760 generates information indicating the duration of each step in a conversation between a similar patient and a healthcare professional. More specifically, the information generation function 760 generates the conversation time information G131 based on the total duration of conversation between the similar patient and the healthcare professional, the duration of speech by the healthcare professional, and the duration of speech by the patient, which are analyzed by the conversation analysis function 758.

[0072] The emotional value information G132 is information that includes a graph showing changes in emotional value and keywords associated with the emotional value. For example, the emotional value is a numerical representation of the similar patient's positive or negative emotions. The graph of the emotional value information G132 shows the emotional value over time in the conversation between the similar patient and the healthcare professional during the medical examination. The keywords indicate the theme of the conversation, characteristic remarks, and remarks that significantly change the patient's emotions. In this way, the information generation function 760 generates information indicating the emotions of the similar patient at each stage in the conversation between the similar patient and the healthcare professional. The information generation function 760 generates information indicating keywords associated with each stage in the conversation between the similar patient and the healthcare professional. More specifically, the information generation function 760 generates the emotional value information G132 based on the emotional value and keywords analyzed by the conversation analysis function 758.

[0073] The process information G133 is information indicating the name of each process of a conversation between a similar patient and a medical professional during a medical examination. The information generation function 760 generates the process information G133 based on the name of each process of the conversation analyzed by the conversation analysis function 758. In the process information G133 shown in FIG. 4, each process has the same length, but the length may also be determined according to the conversation time of each process.

[0074] The emotion distribution information G134 is information that indicates the emotion distribution of similar patients during the consultation. For example, the emotion distribution indicates the distribution of emotions such as joy, anger, sadness, and happiness of similar patients. The information generation function 760 generates the emotion distribution information G134 based on the emotions of joy, anger, sadness, and happiness of similar patients analyzed by the conversation analysis function 758.

[0075] Note that the emotion distribution information G134 shown in FIG. 4 is merely an example and may be changed as desired. For example, the emotion distribution information G134 is not limited to all of the patient summary information G11, treatment selection flow information G12, and conversation flow information G13. It may include some of these pieces of information, or selected pieces of information. For example, when the treatment selection flow information G12 is selected, the information generation function 760 generates emotion distribution information G134 that includes the treatment selection flow information G12. In other words, when the treatment selection flow information G12 is selected, the information generation function 760 generates emotion distribution information G134 that does not include the patient summary information G11 and the conversation flow information G13. In this case, the conversation analysis function 758 does not perform analysis related to the conversation flow information G13. This allows the information generation function 760 to generate the emotion distribution information G134 more quickly.

[0076] Next, various processes executed by the clinical support system 1 will be described.

[0077] 5 is a flowchart showing an example of the display process executed by the clinical support device 70 according to the first embodiment. The display process is a process for displaying features of conversations during medical examinations of similar patients.

[0078] The patient designation function 751 accepts designation of a patient to be the subject of the conversation analysis screen G1 (step S1).

[0079] The information acquisition function 752 acquires patient information such as electronic medical record information 11, radiology report information 21, image information 31, clinical test result information 41, clinical test report information 42, and personal health information 61 (step S2).

[0080] The information acquisition function 752 acquires the medical interview information 12 (step S3).

[0081] The information acquisition function 752 acquires the conversation information 51 (step S4).

[0082] The characteristic analysis function 754 generates patient characteristic information indicating biological, psychological, social, and other aspects of the patient through analytical processing using the patient information, medical interview information 12, and conversation information 51 (step S5).

[0083] The similarity calculation function 755 calculates the similarity between the patient characteristic information generated by the characteristic analysis function 754 and each of the plurality of pieces of patient characteristic information contained in the reference conversation information 742 (step S6).

[0084] The similar patient selection function 756 selects similar patients based on the similarity calculated by the similarity calculation function 755 (step S7).

[0085] The conversation information acquisition function 757 acquires the conversation information 51 of the similar patient from the reference conversation information 742 (step S8).

[0086] The conversation analysis function 758 analyzes the conversation information 51 of similar patients acquired by the conversation information acquisition function 757 (step S9).

[0087] The standard treatment acquisition function 759 acquires treatment policy information indicating standard treatment according to the illness or injury of the similar patient selected by the similar patient selection function 756 from the standard treatment information 743 (step S10).

[0088] The information generation function 760 generates a conversation analysis screen G1 based on the analysis results obtained by the conversation analysis function 758 and the standard of care acquired by the standard of care acquisition function 759 (step S11). The information generation function 760 transmits the generated conversation analysis screen G1 to the display device 80 (step S12). As a result, the display device 80 displays the conversation analysis screen G1.

