Information processing system, information processing method, and program

Abstract

To suppress a work burden for creating a medical sentence of a medical worker.SOLUTION: According to the present invention, there is provided an information processing system for supporting creation of a medical sentence, including a data acquisition unit and a medical sentence generation unit, in which the data acquisition unit is configured to be able to acquire conversation data and medical data, the conversation data includes content of a conversation between a medical worker and a patient, the medical data includes drug data related to the patient, and the medical sentence generation unit is configured to generate the medical sentence by inputting the conversation data and the drug data to a learning model and causing the learning model to output the medical sentence.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing system, an information processing method, and a program. [Background technology]

[0002] In addition to dispensing medicines prescribed by doctors, medical professionals (for example, pharmacists at pharmacies) also have the task of acquiring and managing patient data through conversations with patients. Conventionally, technologies to support the work of medical professionals have been proposed, and Patent Document 1 discloses a system for improving the quality of medication instruction work. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-047624 Summary of the Invention [Problem to be solved by the invention]

[0004] The work of medical professionals involves creating medical documents to record interactions with patients. For example, creating medical documents is a burden for medical professionals when they have other tasks that require their attention, such as dispensing medicines and providing medication advice, but it is a necessary task that cannot be omitted.

[0005] The present invention aims to reduce the workload of medical professionals in creating medical documents. [Means for solving the problem]

[0006] [1] According to the present invention, there is provided an information processing system for assisting in the creation of medical documents in pharmacies, comprising a data acquisition unit and a medical document generation unit, wherein the data acquisition unit is configured to be able to acquire conversation data and medical data, wherein the conversation data includes the content of a conversation relating to medication instructions from a pharmacist to a patient, and the medical data has at least one of pharmaceutical data relating to the patient, medication history data relating to the patient, data relating to medication problems for the patient, and data relating to prescriptions for the patient, and the medical data is associated with the conversation, and the medical document generation unit is configured to generate the medical document by inputting the conversation data relating to the conversation of the medication instructions and the medical data relating to the conversation of the medication instructions into a learning model and outputting the medical document from the learning model.

[0007] According to the present invention, since the above configuration is provided, it is possible to reduce the workload of medical professionals in creating medical documents.

[0008] Various embodiments of the present invention will be described below as examples, and the embodiments shown below can be combined with each other. [2] Preferably, an information processing system as described in [1] is provided, wherein the medical text generation unit inputs the conversation data related to the conversation of the medication instruction and the medical data related to the conversation of the medication instruction into the learning model to generate a first medical text as the medical text from the learning model, and the medical text generation unit is configured to gradually generate a second medical text different from the first medical text based on the first medical text. [3] Preferably, an information processing system as described in [2] is provided, wherein the second medical document has basic data of the patient added to the first medical document that enables at least the patient to be identified. [4] Preferably, there is provided an information processing system according to any one of [1] to [3], wherein the medical data further includes data on the name of a pharmacy. [5] Preferably, there is provided an information processing system according to any one of [1] to [4], wherein the medical data further includes at least one of the name of the prescribing doctor and the name of the hospital. [6] Preferably, the information processing system according to any one of [1] to [5] is provided, wherein the data acquisition unit is configured to be able to acquire the conversation data and the medical data from a terminal in the pharmacy. [7] According to another aspect of an embodiment of the present invention, there is provided an information processing method for assisting in the creation of medical documents in pharmacies, the information processing method comprising an acquisition step and a generation step, wherein in the acquisition step, conversation data and medical data are acquired, the conversation data including the content of a conversation related to medication instructions from a pharmacist to a patient, the medical data having at least one of pharmaceutical data related to the patient, medication history data related to the patient, data related to medication problems for the patient, and data related to prescriptions for the patient, the medical data corresponding to the conversation, and in the generation step, the conversation data related to the conversation of the medication instructions and the medical data related to the conversation of the medication instructions are input into a learning model and the medical document is output from the learning model, thereby generating the medical document. [8] Preferably, a program is provided that causes a computer to execute the information processing method described in [7]. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 shows an example of a system configuration of an information processing system 100 according to an embodiment. [Figure 2] FIG. 2 is a block diagram showing the hardware configuration of the information processing device 1. As shown in FIG. [Figure 3] FIG. 3 is a functional block diagram of the control unit 12 shown in FIG. [Figure 4]Figure 4A is an explanatory diagram of data output from the pharmacy to the information processing device 1, and Figure 4B is an explanatory diagram that schematically shows an example of the type of stored data DT0 as a database stored in the memory unit 11 of the information processing device 1 shown in Figure 4A. [Figure 5] FIG. 5 is an explanatory diagram of data exchanged between the information processing device 1, the large-scale language model server 2, and the speech-to-text server 3. [Figure 6] FIG. 6 is an explanatory diagram of data exchanged between the information processing device 1 and the large-scale language model server 2 after the data exchange shown in FIG. [Figure 7] Figure 7A shows an example of medical data that is later in time series than the data shown in Figure 7B, and Figure 7B shows an example of medical data that is earlier in time series than the data shown in Figure 7A. In Figures 7A and 7B, data shown in gray is non-output medical data (data that is not output to the large-scale language model server 2). [Figure 8] FIG. 8A shows an example of pre-processed character data d2, and FIG. 8B shows an example of processed character data d3. [Figure 9] FIG. 9 is an example of medical text MT1. [Figure 10] FIG. 10 is a flowchart showing an example of an information processing method of the information processing system 100 according to the embodiment. [Figure 11] FIG. 11A is an example of a system configuration of an information processing system 100 according to Modification 1, and FIG. 11B is a functional block diagram of a control unit 12 of the information processing device 1 shown in FIG. 11A. [Figure 12] FIG. 12 shows an example of the system configuration of the information processing system 100 according to the second modification. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. Various features shown in the following embodiments can be combined with each other. Furthermore, each feature can be an invention independently.

