JP2023071545A - Clinical support system and clinical support device - Google Patents

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

The embodiments disclosed in this specification and drawings relate to a clinical support system and a clinical support device.

Conventionally, medical professionals such as doctors are 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.

However, it may be difficult to collect information on, for example, psychological aspects and social aspects, and it is also difficult to utilize this information while overlooking the entirety of the information. Therefore, there is a demand for technology that supports communication in medical care.

Japanese Patent Application Publication No. 2014-503894

One of the problems to be solved by the embodiments disclosed in the specification and drawings is to support communication in medical care. However, the problems to be solved by the embodiments disclosed in this specification and drawings are not limited to the above problems. A problem corresponding to each effect of each configuration shown in the embodiments described later can be positioned as another problem.

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, interview information indicating symptoms of the first patient, and conversation information in which conversation of the first patient is recorded. The first analysis unit generates patient characteristic information indicating the patient's psychological and social aspects through analysis processing using the patient information, the interview information, and the conversation information. The second acquisition unit acquires the conversation information of a second patient whose patient characteristic information is similar to the patient characteristic information of the first patient generated by the first analysis unit. A said 2nd analysis part analyzes the characteristic of a said 2nd patient and a medical worker's conversation based on the said conversation information acquired by the said 2nd acquisition part. The display control unit displays the analysis result by the second analysis unit.

FIG. 1 is a block diagram showing an example configuration of a clinical support system according to the first embodiment. FIG. 2 is a block diagram showing an example of the configuration of the clinical support device according to the first embodiment. FIG. 3 is a diagram showing an example of the data configuration of analysis target information. FIG. 4 is an explanatory diagram showing an example of a conversation analysis screen according to the first embodiment. 5 is a flowchart illustrating an example of display processing executed by the clinical support apparatus according to the first embodiment; FIG. FIG. 6 is a block diagram showing an example of the configuration of a clinical support device according to the second embodiment. FIG. 7 is a flow chart showing an example of display processing executed by the clinical support device according to the second embodiment. FIG. 8 is a flowchart showing an example of display processing executed by the clinical support device according to Modification 1 of the second embodiment.

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, it is assumed that parts denoted by the same reference numerals perform the same operations, and overlapping descriptions will be omitted as appropriate.

(First embodiment)
Medical professionals such as doctors are required to provide holistic medical care based not only on biological aspects but also on the psychological and social aspects of patients. More specifically, medical professionals are required to make decisions on medical care policies that take into consideration various aspects, rather than solely based on biological aspects such as the results of diagnostic imaging and laboratory test results. ing.

For example, patients with chronic diseases such as lifestyle-related diseases may not be cured by treatment from the biological aspect of drug administration alone. For example, in the case of low back pain, it is preferable that the medical staff not only prescribe poultices but also give advice according to the patient's lifestyle such as work content.

Medical professionals can expect further improvement through medical treatment that encourages improvement in lifestyle habits rather than administration of drugs alone. In addition, the patient's adherence to treatment and medication through treatment that fits his/her lifestyle during and after treatment is determined by determining the treatment policy, including whether the patient has children and work status. (e.g., to be involved in health care and receive treatment in line with their decisions), live better lives, and have better outcomes. Furthermore, the patient can concentrate on the treatment with peace of mind because the explanation and treatment are given according to the patient's own personality.

In this way, medical professionals can provide better medical care by determining treatment policies 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 collect information on psychological and social aspects through communication with patients.

However, medical professionals communicate with patients based on self-improvement and experience. And medical professionals may have difficulty gathering information on psychological and social aspects. In addition, the medical staff also has to decide which of the psychological aspects and the social aspects of the information to be recorded in the electronic medical record or the like. Furthermore, medical professionals are required to utilize this information while overlooking it.

On the other hand, experienced medical staff can communicate smoothly with patients based on their own experience and knowledge. Therefore, there is a demand for a technique that supports learning from others by referring to the key points of communication between other medical professionals and patients, and that is, a technique that supports medical care.

FIG. 1 is a block diagram showing an example 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 medical image management system (PACS: Picture Archiving and Communication Systems) 30, and a clinical examination information system. (LIS: Laboratory Information System) 40 , a patient terminal 50 , a patient measurement terminal 60 , a clinical support device 70 and a display device 80 . In addition, each system and each device are connected via a network 90 so as to be able to communicate with each other. Note that the configuration shown in FIG. 1 is an example, and the number of each system and each device may be changed arbitrarily. Devices not shown in FIG. 1 may also be connected to network 90 .

The hospital information system 10, the radiology information system 20, the medical image management system 30, and the laboratory test information system 40 are implemented by computer equipment such as servers and workstations, for example.

A hospital information system 10 stores electronic medical record information 11 and medical interview information 12 . The electronic medical chart information 11 is information in which a patient's clinical progress is recorded. For example, the electronic medical record information 11 includes information for identifying a patient, personal information such as the patient's age, sex and family structure, information such as the name of the disease that the patient suffered from, the name of the prescribed drug, and the duration of treatment. have. The inquiry information 12 is information indicating the content of the patient's answers to the inquiry. For example, the inquiry information 12 includes answers to the patient's current symptoms, answers to the patient's living environment, and answers to the patient's character diagnosis.

