Medical information processing device, medical information processing system, medical care support method, and program

Abstract

To reflect patient's desire in medical treatment.SOLUTION: A medical information processing device includes a first acquisition part, a second acquisition part, an estimation part, a third acquisition part, and a fourth acquisition part. The first acquisition part acquires diagnosis contents diagnosed to a patient. The second acquisition part acquires reaction information about a reaction of the patient to a first therapy to the diagnosis contents. The estimation part estimates feelings of the patient about the first therapy on the basis of the reaction information. The third acquisition part acquires a desire of the patient about the first therapy on the basis of the reaction information. The fourth acquisition part acquires a second therapy suitable to the desire in response to the feelings estimated by the estimation part.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The embodiments disclosed in this specification and the drawings relate to a medical information processing device, a medical information processing system, a medical assistance method, and a program.

[0002] Traditionally, doctors will explain to patients about the condition and treatment of an illness or injury to help them understand the illness or injury.

[0003] However, patients may not be able to communicate their wishes to doctors due to lack of understanding or intimidation of doctors. In such cases, patients may stop visiting the hospital or change to another hospital. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent Publication No. 2021-012437 Summary of the Invention [Problem to be solved by the invention]

[0005] One of the problems to be solved by the embodiments disclosed in this specification and the drawings is to reflect the patient's wishes in treatment. However, the problems to be solved by the embodiments disclosed in this specification and the drawings are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described below can also be positioned as other problems. [Means for solving the problem]

[0006] A medical information processing device according to an embodiment includes a first acquisition unit, a second acquisition unit, an estimation unit, a third acquisition unit, and a fourth acquisition unit. The first acquisition unit acquires a diagnosis diagnosed for a patient. The second acquisition unit acquires reaction information regarding the patient's reaction to a first treatment method for the diagnosis. The estimation unit estimates the patient's feelings regarding the first treatment method based on the reaction information. The third acquisition unit acquires the patient's requests regarding the first treatment method based on the reaction information. The fourth acquisition unit acquires a second treatment method that matches the requests according to the feelings estimated by the estimation unit. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a diagram illustrating an example of a medical support system according to the first embodiment. [Diagram 2] FIG. 2 is a flowchart showing an example of reservation processing executed by the medical image processing apparatus according to the first embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, a medical information processing device, a medical information processing system, a medical assistance method, and a program according to the present embodiment will be described with reference to the drawings. In the following embodiments, parts with the same reference numerals perform the same operations, and duplicated descriptions will be omitted as appropriate.

[0009] (First embodiment) Fig. 1 is a diagram showing an example of a medical support system 1 according to a first embodiment. The medical support system 1 includes an imaging device 10, a microphone 20, an operation terminal 30, and a medical information processing device 40. The imaging device 10, the microphone 20, the operation terminal 30, and the medical information processing device 40 are communicatively connected to a network 50 such as an in-hospital LAN (Local Area Network). The medical support system 1 shown in Fig. 1 includes one each of the imaging device 10, the microphone 20, the operation terminal 30, and the medical information processing device 40, but may include multiple each.

[0010] The imaging device 10, the microphone 20, and the operation terminal 30 are installed at a location where a medical professional interviews a patient who has received an explanation from a doctor about a treatment for an illness or the like. The medical professional may be a nurse, a pharmacist, a physical therapist, or the like. For example, a nurse may check with the patient whether there is anything unclear about the doctor's explanation. For example, a pharmacist may explain to the patient about the medicine prescribed for the patient. For example, a physical therapist may explain to the patient about physical therapy. Note that the medical professional is not limited to a nurse, a pharmacist, and a physical therapist, and may be another type of professional.

[0011] The imaging device 10 is a camera that captures an image of a patient. For example, the imaging device 10 captures a patient image, which is an image of the patient during an interview between a medical professional and the patient. The imaging device 10 then transmits the captured patient image to the medical information processing device 40.

