Medical examination support device, medical examination support method, and medical examination support program

Abstract

To achieve a medial examination support device that can reduce a patient's anxiety in a medical examination.SOLUTION: A medical examination support device comprises: an acquisition unit that acquires information on a medical examination for the patient, detail information representing the details of the exchange between the patient and a person who conducts the medical examination for the patient in the medical examination, and state information on the emotion of the patient in the medical examination; an explanatory sentence preparation unit that prepares an explanatory sentence related to an item for which the patient is estimated to be anxious, by using a language model generated through machine learning, the medical examination information, the detail information, and the state information; and an output unit that outputs the explanatory sentence.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a medical assistance device, a medical assistance method, and a medical assistance program. [Background technology]

[0002] Technologies for supporting medical treatment are known. One example of such a technology is described in Patent Document 1. Patent Document 1 describes an information processing device that receives voice data or text data during remote medical treatment transmitted from a doctor's terminal device to a patient's terminal device, and, if the received data contains medical terms, outputs glossary data corresponding to the medical terms to the patient's terminal device along with the voice data or text data. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2020-113003 Summary of the Invention [Problem to be solved by the invention]

[0004] However, during medical treatment, patients may feel anxious because they are unable to understand the contents of explanations given by doctors, etc. The technology described in Patent Document 1 has the problem of not being able to alleviate the anxiety of patients during medical treatment.

[0005] The present disclosure has been made in view of the above problems, and an exemplary purpose thereof is to provide a technology that reduces patient anxiety during medical treatment. [Means for solving the problem]

[0006] A medical assistance device according to an exemplary aspect of the present disclosure includes an acquisition means for acquiring a patient's medical information, content information representing the content of interactions between the patient and the person examining the patient during the examination, and status information relating to the patient's emotions during the examination, a language model generated by machine learning, an explanatory text generation means for using the medical information, the content information, and the status information to generate an explanatory text relating to matters about which the patient is presumed to be feeling anxious, and an output means for outputting the explanatory text.

[0007] A medical support method according to an exemplary aspect of the present disclosure includes an acquisition process in which at least one processor acquires a patient's medical information, content information representing the content of interactions between the patient and a person examining the patient during a medical examination, and status information relating to the patient's emotions during the medical examination; an explanatory text generation process in which the at least one processor uses a language model generated by machine learning, the medical information, the content information, and the status information to generate an explanatory text relating to an issue about which the patient is presumed to be feeling anxious; and an output process in which the at least one processor outputs the explanatory text.

[0008] A medical assistance program according to an exemplary aspect of the present disclosure is a medical assistance program for causing a computer to function as a medical assistance device, and causes the computer to function as: an acquisition means for acquiring a patient's medical information, content information representing the content of exchanges between the patient and the person examining the patient during the examination, and status information relating to the patient's emotions during the examination; an explanatory text generation means for generating explanatory text relating to matters about which the patient is presumed to be feeling anxious using a language model generated by machine learning, the medical information, the content information, and the status information; and an output means for outputting the explanatory text. [Effects of the Invention]

[0009] According to an exemplary aspect of the present disclosure, an exemplary effect is achieved in that a technology that can reduce patient anxiety during medical treatment can be provided. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a block diagram showing a configuration of a medical assistance device according to the present disclosure. [Figure 2] 1 is a flow chart showing the flow of a medical support method according to the present disclosure. [Figure 3] 1 is a block diagram illustrating a configuration of an information processing device according to the present disclosure. [Figure 4] FIG. 2 is a diagram illustrating a functional configuration of a control unit according to the present disclosure. [Figure 5] 10A and 10B are diagrams illustrating specific examples of output information output by an output control unit according to the present disclosure. [Figure 6] 1 is a flow diagram showing an example of the flow of a medical assistance method according to the present disclosure. [Figure 7] 1 is a block diagram showing the configuration of a computer that functions as a medical assistance device or an information processing device according to the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0011] The following are examples of embodiments of the present invention. However, the present invention is not limited to the exemplary embodiments shown below, and various modifications are possible within the scope of the claims. For example, embodiments obtained by appropriately combining the technologies (part or all of the products or methods) employed in the exemplary embodiments shown below may also be included in the scope of the present invention. Furthermore, embodiments obtained by appropriately omitting some of the technologies employed in the exemplary embodiments shown below may also be included in the scope of the present invention. Furthermore, the effects mentioned in the exemplary embodiments shown below are examples of effects expected in the exemplary embodiments, and do not define the scope of the present invention. In other words, embodiments that do not exhibit the effects mentioned in the exemplary embodiments shown below may also be included in the scope of the present invention.

[0012] First Exemplary Embodiment A first exemplary embodiment, which is an example of an embodiment of the present invention, will be described in detail with reference to the drawings. This exemplary embodiment is a basic form for each of the exemplary embodiments described below. Note that the scope of application of each technique employed in this exemplary embodiment is not limited to this exemplary embodiment. That is, each technique employed in this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical obstacles arise. Furthermore, each technique shown in the drawings referenced to explain this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical obstacles arise.