[0089] With the above, the clinical support device 70 ends the display process.

[0090] As described above, the clinical support system 1 according to the first embodiment acquires patient information, medical interview information 12, and conversation information 51. Furthermore, the clinical support system 1 generates patient characteristic information indicating psychological and social aspects of the patient through an analysis process using the patient information, medical interview information 12, and conversation information 51. Furthermore, the clinical support system 1 acquires the extracted patient characteristic information and conversation information 51 of similar patients whose patient characteristic information is similar. Furthermore, the clinical support system 1 analyzes the characteristics of conversations between similar patients and medical professionals based on the acquired conversation information 51. Then, the clinical support system 1 generates information showing a conversation analysis screen G1 based on the analysis results. This allows medical professionals to refer to conversations between similar patients who are similar to their own patient and other medical professionals. Therefore, the clinical support system 1 can support communication during medical treatment.

[0091] Furthermore, medical professionals can consider treatment plans that take into account the biological, psychological, and social aspects of the patient. Therefore, the clinical support system 1 can promote holistic and patient-centered medical care.

[0092] (Variation 1) It has been explained that the clinical support device 70 according to the first embodiment has the patient characteristic information of similar patients in the reference conversation information 742. That is, it has been explained that the patient characteristic information of similar patients is stored in the memory circuitry 740 of the clinical support device 70. However, the patient characteristic information of similar patients may be stored in a memory circuitry of another device other than the memory circuitry 740 of the clinical support device 70. Alternatively, the clinical support device 70 may generate the patient characteristic information of similar patients each time it compares the patient characteristic information of a patient specified by the patient specification function 751 with that of a patient specified by the patient specification function 751.

[0093] (Second embodiment) The clinical support device 70a according to the second embodiment further includes a condition input function 761 that accepts acquisition conditions for acquiring conversation information 51. FIG. 6 is a block diagram showing an example of the configuration of the clinical support device 70a according to the second embodiment. The processing circuit 750a of the clinical support device 70a further includes the condition input function 761.

[0094] The condition input function 761 accepts acquisition conditions for acquiring the conversation information 51. The condition input function 761 is an example of an input unit. More specifically, the condition input function 761 accepts acquisition conditions for acquiring the conversation information 51 from among the similar patients selected by the similar patient selection function 756.

[0095] For example, the condition input function 761 accepts an input specifying a medical professional as an acquisition condition. That is, the condition input function 761 accepts an input of medical professional identification information for identifying a medical professional who has conversed with a similar patient as an acquisition condition. The medical professional identification information may be the name of the medical professional or code information for identifying the medical professional. Furthermore, the medical professional identification information may be the number of years of experience of the medical professional, the medical department to which the medical professional belongs, the classification of the medical professional such as a specialist, or other conditions.

[0096] The conversation information acquisition function 757 acquires conversation information 51 of a similar patient based on the similarity between the patient characteristic information generated by the characteristic analysis function 754 and the patient characteristic information of a similar patient, and the acquisition conditions. More specifically, the similar patient selection function 756 selects a similar patient based on the similarity between the patient characteristic information generated by the characteristic analysis function 754 and the patient characteristic information of a similar patient. The conversation information acquisition function 757 acquires conversation information 51 of a similar patient selected by the similar patient selection function 756, which satisfies the acquisition conditions.

[0097] For example, when the acquisition condition is medical worker identification information, the conversation information acquisition function 757 acquires conversation information 51 between a designated medical worker and a similar patient from the conversation information 51 of similar patients. That is, the conversation information acquisition function 757 acquires, from the reference conversation information 742, conversation information 51 that is associated with the patient characteristic information of the similar patient and that is associated with the medical worker identification information indicated by the acquisition condition.

[0098] Next, various processes executed by the clinical support system 1 according to the second embodiment will be described.

[0099] FIG. 7 is a flowchart showing an example of a display process executed by the clinical support device 70a according to the second embodiment.

[0100] Steps S21 to S27 are the same as steps S1 to S7 of the display process executed by the clinical support device 70 according to the first embodiment.

[0101] The condition input function 761 receives medical staff identification information for identifying medical staff who have conversed with similar patients as an acquisition condition for acquiring conversation information 51 (step S28).

[0102] The conversation information acquisition function 757 acquires conversation information 51 of similar patients, which is conversation information 51 that matches the medical worker identification information, from the reference conversation information 742 (step S29).

[0103] The conversation analysis function 758 analyzes the conversation information 51 acquired by the conversation information acquisition function 757 (step S30).