[0012] The microphone 20 is a device that converts the voice of a patient into an electrical signal and generates voice data from the electrical signal. For example, the microphone 20 captures voice data of the patient during an interview between a medical professional and the patient. The microphone 20 then transmits the voice data of the patient to the medical information processing device 40.

[0013] The operation terminal 30 is a computer device such as a smartphone, a tablet terminal, or a personal computer. The operation terminal 30 is a terminal operated by a medical professional. The operation terminal 30 displays an image provided from the medical information processing device 40.

[0014] The medical information processing device 40 is a computer device such as a server device. The medical information processing device 40 acquires voice data and an image of a patient who is having an interview with a medical professional. The medical information processing device 40 also determines whether or not the patient has anxiety, dissatisfaction, requests, etc. regarding the explanation of the treatment method from the doctor based on the voice data and the image of the patient who is having an interview with the medical professional.

[0015] When a patient has a request regarding a treatment method, the medical information processing device 40 judges whether or not there is a treatment method that reflects the request. Then, the medical information processing device 40 displays the judgment result on the operation terminal 30. This allows medical personnel to encourage the patient to consult with a doctor again in order to find a treatment method that meets the patient's request.

[0016] Here, the circumstances in which the medical support system 1 is used will be described.

[0017] When a patient has an illness or injury, the doctor explains the treatment to the patient. For example, the doctor may explain, "A stage 2 tumor has been found. By removing this tumor through surgery, the survival rate after 5 years is 80%. However, this surgery will leave you with paralysis in your left leg."

[0018] In addition, after the patient's interview with the doctor, the patient may have an interview with a medical professional such as a nurse. For example, the nurse may ask if the patient needs additional explanation regarding the treatment, such as "Did you understand the treatment?" or "Is there anything you are unclear about, such as what life will be like after you are left paralyzed?"

[0019] At this time, patients may express concerns or dissatisfaction that they had not thought of during their interview with the doctor. For example, a patient may say, "I understand that my left leg will remain paralyzed, but will I be able to drive a car? It will be difficult to live in the area if I cannot drive a car. It will be financially difficult to replace it with a welfare vehicle. How much will the treatment cost? My wife has passed away, and if I will no longer be able to enjoy my hobby of mountain climbing, I would like to leave some money for my grandchildren."

[0020] Pharmacists may also conduct interviews to explain the medications they prescribe to patients. For example, pharmacists may explain the medication, such as, "This medication needs to be taken three times a day after meals. Approximately 30% of patients experience side effects such as fatigue."

[0021] At this time, patients may talk about concerns or dissatisfaction that they had not thought of during their interview with the doctor. For example, patients may ask, "Will I be unable to drive if I take this medicine? Will the medicine affect me in a way that makes me cold towards my grandchildren?"

[0022] In this way, a patient may express their requests during an interview with a medical professional that is held after the patient's interview with a doctor. In this case, it is preferable for the doctor to change the treatment method to one that suits the patient's requests. Therefore, when a request that includes anxiety or dissatisfaction is expressed, the medical support system 1 determines whether or not there is a treatment method that suits the patient's requests. If there is a treatment method that suits the patient's requests, the medical support system 1 encourages the patient to interview the doctor again. This allows the doctor to change the treatment method to one that suits the patient's requests.

[0023] Next, the medical image processing device 40 will be described in detail.

[0024] The medical information processing device 40 includes a NW (network) interface 41 , an input interface 42 , a display 43 , a memory circuitry 44 , and a processing circuitry 45 .

[0025] The NW interface 41 is connected to the processing circuit 45, and controls the transmission and communication of various data between each device connected via the network 50. For example, the NW interface 41 is realized by a network card, a network adapter, a NIC (Network Interface Controller), or the like.

[0026] The input interface 42 is connected to the processing circuit 45, converts an input operation received from an operator (medical worker) into an electrical signal, and outputs the electrical signal to the processing circuit 45. Specifically, the input interface 42 converts an input operation received from an operator into an electrical signal, and outputs the electrical signal to the processing circuit 45. For example, the input interface 42 is realized by a trackball, a switch button, a mouse, a keyboard, a touch pad that performs an input operation by touching the operation surface, a touch screen in which a display screen and a touch pad are integrated, a non-contact input circuit using an optical sensor, and a voice input circuit. In this specification, the input interface 42 is not limited to only those that have physical operation parts such as a mouse and a keyboard. For example, an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the device and outputs the electrical signal to a control circuit is also included in the examples of the input interface 42.