[0013] (Configuration of medical support device) The configuration of the medical support device 1 will be described with reference to Fig. 1. Fig. 1 is a block diagram showing the configuration of the medical support device 1. As shown in Fig. 1, the medical support device 1 includes an acquisition unit 11, an explanatory text generation unit 12, and an output unit 13.

[0014] The acquisition unit 11 acquires medical information of a patient, content information representing the content of exchanges between the patient and the person examining the patient during the examination, and state information relating to the patient's emotions during the examination. The explanatory text generation unit 12 generates explanatory text relating to matters that are estimated to cause anxiety to the patient using a language model generated by machine learning, the medical information, the content information, and the state information. The output unit 13 outputs the explanatory text generated by the explanatory text generation unit 12.

[0015] (Effects of medical support devices) As described above, the medical assistance device 1 includes an acquisition unit 11 that acquires patient medical information, content information representing the content of exchanges between the patient and the examiner during the medical examination, and state information relating to the patient's emotions during the examination, an explanatory text generation unit 12 that uses a language model generated by machine learning, the medical information, the content information, and the state information to generate explanatory text relating to matters that are estimated to cause the patient anxiety, and an output unit 13 that outputs the explanatory text. Therefore, the medical assistance device 1 has the effect of reducing patient anxiety during medical treatment.

[0016] (Flow of medical support method) The flow of the medical care support method S1 will be described with reference to Fig. 2. Fig. 2 is a flow diagram showing the flow of the medical care support method S1. As shown in Fig. 2, the medical care support method S1 includes an acquisition process S11, an explanatory text generation process S12, and an output process S13.

[0017] In an acquisition process S11, at least one processor acquires medical information of a patient, content information representing the content of exchanges between the patient and the examiner during the examination, and state information relating to the patient's emotions during the examination. In an explanatory text generation process S12, at least one processor uses a language model generated by machine learning, the medical information, the content information, and the state information to generate an explanatory text relating to an issue that is estimated to be causing the patient anxiety. In an output process S13, at least one processor outputs the explanatory text.

[0018] (Effects of medical examination support methods) As described above, the medical care support method S1 includes an acquisition process S11 in which at least one processor acquires a patient's medical information, content information representing the content of the exchange between the patient and the examiner during the medical examination, and status information relating to the patient's emotions during the medical examination, an explanatory text generation process S12 in which the at least one processor uses a language model generated by machine learning, the medical information, the content information, and the status information to generate an explanatory text relating to an issue that is estimated to be causing the patient anxiety, and an output process S13 in which the at least one processor outputs the explanatory text. Therefore, the medical care support method S1 has the effect of reducing patient anxiety during medical care.

[0019] Second Exemplary Embodiment A second exemplary embodiment, which is one example of an embodiment of the present invention, will be described in detail with reference to the drawings. Components having the same functions as those described in the above exemplary embodiment will be assigned the same reference numerals, and their description will be omitted as appropriate. The scope of application of each technology employed in this exemplary embodiment is not limited to this exemplary embodiment. That is, each technology employed in this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical hindrance occurs. Furthermore, each technology shown in each drawing referenced to explain this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical hindrance occurs.

[0020] (Configuration of information processing device) The configuration of the information processing device 1A will be described with reference to Fig. 3. Fig. 3 is a block diagram showing the configuration of the information processing device 1A. The information processing device 1A is an example of a medical assistance device according to the present disclosure. The information processing device 1A includes a control unit 10A, a storage unit 20A, a communication unit 30A, an input unit 40A, and an output unit 50A.

[0021] (Communications Department) The communication unit 30A communicates with devices external to the information processing device 1A via a communication line. While the specific configuration of the communication line does not limit the present exemplary embodiment, examples of the communication line include a wireless LAN (Local Area Network), a wired LAN, a WAN (Wide Area Network), a public line network, a mobile data communication network, or a combination thereof. The communication unit 30A transmits data supplied from the control unit 10A to other devices, and supplies data received from other devices to the control unit 10A.

[0022] (Input section) The input unit 40A is configured to receive input to the information processing device 1A, and includes, for example, input devices such as a keyboard, a mouse, a touch panel, a camera, a microphone, etc. The input unit 40A may also be configured to receive data from the input devices via an interface such as a USB (Universal Serial Bus).

[0023] (output section) The output unit 50A is a component for performing output from the information processing device 1A, and includes, for example, output devices such as a display, a printer, a touch panel, a speaker, etc. The output unit 50A may also be configured to include, for example, an interface such as a USB, and to output data to the output device via the interface.

[0024] (Storage part) The storage unit 20A stores various types of information referenced by the control unit 10A. Examples of such information include medical information 201, content information 202, condition information 203, and explanatory text 204.