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

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

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 indicating the results of clinical tests. The clinical test report information 42 is information having findings on clinical test results.

A patient terminal 50 is a terminal used by a patient. The patient terminal 50 is implemented by, for example, a smart phone, tablet terminal, wearable terminal, or 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 on conversations with medical professionals such as doctors, nurses, and counselors. Specifically, the conversation information 51 has audio information of recorded conversations. Also, the conversation information 51 may include video information such as moving images and still images including conversation. When image information is included, the conversation information 51 has an image showing the patient's facial expression.

The patient measuring terminal 60 is a terminal for measuring patient's vital signs. The patient measurement terminal 60 is implemented by, for example, a wearable terminal. The patient measurement terminal 60 measures vital signs such as blood pressure, pulse, body temperature, and respiration. Also, the patient measurement terminal 60 may record the conversation. Then, the patient measurement terminal 60 stores personal health information 61 having the measurement results of vital signs.

The patient measurement terminal 60 is not limited to a wearable terminal, and may be a body fat meter, an activity meter, or other devices. In addition, the patient measurement terminal 60 may measure body fat, measure the amount of activity, or measure other items. Furthermore, the clinical support system 1 may have a plurality of patient measurement terminals 60 . Further, in the clinical support system 1, the measurement targets of the plurality of patient measurement terminals 60 may be different.

The clinical support device 70 is a device that supports a medical worker such as a doctor in clinical practice. The clinical support device 70 is realized by, for example, computer equipment such as a server or workstation. The clinical support device 70 analyzes the patient's biological, social, and psychological aspects based on the information acquired from each system and device of the clinical support system 1 . In addition, the clinical support device 70 analyzes other aspects of information that do not belong to the patient's biological, social, and psychological aspects. In addition, the clinical support device 70 generates information indicating biological aspects, social aspects, psychological aspects, 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.

The display device 80 is a device capable of displaying various information. The display device 80 is realized by computer equipment such as a personal computer and a tablet terminal. The display device 80 displays various information generated by the clinical support device 70 . For example, the display device 80 displays a conversation analysis screen G1 (see FIG. 4). Further, the display device 80 may be a transmissive display that displays different information on the medical staff side and the patient side. The display device 80 is an example of a display controller.

By viewing the information displayed on the display device 80, a medical worker such as a doctor can refer to the analysis result of the conversation between the other medical worker and the patient during medical treatment. Therefore, medical staff can refer to conversations between other medical staff and patients during medical care.

Next, the clinical support device 70 will be described.

FIG. 2 is a block diagram showing an example of the configuration of the clinical support device 70 according to the first embodiment. The clinical support device 70 has a NW (network) interface 710 , an input interface 720 , a display 730 , a memory circuit 740 and a processing circuit 750 .

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

The input interface 720 is connected to the processing circuit 750 , converts an input operation received from an operator (medical worker) into an electrical signal, and outputs the electrical signal to the processing circuit 750 . Specifically, the input interface 720 converts an input operation received from the operator into an electrical signal and outputs the electrical signal to the processing circuit 750 . For example, the input interface 720 includes a trackball, a switch button, a mouse, a keyboard, a touch pad that performs input operations by touching an operation surface, a touch screen that integrates a display screen and a touch pad, and a non-optical sensor. It is implemented by a contact input circuit, an audio input circuit, and the like. In this specification, the input interface 720 is not limited to having physical operation parts such as a mouse and a keyboard. For example, the input interface 720 also includes 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 the control circuit.

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

The storage circuit 740 is connected to the processing circuit 750 and stores various data. In addition, the storage circuit 740 stores various programs for realizing various functions by being read and executed by the processing circuit 750 . For example, the storage circuit 740 is implemented by a semiconductor memory device such as a RAM (Random Access Memory) or flash memory, a hard disk, an optical disk, or the like.

The storage circuit 740 stores analysis target information 741 , reference conversation information 742 and standard treatment information 743 .

FIG. 3 is a diagram showing an example of the data configuration of the analysis target information 741. As shown in FIG. The analysis target information 741 is information specifying information to be analyzed as a patient's biological aspect, social aspect, psychological aspect, and other aspects among the information acquired by each system and device of the clinical support system 1. be.

The analysis target information 741 associates a classification, an analysis item, and an analysis target item. Classifications are divisions such as biological aspects, psychological aspects, social aspects, and others. A biology is a classification that indicates a biological aspect of a patient. For example, the biological aspect is associated with biological analysis items such as various test results, disease, and physical condition of the patient. In addition, the biological aspects are not limited to biological aspects, and may be associated with analysis items related to physical aspects, medical aspects, and pathological aspects. The psychological aspect is a classification that indicates the psychological aspect of the patient. For example, the psychological aspect is associated with psychological aspect analysis items such as the patient's intention and character. Psychological aspects may also include the patient's emotional state. The emotional state is an emotion such as depressed or angry. The social aspect is a classification that indicates the patient's social aspect. For example, social aspects are associated with analysis items of social aspects such as work, human relationships, hobbies, and educational background. Others are associated with analysis items that do not belong to any of the biological, psychological, and social aspects. An analysis item is an item for analyzing a patient. The analysis target item is information in which whether or not to analyze the analysis item is set. That is, the clinical support device 70 analyzes the analysis item set as the analysis target in the analysis target item. Further, 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 arbitrarily.