[0011] 1. System Configuration of Information Processing System 100 The information processing system 100 can support the creation of medical documents that need to be created by medical professionals such as pharmacists. In other words, the information processing system 100 has a function of reducing the workload of medical professionals in creating medical documents by generating medical documents.

[0012] As shown in Fig. 1, the information processing system 100 includes an information processing device 1, a large-scale language model server 2 (an example of a large-scale language model), a speech-to-text server 3, a terminal 4, and a speech acquisition device 5. These are connected to each other so as to enable the exchange of information via a communication network 6 (e.g., the Internet, etc.). Note that the communication network 6 may be a closed network, partly or entirely separated from the Internet. Each component of the information processing system 100, such as the information processing device 1, has one or more functions (functional units), and each component may be configured as a single device as shown in Fig. 1, or may be configured as multiple independent devices configured to be able to exchange information. The same applies to each functional unit, such as the control unit 12 of the information processing device 1, which will be described later. Each component included in the information processing system 100 will be further described below.

[0013] 1-1. Information processing device 1 2, the information processing device 1 has a communication unit 10, a storage unit 11, a control unit 12, an output unit 13, and an input unit 14, and these components are electrically connected via a communication bus 15 inside the information processing device 1. The control unit 12 has a data acquisition unit 121, a medical text generation unit 122, and an encoding unit 123. Furthermore, the medical text generation unit 122 has an output generation unit 122a and an adjustment unit 122b.

[0014] Each of the above components may be implemented by software or hardware. When implemented by software, various functions can be realized by a CPU executing a computer program. The program may be stored on a non-transitory computer-readable recording medium, provided as a downloadable file from an external server, or implemented by so-called cloud computing, in which a program stored in an external storage unit is read and functions are realized. When implemented by hardware, various circuits such as an ASIC, FPGA, or DRP can be used. The embodiments deal with various information and concepts encompassing such information. These are represented by high and low signal values ​​or quantum bits as a binary bit set consisting of 0s or 1s, and communication and calculations can be performed by the above software or hardware aspects. The software may be a general-purpose OS or a dedicated OS.

[0015] The communication unit 10 can employ wired communication means such as USB, IEEE1394, Thunderbolt (registered trademark), wired LAN network communication, etc. The communication unit 10 may be configured to be connected to the communication network 6 via wireless communication means such as wireless LAN network communication, mobile communication such as 3G / LTE / 5G, Bluetooth (registered trademark) communication, etc. The communication unit 10 may also be configured to use both the wired communication means and wireless communication means described above.

[0016] The storage unit 11 stores various values ​​such as various programs, constants, variables, and setting values ​​of the information processing device 1 executed by the control unit 12. The storage unit 11 may be a storage device such as a solid state drive (SSD), or a random access memory (RAM) that stores temporarily required information (arguments, arrays, etc.) related to program calculations. In addition to the storage unit 11, the information processing device 1 may also use an external storage unit (for example, an external storage medium, a cloud, etc.).

[0017] The control unit 12 is configured to execute processing and control related to information processing of the information processing device 1. The control unit 12 can be configured, for example, by a central processing unit (CPU), and in the embodiment, the control unit 12 is an example of a processor capable of executing programs related to each step of a flowchart described below. The control unit 12 realizes various functions related to the information processing device 1, for example, by reading out programs stored in the storage unit 11. Furthermore, information processing by software in the information processing device 1 is realized, for example, by the control unit 12 as hardware processing various programs stored in the storage unit 11.