[0025] (Medical Information) The medical information 201 is information relating to the patient's medical care. An example of the medical information 201 is chart information. The medical information 201 includes, for example, at least one of the patient's personal information, the patient's examination findings, and the patient's medical history information. The patient's personal information is the patient's personal information, and includes, for example, information indicating the patient's age, gender, smoking amount, etc. The patient's examination findings information is information indicating findings by a medical professional such as a doctor, and includes, for example, information obtained by the medical professional through a medical interview or visual examination. The information obtained through a medical interview or visual examination may be, for example, a sentence such as "there is pain when pressing the abdomen." The patient's medical history information is information relating to the patient's medical history, and includes, for example, information indicating the patient's medical history, the family's medical history, the patient's smoking amount, etc.

[0026] The medical information 201 may also include diagnostic images of patients. Examples of diagnostic images include at least one of X-ray images, endoscopic images, pathological images, MRI images, and CT images. The storage unit 20A stores medical information 201 for each of a plurality of patients.

[0027] (Content information) The content information 202 is information that represents the content of the exchange between the patient and the person examining the patient during the medical examination. Here, the person examining the patient is, for example, a medical professional such as a doctor. The content information 202 may be, for example, audio data of a conversation between the patient and the medical professional during the medical examination. In this case, the conversation between the patient and the medical professional includes, for example, complaints and questions uttered by the patient. The content information 202 may also be, for example, information that represents the content of the complaints and questions the patient has written in a questionnaire.

[0028] The content information 202 may be data in text format or may be audio data. The content information 202 is text including a question such as "Are you okay? What kind of surgery or examination is this?"

[0029] (Status information) The state information 203 is information relating to the patient's emotions. As an example, the state information 203 includes information indicating at least one of the patient's facial expression, speech pattern, vital signs, and emotion analysis results during the examination. As an example, the emotion analysis is performed by the emotion analysis unit 14A, which will be described later. In other words, the state information 203 may include information indicating the analysis results of the patient's emotions performed by the emotion analysis unit 14A, which will be described later. As an example, the information indicating the emotion analysis results indicates states such as "tension" and "anxiety."

[0030] (Explanatory text) The explanatory text 204 is text about matters that are presumed to cause anxiety to the patient. For example, the explanatory text 204 is text that simply explains the contents of the treatment, or text intended to alleviate the patient's anxiety. More specifically, the explanatory text 204 includes, for example, a sentence such as, "There is a risk of acute appendicitis (appendicitis). The success rate of treatment is very high," or a sentence such as, "In the case of surgery, anesthesia is administered and a small hole is made in the abdomen to remove the lesion with a camera and instruments. The surgical scar is small, the hospital stay is short, and recovery is quick. First, a CT scan will be performed to confirm the symptoms."

[0031] (Control unit) 4 is a diagram showing the functional configuration of the control unit 10A. The control unit 10A includes an acquisition unit 11A, an explanatory sentence generation unit 12A, an output control unit 13A, and a sentiment analysis unit 14A. The acquisition unit 11A is an example of an acquisition means according to the present disclosure. The explanatory sentence generation unit 12A is an example of an explanatory sentence generation means according to the present disclosure. The output control unit 13A is an example of an output means according to the present disclosure. The sentiment analysis unit 14A is an example of an acquisition means and a sentiment analysis means according to the present disclosure.

[0032] (Acquisition Department) The acquisition unit 11A acquires medical information 201 of a patient to be examined and content information 202 representing the content of the exchange between the patient and a medical professional or the like during the examination, and supplies the acquired medical information 201 and content information 202 to the explanation sentence generation unit 12A. As an example, the acquisition unit 11A may acquire the medical information 201 and content information 202 by reading the medical information 201 and content information 202 from a storage destination (which may be a storage device within the information processing device 1A or a storage device external to the information processing device 1A) designated by the user of the information processing device 1A. Alternatively, the acquisition unit 11A may acquire the medical information 201 and content information 202 by receiving the medical information 201 and content information 202 from another device via the communication unit 30A. Alternatively, the acquisition unit 11A may acquire the medical information 201 and content information 202 input to the input unit 40A.

[0033] As an example, the acquisition unit 11A acquires voice data representing voices collected during a medical examination, and converts the voice data into text-format content information 202. In this case, the acquisition unit 11A may perform speaker recognition from the voice data and generate content information 202 representing the content of the utterances of each speaker by transcribing the utterances of each speaker.

[0034] (Sentiment Analysis Department) The emotion analysis unit 14A analyzes the emotions of the patient during the medical examination and generates an analysis result. For example, the emotion analysis unit 14A may analyze the emotions of the patient by analyzing at least one of voice data of the patient collected during the medical examination and image data captured during the medical examination. Here, the image data may be data representing a still image or data representing a moving image. The emotion analysis method may be, for example, a method using a dictionary prepared in advance, or a method using a trained model generated by machine learning. When a trained model is used, the input of the trained model may include, for example, at least one of text, voice data, and image data. Furthermore, the output of the trained model includes information indicating the emotion classification result.