Reference conversation information 742 is information having a plurality of conversation information 51 to be referred to. The reference conversation information 742 has a plurality of pieces of information in which patient characteristic information, conversation information 51, and medical worker identification information are associated with each other. The patient characteristic information is an analysis result for each analysis item of a patient's biological aspect, psychological aspect, social aspect, or other aspects. The conversation information 51 is information in which the conversation between the patient of the patient characteristic information and the medical staff is recorded. The medical worker identification information is identification information for identifying the medical worker who has had a conversation with the patient in the conversation information 51 . In addition, the medical worker identification information may be the number of years of experience of the medical worker, the department to which the medical worker belongs, or the classification of the medical worker such as a specialist. However, it may be a combination of these information, or information other than these.

The standard treatment information 743 is information in which standard treatment policies for injuries and diseases are registered. The standard treatment information 743 has injury/disease identification information for specifying an injury and disease, and treatment policy information indicating a treatment policy. The disease identification information is, for example, the name of the disease or code information for identifying the disease. Treatment policy information is information indicating a standard treatment for an injury or disease. 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.

The processing circuit 750 controls the overall operation of the clinical support device 70 . The processing circuit 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, and a conversation analysis function 758. , standard treatment acquisition function 759 , and information generation function 760 . In the embodiment, a patient specification function 751, an information acquisition function 752, an analysis target specification function 753, a feature analysis function 754, a similarity calculation function 755, a similar patient selection function 756, a conversation information acquisition function 757, and a conversation analysis function, which are constituent elements. 758, the standard treatment acquisition function 759, and the information generation function 760 are stored in the storage circuit 740 in the form of a computer-executable program. The processing circuit 750 is a processor that reads a program from the storage circuit 740 and executes it to implement a function corresponding to each program. In other words, the processing circuit 750 with each program read has each function shown in the processing circuit 750 of FIG.

In FIG. 2, a single processor includes a patient specification function 751, an information acquisition function 752, an analysis target specification function 753, a feature analysis function 754, a similarity calculation function 755, a similar patient selection function 756, and a conversation information acquisition function. 757, conversation analysis function 758, standard treatment acquisition function 759, and information generation function 760, but the processing circuit 750 is configured by combining a plurality of independent processors, and each processor executes a program. It does not matter if the function is realized by Also, in FIG. 2, a single memory circuit such as the memory circuit 740 has been described as storing a program corresponding to each processing function. , the corresponding programs may be read from individual storage circuits.

The term "processor" used in the above description includes, for example, a CPU (Central Processing Unit), a GPU (Graphical Processing Unit), an application specific integrated circuit (ASIC), a programmable logic device (for example, a simple Circuits such as a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA). The processor implements its functions by reading and executing programs stored in the storage circuit 740 . Note that instead of storing the program in the memory circuit 740, the program may be configured to be directly embedded in the circuit of the processor. In this case, the processor realizes its function by reading and executing the program embedded in the circuit.

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

The information acquisition function 752 acquires patient information related to the patient designated by the patient designation function 751, inquiry information 12 indicating the content of the patient's answers to the inquiry, and conversation information 51 in which the patient's conversation is recorded. The information acquisition function 752 is an example of a first acquisition unit. Here, the patient information is patient monitoring information such as the electronic medical record information 11 , the interpretation report information 21 , the image information 31 , the clinical test result information 41 , the clinical test report information 42 , and the personal health information 61 . For example, when a patient is designated by the patient designation function 751, the information acquisition function 752 acquires patient information, interview information 12, and conversation information 51 of the designated patient.

The analysis target designation function 753 designates an analysis target item to be analyzed by the feature analysis function 754 among the analysis items of the patient's biological, psychological, and social aspects. That is, the analysis target specification function 753 specifies the analysis target item of the analysis target information 741 .

More specifically, the analysis target specification function 753 accepts specification of an analysis target to be analyzed using the patient information acquired by the information acquisition function 752, the inquiry information 12, and the conversation information 51. FIG. For example, the analysis target designation function 753 receives designation of the analysis target based on information received by the NW interface 710 or an operation received by the input interface 720 . For example, the analysis target specification function 753 receives specification of the analysis target from the display device 80 via the NW interface 710 . Then, the analysis target specifying function 753 specifies the analysis target item of the specified analysis target information 741 as the analysis target.

The feature analysis function 754 performs analysis processing using the patient information, the interview information 12, and the conversation information 51 to obtain patient feature information indicating the patient's biological, psychological, social, and other aspects. to generate Feature analysis function 754 is an example of a first analysis unit. The analysis process is a process of extracting information from the patient information, the inquiry information 12, and the conversation information 51, judging, analyzing, and generating new information. Patient characteristic information is information that indicates characteristics of a patient in biological, psychological, social, or other aspects. Further, the patient characteristic information is the analysis result for each analysis item of the patient's biological aspect, psychological aspect, social aspect, or other aspects.