[0018] The output unit 13 is, for example, a display unit of the information processing device 1. The output unit 13 may be included in the housing of the information processing device 1 or may be externally attached. The output unit 13 displays a graphical user interface (GUI) screen that can be operated by a user. The output unit 13 may be, for example, a display device such as a CRT display, a liquid crystal display, an organic EL display, a plasma display, or an electronic paper display, as well as a display device such as an illuminable light or a projector. It is optional whether or not the information processing device 1 includes the output unit 13. For example, the output of the information processing device 1 may be displayed on a display unit located at a location independent of the location where the information processing device 1 is installed. The output unit 13 may also have a device that outputs audio.

[0019] The input unit 14 is configured to receive, for example, an operation input made by a user of the information processing device 1. The input unit 14 may be included in the housing of the information processing device 1 or may be externally attached. For example, the input unit 14 may be a touch panel, a switch button, a mouse, a keyboard, or the like. It is optional whether or not the information processing device 1 includes the input unit 14. For example, the information processing device 1 may receive an operation input to the information processing device 1 via an information processing terminal located at a location separate from the location where the information processing device 1 is installed.

[0020] 1-2. Large-scale language model server 2 and speech-to-text server 3 The large-scale language model server 2 and the speech-to-text server 3 are communicatively connected to the information processing device 1 via a communication network 6. The large-scale language model server 2 has a sentence generation unit 21. This sentence generation unit 21 has, for example, a neural network trained using a large amount of teacher data, and has the function of generating a response to data (prompts) output from the information processing device 1 and outputting it to the information processing device 1. The large-scale language model server 2 is not particularly limited, but for example, ChatGPT by OpenAI Inc. can be adopted.

[0021] The speech-to-text server 3 has a transcription unit 31. The transcription unit 31 is configured to generate text conversation data in which the content of the conversation is transcribed using audio conversation data. The transcription unit 31 has, for example, a neural network trained using a large amount of training data (audio training data). The transcription unit 31 has a function of receiving data (audio data) output from the information processing device 1 and outputting the data as text data to the information processing device 1. Note that, in the embodiment, the transcription unit 31 has been described as being configured as a trained neural network, but is not limited to this. For example, the transcription unit 31 may be configured to analyze and transcribing audio data according to a predetermined algorithm. The speech-to-text server 3 is not particularly limited, but may be, for example, Whisper (registered trademark) by OpenAI Corporation.

[0022] 1-3. Terminal 4 and voice capture device 5 The terminal 4 is an information processing device (for example, a personal computer) placed in, for example, a pharmacy, and is configured to allow a medical professional to input patient data. The terminal 4 is also configured to display the patient data on a display unit (not shown) so that the medical professional can obtain the patient data using the terminal 4. The patient data exchanged in the information processing system 100 will be described in detail later. The voice capture device 5 has a device such as a microphone and is configured to capture conversation data between a medical professional and a patient at a pharmacy. The conversation data is voice conversation data. The voice capture device 5 may be integrated with the terminal 4 or may be separate. The voice capture device 5 may be, for example, a dedicated voice recorder or a mobile phone (e.g., a smartphone) equipped with a voice capture application.

[0023] In one example of the embodiment, when the voice capturing device 5 acquires conversation data (voice data), the voice data is transmitted from the voice capturing device 5 to the terminal 4. Then, the terminal 4 outputs the patient data and the conversation data together to the information processing device 1. This prevents the patient data related to the current prescription and the conversation data related to the current prescription from being separated, and allows them to be appropriately processed by the information processing device 1. Note that the configuration of data transmission is not limited to this, and a configuration may also be adopted in which, when the voice capturing device 5 acquires the conversation data, the voice capturing device 5 outputs the data to the information processing device 1 via the communication network 6, and the information is compiled on the information processing device 1 side.

[0024] 2. Functional configuration The functional configuration of the information processing device 1 according to this embodiment will be described with reference to Fig. 3. Information processing by software stored in the storage unit 11 is specifically realized by the control unit 12, which is an example of hardware, and each functional unit included in the control unit 12 is executed.

[0025] The data acquisition unit 121 is configured to be able to acquire various types of data from the information processing device 1, the large-scale language model server 2, the speech-to-text server 3, etc. In the embodiment, the data acquisition unit 121 is configured to be able to acquire conversation data and medical data. The data acquisition unit 121 is also configured to be able to acquire text conversation data from the transcription unit 31. These types of data will be described in detail later.