[0035] The emotion analysis unit 14A supplies the analysis results to the explanation sentence generation unit 12A. The emotion analysis unit 14A may also output the analysis results by writing them to a storage destination (which may be a storage device within the information processing device 1A or a storage device external to the information processing device 1A) designated by the user of the information processing device 1A. The emotion analysis unit 14A may also transmit the analysis results to another device via the communication unit 30A, or may output the analysis results to an output device such as a display.

[0036] (Explanatory text generation section) The explanatory sentence generation unit 12A acquires the status information 203. As an example, the explanatory sentence generation unit 12A may acquire the status information 203 by reading the status information 203 from a storage location (which may be a storage device within the information processing device 1A or a storage device external to the information processing device 1A) designated by the user of the information processing device 1A. Alternatively, the explanatory sentence generation unit 12A may acquire the status information 203 by receiving the status information 203 from another device via the communication unit 30A. Alternatively, the explanatory sentence generation unit 12A may acquire the status information 203 input to the input unit 40A.

[0037] As an example, the status information 203 includes an analysis result supplied from the emotion analysis unit 14A. The status information 203 may also include information input by a medical professional examining the patient using the input unit 40A, or information transmitted to the information processing device 1A from a communication device operated by the medical professional. In this case, the medical professional examining the patient observes the facial expression of the patient during the examination, and if the medical professional thinks that the patient is feeling nervous or anxious or does not understand the explanation, the medical professional inputs information indicating that fact.

[0038] The condition information 203 may also include information input by the patient using the input unit 40A or information transmitted from a communication device operated by the patient to the information processing device 1A. In this case, if the patient is unable to understand the explanation given by the medical staff or feels nervous or anxious about the treatment, the patient inputs information indicating that fact.

[0039] The explanatory text generation unit 12A generates explanatory text 204 using a large-scale language model M1 generated by machine learning, medical information 201, content information 202, and condition information 203. The large-scale language model M1 is an example of a language model according to the present disclosure. Here, the large-scale language model M1 may be stored in a storage unit 20A of the information processing device 1A, or may be stored in a device other than the information processing device 1A. Note that the large-scale language model M1 being stored in the storage unit 20A means that parameters defining the large-scale language model M1 are stored in the storage unit 20A.

[0040] The large-scale language model M1 is a language model generated by machine learning and composed of an artificial neural network with many parameters. Examples of the large-scale language model M1 include, but are not limited to, generative AI such as ChatGPT (Chat Generative Pre-trained Transformer) and GPT-4 (Generative Pre-trained Transformer 4), or a generative AI fine-tuned using medical data.

[0041] The input information input by the explanatory sentence generation unit 12A to the large-scale language model M1 includes at least one of medical information 201, content information 202, and condition information 203. The input information may also include information instructing the generation of an explanatory sentence. For example, the information may be text such as "Please explain the treatment in simple terms" or "Please generate an explanatory sentence that will reduce the patient's anxiety." The output information output from the large-scale language model M1 includes an explanatory sentence 204.

[0042] When the large-scale language model M1 is stored in a device other than the information processing device 1A, the explanatory sentence generation unit 12A, for example, inputs the input information to the large-scale language model M1 by transmitting the input information to the device storing the large-scale language model M1 via the communication unit 30A. In this case, the explanatory sentence generation unit 12A receives information output by the large-scale language model M1 from the device via the communication unit 30A.

[0043] Furthermore, the explanatory sentence generation unit 12A may determine whether the patient is feeling anxious by referring to the condition information 203. In this case, the explanatory sentence generation unit 12A may determine whether the patient is feeling anxious by referring to the condition information 203, and may generate the explanatory sentence 204 when it is determined that the patient is feeling anxious.

[0044] (Output control section) The output control unit 13A outputs output information 205 including the explanatory text 204 generated by the explanatory text generation unit 12A. As an example, the output control unit 13A may output the output information 205 by writing it to a storage destination (which may be a storage device within the information processing device 1A or a storage device external to the information processing device 1A) designated by the user of the information processing device 1A. Furthermore, the output control unit 13A may transmit the output information 205 via the communication unit 30A, or may output the output information 205 to an output device such as a display.

[0045] 5 is a diagram showing a specific example of output information 205 output by the output control unit 13A. As an example, the output control unit 13A may cause the display device to display the output information 205 shown in FIG. 5. In this case, the screen displayed on the display device includes explanatory text 204. The screen also includes content information 202 and status information 203, which are input to the information processing device 1A. As shown in FIG. 5, the output control unit 13A may output at least one of medical information 201, content information 202, and status information 203 together with the explanatory text 204.

[0046] In the example of FIG. 5, the output information 205 includes content information 202 in which the "Explanation of Treatment" is "You have acute appendicitis. Let's first examine how advanced the condition is to see if medication alone is sufficient or if surgery is necessary." The output information 205 also includes state information 203 in which the "Facial Expression and Attitude" is "Tense / Anxious." The output information 205 also includes content information 202 in which the "Complaint / Question" is "Are you okay? What kind of surgery / examination?"