[0027] The display 43 is connected to the processing circuit 45, and displays various information and various image data output from the processing circuit 45. For example, the display 43 is realized by a liquid crystal display, a CRT (Cathode Ray Tube) display, an organic EL display, a plasma display, a touch panel, or the like.

[0028] The memory circuitry 44 is connected to the processing circuitry 45 and stores various data. The memory circuitry 44 also stores various programs that are read and executed by the processing circuitry 45 to realize various functions. For example, the memory circuitry 44 is realized by a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, a hard disk, an optical disk, or the like.

[0029] The processing circuitry 45 controls the operation of the entire medical information processing device 40. The processing circuitry 45 has, for example, a diagnosis content acquisition function 451, a data acquisition function 452, a phrase analysis function 453, an emotion estimation function 454, a search function 455, a display control function 456, and a reservation function 457. In the embodiment, each processing function performed by the components of the diagnosis content acquisition function 451, the data acquisition function 452, the phrase analysis function 453, the emotion estimation function 454, the search function 455, the display control function 456, and the reservation function 457 is stored in the storage circuitry 44 in the form of a program executable by a computer. The processing circuitry 45 is a processor that reads out the program from the storage circuitry 44 and executes it to realize the function corresponding to each program. In other words, the processing circuitry 45 in a state in which each program has been read out has each function shown in the processing circuitry 45 in FIG. 1.

[0030] 1, the diagnosis content acquisition function 451, the data acquisition function 452, the phrase analysis function 453, the emotion estimation function 454, the search function 455, the display control function 456, and the reservation function 457 are realized by a single processor, but the processing circuit 45 may be configured by combining a plurality of independent processors, and each processor may execute a program to realize the function. Also, in FIG. 1, the single memory such as the storage circuit 44 stores the programs corresponding to the processing functions, but the processing circuit 45 may be configured by distributing a plurality of memories and reading out the corresponding programs from the individual memories.

[0031] The term "processor" used in the above description refers to a circuit such as a CPU (Central Processing Unit), a GPU (Graphical Processing Unit), an Application Specific Integrated Circuit (ASIC), a programmable logic device (e.g., a Simple Programmable Logic Device (SPLD), a Complex Programmable Logic Device (CPLD), and a Field Programmable Gate Array (FPGA)). The processor realizes its function by reading and executing a program stored in the memory circuit 44. Note that instead of storing a program in the memory circuit 44, the processor may be configured to directly incorporate the program into its circuit. In this case, the processor realizes its function by reading and executing the program incorporated in the circuit.

[0032] The diagnostic content acquisition function 451 acquires diagnostic content diagnosed for a patient. The diagnostic content acquisition function 451 is an example of a first acquisition unit. The diagnostic content is a diagnosis result of an illness, injury, or the like diagnosed for a patient.

[0033] More specifically, when patient identification information for identifying a patient is transmitted from the operation terminal 30, the diagnostic content acquisition function 451 acquires the diagnostic content of a patient specified by the patient identification information. The diagnostic content acquisition function 451 may acquire the diagnostic content from the memory circuit 44, may acquire the diagnostic content from an electronic medical record stored in an HIS (Hospital Information System), may acquire the diagnostic content from another device via the NW interface 41, or may acquire the diagnostic content via the input interface 42.

[0034] The data acquisition function 452 acquires reaction information regarding the patient's reaction to the treatment method for the diagnosis. For example, the reaction information is voice data of the patient's speech and a patient image of the patient. That is, the data acquisition function 452 acquires voice data and a patient image during an interview between a medical professional and a patient. Specifically, the data acquisition function 452 acquires voice data of the patient's speech regarding the treatment method for the diagnosis as the reaction information. In addition, the data acquisition function 452 acquires an image of the patient during an interview regarding the treatment method for the diagnosis as the reaction information. The data acquisition function 452 is an example of a second acquisition unit.