[0026] The medical text generation unit 122 is configured to generate medical text based on conversation data (text conversation data) and drug data. The output generation unit 122a of the medical sentence generation unit 122 has a function of generating various prompts (for example, sentence generation prompts and accuracy improvement prompts, which will be described later). The adjustment unit 122b of the medical text generation unit 122 has a post-processing function. That is, the adjustment unit 122b has a function of adjusting the content of the medical text MT1 by adding information to the medical text MT1 generated by the large-scale language model server 2 (for example, adding information such as a name that can identify an individual to the medical text MT1). Note that it is optional whether the information processing device 1 has the adjustment unit 122b.

[0027] The encoding unit 123 has a function of encoding part or all of the non-output medical data among the data related to the patient. In other words, the encoding unit 123 has a function of encoding the non-output medical data so that the content of the non-output medical data is anonymized or abstracted. The non-output medical data is the patient data that is not output to the large-scale language model server 2. The information processing device 1 prevents individuals from being identified by outputting the encoded data. In other words, since data related to patients includes, for example, data that is personal information, in the embodiment, data that is not output to the large-scale language model server 2 (non-output medical data) is predetermined, but the information processing device 1 is able to encode the non-output medical data and include it in the output medical data.

[0028] 3. Data exchanged in the information processing system 100 In the information processing system 100 according to the embodiment, various types of data are exchanged between the information processing device 1, the large-scale language model server 2, the speech-to-text server 3, and the terminal 4. The data will be described with reference to Figures 4A to 8B.

[0029] The data exchanged in the information processing system 100 includes medical data, conversation data, and medical documents. In one embodiment, the types of medical data include items shown as data D1 to data D19 in Fig. 4B. The numbers of data D1 to data D19 are assigned for convenience to distinguish these items. Also, not all of data D1 to data D19 are necessarily required items.

[0030] 3-1.Medical Data In the information processing system 100 according to the embodiment, the storage unit 11 functions as a database, and therefore all data exchanged in the information processing system 100 is accumulated in the storage unit 11. First, the types of medical data among this all data will be listed.

[0031] As shown in FIG. 4B, in an embodiment, the medical data includes items such as ID (data D1), name (data D2), gender (data D3), date of birth (data D4), age (data D5), generation (data D5t), health insurance card information (data D6), allergy history (data D7), current illness history (data D8), medical history (data D9), history of drug side effects (data D10), history of concomitant drug use (data D11), information related to medication problems (data D12), pharmacy name (data D13), prescribing doctor name (data D14), hospital name (data D15), prescription date (data D16), drug name (data D17), drug quantity, usage, and dosage (data D18), and medication history (data D19).

[0032] The ID (data D1) is a unique symbol assigned to each individual, and is represented by, for example, numbers or letters. The generation (data D5t) may be acquired from the terminal 4 on the pharmacy side, or may be acquired by the information processing device 1 having a function of calculating the generation based on the age and using this function. This function can be realized by the encoding unit 123, for example. The health insurance card information (data D6) includes the insurer number, symbol, and number. Information relating to medication problems (data D12) includes, for example, information about circumstances that are different from allergies, such as an individual's inability to tolerate a medication or their body not agreeing with the medication (for example, a desire to avoid herbal medicines because they do not agree with the taste).

[0033] The drug name (data D17) is an example of drug data. This drug name may be the exact name of the drug, or an abbreviated name. Name (data D2), sex (data D3), date of birth (data D4), age (data D5), and health insurance card information (data D6) are examples of basic patient data. Medication history (data D19) is an example of medication history data.

[0034] <Stored data DT0 and medical data DT1> In one example of the embodiment, all of these 20 items of data are stored as a database in the storage unit 11 of the information processing device 1. Then, when a patient visits a pharmacy and a medical professional receives, for example, a prescription, the medical professional inputs the prescription data into the terminal 4. As a result, the prescription data (medical data DT1) is output to the information processing device 1, as shown in FIG. 4A. For example, if the medical data DT1 is prescription data, it includes at least data such as the name of the pharmacy (data D13), the name of the medicine (data D17), and the quantity, usage, and dosage of the medicine (data D18).

[0035] Furthermore, for example, if the medical data DT1 is dispensing data, it includes data such as the name of the medicine (data D17) and the quantity, dosage, and administration of the medicine (data D18). Furthermore, for example, if the medical data DT1 is pharmaceutical data, data such as the name of the pharmaceutical (data D17) is included. The types of data included in the medical data DT1 are not limited to prescription data, dispensing data, and drug data, and various modifications are possible. In other words, the medical data DT1 may include all or some of the 20 items of data described above, and various settings are possible. The information processing system 100 is preferably configured to be able to change the type of data included in the medical data.