[0047] In the example of Figure 5, the output information 205 includes an explanatory text 204 that reads, "There is a possibility that you have acute appendicitis (appendicitis). The success rate of treatment is very high. If the symptoms are mild, you can be treated with a prescription for antibiotics, but if the condition progresses, surgery is required. In the case of surgery, you will be given anesthesia and a small hole will be made in the abdomen to remove the lesion with a camera and instruments. The surgical scar will be small, the hospital stay will be short, and the recovery will be quick. First, a CT scan will be performed to confirm your symptoms."

[0048] The explanatory text 204 is used, for example, for patient decision-making regarding medical treatment. For example, by reviewing the explanatory text 204, the patient can decide on an action such as consulting with a doctor again about the course of treatment.

[0049] (Flow of medical support method) 6 is a flow diagram showing an example of the flow of a medical care support method S1A executed by the information processing device 1A. The steps included in the flow diagram of FIG. 6 may be executed in parallel or in a different order. In step S101, the emotion analysis unit 14A analyzes the facial expression and speech content of the patient during the examination, and generates the analysis result as status information 203.

[0050] In step S102, the emotion analysis unit 14A outputs the analysis result, ie, state information 203, to an output device. As an example, the emotion analysis unit 14A may output the state information 203 to a display and cause the state information 203 to be displayed on the display.

[0051] A medical professional examining a patient checks the condition information 203 displayed on the display and determines whether to output explanatory text 204. If explanatory text 204 is to be output, the medical professional operates an operator or the like connected to input unit 40A to input an instruction to output explanatory text 204 to information processing device 1A. For example, if the output condition information 203 includes at least one of information representing "tension" and information representing "anxiety," the medical professional determines to output explanatory text 204. On the other hand, if it is determined that there is no need to output explanatory text 204, the medical professional does not input an output instruction. For example, if the output condition information 203 does not include at least one of information representing "tension" and information representing "anxiety," the medical professional determines not to output explanatory text 204.

[0052] In step S103, the explanatory text generation unit 12A determines whether to generate explanatory text 204. As an example, the explanatory text generation unit 12A makes this determination based on whether or not an output instruction has been input by a user such as a medical professional. If it is determined that explanatory text 204 will be generated, the explanatory text generation unit 12A proceeds to the processing of step S104. On the other hand, if it is determined that explanatory text 204 will not be generated, the explanatory text generation unit 12A skips the processing of steps S104 to S108 and returns to the processing of step S101.

[0053] In step S103, the determination method used by the explanatory sentence generation unit 12A is not limited to the method described above, and the explanatory sentence generation unit 12A may determine whether to generate the explanatory sentence 204 using another method. As an example, the explanatory sentence generation unit 12A may determine whether to output the explanatory sentence 204 by referring to the state information 203 generated by the emotion analysis unit 14A. In this case, as an example, the explanatory sentence generation unit 12A may determine to output the explanatory sentence 204 if the state information 203 includes at least one of information representing "tension" and information representing "anxiety." Furthermore, as an example, the explanatory sentence generation unit 12A may determine not to output the explanatory sentence 204 if the state information 203 does not include at least one of information representing "tension" and information representing "anxiety."

[0054] In step S104, the acquisition unit 11A acquires medical information 201 from the storage unit 20A. In step S105, the acquisition unit 11A acquires voice data collected during the examination. The voice data represents a conversation between a patient and a medical professional during the examination. In step S106, the acquisition unit 11A performs speaker recognition processing on the voice data and transcribes the utterances of each speaker, thereby generating content information representing the content of the utterances of each speaker. The acquisition unit 11A supplies the medical information 201 and the generated content information 202 to the explanation sentence generation unit 12A.

[0055] In step S107, the explanatory sentence generation unit 12A inputs the medical information 201, the content information 202, and the condition information 203 into the large-scale language model M1 to generate the explanatory sentence 204. In step S108, the output control unit 13A outputs the explanatory sentence 204. After completing the processing of step S108, the control unit 10A returns to the processing of step S101.

[0056] (Effects of information processing devices) As described above, the information processing device 1A includes the emotion analysis unit 14A that analyzes the emotions of the patient during the medical examination, and the explanatory sentence generation unit 12A is configured to acquire the status information 203 that includes the analysis results of the emotion analysis unit 14A. Therefore, the information processing device 1A can output explanatory sentences 204 that reflect the results of the emotion analysis of the patient, and by having the patient check the output explanatory sentences 204, it is possible to obtain the effect of reducing the patient's tension or anxiety.

[0057] Furthermore, the information processing device 1A employs a configuration in which the acquisition unit 11A acquires audio data representing sounds picked up during the medical examination and converts the acquired audio data into text-format content information 202. Therefore, the information processing device 1A has the effect of being able to output explanatory text 204 that reflects the content of the conversation during the medical examination.

[0058] Furthermore, the information processing device 1A employs a configuration in which speaker recognition is performed from the voice data, and the utterances of each speaker are transcribed to generate content information 202 representing the content of each speaker's utterance. Therefore, the information processing device 1A has the effect of being able to output explanatory text 204 that reflects the content of each speaker's utterance during the consultation.