[0035] More specifically, the data acquisition function 452 acquires a patient image from the imaging device 10 via the NW interface 41. The data acquisition function 452 also acquires voice data from the microphone 20 via the NW interface 41. Note that the data acquisition function 452 may acquire patient image and voice data not only from the imaging device 10 and the microphone 20 but also from other devices.

[0036] The phrase analysis function 453 performs phrase analysis on the voice data. As a result, the phrase analysis function 453 acquires the contents of the patient's remarks during an interview between a medical professional and a patient. For example, the phrase analysis function 453 acquires the contents of the patient's remarks by inputting the voice data into a trained model.

[0037] The emotion estimation function 454 estimates the emotion of the patient in an interview between a medical professional and a patient. For example, the emotion estimation function 454 estimates the emotion of the patient regarding a treatment method explained by a doctor based on voice data. The emotion estimation function 454 is an example of an estimation unit. The emotion estimation function 454 also estimates the emotion of the patient based on a patient image. That is, the emotion estimation function 454 estimates the emotion of the patient based on the voice data and the patient image. Note that the emotion estimation function 454 may estimate the emotion of the patient based on either one of the voice data and the patient image, not limited to both.

[0038] More specifically, the emotion estimation function 454 acquires the negativity and positivity of the patient at each time point when the medical professional explains the treatment method during a meeting between the medical professional and the patient. The negativity is the degree of the patient's negative emotion. For example, the negativity is the degree of anxiety or dissatisfaction. The positivity is the degree of the patient's positive emotion. For example, the positivity is the degree of joy.

[0039] The emotion estimation function 454 estimates a negativity level indicating the degree of the patient's negative emotion during an interview between a medical professional and a patient. For example, the emotion estimation function 454 estimates the patient's negativity level for each time point of the interview. Note that the emotion estimation function 454 may estimate the patient's negativity level for the entire interview, not limited to each time point of the interview.

[0040] For example, the emotion estimation function 454 estimates the negativity of the patient based on the tone of the patient's voice indicated by the voice data. Specifically, the emotion estimation function 454 acquires the negativity associated with the scale that matches the patient's voice based on the scale-specific negativity information in which the negativity is set for each scale. In this way, the emotion estimation function 454 estimates the negativity of the patient.

[0041] Alternatively, the emotion estimation function 454 inputs the voice data to a trained model that has trained the correlation between tone of voice and negativity. Then, the emotion estimation function 454 estimates the patient's negativity by acquiring the negativity output from the trained model.

[0042] For example, the emotion estimation function 454 estimates the negativity of the patient based on the utterance content analyzed by the phrase analysis function 453. Specifically, the emotion estimation function 454 acquires the negativity associated with a phrase that matches the utterance content based on phrase-specific negativity information in which a negativity is set for each phrase. Alternatively, the emotion estimation function 454 inputs the voice data to a trained model that has learned the correlation between phrases and negativity. Then, the emotion estimation function 454 estimates the negativity of the patient by acquiring the negativity output from the trained model.

[0043] For example, the emotion estimation function 454 estimates the negativity of a patient based on a patient image. Specifically, the emotion estimation function 454 inputs the patient image to a trained model that has learned the correlation between facial expressions and negativity. Then, the emotion estimation function 454 estimates the negativity of the patient by acquiring the negativity output from the trained model.

[0044] In addition, the emotion estimation function 454 may estimate the degree of negativity of the patient based on one or two elements, not limited to the three elements of the voice data, the utterance content analyzed by the phrase analysis function 453, and the patient image.

[0045] Furthermore, the emotion estimation function 454 may estimate the degree of negativity by integrating three elements, namely, the voice data, the utterance contents analyzed by the phrase analysis function 453, and the patient image, or may estimate the degree of negativity by adding them together. Furthermore, the emotion estimation function 454 may estimate the degree of negativity of the patient by elements other than the three elements, namely, the voice data, the utterance contents analyzed by the phrase analysis function 453, and the patient image.