[0036] When the medical data DT1 is output to the information processing device 1, the contents of the stored data DT0 as a database are updated each time. Also, for example, if there is a change in the patient's status (e.g., name), data related to the change is input to the terminal 4, and the stored data DT0 is updated. Note that the storage unit 11 also holds data before the update (old data). In this way, the storage unit 11 comprehensively stores data exchanged in the information processing system 100, and the data accumulates.

[0037] Incidentally, medical data can be divided into the above-mentioned non-output medical data and output medical data. The non-output data is data that is not output to the large-scale language model server 2 because it contains personal information, while the output data is data that is output to the large-scale language model server 2. These types of data are explained below.

[0038] 3-1-1. Non-printed medical data The non-output medical data will be described with reference to Fig. 7A. In the embodiment, the non-output medical data may include, for example, name (data D2), date of birth (data D4), age (data D5), health insurance card information (data D6), pharmacy name (data D13), prescribing doctor name (data D14), hospital name (data D15), and prescription date (data D16). The data is not limited to the examples shown here, and for example, information that is considered to be relatively more anonymous than name, etc. (date of birth and age) may be included in the output medical data, and this can be changed as appropriate.

[0039] 3-1-2. Output medical data The output medical data will be described with reference to FIG. 7A. In this embodiment, the output medical data may include, for example, ID (data D1), gender (data D3), age (data D5), allergy history (data D7), current illness history (data D8), medical history (data D9), history of side effects from medication (data D10), history of concomitant medication (data D11), information related to medication problems (data D12), drug name (data D17), drug quantity, dosage, and administration (data D18), and medication history (data D19). The data is not limited to those exemplified here, and other information may be added or removed as necessary.

[0040] <Medical Data DT2> Here, the medical data DT2 shown in FIG. 5 and the medical data DT3 shown in FIG. 6 will be described. 5 shows how medical data DT2 is output to the large-scale language model server 2 in order to improve the accuracy (precision) of the sentences in the raw character data d2 (described later). The medical data DT2 may include all or part of the output medical data described above. The medical data DT2 includes drug names (data D17). This is because drug names are often long and not commonly used, making them difficult to transcribe properly. Preferably, the medical data DT2 includes at least one of the quantity, dosage, and administration of the medicine (data D18) and the medication history (data D19). Furthermore, the medical data DT2 may include not only the content related to the current prescription (see FIG. 7A) but also the content related to past prescriptions (see FIG. 7B). The content related to past prescriptions may include those from multiple times (for example, the previous time and the time before that).

[0041] <Medical Data DT3> 6 shows how medical data DT3 is output to the large-scale language model server 2 together with processed character data d3, which will be described later, to generate medical text MT1, which will be described later. The medical data DT3 may include all or part of the output medical data described above. The medical data DT3 includes drug names (data D17), which are an example of drug data, in order to appropriately generate the medical text MT1 output from the large-scale language model server 2. Preferably, the medical data DT3 includes at least one of the quantity, usage, and dosage of the medicine (data D18) and the medication history (data D19). More preferably, the medical data DT3 includes at least one of ID (data D1), gender (data D3), age (data D5t), allergy history (data D7), current illness history (data D8), medical history (data D9), history of drug side effects (data D10), and history of concomitant drug use (data D11).

[0042] Furthermore, the medical data DT3 may include not only the content related to the current prescription (see FIG. 7A) but also the content related to past prescriptions (see FIG. 7B), similar to the medical data DT3. The content related to past prescriptions may include those from multiple times (for example, the previous time and the time before that). Specifically, a drug name (an example of drug data) may have new drug data and old drug data. The new drug data corresponds to the conversation in the conversation data. In other words, the new drug data is data on the drug name written on, for example, a prescription submitted by the patient this time. The old drug data is data that predates the new drug data. In other words, the old drug data is data on drugs written on, for example, a prescription submitted by the patient earlier than this time. "Earlier than this time" may be data related to the prescription one prescription before the prescription in the new drug data, or data related to the prescription two prescriptions before, or even earlier than that. Furthermore, the old drug data may have multiple pieces of data that predate this time.

[0043] 3-2. Conversation data The conversation data includes the content of the conversation between a medical professional and a patient. Types of conversation data include voice conversation data d1 (voice data acquired by the voice acquisition device 5 in the embodiment) shown in FIGS. 4A and 5, and text conversation data. Types of text conversation data include raw character data d2 shown in FIG. 5 and processed character data d3 shown in FIGS. 5 and 6. Here, the raw character data d2 is data obtained by transcribing the voice conversation data by the transcription unit 31. The processed character data d3 is text conversation data used by the medical text generation unit 122 when generating medical text. In the embodiment, the processed character data d3 is text data obtained after the text generation unit 21 processes the raw character data d2 to improve the accuracy of the content.

