Information processing system, information processing method, and program

Abstract

To reduce the work burden on medical workers in creating medical documents.SOLUTION: An information processing system for supporting creation of medical documents includes a data acquisition unit and a medical document generation unit. The data acquisition unit is configured to obtain conversation data and medical data. The conversation data includes the content of a conversation between a medical worker and a patient. The medical data includes medicine data. The medicine data includes new medicine data and old medicine data. The new medicine data corresponds to the conversation of the conversation data. The old medicine data is data older than the new medicine data. The medical document generation unit is configured to generate the medical document based on the conversation data and the medicine data.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 Art

[0002] Medical workers (for example, pharmacists in a pharmacy) have tasks of obtaining and managing patient data through conversations with patients in addition to dispensing the drugs prescribed by doctors. Conventionally, technologies for supporting the work of medical workers have been proposed. Patent Document 1 discloses a system for improving the quality of the task of medication guidance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Medical workers' tasks include creating medical documents to record interactions with patients. For example, creating medical documents is a burden for medical workers in situations where they have other tasks that require attention, such as dispensing and medication guidance, but it is a necessary task and cannot be omitted.

[0005] An object of the present invention is to suppress the work burden of medical workers in creating medical documents.

Means for Solving the Problems

[0006] [1] According to the present invention, there is provided an information processing system for assisting in creating medical texts, comprising a data acquisition unit and a medical text generation unit. The data acquisition unit is configured to be able to acquire conversation data and medical data. The conversation data includes the content of conversations between medical staff and patients. The medical data has pharmaceutical data, and the pharmaceutical data has new pharmaceutical data and old pharmaceutical data. The new pharmaceutical data corresponds to the conversation in the conversation data, and the old pharmaceutical data is data prior to the new pharmaceutical data. The medical text generation unit is configured to generate the medical text based on the conversation data and the pharmaceutical data.

[0007] According to the present invention, it is possible to generate a medical text using conversation data between medical staff and patients and pharmaceutical data of medical data, and it is possible to suppress the workload of creating medical texts by medical staff.

[0008] Hereinafter, various embodiments of the present invention will be exemplified. The embodiments shown below can be combined with each other. [2] Preferably, in the information processing system according to [1], the conversation data is voice conversation data, the data acquisition unit is configured to be able to acquire text conversation data from a speech recognition unit, and the speech recognition unit is configured to generate the text conversation data obtained by converting the content of the conversation into text using the voice conversation data. The medical text generation unit is configured to generate the medical text based on the text conversation data and the pharmaceutical data. [3] Preferably, in the information processing system according to [2], the medical text generation unit has an output generation unit. The output generation unit generates a text generation prompt to be output to a large language model. The text generation prompt has the text conversation data and the pharmaceutical data, and has an instruction to generate the medical text. The data acquisition unit acquires the medical text as an output from the large language model for the text generation prompt. [4] Preferably, the information processing system according to [3], wherein the output generation unit generates an accuracy improvement prompt to be output to the large language model, the accuracy improvement prompt has the pre - processing character data and the pharmaceutical data, and has an instruction to generate post - processing character data with reference to the pharmaceutical data, the pre - processing character data is the data obtained by transcribing the voice conversation data, the post - processing character data is the character conversation data used when the medical article generation unit generates the medical article, and the data acquisition unit acquires the post - processing character data as the output from the large language model for the accuracy improvement prompt. An information processing system is provided. [5] Preferably, the information processing system according to [4], wherein the accuracy improvement prompt has an instruction to generate the post - processing character data by distinguishing the content spoken by the medical staff from the content spoken by the patient, and the character conversation data has the content spoken by the medical staff and the content spoken by the patient distinguished from each other. An information processing system is provided. [6] Preferably, the information processing system according to [1], wherein the conversation data is character data, and the content spoken by the medical staff and the content spoken by the patient are distinguished from each other. An information processing system is provided. [7] Preferably, the information processing system according to any one of [1] to [6], wherein the medical data further has the basic data of the patient, the basic data has the name of the patient and the gender of the patient, and the medical article generation unit generates the medical article based on the conversation data, the pharmaceutical data and the basic data. An information processing system is provided. [8] Preferably, the information processing system according to any one of [1] to [7], wherein the medical data further has the medication history data of the patient, and the medical article generation unit generates the medical article based on the conversation data, the pharmaceutical data and the medication history data. An information processing system is provided. [9] Preferably, there is provided an information processing system according to any one of [1] to [8], wherein the medical text is generated with subjective information, objective information, evaluation, and plan being distinguished.