[0059] Furthermore, the information processing device 1A employs a configuration in which the explanatory sentence generation unit 12A refers to the state information 203 to determine whether the patient is feeling anxious, and when it is determined that the patient is feeling anxious, generates the explanatory sentence 204. Therefore, the information processing device 1A can prevent unnecessary output of the explanatory sentence 204.

[0060] Furthermore, the information processing device 1A employs a configuration in which the status information 203 includes information indicating at least one of the patient's facial expression, speech style, vital signs, and emotion analysis results during the examination. Therefore, the information processing device 1A has the effect of being able to output the explanatory text 204 that reflects information indicating at least one of the patient's facial expression, speech style, vital signs, and emotion analysis results during the examination.

[0061] Furthermore, the information processing device 1A employs a configuration in which the medical information 201 includes at least one of the patient's personal information, the patient's medical findings, and the patient's medical history information. Therefore, the information processing device 1A has the effect of being able to output an explanatory text 204 that reflects at least one of the patient's personal information, the patient's medical findings, and the patient's medical history information.

[0062] Furthermore, the information processing device 1A employs a configuration in which the explanatory text 204 is used for patient decision-making regarding medical treatment. Therefore, the information processing device 1A enables patients receiving medical treatment to make more appropriate decisions.

[0063] [Software implementation example] Some or all of the functions of the medical assistance device 1 and the information processing device 1A (hereinafter also referred to as "each of the above devices") may be realized by hardware such as an integrated circuit (IC chip), or by software.

[0064] In the latter case, each of the above devices is realized by, for example, a computer that executes instructions of a program, which is software that realizes each function. An example of such a computer (hereinafter referred to as computer C) is shown in Figure 7. Figure 7 is a block diagram showing the hardware configuration of computer C that functions as each of the above devices.

[0065] The computer C includes at least one processor C1 and at least one memory C2. The memory C2 stores a program P for causing the computer C to operate as each of the above-mentioned devices. In the computer C, the processor C1 reads and executes the program P from the memory C2, thereby realizing the functions of each of the above-mentioned devices.

[0066] The processor C1 may be, for example, a central processing unit (CPU), a graphic processing unit (GPU), a digital signal processor (DSP), a micro processing unit (MPU), a floating point number processing unit (FPU), a physics processing unit (PPU), a tensor processing unit (TPU), a quantum processor, a microcontroller, or a combination thereof. The memory C2 may be, for example, a flash memory, a hard disk drive (HDD), a solid state drive (SSD), or a combination thereof.

[0067] The computer C may further include a RAM (Random Access Memory) for expanding the program P during execution and for temporarily storing various data. The computer C may also include a communication interface for transmitting and receiving data to and from other devices. The computer C may also include an input / output interface for connecting input / output devices such as a keyboard, mouse, display, and printer.

[0068] Furthermore, the program P can be recorded on a non-transitory tangible recording medium M that can be read by the computer C. Such a recording medium M can be, for example, a tape, a disk, a card, a semiconductor memory, or a programmable logic circuit. The computer C can acquire the program P via such a recording medium M. The program P can also be transmitted via a transmission medium. Such a transmission medium can be, for example, a communication network or broadcast waves. The computer C can also acquire the program P via such a transmission medium.

[0069] Furthermore, the functions of each of the devices may be realized by a single processor provided in a single computer, by multiple processors provided in a single computer working in cooperation, or by multiple processors provided in each of multiple computers working in cooperation. Furthermore, the programs for causing each of the devices to realize the functions may be stored in a single memory provided in a single computer, or may be distributed and stored in multiple memories provided in a single computer, or may be distributed and stored in multiple memories provided in each of multiple computers.

[0070] [Appendix A] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims. (Appendix A1) an acquisition means for acquiring medical information of a patient, content information representing the content of exchanges between the patient and a person examining the patient during the medical examination, and state information relating to the patient's emotions during the medical examination; an explanatory sentence generation means for generating explanatory sentences relating to matters that are estimated to cause anxiety to the patient, using a language model generated by machine learning, the medical information, the content information, and the condition information; an output means for outputting the explanatory text; A medical support device comprising:

[0071] (Appendix A2) Further comprising emotion analysis means for analyzing the emotion of the patient during the consultation, the acquiring means acquires the state information including an analysis result of the emotion analyzing means. 1. A medical support device as described in Appendix A1.

[0072] (Appendix A3) the acquiring means acquires voice data representing voices collected during the medical examination and converts the voice data into the content information in text format. A medical support device according to appendix A1 or A2.

[0073] (Appendix A4) the acquiring means performs speaker recognition from the voice data and generates the content information representing the content of the utterance of each speaker by transcribing the utterance of each speaker; A medical support device as described in Appendix A3.

[0074] (Appendix A5) the explanatory sentence generation means refers to the condition information to determine whether the patient is feeling anxious, and generates the explanatory sentence when it is determined that the patient is feeling anxious. A medical support device according to any one of appendices A1 to A4.