[0046] Furthermore, the emotion estimation function 454 estimates the positivity level of the patient at each time point during an interview between the medical professional and the patient. That is, the emotion estimation function 454 estimates the positivity level indicating the degree of the patient's positive emotion at each time point of the voice data. For example, the emotion estimation function 454 estimates the positivity level of the patient using a method similar to the negativity level.

[0047] For example, the emotion estimation function 454 estimates the positivity of the patient based on the tone of the patient's voice indicated by the voice data. Specifically, the emotion estimation function 454 acquires a positivity associated with a scale that matches the patient's voice based on scale positivity information in which a positivity is set for each scale. In this way, the emotion estimation function 454 estimates the positivity of the patient. Alternatively, the emotion estimation function 454 inputs the voice data to a trained model that has learned the correlation between the tone of voice and the positivity. Then, the emotion estimation function 454 estimates the positivity of the patient by acquiring the positivity output from the trained model.

[0048] For example, the emotion estimation function 454 estimates the positivity level of the patient based on the utterance content analyzed by the phrase analysis function 453. Specifically, the emotion estimation function 454 acquires the positivity level associated with a phrase that matches the utterance content based on phrase-specific positivity level information in which a positivity level is set for each phrase. Alternatively, the emotion estimation function 454 inputs the voice data to a trained model that has learned the correlation between phrases and positivity levels. Then, the emotion estimation function 454 estimates the positivity level of the patient by acquiring the positivity level output from the trained model.

[0049] For example, the emotion estimation function 454 estimates the positivity level of a patient based on a patient image. Specifically, the emotion estimation function 454 inputs the patient image to a trained model that has learned the correlation between facial expressions and positivity levels. Then, the emotion estimation function 454 estimates the positivity level of the patient by acquiring the positivity level output from the trained model.

[0050] In addition, the emotion estimation function 454 may estimate the positivity level of the patient based on one or two elements, not limited to the three elements of the voice data, the utterance content analyzed by the phrase analysis function 453, and the patient image.

[0051] Furthermore, the emotion estimation function 454 may estimate the positivity level by integrating three elements, namely, the voice data, the utterance content analyzed by the phrase analysis function 453, and the patient image, or may estimate the positivity level by adding up the positivity of the three elements. Furthermore, the emotion estimation function 454 may estimate the positivity level of the patient using elements other than the three elements, namely, the voice data, the utterance content analyzed by the phrase analysis function 453, and the patient image.

[0052] The search function 455 searches for a treatment method that matches the patient's needs according to the patient's emotions estimated by the emotion estimation function 454. More specifically, the search function 455 determines whether to search for a treatment method that matches the patient's needs based on the patient's negativity estimated by the emotion estimation function 454. For example, the search function 455 determines to search for a treatment method that matches the patient's needs when the patient's negativity estimated by the emotion estimation function 454 is equal to or greater than a threshold.

[0053] On the other hand, when the negativity of the patient estimated by the emotion estimation function 454 is less than the threshold, the search function 455 determines not to search for a treatment method that matches the patient's request. Note that the negativity may be a cumulative value of the negativity at each time point estimated by the emotion estimation function 454, or may be the negativity at each time point.

[0054] The search function 455 acquires the patient's request regarding the treatment explained by the doctor based on the voice data. The search function 455 is an example of a third acquisition unit. More specifically, the search function 455 extracts the patient's request from the patient's statement analyzed by the phrase analysis function 453. For example, the search function 455 extracts keywords from the patient's statement analyzed by the phrase analysis function 453 based on request list information in which the patient's request is associated with the keyword. Then, the search function 455 extracts the request associated with the extracted keyword in the request list information as the patient's request.

[0055] The search function 455 may extract the patient's request using a trained model. For example, the search function 455 inputs voice data to a trained model that has learned the correlation between requests and keywords. Then, the emotion estimation function 454 may acquire the request output from the trained model. Furthermore, the search function 455 may extract the request using other methods.