[10] According to another aspect of the embodiment of the present invention, there is provided an information processing method for assisting in creating a medical text, the method including an acquisition step and a generation step. In the acquisition step, conversation data and medical data are acquired. The conversation data includes the content of a conversation between a medical worker and a patient. The medical data has pharmaceutical data, and the pharmaceutical data has new pharmaceutical data and old pharmaceutical data. The new pharmaceutical data corresponds to the conversation in the conversation data, and the old pharmaceutical data is data prior to the new pharmaceutical data. In the generation step, the medical text is generated based on the conversation data and the pharmaceutical data.

[11] According to another aspect of the embodiment of the present invention, there is provided a program for causing a computer to execute the information processing method described in

[10] .

Brief Description of the Drawings

[0009]

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[0010] Hereinafter, embodiments of the present invention will be described with reference to the drawings. The various characteristic matters shown in the following embodiments can be combined with each other. Also, an invention can be established independently for each characteristic matter.

[0011] 1. System Configuration of Information Processing System 100 The information processing system 100 can assist in creating medical texts that need to be created by medical professionals such as pharmacists. That is, the information processing system 100 has a function of suppressing the workload of medical professionals in creating medical texts by generating medical texts.

[0012] As shown in FIG. 1, the information processing system 100 includes an information processing device 1, a large language model server 2 (an example of a large language model), a speech-to-text server 3, a terminal 4, and a voice acquisition device 5. These are connected via a communication network 6 (e.g., the Internet, etc.) so that information can be exchanged. Note that part or all of the communication network 6 may be a closed network 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). Each component may be configured as a single device as shown in FIG. 1, or may be configured by a plurality of independent devices capable of exchanging information. The same applies to each functional unit such as the control unit 12 of the information processing device 1 described later. Hereinafter, each component included in the information processing system 100 will be further described.

[0013] 1-1. Information Processing Device 1 As shown in FIG. 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. Further, 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 by hardware. When implemented by software, various functions can be realized by a CPU executing a computer program. The program may be stored in a computer-readable non-transitory recording medium, may be provided for download from an external server, or may also be realized by so-called cloud computing that reads a program stored in an external storage unit to realize functions. When implemented by hardware, it can be realized by various circuits such as ASIC, FPGA, or DRP. In the embodiments, various information and concepts including the same are handled, and these are represented by the high and low of signal values or quantum bits as a set of binary bits composed of 0 or 1, and communication and calculation can be executed by the above software or hardware modes. Note that the software may be a general-purpose OS or a dedicated OS.

[0015] The communication unit 10 can adopt wired communication means such as, for example, USB, IEEE1394, Thunderbolt (registered trademark), wired LAN network communication, etc. Note that the communication unit 10 may adopt a configuration connected to the communication network 6 via wireless communication means such as, for example, wireless LAN network communication, mobile communication such as 3G / LTE / 5G, Bluetooth (registered trademark) communication, etc. Also, the communication unit 10 may have a configuration that combines the above-mentioned wired communication means and wireless communication means.

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

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

[0018] The output unit 13 is, for example, a display unit of the information processing apparatus 1. The output unit 13 may be included in the housing of the information processing apparatus 1 or may be externally attached. The output unit 13 displays a screen of a graphical user interface (GUI) operable by the user. The output unit 13 may employ, for example, a display device such as a CRT display, a liquid crystal display, an organic EL display, a plasma display, an electronic paper display, or other display devices such as a lit light or a projector. Note that whether or not the information processing apparatus 1 includes the output unit 13 is optional. For example, the output of the information processing apparatus 1 may be displayed on a display unit at a location separate from the location where the information processing apparatus 1 is installed. Further, the output unit 13 may have a device that outputs in voice.

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

[0020] 1-2. Large Language Model Server 2 and Speech-to-Text Server 3 The large language model server 2 and the speech-to-text server 3 are communicably connected to the information processing device 1 via the communication network 6. The large language model server 2 has a text generation unit 21. This text generation unit 21 has, for example, a neural network trained using a large number of teacher data, and also has a function of generating an answer to the data (prompt) output from the information processing device 1 and outputting it to the information processing device 1. Note that the large language model server 2 is not particularly limited, but for example, it is possible to adopt ChatGPT of OpenAI.