More specifically, the feature analysis function 754 generates patient feature information for items to be analyzed for each of the patient's biological, psychological, and social aspects. The feature analysis function 754 generates patient feature information corresponding to the analysis target item of the biological aspect specified by the analysis target information 741 among the information included in the patient information. Specifically, the feature analysis function 754 generates patient feature information indicating co-morbidities and physical conditions included in the patient information.

In addition, the feature analysis function 754 analyzes patient features of psychological and social analysis target items specified by the analysis target information 741 among the information included in the patient information, the interview information 12, and the conversation information 51. Generate information. For example, the feature analysis function 754 generates patient feature information corresponding to the psychological and social analysis target items specified by the analysis target information 741 from the medical interview information 12 . Specifically, the feature analysis function 754 extracts answers to questions such as character judgment, work, and human relations included in the medical interview information 12 .

Also, the feature analysis function 754 analyzes the conversation information 51 to generate patient feature information including conversation content information and emotion information of the conversation. The conversation content information is information indicating the content of the conversation. The emotion information is information indicating the patient's emotion when each sentence in the conversation is uttered.

More specifically, the feature analysis function 754 generates patient feature information including conversation content information and emotional information corresponding to psychological and social analysis target items 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 executing natural language processing on the conversation information 51 . For example, if the conversation information 51 includes an utterance "My back hurts", the feature analysis function 754 generates a sentence "My back hurts".

Also, the feature analysis function 754 generates emotion information indicating the emotion of the patient who made the utterance. Here, when the symptoms are aggravated, the patient often speaks with negative emotions. On the other hand, when symptoms are improving, patients often speak with positive emotions. For example, the feature analysis function 754 generates emotion information that indicates the patient's emotion expressed by tone of voice, hesitation, nuances, etc. in the utterance "My back hurts." In this manner, the feature analysis function 754 generates patient feature information in which emotion information is associated with conversation content information.

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 the speaker of the conversation included in the conversation information 51 using a learned model generated by machine learning. The trained model is generated by supervised learning in which conversation information 51 is input as teacher data on the input side, and emotion information indicating the emotion of the speaker is input as teacher data on the output side. Note that the learned model may be generated by other methods, not limited to such machine learning.

Also, the feature analysis function 754 may generate information indicating the patient's emotional information and sense of values by comparing the conversation information 51 with other information such as the patient information and the interview information 12 . For example, the feature analysis function 754 recognizes that 6 hours is medium intensity when the answer is medium intensity standing work in the inquiry information 12 and 6 hours of standing work is answered in the conversation information 51. Information indicative of the patient's values can be generated.

In addition, the patient's social and psychological conditions change over time. Therefore, the characteristic analysis function 754 may generate patient characteristic information for each fixed period, or may generate patient characteristic information for each clinical event such as an examination or notification.

Note that the feature analysis function 754 generates patient feature information indicating not only the patient's biological aspects, psychological aspects, social aspects, and other aspects, but also psychological aspects and social aspects. may be

The similarity calculation function 755 calculates similarities between the patient characteristic information generated by the characteristic analysis function 754 and a plurality of pieces of patient characteristic information included in the reference conversation information 742 . More specifically, the similarity calculation function 755 calculates the similarity by comparing each analysis item included in the patient characteristic information. For example, the similarity calculation function 755 calculates the similarity by counting the number of matching analysis items. It should be noted that the similarity calculation function 755 may determine that the analysis items match when it can be determined that the items are approximately the same, not limited to a perfect match. Also, the similarity calculation method described above is merely an example, and other methods may be used for calculation.

The similar patient selection function 756 selects similar patients who are similar to the patient designated by the patient designation function 751 . A similar patient is an example of a second patient. More specifically, the similar patient selection function 756 selects similar patients based on the degree of similarity between the patient's patient characteristic information generated by the characteristic analysis function 754 and the respective patient characteristic information of the reference conversation information 742 . For example, the similar patient selection function 756 selects patients whose similarity is equal to or greater than the threshold as similar patients. The similar patient selection function 756 is not limited to the threshold value, and may select patients with patient characteristic information up to the setting number having a high degree of similarity as similar patients, or select similar patients by other methods. good too.

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 similar patients having similar patient characteristic information. 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 similar patients 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 acquires the conversation information 51 of the similar patient selected by the similar patient selection function 756 from the reference conversation information 742. good too.

The conversation analysis function 758 analyzes the features of the conversation between the patient and the medical staff based on the conversation information 51 acquired by the conversation information acquisition function 757 . Conversation analysis function 758 is an example of a second analysis unit. More specifically, the conversation analysis function 758 analyzes the name of each process of conversation based on the conversation information 51 acquired by the conversation information acquisition function 757 . For example, in the process of conversation in a medical examination, there are an introductory part to ask the patient's recent condition, an announcement part to inform the patient of the disease, an explanatory part to explain the patient's disease, and an explanation of the future schedule. There is a tightening part etc. 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 it corresponds to.

Also, the conversation analysis function 758 analyzes the time of each process in the conversation between the similar patient and the medical staff based on the conversation information 51 acquired by the conversation information acquisition function 757 . In other words, the conversation analysis function 758 analyzes how many minutes the introduction part talks, how many minutes the announcement part talks, how many minutes the explanation part talks, and how many minutes the conclusion part talks. In addition, the conversation analysis function 758 analyzes the total time spent speaking by the similar patient and the health care worker, the time spent speaking by the health care worker, and the time spent speaking by the patient.