[0075] (Appendix A6) The condition information includes information indicating at least one of facial expression, speech pattern, vital signs, and emotion analysis results of the patient during the medical examination. A medical support device according to any one of appendices A1 to A5.

[0076] (Appendix A7) The medical information includes at least one of personal information of the patient, medical examination findings information of the patient, and medical history information of the patient. A medical support device according to any one of appendices A1 to A6.

[0077] (Appendix A8) The explanatory text is used to help the patient make decisions about medical treatment. A medical support device according to any one of appendices A1 to A7.

[0078] [Appendix B] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims. (Appendix B1) an acquisition process in which at least one processor acquires medical information of a patient, content information representing the content of an exchange between the patient and a person examining the patient during a medical examination, and state information relating to the patient's emotions during the medical examination; an explanatory sentence generation process in which the at least one processor generates explanatory sentences regarding matters that are estimated to cause anxiety to the patient, using a language model generated by machine learning, the medical information, the content information, and the condition information; an output process in which the at least one processor outputs the explanatory text; A medical support method including:

[0079] (Appendix B2) the at least one processor further includes a sentiment analysis process for performing a sentiment analysis of the patient during the consultation; In the acquisition process, the at least one processor acquires the state information including an analysis result of the emotion analysis process. A medical support method as described in Appendix B1.

[0080] (Appendix B3) In the acquisition process, the at least one processor acquires audio data representing sounds collected during the medical examination and converts the audio data into the content information in text format. A medical support method as described in Appendix B1 or B2.

[0081] (Appendix B4) In the acquisition process, the at least one processor performs speaker recognition from the voice data and generates the content information representing the content of the utterance of each speaker by transcribing the utterance of each speaker. The medical support method described in Appendix B3.

[0082] (Appendix B5) In the explanatory sentence generation process, the at least one processor refers to the state information to determine whether the patient is feeling anxious, and generates the explanatory sentence when it is determined that the patient is feeling anxious. A medical support method according to any one of Appendices B1 to B4.

[0083] (Appendix B6) The condition information includes information indicating at least one of facial expression, speech pattern, vital signs, and emotion analysis results of the patient during the medical examination. A medical support method according to any one of Appendices B1 to B5.

[0084] (Appendix B7) The medical information includes at least one of personal information of the patient, medical examination findings information of the patient, and medical history information of the patient. A medical support method according to any one of Appendices B1 to B6.

[0085] (Appendix B8) The explanatory text is used to help the patient make decisions about medical treatment. A medical support method according to any one of Appendices B1 to B7.

[0086] [Appendix C] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims. (Appendix C1) A program for causing a computer to function as a medical assistance device, The computer an acquisition means for acquiring medical information of a patient, content information representing the content of exchanges between the patient and a person examining the patient during the medical examination, and state information relating to the patient's emotions during the medical examination; an explanatory sentence generation means for generating explanatory sentences relating to matters that are estimated to cause anxiety to the patient, using a language model generated by machine learning, the medical information, the content information, and the condition information; an output means for outputting the explanatory text; A medical support program to function as a

[0087] (Appendix C2) The computer further functioning as emotion analysis means for analyzing the emotion of the patient during the consultation; the acquiring means acquires the state information including an analysis result of the emotion analyzing means. The clinical support program described in Appendix C1.

[0088] (Appendix C3) the acquiring means acquires voice data representing voices collected during the medical examination and converts the voice data into the content information in text format. A medical support program as described in Appendix C1 or C2.

[0089] (Appendix C4) the acquiring means performs speaker recognition from the voice data and generates the content information representing the content of the utterance of each speaker by transcribing the utterance of each speaker; The clinical support program described in Appendix C3.

[0090] (Appendix C5) the explanatory sentence generation means refers to the condition information to determine whether the patient is feeling anxious, and generates the explanatory sentence when it is determined that the patient is feeling anxious. A clinical support program as set forth in any one of Appendices C1 to C4.

[0091] (Appendix C6) The condition information includes information indicating at least one of facial expression, speech pattern, vital signs, and emotion analysis results of the patient during the medical examination. A clinical support program as set forth in any one of Appendices C1 to C5.

[0092] (Appendix C7) The medical information includes at least one of personal information of the patient, medical examination findings information of the patient, and medical history information of the patient. A clinical support program as set forth in any one of Appendices C1 to C6.

[0093] (Appendix C8) The explanatory text is used to help the patient make decisions about medical treatment. A clinical support program as set forth in any one of Appendices C1 to C7.

[0094] [Appendix D] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims. (Appendix D1) at least one processor, an acquisition process for acquiring medical information of a patient, content information representing the content of the exchange between the patient and the examiner during the medical examination, and state information relating to the patient's emotions during the medical examination; an explanatory sentence generation process that generates explanatory sentences regarding matters that are estimated to cause anxiety to the patient, using a language model generated by machine learning, the medical information, the content information, and the condition information; an output process for outputting the explanatory text; A medical support device that performs the above.