[0021] The speech-to-text server 3 has a speech recognition unit 31. The speech recognition unit 31 is configured to generate text conversation data by converting the content of the conversation into text using the voice conversation data. The speech recognition unit 31 has, for example, a neural network trained using a large number of teacher data (voice teacher data). When the speech recognition unit 31 receives the data (voice data) output from the information processing device 1, it has a function of outputting it to the information processing device 1 as text data. In the embodiment, the speech recognition unit 31 is described as being composed of a pre-trained neural network, but it is not limited to this. For example, it may be configured to analyze and convert voice data into text according to a predetermined algorithm. Note that the speech-to-text server 3 is not particularly limited, but for example, it is possible to adopt Whisper (registered trademark) of OpenAI.

[0022] 1-3. Terminal 4 and Voice Acquisition Device 5 The terminal 4 is an information processing device (e.g., a personal computer) arranged, for example, in a pharmacy or the like, and is configured such that medical staff can input patient data. Further, the medical staff can display patient data on a display unit (not shown) of the terminal 4 so as to be able to acquire the patient data using the terminal 4. Note that the patient data exchanged in the information processing system 100 will be described in detail later. The voice acquisition device 5 has a device such as a microphone and is configured to acquire conversation data between medical staff and patients made in a pharmacy. The conversation data is voice conversation data. Note that the voice acquisition device 5 may be integrated with the terminal 4 or may be separate. The voice acquisition device 5 can adopt, for example, a dedicated voice recorder or a mobile phone (e.g., a smartphone or the like) on which a voice acquisition application is installed.

[0023] In an example of the embodiment, when the voice acquisition device 5 acquires conversation data (voice data), the voice data is transmitted from the voice acquisition 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. Thereby, the patient data related to the current prescription and the conversation data related to the current prescription are not separated and can be appropriately processed by the information processing device 1. Note that the configuration of data transmission is not limited to this, and when the voice acquisition device 5 acquires conversation data, it may be configured to output from the voice acquisition device 5 to the information processing device 1 via the communication network 6 and the information processing device 1 side summarizes the information.

[0024] 2. Functional configuration With reference to FIG. 3, the functional configuration of the information processing device 1 according to the present embodiment will be described. 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 thus 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 data from the information processing device 1, the large language model server 2, the speech-to-text server 3, and the like. In the embodiment, the data acquisition unit 121 is configured to be able to acquire conversation data and medical data. Also, the data acquisition unit 121 is configured to be able to acquire text conversation data from the speech-to-text unit 31. These data will be described in detail later.

[0026] The medical text generation unit 122 is configured to generate a medical text based on conversation data (text conversation data) and pharmaceutical data. The output generation unit 122a of the medical text generation unit 122 has a function of generating various prompts (for example, a text generation prompt and an accuracy improvement prompt 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 adding information to the medical text MT1 generated by the large language model server 2 (for example, adding information such as a name that can identify an individual to the medical text MT1) to adjust the content of the medical text MT1. Whether the information processing device 1 has the adjustment unit 122b is optional.

[0027] The encoding unit 123 has a function of encoding some 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 so that the content of the non-output medical data is anonymized or abstracted. Note that the non-output medical data is data among the patient's data that is not output to the large language model server 2. The information processing device 1 outputs the encoded data so that an individual cannot be identified. That is, since the data related to the patient includes, for example, data that is personal information, in the embodiment, non-output medical data (data that is not output to the large language model server 2) is predetermined, but the information processing device 1 can encode the non-output medical data and include it in the output medical data.

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

[0029] The data exchanged in the information processing system 100 includes medical data, conversation data, and medical texts. In an example of the embodiment, the types of medical data include the items shown in data D1 to data D19 shown in FIG. 4B. The numbers of data D1 to data D19 are arbitrarily assigned for the purpose of distinguishing these items. Also, not all of data D1 to data D19 are necessarily essential items.

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

[0031] As shown in FIG. 4B, in the embodiment, medical data includes items such as ID (data D1), name (data D2), gender (data D3), date of birth (data D4), age (data D5), age group (data D5t), insurance card information (data D6), allergy history (data D7), current medical history (data D8), past medical history (data D9), drug side effect history (data D10), drug combination history (data D11), information related to problems in taking drugs (data D12), pharmacy name (data D13), prescribing doctor's name (data D14), hospital name (data D15), prescribing date (data D16), name of medicine (data D17), dosage, usage, and frequency of medicine (data D18), and drug history (data D19).