[0044] Specifically, as shown in the example of FIG. 8A, the pre-processed character data d2 may contain errors in the name of a drug, etc. (see the underlined portion). However, as shown in the example of FIG. 8B, the errors have been corrected in the processed character data d3 (see the underlined portion), improving the accuracy of the text. In addition, the processed character data d3 distinguishes between what was said by the medical professional and what was said by the patient. Because this type of processed character data d3 is output to the large-scale language model server 2, the output from the large-scale language model server 2 (medical text MT1) is optimized and its accuracy is improved.

[0045] 3-3. Medical Writing In the embodiment, the medical text is divided into medical text MT1 output from the large-scale language model server 2 to the information processing device 1 and medical text MT2 output from the information processing device 1 to the pharmacy side. The medical text MT1 has a predetermined format, as shown in Figure 9. One example of this predetermined format is SOAP. Here, SOAP refers to a recording method (recording format) used in medical settings, etc., that distinguishes between subjective information (Subject), objective information (Object), assessment, and plan. In other words, the medical text MT1 was generated by distinguishing between subjective information, objective information, assessment, and plan.

[0046] The medical document MT2 is data configured to identify an individual by adding at least one of the following data to the medical document MT1: name (data D2), gender (data D3), date of birth (data D4), age (data D5), and health insurance card information (data D6). In other words, the medical document MT2 is data to which elements of basic data are also added. Note that the information processing device 1 may not generate the medical document MT2, and the medical document MT1 itself may be output to the pharmacy. In this case, for example, the process of identifying the individual may be performed at the pharmacy's terminal 4.

[0047] 4. Information Processing Flow of Information Processing System 100 The program according to the embodiment executes each step (information processing method) described below. This information processing method is a process for supporting the creation of medical documents. An example of the information processing method according to the embodiment will be described with reference to FIG. 10.

[0048] <Start: Step S001> A medical professional receives a prescription from a patient and begins dispensing medication according to the prescription. Once dispensing is complete, the medical professional begins a conversation with the patient to hand over the medication to them. However, prior to this conversation, the medical professional has activated the voice capture device 5. Medical professionals at pharmacies are required to record medical procedures such as dispensing medication and conversations as medical documents, which is time-consuming. The information processing system 100 according to the embodiment reduces this time-consuming effort by automatically generating all or part of the medical documents, thereby reducing the burden on medical professionals.

[0049] <Step S002> When the medical worker completes the conversation with the patient, he stops the voice capture device 5 and transmits the voice conversation data d1 from the memory of the voice capture device 5 to the memory of the terminal 4, and the terminal 4 thereby acquires the medical data DT1. The medical worker also inputs the contents of the current prescription into the terminal 4 as the medical data DT1, and the memory of the terminal 4 acquires this medical data DT1. Alternatively, the medical data DT1 may be acquired by scanning an image of the prescription and reading information such as the text of the prescription from the scanned data.

[0050] In the explanation here, it is assumed that the patient has already used one of the pharmacies utilized by the information processing system 100, and the patient's basic data (such as name (data D2)) has already been stored in the memory unit 11 of the information processing device 1 as a database. Therefore, in the example of medical data DT1 in this step S001, it is not necessarily necessary to input all of the basic data (for example, age (data D5)).

[0051] <Step S003> The data acquisition unit 121 of the information processing device 1 acquires the voice conversation data d1 and the medical data DT1 transmitted from the pharmacy side, and stores them in the storage unit 11.

[0052] Step S003 is an example of an acquisition step.

[0053] <Step S004> The control unit 12 of the information processing device 1 outputs the voice conversation data d1 stored in the memory unit 11 to the speech-to-text server 3. The transcription unit 31 of the speech-to-text server 3 transcribes the voice conversation data d1 to generate raw character data d2, which is character data.

[0054] <Step S005> The speech-to-text server 3 outputs the raw character data d2 to the information processing device 1. The data acquisition unit 121 of the information processing device 1 acquires the raw character data d2 and stores it in the storage unit 11.

[0055] <Step S006> The control unit 12 of the information processing device 1 outputs the raw character data d2 and medical data DT2 stored in the storage unit 11 to the large-scale language model server 2, and in doing so generates an output prompt. Specifically, the output generation unit 122a of the medical text generation unit 122 generates an accuracy improvement prompt to be output to the large-scale language model server 2. The accuracy improvement prompt includes the raw character data d2 and the medical data DT2, and has an instruction to generate processed character data d3 by referencing the medical data DT2. The accuracy improvement prompt also has an instruction to generate processed character data d3 by distinguishing between what was spoken by the medical professional and what was spoken by the patient (see FIG. 8B), which is expected to have the effect of improving the generation accuracy of the medical text MT1.