Also, the conversation analysis function 758 analyzes the similar patient's emotions in each process in the conversation between the similar patient and the medical staff based on the conversation information 51 acquired by the conversation information acquisition function 757 . For example, the conversation analysis function 758 analyzes emotional values obtained by quantifying positive and negative emotions of similar patients in each process of conversation. Note that the conversation analysis function 758 may analyze whether the similar patient is feeling joy, anger, sorrow, or joy, not just positive or negative. Here, the feature analysis function 754 generates emotion information indicating the emotion of the patient speaking in each process. For example, the conversation analysis function 758 analyzes the emotions of similar patients in each process by obtaining the emotion information in each process.

Also, the conversation analysis function 758 analyzes the keywords of each process in the conversation between the similar patient and the medical staff based on the conversation information 51 . For example, conversation analysis function 758 analyzes keywords that indicate the theme of the conversation in each process. The conversation analysis function 758 analyzes themes by executing natural language processing on the language information of the conversation information 51 in each process. Then, the conversation analysis function 758 acquires the analyzed theme as a keyword for each process. It should be noted that the conversation analysis function 758 may acquire not only the theme of the conversation in each process, but also characteristic utterances as keywords.

Also, the conversation analysis function 758 extracts the treatment considered to be implemented in the conversation between the second patient and the medical staff based on the conversation information 51 . For example, the conversation analysis function 758 extracts suggested treatment information indicating the treatment suggested to the patient by the healthcare professional through natural language processing. Speech analysis function 758 also extracts treatment selection information indicating the treatment selected to be delivered through natural language processing.

The standard treatment acquisition function 759 acquires from the standard treatment information 743 the standard treatment that is normally performed for the patient designated by the patient designation function 751 . More specifically, the standard treatment acquisition function 759 acquires the disease identification information such as the name of the patient specified by the patient specification function 751 from the electronic medical record information 11 . Also, the standard treatment acquisition function 759 acquires from the standard treatment information 743 the treatment policy information indicating the standard treatment corresponding to the acquired injury/disease identification information.

The information generation function 760 generates information to be displayed on the display device 80 . The display device 80 displays various information generated by the information generation function 760 . More specifically, information generation function 760 generates information indicating the results of analysis by conversation analysis function 758 . 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 showing analysis results of the conversation between the similar patient and the medical staff by the conversation analysis function 758 .

Here, FIG. 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.

The patient summary information G11 is information indicating a summary of similar patients. More specifically, the patient summary information G11 is information indicating similarities of similar patients, medical staff who have spoken with the similar patients, and current situations of the similar patients. The information generation function 760 generates the patient characteristic information of the analysis items determined to match by the similarity calculation function 755, the medical staff identification information associated with the conversation information 51 in the reference conversation information 742, and similar patient selection. The electronic medical record information 11 of the similar patient selected by the function 756 is acquired.

The information generation function 760 also generates patient summary information G11 based on the patient characteristic information, the electronic medical record information 11, and the medical staff identification information. That is, the information generating function 760 generates information indicating similarities of similar patients based on the patient characteristic information of the analysis items determined to match by the similarity calculating function 755 . By presenting the similarities in this way, the medical staff can explain the treatment policy and the like after informing the patient of the similarities in the similar patients. As a result, patients often feel that they are being treated empathetically. Therefore, medical staff can communicate smoothly. In addition, the information generation function 760 generates information indicating the medical worker who had a conversation with the similar patient based on the medical worker identification information associated with the conversation information 51 . The information generation function 760 also generates information indicating the current situation of the similar patient based on the electronic medical record information 11 of the similar patient selected by the similar patient selection function 756 .

The information generation function 760 may present information indicating patient characteristics in the patient summary information G11. For example, the information generation function 760 may indicate whether a patient avoids a treatment that is at risk, or a patient that would choose a treatment even if it is at risk if a favorable effect could be expected. . This allows the healthcare professional to determine how the explanation is appropriate for the patient. Additionally, the information generator function 760 may present a list of paraphrases to paraphrase according to patient type. The information generation function 760 may also present examples of failed communications in similar patients.

The treatment selection flow information G12 is information indicating a flow until a treatment to be performed on a similar patient is selected. For example, the treatment selection flow information G12 indicates a standard treatment that is normally performed for an injury or disease of a similar patient, a suggested treatment proposed by a medical professional to the similar patient, and a selected treatment indicating the selected treatment. Information. The information generation function 760 generates treatment selection flow information G12 based on the treatment policy information acquired by the standard treatment acquisition function 759, the suggested 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 treatments that are standard practice for similar patient injuries, treatments that medical personnel have suggested for similar patients, and treatments that have been selected. By referring to the flow for narrowing down the selected treatment shown in the treatment selection flow information G12, the medical staff can refer to the flow of conversation in which the treatment was explained. The treatment selection flow information G12 shown in FIG. 4 is narrowed down to the selected treatment by three stages of standard treatment, proposed treatment, and selected treatment. However, the treatment selection flow information G12 may have four or more stages.