[0032] The ID (data D1) is a unique symbol assigned to each individual and is represented by, for example, numbers, alphabets, etc. The age (data D5t) may be obtained from the terminal 4 on the pharmacy side, or the information processing device 1 may have a function of calculating the age based on the age and obtain it by exerting the function. This function can be realized, for example, by the encoding unit 123. The insurance certificate information (data D6) includes the insurer number, symbol, and number. The information related to problems in taking medicine (data D12) includes, for example, information on circumstances such as unsuitable for an individual's mouth or body, different from allergies (for example, wanting to avoid Chinese herbal medicine because it doesn't suit the taste).

[0033] Note that the medicine name (data D17) is an example of medicine data. This medicine name may be the name of the medicine itself or an abbreviated name. The name (data D2), gender (data D3), date of birth (data D4), age (data D5), and insurance certificate information (data D6) are examples of the patient's basic data. The medication history (data D19) is an example of medication history data.

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

[0035] Also, for example, if the medical data DT1 is dispensing data, it includes data such as the medicine name (data D17) and dosage, usage, and amount of the medicine (data D18). Also, for example, if the medical data DT1 is pharmaceutical data, data such as the name of the pharmaceutical product (data D17) is included. Note that the types of data included in the medical data DT1 are not limited to prescription data, dispensing data, and pharmaceutical data, and various changes are possible. That is, the medical data DT1 may include all of the above-mentioned 20 items of data, or may include only a part of them, and various settings are possible. The information processing system 100 is preferably configured to be able to change the types of data included in the medical data.

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

[0037] By the way, medical data can be distinguished into the above-mentioned non-output medical data and output medical data. The non-output data is data that does not include personal information and is not output to the large language model server 2, and the output data is data that is output to the large language model server 2. These data will be described below.

[0038] 3-1-1. Non-output medical data The non-output medical data will be described with reference to FIG. 7A. In an embodiment, the non-output medical data may include, for example, name (data D2), date of birth (data D4), age (data D5), insurance card information (data D6), pharmacy name (data D13), prescribing doctor name (data D14), hospital name (data D15), and prescribing date (data D16). The data is not limited to those exemplified herein. For example, information that is considered to be relatively highly anonymous compared to the name etc. (date of birth and age) may be included in the output medical data and can be appropriately changed.

[0039] 3-1-2. Output Medical Data The output medical data will be described with reference to FIG. 7A. In an embodiment, the output medical data may include, for example, ID (data D1), gender (data D3), age group (data D5t), allergy history (data D7), current medical history (data D8), past medical history (data D9), drug side effect history (data D10), drug combination history (data D11), information related to problems in taking drugs (data D12), drug name (data D17), dosage, usage, and dose of the drug (data D18), and drug history (data D19). The data is not limited to those exemplified herein, and other information may be added or reduced 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. In FIG. 5, in order to improve the accuracy (correctness) of the text of the pre-processing character data d2 described later, the state in which the medical data DT2 is output to the large language model server 2 is shown. The medical data DT2 may include all or part of the above-described output medical data. Note that the medical data DT2 includes a drug name (data D17). This is because many drug names are long and many are names that are not commonly used, making it difficult to appropriately perform character recognition. Preferably, the medical data DT2 includes at least one of the dosage, usage, and amount of medicine (data D18) and the medication history (data D19). In addition, 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 of multiple times (for example, the previous time and the time before that, etc.).

[0041] <Medical data DT3> In FIG. 6, in order to generate the medical text MT1 described later, the state in which the medical data DT3 is output to the large language model server 2 together with the processed character data d3 described later is shown. The medical data DT3 may include all or part of the above-described output medical data. Note that the medical data DT3 includes the name of the medicine (data D17), which is an example of the medicine data, in order to appropriately generate the medical text MT1 output from the large language model server 2. Preferably, the medical data DT3 includes at least one of the dosage, usage, and amount of medicine (data D18) and the medication history (data D19). More preferably, the medical data DT3 includes at least one of the ID (data D1), gender (data D3), age (data D5t), allergy history (data D7), current medical history (data D8), past medical history (data D9), history of drug side effects (data D10), and history of drug combination (data D11).

[0042] In addition, similar to the medical data DT3, 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). The content related to past prescriptions may include those of multiple times (for example, the previous time and the time before that, etc.). 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, for example, the data of the drug name described in the prescription submitted by the patient this time. The old drug data is data prior to the new drug data. In other words, the old drug data is, for example, the data of the drug described in the prescription submitted by the patient before this time. Before this time may refer to the data related to the prescription one time before the prescription in the new drug data, the data related to the prescription two times before, or even earlier. Also, the old drug data may have a plurality of data prior to this time.