[0056] The control unit 12 of the information processing device 1 outputs this accuracy improvement prompt to the large-scale language model server 2. The number of characters in the accuracy improvement prompt may be, for example, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1200, 1400, 1600, 1800, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 12000, or 14000, or may be within a range between any two of the numbers exemplified here. The accuracy improvement prompt may also be composed of multiple separate prompts.

[0057] The sentence generation unit 21 of the large-scale language model server 2 refers to the accuracy improvement prompt, generates processed character data d3 by correcting the contents of the raw character data d2 so as to improve the accuracy of the raw character data d2, and outputs the processed character data d3 to the information processing device 1.

[0058] <Step S007> The data acquisition unit 121 of the information processing device 1 acquires the processed character data d3 as an output from the large-scale language model in response to the accuracy improvement prompt, and stores the processed character data d3 in the storage unit 11.

[0059] <Step S008> Subsequently, the control unit 12 of the information processing device 1 outputs the processed character data d3 and medical data DT3 stored in the storage unit 11 to the large-scale language model server 2, and in so doing generates an output prompt. Specifically, the output generation unit 122a of the medical text generation unit 122 generates a text generation prompt to be output to the large-scale language model server 2. The text generation prompt includes the processed character data d3 and the medical data DT3, and also includes an instruction to generate medical text MT1. In this embodiment, this prompt includes an instruction to create the content of the processed character data d3 and the medical data DT3 in a predetermined format (SOAP in this embodiment). The prompt may also include supplemental data, such as a definition of what the SOAP is, or a limit on the number of characters in the medical text MT1 to be generated. Note that the medical data DT3 in this prompt excludes information that could identify an individual (for example, name, which is an example of basic data), but the information may be included as coded data (abstracted data) that has been coded (abstracted) by the coding unit 123.

[0060] The control unit 12 of the information processing device 1 outputs this sentence generation prompt to the large-scale language model server 2. The number of characters in the sentence generation prompt may be, for example, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1200, 1400, 1600, 1800, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 12000, or 14000, or may be within a range between any two of the numbers exemplified here. The sentence generation prompt may also be composed of multiple separate prompts.

[0061] The sentence generation unit 21 of the large-scale language model server 2 references the sentence generation prompt, generates medical sentence MT1, and outputs it to the information processing device 1.

[0062] <Step S009> The data acquisition unit 121 of the information processing device 1 acquires medical text MT1 as output from the large-scale language model in response to a text generation prompt, and stores it in the memory unit 11. Here, the medical data DT3 output to the large-scale language model server 2 is processed so as not to include personal information. For this reason, the adjustment unit 122b of the medical text generation unit 122 generates medical text MT2 by adding basic data (such as name) to the medical text MT1 so that individuals can be identified, and stores it in the memory unit 11. In other words, the medical text generation unit 122 generates medical text MT2 based on the conversation data (processed character data), pharmaceutical data (medical data), and the basic data.

[0063] Steps S008 and S009 are an example of a generating step.

[0064] <Step S010> The medical staff acquires the medical text MT2 via the terminal 4, and the medical text MT2 is stored in the memory of the terminal 4. If the information processing system 100 does not have the adjustment unit 122b, the terminal 4 acquires the medical text MT1.

[0065] <End: Step S011> By utilizing Medical Text MT2, medical professionals can reduce the effort required to create medical documents.

[0066] 5. Variations 5-1. Variation 1 In the embodiment, a configuration has been described in which medical text is generated using servers external to the information processing device 1, such as the large-scale language model server 2 and the speech-to-text server 3, but the present invention is not limited to this. As shown in FIGS. 11A and 11B , the information processing device 1 may have the functions of the large-scale language model server 2 and the speech-to-text server 3. In other words, the information processing device 1 may have a text generation unit 21 and a transcription unit 31.

[0067] 5-2. Variation 2 In the first modification, the information processing system 100 is described as a system in which information is exchanged between a medical institution (pharmacy) and an information processing device 1 located in a place independent of the medical institution, but the present invention is not limited to this. As shown in Fig. 12, the information processing device 1 may be located in the medical institution (pharmacy), and the system may be one in which data is handled entirely within the medical institution (pharmacy).