The first conversation flow information G13a and the second conversation flow information G13b are information indicating the flow of conversation between the similar patient and the medical staff when the similar patient is examined by the medical staff. When the first conversation flow information G13a and the second conversation flow information G13b are not distinguished from each other, 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 the first conversation flow information G13a and the second conversation flow information G13b because the similar patient and the medical staff have been examined twice. One conversation flow information G13 is generated. That is, the information generation function 760 generates information indicating the number of pieces of conversation flow information G13 corresponding to the number of consultations. The conversation flow information G13 includes conversation time information G131, emotion value information G132, process information G133, and emotion distribution information G134.

The conversation time information G131 is information indicating the conversation time between the similar patient and the medical staff. For example, the conversation time information G131 includes the total time spent speaking by the similar patient and the medical staff, the speaking time spent by the medical staff, and the speaking time spent by the patient. Thus, the information generation function 760 generates information indicative of the time of each step in the conversation between the similar patient and the healthcare professional. More specifically, the information generation function 760 analyzes the total time of speech between the similar patient and the medical staff, the speech time of the medical staff, and the speech time of the patient, analyzed by the conversation analysis function 758. Conversation time information G131 is generated based on.

The emotional value information G132 is information having a graph showing changes in emotional values and keywords for emotional values. For example, the emotional value is a quantification of positive or negative emotions of similar patients. The graph of the emotional value information G132 shows emotional values according to the passage of time in the conversation between the similar patient and the medical staff in the medical examination. Also, the keywords indicate conversation themes, characteristic remarks, and remarks that greatly change the patient's emotions. In this way, the information generation function 760 generates information indicative of the similar patient's emotions during each step of the conversation between the similar patient and the medical staff. The information generation function 760 generates information indicating keywords for each process in the conversation between the similar patient and the medical staff. More specifically, the information generation function 760 generates emotion value information G132 based on the emotion value analyzed by the conversation analysis function 758 and keywords.

The process information G133 is information indicating the name of each process of the conversation between the similar patient and the medical staff in the medical examination. Information generation function 760 generates process information G133 based on the name of each process of the conversation analyzed by conversation analysis function 758 . In the process information G133 shown in FIG. 4, the length of each process is equal, but the length may correspond to the conversation time of each process.

The emotion distribution information G134 is information indicating the emotion distribution of similar patients in medical examinations. For example, the emotion distribution shows the distribution of emotions of joy, anger, sadness, and joy of similar patients. The information generation function 760 generates emotion distribution information G134 based on the similar patient's emotions of joy, anger, sadness, and joy analyzed by the conversation analysis function 758 .

It should be noted that the emotion distribution information G134 shown in FIG. 4 is an example and may be changed arbitrarily. For example, the emotion distribution information G134 is not limited to all of the patient summary information G11, the treatment selection flow information G12, and the conversation flow information G13, and may contain some of these information. It may have selected information. For example, when the treatment selection flow information G12 is selected, the information generation function 760 generates the emotion distribution information G134 having the treatment selection flow information G12. In other words, the information generation function 760 generates the emotion distribution information G134 without the patient summary information G11 and the conversation flow information G13 when the treatment selection flow information G12 is selected. In this case, conversation analysis function 758 does not perform analysis related to conversation flow information G13. Therefore, information generation function 760 can generate emotion distribution information G134 more quickly.

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

FIG. 5 is a flowchart showing an example of display processing executed by the clinical support device 70 according to the first embodiment. The display process is a process of displaying the characteristics of conversations in medical examinations of similar patients.

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

The information acquisition function 752 acquires patient information such as electronic chart information 11, interpretation report information 21, image information 31, clinical test result information 41, clinical test report information 42, and personal health information 61 (step S2).

The information acquisition function 752 acquires the inquiry information 12 (step S3).

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

The feature analysis function 754 performs analysis processing using the patient information, the interview information 12, and the conversation information 51 to obtain patient feature information indicating the patient's biological, psychological, social, and other aspects. is generated (step S5).

The similarity calculation function 755 calculates similarities between the patient characteristic information generated by the characteristic analysis function 754 and a plurality of pieces of patient characteristic information included in the reference conversation information 742 (step S6).

The similar patient selection function 756 selects similar patients based on the similarity of the similarity calculation function 755 (step S7).

The conversation information acquisition function 757 acquires the similar patient's conversation information 51 from the reference conversation information 742 (step S8).

The conversation analysis function 758 analyzes the similar patient's conversation information 51 acquired by the conversation information acquisition function 757 (step S9).

The standard treatment acquisition function 759 acquires, from the standard treatment information 743, treatment policy information indicating the standard treatment according to the disease of the similar patient selected by the similar patient selection function 756 (step S10).

The information generation function 760 generates a conversation analysis screen G1 based on the analysis result analyzed by the conversation analysis function 758 and the standard treatment acquired by the standard treatment acquisition function 759 (step S11). The information generation function 760 transmits the generated conversation analysis screen G1 to the display device 80 (step S12). Thereby, the display device 80 displays the conversation analysis screen G1.

With the above, the clinical support device 70 ends the display processing.