[0043] 3-2. Conversation Data The conversation data includes the content of the conversation between the medical staff and the patient. The types of conversation data include the voice conversation data d1 shown in FIGS. 4A and 5 (the voice data acquired by the voice acquisition device 5 in the embodiment) and the text conversation data. Also, the types of text conversation data include the pre-processed text data d2 shown in FIG. 5 and the post-processed text data d3 shown in FIGS. 5 and 6. Here, the pre-processed text data d2 is the data obtained by performing speech recognition on the voice conversation data by the speech recognition unit 31. The post-processed text data d3 is the text conversation data used when the medical article generation unit 122 generates a medical article. In the embodiment, the post-processed text data d3 is the text data after processing the pre-processed text data d2 by the text generation unit 21 so that the accuracy of the content is improved.

[0044] Specifically, as shown in an example in FIG. 8A, the pre-processed text data d2 may have errors in the drug name or the like (see the underlined part). However, as shown in an example in FIG. 8B, the errors in the post-processed text data d3 are corrected (see the underlined part), and the accuracy as a text is improved. In addition, in the post-processed text data d3, the content spoken by the medical staff and the content spoken by the patient are distinguished. Since such post-processed text data d3 is output to the large language model server 2, the output (medical article MT1) from the large language model server 2 is optimized and the accuracy is also improved.

[0045] 3-3. Medical Article In the medical article in the embodiment, a medical article MT1 output from the large language model server 2 to the information processing apparatus 1 and a medical article MT2 output from the information processing apparatus 1 to the pharmacy side are distinguished. As shown in FIG. 9, the medical article MT1 has a predetermined format. An example of this predetermined format is SOAP. Here, SOAP refers to a recording method (recording format) used at the site of medical treatment or the like in which subjective information (Subject), objective information (Object), assessment (Assessment), and plan (Plan) are distinguished. That is, the medical article MT1 is generated with subjective information, objective information, assessment, and plan being distinguished.

[0046] In addition to the medical article MT1, the medical article MT2 is data configured to be able to identify an individual by adding at least one piece of data among the name (data D2), gender (data D3), date of birth (data D4), age (data D5), and insurance card information (data D6). In other words, the medical article MT2 is data to which elements of basic data are added. Note that the information processing apparatus 1 may be configured not to generate the medical article MT2 and output the medical article MT1 itself to the pharmacy side. In this case, for example, the pharmacy-side terminal 4 may perform processing for identifying an individual.

[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 assisting in creating a medical article. With reference to FIG. 10, an example of the information processing method according to the embodiment will be described.

[0048] <Start: Step S001> Medical staff receive a prescription from a patient and start dispensing according to the prescription. When the dispensing is completed, they start a conversation with the patient to hand over the medicine. Prior to this conversation, the medical staff have activated the voice acquisition device 5. Medical staff at a pharmacy are required to leave records as medical documents for medical acts such as dispensing and conversation, which is time-consuming. The information processing system 100 according to the embodiment can reduce this time-consuming task by automatically generating all or part of the medical document, and can suppress the burden on the medical staff.

[0049] <Step S002> When the conversation with the patient is completed, the medical staff stop the voice acquisition device 5 and transmit the voice conversation data d1 from the memory of the voice acquisition device 5 to the memory of the terminal 4. As a result, the terminal 4 acquires the medical data DT1. In addition, the medical staff input the content of this prescription as the medical data DT1 into the terminal 4, and the memory of the terminal 4 acquires this medical data DT1. Note that the image of the prescription may be scanned, information such as the characters of the prescription may be read from the scanned data, and the medical data DT1 may be acquired.

[0050] Here, in the description, the patient has already used one of the pharmacies utilized by the information processing system 100, and the basic data of the patient (such as name (data D2), etc.) has already been stored in the storage unit 11 of the information processing device 1 serving as a database. Therefore, in an example of the medical data DT1 in this step S001, it is not necessarily required to input all the basic data (for example, age (data D5), etc.).

[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] This step S003 is an example of an acquisition step.