[0056] The search function 455 searches for a treatment method that matches the patient's needs, according to the patient's emotion estimated by the emotion estimation function 454. The search function 455 is an example of a fourth acquisition unit. More specifically, the search function 455 searches for a treatment method that matches the patient's needs when the patient's negativity is equal to or greater than a threshold.

[0057] For example, the search function 455 generates search conditions for a treatment that matches the patient's request. The search conditions are attributes of the treatment, such as "no residual disability" and "low cost of treatment." The search function 455 generates search conditions for a treatment by identifying which attributes the request has.

[0058] For example, the search function 455 acquires the attributes of the treatment method to which the request belongs by inputting the request to a trained model that has learned the correlation between the request and the attributes of the treatment method. In other words, the search function 455 acquires search conditions that match the request. In this way, the search function 455 generates one or more search conditions. The search function 455 may acquire search conditions that match the request based on information that associates keywords included in the request with the search conditions.

[0059] The search function 455 searches for treatments that match the patient's needs for a diagnosis based on one or more search criteria. For example, the search function 455 retrieves treatments for the patient from a treatment database that match the diagnosis and the search criteria.

[0060] The treatment method database is information in which diagnostic content information, one or more pieces of treatment method information for the diagnostic content, and treatment result information for the treatment method are associated with each other. Diagnostic content information is information for identifying diagnostic content, such as the name of a disease or injury, or an identification number. Treatment method information is information for identifying a treatment method, such as a name or an identification number. Treatment result information is information indicating the results of performing a treatment method for a diagnostic content. For example, treatment result information includes information such as the degree of residual disability and the cost of treatment.

[0061] The search function 455 may acquire a treatment method that matches the search conditions using a trained model, not limited to a treatment method database. That is, the search function 455 may acquire one or more treatment methods that match the patient's needs. For example, the search function 455 may acquire one or more treatment methods that match the needs or search conditions by inputting the needs or search conditions to a trained model that has learned the correlation between the needs or search conditions and the treatment methods.

[0062] The search function 455 acquires a treatment method identified by the content of the statement at the time when the positivity is equal to or greater than a threshold. That is, the search function 455 acquires a treatment method based on the patient's treatment preference criteria estimated from the patient's statement at the time when the positivity is equal to or greater than a threshold. For example, the search function 455 sets a priority order for the treatment method based on the patient's statement at the time when the positivity is equal to or greater than a threshold. As a result, the search function 455 acquires a treatment method based on the patient's treatment preference criteria.

[0063] More specifically, the search function 455 acquires the patient's utterance content when the patient's positivity level reaches or exceeds a threshold, from among the utterance content analyzed by the phrase analysis function 453 during an interview between a medical professional and a patient. The search function 455 also identifies attributes of a treatment method corresponding to the acquired utterance content, based on treatment result information in the treatment method database. The search function 455 also increases the priority of a treatment method having the identified attributes. The search function 455 then sets a priority order for the acquired treatment methods according to the priority level.

[0064] The display control function 456 displays the results obtained by the search function 455 on the operation terminal 30. For example, the display control function 456 displays the treatment method and the patient's request obtained by the search function 455 on the operation terminal 30. The display control function 456 is an example of a display control unit. The operation terminal 30 is an example of a display unit. Then, the medical worker suggests having another consultation with the doctor based on the content displayed on the operation terminal 30.

[0065] For example, when a treatment method is acquired by the search function 455, the display control function 456 displays on the operation terminal 30 that a treatment method that meets the patient's needs is available. Furthermore, the display control function 456 may display on the operation terminal 30 the treatment method acquired by the search function 455.

[0066] Furthermore, when a plurality of treatment methods are acquired by the search function 455, the display control function 456 may display the treatment methods on the operation terminal 30 in accordance with the priority order. For example, the display control function 456 may display the treatment methods in association with the priority order on the operation terminal 30, or may display the treatment methods on the operation terminal 30 in the order of priority order. Furthermore, the display control function 456 may display not only the treatment methods, but also the patient's request, search conditions, or the contents of the patient's statement when the patient's positivity level reaches or exceeds a threshold.