[0068] 5-3.Other modifications In the embodiment, the medical institution is described as a pharmacy, but the present invention is not limited to this. For example, the present invention can be applied to a place that provides medicines installed in a hospital. The present invention can also be applied to fields where documents in a predetermined format must be created. For example, the present invention can be applied to hospital work, since SOAPs are created not only in places where medicines are prescribed but also in hospitals.

[0069] The sentence generation unit 21 may be configured to have a neural network dedicated to generating medical sentences. "Dedicated" means specialized for a specific field, such as the field of medicine or medical care when applied to a pharmacy as in the embodiment. For example, the sentence generation unit 21 can be configured to perform calculations based on a predetermined learning model. Here, the learning model is a model that is trained using a large amount of training data and makes future output predictable. For example, a large amount of medical data (such as drug names) can be used as input data, and a large amount of medical sentences can be used as output data. This allows the sentence generation unit 21 to generate medical sentences based on a learning model that inputs medical data and outputs medical sentences.

[0070] In the embodiment, the information processing system 100 has been described as having the transcription unit 31 within it, but this is not limited to this. In other words, the transcription unit 31 may exist outside the information processing system 100, and the information processing system 100 may be configured to use conversation data that has already been transcribed externally. In this case, the information processing device 1 can acquire the conversation data that has been transcribed externally and include it in the medical data DT3 to generate a sentence generation prompt.

[0071] Although the embodiments have been described above, they are presented as examples and are not intended to limit the scope of the invention. The novel embodiments can be embodied in various other forms, and various omissions, substitutions, and modifications can be made. The embodiments and their modifications are included within the scope and spirit of the invention, and are also included in the scope of the invention and its equivalents as defined in the claims. [Explanation of symbols]

[0072] 100: Information Processing Systems 1: Information processing equipment 10: Communications Department 11: Storage section 12: Control section 121: Data acquisition section 122: Medical text generation unit 122a: Output generation section 122b: Adjustment section 123: Encoding section 13: Output section 14: Input section 15: Communication bus 2: Large-scale language model server 21: Sentence generation section 3: Speech-to-text server 31: Transcription Department 4: Terminal 5: Audio capture device 6: Communication network DT0: Stored data DT1: Medical Data DT2: Medical Data DT3: Medical Data MT1: Medical Texts MT2: Medical Texts d1: Voice conversation data d2: raw character data d3: Processed character data

Claims

1. An information processing system for supporting the creation of medical documents in pharmacies, A data acquisition unit and a medical text generation unit are provided, the data acquisition unit is configured to be able to acquire conversation data and medical data; The conversation data includes the content of a conversation related to medication instructions from a pharmacist to a patient, the medical data includes at least one of pharmaceutical data relating to the patient, medication history data relating to the patient, data relating to medication problems of the patient, and data relating to prescriptions for the patient; the medical data is associated with the conversation; The medical text generation unit is configured to generate the medical text by inputting the conversation data related to the conversation for medication instructions and the medical data related to the conversation for medication instructions into a learning model and outputting the medical text from the learning model.

2. 2. The information processing system according to claim 1, an information processing system, wherein the medical text generation unit inputs the conversation data related to the conversation of the medication instruction and the medical data related to the conversation of the medication instruction into the learning model to generate a first medical text as the medical text from the learning model, and the medical text generation unit is configured to gradually generate a second medical text different from the first medical text based on the first medical text.

3. 3. The information processing system according to claim 2, An information processing system, wherein the second medical document is added to the first medical document with basic data of the patient that enables at least the patient to be identified.

4. An information processing system according to any one of claims 1 to 3, An information processing system in which the medical data further includes data on the name of a pharmacy.

5. An information processing system according to any one of claims 1 to 3, An information processing system, wherein the medical data further includes at least one of the name of a prescribing doctor and the name of a hospital.

6. An information processing system according to any one of claims 1 to 3, The information processing system is configured so that the data acquisition unit can acquire the conversation data and the medical data from a terminal in the pharmacy.

7. 1. A computer-implemented information processing method for supporting the creation of medical documents in pharmacies, comprising: The method includes an acquisition step and a generation step, In the acquiring step, conversation data and medical data are acquired, The conversation data includes the content of a conversation related to medication instructions from a pharmacist to a patient, the medical data includes at least one of pharmaceutical data relating to the patient, medication history data relating to the patient, data relating to medication problems of the patient, and data relating to prescriptions for the patient; the medical data is associated with the conversation; In the generation step, the medical text is generated by inputting the conversation data related to the conversation of the medication instruction and the medical data related to the conversation of the medication instruction into a learning model and outputting the medical text from the learning model.

8. A program causing a computer to execute the information processing method according to claim 7.

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