[0104] The standard treatment acquisition function 759 acquires treatment policy information indicating standard treatment according to the illness or injury of the similar patient selected by the similar patient selection function 756 from the standard treatment information 743 (step S31).

[0105] The information generation function 760 generates a conversation analysis screen G1 based on the analysis results by the conversation analysis function 758 (step S32). The information generation function 760 transmits the generated conversation analysis screen G1 to the display device 80 (step S33). As a result, the display device 80 displays the conversation analysis screen G1.

[0106] With the above, the clinical support device 70a ends the display process.

[0107] As described above, the clinical support system 1 according to the second embodiment accepts acquisition conditions for acquiring conversation information 51. For example, the clinical support system 1 accepts medical worker identification information as an acquisition condition. This allows medical workers to view the analysis results of the communication of medical workers that they wish to use as reference.

[0108] (Variation 1) In the second embodiment, it has been explained that the condition input function 761 accepts input of medical worker identification information as an acquisition condition. The condition input function 761 according to the first modification of the second embodiment accepts input of setting an importance level for patient characteristic information as an acquisition condition. Furthermore, the conversation information acquisition function 757 acquires conversation information 51 of similar patients based on the importance level, the patient characteristic information of the patient extracted by the characteristic analysis function 754, and the patient characteristic information of similar patients.

[0109] More specifically, the condition input function 761 accepts input for setting the importance of classifications and analysis items of patient characteristic information. For example, if the importance of the "psychological aspects" of patient characteristic information is increased as an acquisition condition, the similarity calculation function 755 calculates the similarity by weighting the "psychological aspects." This increases the similarity of patient characteristic information with similar "psychological aspects." Therefore, the similar patient selection function 756 selects patients with similar "psychological aspects" as similar patients. Therefore, the conversation information acquisition function 757 acquires conversation information 51 of similar patients whose psychological aspects are similar to those of the patient specified by the patient specification function 751.

[0110] Furthermore, when the importance of "educational background," an analysis item of the social aspects of patient characteristic information, is increased as an acquisition condition, the similarity calculation function 755 calculates the similarity by weighting "educational background." This increases the similarity of patient characteristic information with similar "educational background." Therefore, the similar patient selection function 756 selects patients with similar "educational background" as similar patients. Therefore, the conversation information acquisition function 757 acquires conversation information 51 of similar patients whose "educational background" is similar to that of the patient specified by the patient specification function 751.

[0111] The condition input function 761 may also accept input for setting the importance for multiple classifications or analysis items.

[0112] Next, various processes executed by the clinical support system 1 according to the first modification of the second embodiment will be described.

[0113] FIG. 8 is a flowchart showing an example of a display process executed by the clinical support device 70a according to the first modification of the second embodiment.

[0114] Steps S41 to S45 are similar to steps S1 to S5 of the display process executed by the clinical support device 70 according to the first embodiment.

[0115] The condition input function 761 receives an input for setting the importance as an acquisition condition for acquiring the conversation information 51 (step S46).

[0116] The similarity calculation function 755 calculates the similarity between each of the patient characteristic information generated by the characteristic analysis function 754 and the multiple patient characteristic information contained in the reference conversation information 742, according to the importance set by the condition input function 761 (step S47).

[0117] The similar patient selection function 756 selects similar patients based on the similarity calculated by the similarity calculation function 755 (step S48).

[0118] The conversation information acquisition function 757 acquires the conversation information 51 of the similar patient from the reference conversation information 742 (step S49).

[0119] The conversation analysis function 758 analyzes the conversation information 51 acquired by the conversation information acquisition function 757 (step S50).

[0120] The standard treatment acquisition function 759 acquires treatment policy information indicating standard treatment according to the illness or injury of the similar patient selected by the similar patient selection function 756 from the standard treatment information 743 (step S51).

[0121] The information generation function 760 generates a conversation analysis screen G1 based on the analysis results by the conversation analysis function 758 (step S52). The information generation function 760 transmits the generated conversation analysis screen G1 to the display device 80 (step S53). As a result, the display device 80 displays the conversation analysis screen G1.

[0122] With the above, the clinical support device 70a ends the display process.

[0123] As described above, the clinical support system 1 according to the first modification of the second embodiment accepts inputs for setting the classification of patient characteristic information and the importance of analysis items as acquisition conditions, thereby enabling medical professionals to specify similar patients in more detail.

[0124] (Variation 2) In this embodiment, the information generation function 760 generates the conversation analysis screen G1. Then, it has been explained that the display device 80 displays the conversation analysis screen G1. The information generation function 760 may display the conversation analysis screen G1 on the display 730 of the clinical support device 70, or may display it on another device.