[0067] The reservation function 457 reserves an interview regarding the treatment method acquired by the search function 455 in a schedule system that manages the nurses' schedules. The reservation function 457 is an example of a reservation unit. If a treatment method that meets the patient's needs is acquired, the medical professional proposes another interview with the doctor. If the patient accepts this proposal, the reservation function 457 reserves an interview with the doctor.

[0068] More specifically, the reservation function 457 acquires the schedule of the doctor who will conduct the interview again from the schedule system. The reservation function 457 also transmits the doctor's schedule to the operation terminal 30. The operation terminal 30 displays the doctor's schedule. The operation terminal 30 also accepts input of the date and time for the interview again. The reservation function 457 also acquires the date and time for the interview again from the operation terminal 30. The operation terminal 30 then registers the interview date and time acquired from the operation terminal 30 in the schedule system. In this way, the reservation function 457 reserves an interview with the doctor.

[0069] The reservation function 457 may also transmit the patient's request, the searched one or more treatments, and the priority of each treatment to the schedule system. This allows the terminal used by the doctor to display the patient's request, the searched one or more treatments, and the priority of each treatment. This allows the doctor to propose a treatment to the patient based on the patient's request, the searched one or more treatments, and the priority of each treatment.

[0070] Next, the processing executed by the medical image processing device 40 will be described.

[0071] 2 is a flowchart showing an example of reservation processing executed by the medical information processing device 40 according to the first embodiment. A patient receives an explanation from a doctor regarding treatment for an illness, injury, etc. After the explanation from the doctor, the patient interviews a medical professional such as a nurse or pharmacist. If the patient has a request regarding treatment during this interview, the reservation processing is a process of booking another interview with the doctor.

[0072] The diagnosis content acquisition function 451 acquires the diagnosis content of a patient (step S1).

[0073] The data acquisition function 452 acquires a patient image captured by the imaging device 10 and voice data captured by the microphone 20 during an interview between a patient and a medical professional (step S2).

[0074] The phrase analysis function 453 analyzes the conversation content contained in the voice data (step S3).

[0075] The emotion estimation function 454 estimates the emotion of the patient at each time point during the interview between the patient and the medical staff based on the patient image and voice data (step S4). That is, the emotion estimation function 454 estimates the degree of negativity and positivity of the patient at each time point during the interview.

[0076] The search function 455 determines whether or not it is necessary to search for a treatment method again based on the degree of negativity (step S5). If the degree of negativity is less than the threshold (step S5; No), the search function 455 determines that it is not necessary to search for a treatment method. That is, the medical information processing device 40 ends the reservation process.

[0077] When the degree of negativity is equal to or greater than the threshold (step S5; Yes), the search function 455 generates search conditions to be used for searching for a treatment method from the patient's request regarding the treatment method obtained from the conversation content (step S6).

[0078] The search function 455 searches for a treatment method using the generated search conditions (step S7).

[0079] The display control function 456 displays the search results for the treatment method on the operation terminal 30 (step S8). Based on the search results displayed on the operation terminal 30, the medical staff suggests to the patient that they consult again with the doctor.

[0080] The reservation function 457 reserves another interview (step S9).

[0081] With the above, the medical image processing device 40 ends the reservation process.

[0082] As described above, the medical information processing device 40 according to the first embodiment acquires the diagnosis given to the patient. The medical information processing device 40 also acquires voice data of what the patient has said regarding the treatment for the diagnosis. The medical information processing device 40 also estimates the patient's feelings regarding the treatment based on the voice data. The medical information processing device 40 also acquires the patient's requests regarding the treatment based on the voice data. Then, the medical information processing device 40 acquires a treatment that matches the requests according to the estimated feelings. This allows the medical professional to encourage the patient to have another interview with the doctor regarding a treatment that matches the patient's requests. Therefore, the medical information processing device 40 can reflect the patient's requests in the treatment.