As described above, the clinical support system 1 according to the first embodiment acquires patient information, interview information 12, and conversation information 51. FIG. In addition, the clinical support system 1 generates patient characteristic information indicating the patient's psychological and social aspects through analysis processing using the patient information, interview information 12, and conversation information 51. FIG. The clinical support system 1 also acquires the extracted patient characteristic information and conversation information 51 of similar patients with similar patient characteristic information. Also, the clinical support system 1 analyzes the features of the conversation between the similar patient and the medical staff based on the acquired conversation information 51 . Then, the clinical support system 1 generates information indicating the conversation analysis screen G1 based on the analysis result. Thereby, medical staff can refer to similar patients who are similar to their own patients and conversations with other medical staff. Therefore, the clinical support system 1 can support communication in medical care.

Furthermore, medical professionals can understand the patient from biological, psychological, and social aspects, and consider treatment strategies. Therefore, the clinical support system 1 can promote holistic and patient-centered medical care.

(Modification 1)
The clinical support apparatus 70 according to the first embodiment has explained that the reference conversation information 742 has patient characteristic information of similar patients. That is, it has been explained that the patient characteristic information of similar patients is stored in the storage circuit 740 of the clinical support device 70 . However, the patient characteristic information of similar patients may be stored in the memory circuit 740 of the clinical support device 70 in the memory circuit of another device. Alternatively, the clinical support apparatus 70 may generate patient characteristic information of similar patients each time the patient characteristic information of the patient designated by the patient designation function 751 is compared.

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

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 section. More specifically, the condition input function 761 receives acquisition conditions for acquiring the conversation information 51 from among the similar patients selected by the similar patient selection function 756 .

For example, the condition input function 761 receives an input designating a medical worker as an acquisition condition. That is, the condition input function 761 receives, as an acquisition condition, input of medical worker identification information for identifying the medical worker who had a conversation with the similar patient. The medical worker identification information may be the name of the medical worker, or code information for identifying the medical worker. In addition, the medical worker identification information may be the number of years of experience of the medical worker, the department to which the medical worker belongs, or the classification of the medical worker such as a specialist. However, conditions other than these may be used.

The conversation information acquisition function 757 acquires the conversation information 51 of the similar patient based on the degree of similarity between the patient characteristic information generated by the characteristic analysis function 754 and the patient characteristic information of the similar patient, and acquisition conditions. More specifically, the similar patient selection function 756 selects similar patients based on the degree of similarity between the patient characteristic information generated by the characteristic analysis function 754 and the patient characteristic information of the similar patients. The conversation information acquisition function 757 acquires the conversation information 51 of the similar patient selected by the similar patient selection function 756 and satisfying the acquisition conditions.

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

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

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

Steps S21 to S27 execute processing similar to steps S1 to S7 of the display processing executed by the clinical support apparatus 70 according to the first embodiment.

The condition input function 761 receives, as an acquisition condition for acquiring the conversation information 51, medical worker identification information for identifying the medical worker who had a conversation with the similar patient (step S28).

The conversation information acquisition function 757 acquires the conversation information 51 of the similar patient that matches the medical worker identification information from the reference conversation information 742 (step S29).

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

The standard treatment acquisition function 759 acquires, from the standard treatment information 743, treatment policy information indicating the standard treatment according to the disease of the similar patient selected by the similar patient selection function 756 (step S31).

The information generation function 760 generates a conversation analysis screen G1 based on the analysis result 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). Thereby, the display device 80 displays the conversation analysis screen G1.

With the above, the clinical support device 70a ends the display processing.

As described above, the clinical support system 1 according to the second embodiment receives acquisition conditions for acquiring the conversation information 51 . For example, the clinical support system 1 receives medical worker identification information as an acquisition condition. With this, the medical staff can see the analysis result of the medical staff's communication that they want to refer to.

(Modification 1)
In the second embodiment, it has been described that the condition input function 761 receives input of medical worker identification information as an acquisition condition. A condition input function 761 according to Modification 1 of the second embodiment receives an input for setting the importance of patient characteristic information as an acquisition condition. Also, the conversation information acquisition function 757 acquires the conversation information 51 of the similar patient based on the degree of importance, the patient characteristic information of the patient extracted by the characteristic analysis function 754, and the patient characteristic information of the similar patient.

More specifically, the condition input function 761 receives inputs for setting the importance levels for classifications of patient characteristic information and analysis items. For example, when the acquisition condition is set such that the importance of the "psychological aspect" of the patient characteristic information is raised, the similarity calculation function 755 calculates the similarity by weighting the "psychological aspect". As a result, the degree of similarity of patient characteristic information having similar "psychological aspects" increases. 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 specifying function 751 .

In addition, when the importance of the analysis item “educational background” of the social aspect of the patient characteristic information is raised as an acquisition condition, the similarity calculation function 755 calculates the similarity by weighting “educational background”. As a result, the degree of similarity of patient characteristic information having similar "educational background" is increased. Therefore, the similar patient selection function 756 selects patients with similar “educational background” as similar patients. Therefore, the conversation information acquisition function 757 acquires the conversation information 51 of similar patients whose “educational background” is similar to that of the patient designated by the patient designation function 751 .

In addition, the condition input function 761 may receive input for setting importance levels for multiple classifications and analysis items.

Next, various processes executed by the clinical support system 1 according to Modification 1 of the second embodiment will be described.