[0125] According to at least one of the embodiments described above, communication during medical treatment can be supported.

[0126] Although several embodiments have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, modifications, and combinations of embodiments can be made without departing from the spirit of the invention. These embodiments and their modifications are included within the scope and spirit of the invention, as well as within the scope of the invention and its equivalents as defined in the claims. [Explanation of symbols]

[0127] 1 Clinical support system 10 Hospital Information System (HIS) 11 Electronic medical record information 12 Medical Interview Information 20 Radiology Information Systems (RIS) 21 Image interpretation report information 30 Medical Image Management Systems (PACS: Picture Archiving and Communication Systems) 31 Image information 40 Laboratory Information System (LIS) 41 Clinical test result information 42 Clinical Test Report Information 50 Patient terminals 51 Conversation Information 60 Patient measurement terminal 61 Personal health information 70, 70a Clinical support equipment 80 Display device 741 Analysis target information 742 Reference Conversation Information 743 Standard treatment information 751 Patient specification function 752 Information Acquisition Function 753 Analysis target specification function 754 Feature Analysis Function 755 Similarity calculation function 756 Similar patient selection function 757 Conversation information acquisition function 758 Conversation Analysis Function 759 Standard treatment acquisition function 760 Information generation function 761 Conditional Input Function G1 Conversation Analysis Screen G11 Patient Profile Information G12 Treatment Selection Flow Information G13 Conversation Flow Information G13a 1st conversation flow information G13b Second conversation flow information G131 Conversation Time Information G132 Emotional Value Information G133 Process information G134 Emotion distribution information

Claims

1. a first acquisition unit that acquires patient information related to a first patient, medical interview information indicating symptoms of the first patient, and conversation information in which a conversation of the first patient is recorded; a first analysis unit that generates patient characteristic information indicating psychological and social aspects of the patient by performing an analysis process using the patient information, the medical interview information, and the conversation information; a second acquisition unit that acquires the patient characteristic information of a first patient generated by the first analysis unit and the conversation information of a second patient whose patient characteristic information is similar to the patient characteristic information of the first patient; a second analysis unit that analyzes characteristics of a conversation between the second patient and a medical professional based on the conversation information acquired by the second acquisition unit; a display control unit that displays the analysis result by the second analysis unit; A clinical support system equipped with:

2. the second analysis unit analyzes the time of each process in the conversation between the second patient and the medical professional based on the conversation information; The display control unit displays the time of each process in the conversation between the second patient and the medical professional. The clinical support system according to claim 1 .

3. the second analysis unit analyzes the second patient's emotions at each stage of the conversation between the second patient and the medical professional based on the conversation information; The display control unit displays the emotion of the second patient at each stage of the conversation between the second patient and the medical professional. The clinical support system according to claim 1 or 2.

4. the second analysis unit analyzes keywords in each step of the conversation between the second patient and the medical professional based on the conversation information; the display control unit displays the keywords for each step in the conversation between the second patient and the medical professional. The clinical support system according to any one of claims 1 to 3.

5. further comprising an input unit that accepts an acquisition condition for acquiring the conversation information; the second acquisition unit acquires the conversation information of the second patient based on the acquisition condition. The clinical support system according to any one of claims 1 to 4.

6. the input unit accepts an input specifying the medical worker; the second acquisition unit acquires the conversation information between the designated medical worker and the second patient from the conversation information of the second patient. The clinical support system according to claim 5 .

7. the input unit accepts an input for setting an importance level for the patient characteristic information; the second acquisition unit acquires the conversation information of the second patient based on the importance, the patient characteristic information of the first patient generated by the first analysis unit, and the patient characteristic information of the second patient. The clinical support system according to claim 5 or 6.

8. The display control unit displays a standard treatment for the illness or injury of the second patient, a treatment proposed to the second patient by a medical professional, and a selected treatment. The clinical support system according to any one of claims 1 to 7.

9. a first acquisition unit that acquires patient information related to a first patient, medical interview information indicating symptoms of the first patient, and conversation information in which a conversation of the first patient is recorded; a first analysis unit that generates patient characteristic information indicating psychological and social aspects of the patient by performing an analysis process using the patient information, the medical interview information, and the conversation information; a second acquisition unit that acquires the conversation information of the second patient based on a similarity between the patient characteristic information of the first patient generated by the first analysis unit and the patient characteristic information of the second patient; a second analysis unit that analyzes characteristics of a conversation between the second patient and a medical professional based on the conversation information acquired by the second acquisition unit; an information generating unit that generates information indicating the analysis result by the second analyzing unit; A clinical support device comprising:

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