[0095] The medical assistance device may further include a memory, and the memory may store a program for causing the at least one processor to execute each of the processes.

[0096] (Appendix D2) the at least one processor: further performing a sentiment analysis process for analyzing the sentiment of the patient during the consultation; In the acquisition process, the at least one processor acquires the state information including an analysis result of the emotion analysis process. A medical support device according to appendix D1.

[0097] (Appendix D3) In the acquisition process, the at least one processor acquires audio data representing sounds collected during the medical examination and converts the audio data into the content information in text format. A medical support device according to appendix D1 or D2.

[0098] (Appendix D4) In the acquisition process, the at least one processor performs speaker recognition from the voice data and generates the content information representing the content of the utterance of each speaker by transcribing the utterance of each speaker. 1. A medical support device as described in Appendix D3.

[0099] (Appendix D5) In the explanatory sentence generation process, the at least one processor refers to the state information to determine whether the patient is feeling anxious, and generates the explanatory sentence when it is determined that the patient is feeling anxious. A medical support device according to any one of appendices D1 to D4.

[0100] (Appendix D6) The condition information includes information indicating at least one of facial expression, speech pattern, vital signs, and emotion analysis results of the patient during the medical examination. A medical support device according to any one of appendices D1 to D5.

[0101] (Appendix D7) The medical information includes at least one of personal information of the patient, medical examination findings information of the patient, and medical history information of the patient. A medical support device according to any one of appendices D1 to D6.

[0102] (Appendix D8) The explanatory text is used to help the patient make decisions about medical treatment. A medical support device according to any one of appendices D1 to D7.

[0103] [Appendix E] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims. (Appendix E1) A program that causes a computer to function as a medical assistance device, The computer, an acquisition process for acquiring medical information of a patient, content information representing the content of the exchange between the patient and the examiner during the medical examination, and state information relating to the patient's emotions during the medical examination; an explanatory sentence generation process that generates explanatory sentences regarding matters that are estimated to cause anxiety to the patient, using a language model generated by machine learning, the medical information, the content information, and the condition information; an output process for outputting the explanatory text; A non-transitory recording medium on which a medical support program for executing the above is recorded. [Explanation of symbols]

[0104] 1 Medical support equipment 1A Information processing equipment 11, 11A Acquisition Department 12, 12A Explanation text generation section 13, 50A output section 13A Output control section 14A Sentiment Analysis Department

Claims

1. an acquisition means for acquiring medical information of a patient, content information representing the content of exchanges between the patient and a person examining the patient during the medical examination, and state information relating to the patient's emotions during the medical examination; an explanatory sentence generation means for generating explanatory sentences relating to matters that are estimated to cause anxiety to the patient, using a language model generated by machine learning, the medical information, the content information, and the condition information; an output means for outputting the explanatory text; A medical support device comprising:

2. Further comprising emotion analysis means for analyzing the emotion of the patient during the consultation, the acquiring means acquires the state information including an analysis result of the emotion analyzing means. The medical support device according to claim 1.

3. the acquiring means acquires voice data representing voices collected during the medical examination and converts the voice data into the content information in text format. The medical support device according to claim 1 or 2.

4. the acquiring means performs speaker recognition from the voice data and generates the content information representing the content of the utterance of each speaker by transcribing the utterance of each speaker; The medical support device according to claim 3.

5. the explanatory sentence generation means determines whether the patient is feeling anxious by referring to the condition information, and generates the explanatory sentence when it is determined that the patient is feeling anxious. The medical support device according to claim 1 or 2.

6. The condition information includes information indicating at least one of facial expression, speech pattern, vital signs, and emotion analysis results of the patient during the medical examination. The medical support device according to claim 1 or 2.

7. The medical information includes at least one of personal information of the patient, medical examination findings information of the patient, and medical history information of the patient. The medical support device according to claim 1 or 2.

8. The explanatory text is used to help the patient make decisions about medical treatment. The medical support device according to claim 1 or 2.

9. an acquisition process in which at least one processor acquires medical information of a patient, content information representing content of interactions between the patient and a person examining the patient during a medical examination, and state information regarding emotions of the patient during the medical examination; an explanatory sentence generation process in which the at least one processor generates explanatory sentences regarding matters that are estimated to cause anxiety to the patient, using a language model generated by machine learning, the medical information, the content information, and the condition information; an output process in which the at least one processor outputs the explanatory text; A medical support method including:

10. A medical assistance program for causing a computer to function as a medical assistance device, the computer comprising: an acquisition means for acquiring medical information of a patient, content information representing the content of exchanges between the patient and a person examining the patient during the medical examination, and state information relating to the patient's emotions during the medical examination; an explanatory sentence generation means for generating explanatory sentences relating to matters that are estimated to cause anxiety to the patient, using a language model generated by machine learning, the medical information, the content information, and the condition information; an output means for outputting the explanatory text; A medical support program to function as a

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