FIG. 8 is a flowchart showing an example of display processing executed by the clinical support device 70a according to Modification 1 of the second embodiment.

Steps S41 to S45 execute processing similar to steps S1 to S5 of the display processing executed by the clinical support apparatus 70 according to the first embodiment.

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

The similarity calculation function 755 calculates the similarity between the patient characteristic information generated by the characteristic analysis function 754 and a plurality of pieces of patient characteristic information included in the reference conversation information 742 according to the degree of importance set by the condition input function 761. degree is calculated (step S47).

The similar patient selection function 756 selects similar patients based on the similarity of the similarity calculation function 755 (step S48).

The conversation information acquisition function 757 acquires the similar patient's conversation information 51 from the reference conversation information 742 (step S49).

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

The standard treatment acquisition function 759 acquires, from the standard treatment information 743, treatment policy information indicating the standard treatment corresponding to the disease of the similar patient selected by the similar patient selection function 756 (step S51).

The information generation function 760 generates a conversation analysis screen G1 based on the analysis result 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). Thereby, the display device 80 displays the conversation analysis screen G1.

With the above, the clinical support device 70a ends the display processing.

As described above, the clinical support system 1 according to Modification 1 of the second embodiment receives an input for setting the importance of the classification of patient characteristic information and analysis items as acquisition conditions. This allows medical professionals to specify similar patients in more detail.

(Modification 2)
In this embodiment, the information generation function 760 generates the conversation analysis screen G1. Further, 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.

According to at least one embodiment and the like described above, communication in medical treatment can be supported.

While several embodiments have been described, these embodiments are provided by way of example and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, changes, and combinations of embodiments can be made without departing from the scope of the invention. These embodiments and their modifications are included in the scope and spirit of the invention, as well as the scope of the invention described in the claims and equivalents thereof.

1 Clinical Support System 10 Hospital Information System (HIS)
11 Electronic chart information 12 Interview information 20 Radiology information system (RIS: Radiology Information Systems)
21 Interpretation report information 30 Medical image management system (PACS: Picture Archiving and Communication Systems)
31 image information 40 laboratory information system (LIS: Laboratory Information System)
41 clinical test result information 42 clinical test report information 50 patient terminal 51 conversation information 60 patient measurement terminal 61 personal health information 70, 70a clinical support device 80 display device 741 analysis target information 742 reference conversation information 743 standard treatment information 751 patient designation function 752 Information Acquisition Function 753 Analysis Target Designation 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 Condition Input Function G1 Conversation Analysis Screen G11 Patient Overview information G12 Treatment selection flow information G13 Conversation flow information G13a First conversation flow information G13b Second conversation flow information G131 Conversation time information G132 Emotion value information G133 Process information G134 Emotion distribution information

Claims (9)

a first acquisition unit that acquires patient information related to the first patient, interview information indicating the symptoms of the first patient, and conversation information in which conversation of the first patient is recorded;
a first analysis unit that generates patient characteristic information indicating psychological and social aspects of a patient by analysis processing using the patient information, the medical interview information, and the conversation information;
a second acquisition unit that acquires the patient characteristic information of the first patient generated by the first analysis unit and the conversation information of a second patient similar to the patient characteristic information;
a second analysis unit that analyzes features of the conversation between the second patient and the medical staff based on the conversation information acquired by the second acquisition unit;
a display control unit for displaying analysis results by the second analysis unit;
clinical support system. The second analysis unit analyzes the time of each process in the conversation between the second patient and the medical staff 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 staff.
The clinical support system according to claim 1. The second analysis unit analyzes the emotion of the second patient in each process in the conversation between the second patient and the medical staff based on the conversation information,
The display control unit displays the emotions of the second patient in each process of the conversation between the second patient and the medical staff.
The clinical support system according to claim 1 or 2. The second analysis unit analyzes keywords in each process in the conversation between the second patient and the medical staff based on the conversation information,
The display control unit displays the keywords for each process in the conversation between the second patient and the medical staff.
The clinical support system according to any one of claims 1 to 3. further comprising an input unit that receives 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. The input unit receives an input designating the medical staff,
The second acquisition unit acquires the conversation information between the specified medical staff and the second patient, from among the conversation information of the second patient.
The clinical support system according to claim 5. The input unit receives an input for setting the importance of the patient characteristic information,
The second acquisition unit, based on the degree of importance, the patient characteristic information of the first patient generated by the first analysis unit, and the patient characteristic information of the second patient, obtaining said conversation information of
The clinical support system according to claim 5 or 6. The display control unit displays the standard treatment for the injury or disease of the second patient, the treatment proposed by the medical staff to the second patient, and the selected treatment.
The clinical support system according to any one of claims 1 to 7. a first acquisition unit that acquires patient information related to the first patient, interview information indicating the symptoms of the first patient, and conversation information in which conversation of the first patient is recorded;
a first analysis unit that generates patient characteristic information indicating psychological and social aspects of a patient by analysis processing 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 the degree of 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. and,
a second analysis unit that analyzes features of the conversation between the second patient and the medical staff based on the conversation information acquired by the second acquisition unit;
an information generation unit that generates information indicating the analysis result by the second analysis unit;
A clinical support device comprising:

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