[0083] (Variation 1) It has been described that the medical information processing device 40 according to the first embodiment has a diagnosis content acquisition function 451, a data acquisition function 452, a phrase analysis function 453, a feeling estimation function 454, a search function 455, a display control function 456, and a reservation function 457. However, all or a part of these functions are not limited to the medical information processing device 40, and may be included in the operation terminal 30 or other devices.

[0084] According to at least one of the embodiments described above, the patient's wishes can be reflected in the treatment.

[0085] Although some embodiments have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, modifications, and combinations of embodiments can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents as described in the claims, as well as in the scope and spirit of the invention. [Explanation of symbols]

[0086] 1 Medical Support System 10. Imaging device 20. Mike 30 Operation terminal 40 Medical information processing device 41 NW (network) interface 42 Input Interface 43 Display 44 Memory circuit 45 Processing circuit 50 Network 451 Diagnostic content acquisition function 452 Data Acquisition Function 453 Phrase analysis function 454 Emotion estimation function 455 Search Function 456 Display Control Function 457 Reservation Function

Claims

1. A first acquisition unit that acquires a diagnosis made to a patient; A second acquisition unit that acquires reaction information regarding a reaction of the patient to a first treatment method for the diagnosis content; an estimation unit that estimates the patient's feelings regarding the first treatment method based on the reaction information; A third acquisition unit that acquires a request of the patient regarding the first treatment method based on the reaction information; A fourth acquisition unit that acquires a second treatment method that matches the request according to the emotion estimated by the estimation unit; A medical information processing device comprising:

2. The second acquisition unit acquires the reaction information and an image of the patient, The estimation unit estimates an emotion of the patient based on the reaction information and the image. The medical information processing device according to claim 1 .

3. The estimation unit estimates a negativity degree indicating a degree of the negative emotion of the patient, The fourth acquisition unit acquires the second treatment method when the negativity is equal to or greater than a threshold. The medical information processing device according to claim 1 .

4. The estimation unit estimates a positivity level indicating a degree of a positive emotion of the patient for each time point of the reaction information, The fourth acquisition unit acquires the second treatment method identified by the content of the statement at the time when the positivity is equal to or greater than a threshold. The medical information processing device according to claim 1 .

5. The fourth acquisition unit acquires the second treatment method based on a treatment method preference criterion of the patient estimated from the content of the statement. The medical information processing device according to claim 4 .

6. Further comprising a display control unit that displays the second treatment method and the request on a display unit. The medical information processing device according to claim 1 .

7. The second acquisition unit acquires, as the reaction information, voice data of the patient's speech regarding the first treatment method. The medical information processing device according to claim 1 .

8. The system for managing a doctor's schedule further includes a reservation unit for reserving an interview regarding the second treatment method. The medical information processing device according to claim 1 .

9. A first acquisition unit that acquires a diagnosis made to a patient; A second acquisition unit that acquires reaction information regarding a reaction of the patient to a first treatment method for the diagnosis content; an estimation unit that estimates the patient's feelings regarding the first treatment method based on the reaction information; A third acquisition unit that acquires a request of the patient regarding the first treatment method based on the reaction information; A fourth acquisition unit that acquires a second treatment method that matches the request according to the emotion estimated by the estimation unit; A medical information processing system comprising:

10. Obtaining a diagnosis made for the patient; obtaining response information regarding the patient's response to a first treatment for the diagnosis; Inferring a sentiment of the patient regarding the first treatment method based on the response information; obtaining a request from the patient regarding the first treatment method based on the response information; obtaining a second treatment method that matches the desire in response to the estimated emotion; A medical support method including the steps of:

11. On the computer, Obtaining a diagnosis made for the patient; obtaining response information regarding the patient's response to a first treatment for the diagnosis; Inferring a sentiment of the patient regarding the first treatment method based on the response information; obtaining a request from the patient regarding the first treatment method based on the response information; obtaining a second treatment method that matches the desire in response to the estimated emotion; A program to make this happen.

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