[0053] <Step S004> The control unit 12 of the information processing apparatus 1 outputs the voice conversation data d1 stored in the storage unit 11 to the speech-to-text server 3. The speech recognition unit 31 of the speech-to-text server 3 performs speech recognition on the voice conversation data d1 and generates pre-processed character data d2 which is character data.

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

[0055] <Step S006> The control unit 12 of the information processing apparatus 1 outputs the pre-processed character data d2 stored in the storage unit 11 and the medical data DT2 to the large 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 an accuracy improvement prompt to be output to the large language model server 2. The accuracy improvement prompt includes the pre-processed character data d2 and the medical data DT2, and has an instruction to generate post-processed character data d3 with reference to the medical data DT2. Further, the accuracy improvement prompt has an instruction to generate the post-processed character data d3 by distinguishing between the content spoken by the medical staff and the content spoken by the patient (see FIG. 8B), and thus, an effect of improving the generation accuracy of the medical text MT1 can be expected.

[0056] The control unit 12 of the information processing apparatus 1 outputs this accuracy improvement prompt to the large language model server 2. Specifically, the number of characters of the accuracy improvement prompt is, 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, 14000, and it may also be within the range between any two of the numerical values exemplified here. Further, the accuracy improvement prompt may be composed of a plurality of divided prompts.

[0057] The text generation unit 21 of the large language model server 2 refers to the accuracy improvement prompt, generates processed text data d3 by modifying the content of the pre - processed text data d2 so that the accuracy of the pre - processed text data d2 is improved, and outputs it to the information processing device 1.

[0058] <Step S007> The data acquisition unit 121 of the information processing device 1 acquires the processed text data d3 as the output from the large language model for the accuracy improvement prompt and stores it in the storage unit 11.

[0059] <Step S008> Subsequently, the control unit 12 of the information processing device 1 outputs the processed text data d3 stored in the storage unit 11 and the medical data DT3 to the large language model server 2. In doing so, it 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 language model server 2. The text generation prompt includes the processed text data d3 and the medical data DT3, and has an instruction to generate the medical text MT1. In the embodiment, this prompt has an instruction to create the content of the processed text data d3 and the medical data DT3 in a predetermined format (SOAP in the embodiment). Also, this prompt may include supplementary data. The supplementary data can include, for example, content that defines what SOAP is, content that limits the number of characters of the generated medical text MT1, and the like. Note that the medical data DT3 in this prompt excludes information by which an individual can be identified (for example, name, which is an example of basic data), but may be included as encoded data (abstracted data) in which the information is encoded (abstracted) by the encoding unit 123.

[0060] The control unit 12 of the information processing apparatus 1 outputs this text generation prompt to the large language model server 2. Specifically, the number of characters in the text generation prompt is, 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, 14000, and it may also be within the range between any two of the numerical values exemplified here. Also, the text generation prompt may be composed of a plurality of divided prompts.

[0061] The text generation unit 21 of the large language model server 2 refers to the text generation prompt, generates a medical text MT1, and outputs it to the information processing apparatus 1.

[0062] <Step S009> The data acquisition unit 121 of the information processing apparatus 1 acquires the medical text MT1 as the output from the large language model for the text generation prompt and stores it in the storage unit 11. Here, the medical data DT3 output to the large 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 a medical text MT2 with basic data (such as name) added so that an individual can be identified in the medical text MT1 and stores it in the storage unit 11. That is, the medical text generation unit 122 generates the medical text MT2 based on the conversation data (processed character data), the pharmaceutical data (medical data), and the basic data.

[0063] Steps S008 and S009 are an example of the generation step.

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

[0065] <End: Step S011> Medical workers save the trouble of creating medical texts by utilizing the medical text MT2.

[0066] 5. Variations 5-1. Variation 1 In the embodiment, a configuration for generating medical texts by utilizing servers external to the information processing device 1, such as the large language model server 2 and the speech-to-text server 3, has been described, but it is not limited thereto. As shown in FIGS. 11A and 11B, the information processing device 1 may have the functions of the large language model server 2 and the speech-to-text server 3. That is, the information processing device 1 may have a text generation unit 21 and a speech recognition unit 31.

[0067] 5-2. Variation 2 In Variation 1, the information processing system 100 has been described as a system in which the medical institution (pharmacy) side and the information processing device 1 arranged at a location independent of the medical institution exchange information, but it is not limited thereto. As shown in FIG. 12, the information processing device 1 may be arranged in the medical institution (pharmacy), and a system in which data is completed within the medical institution (pharmacy) may be used.

[0068] 5-3. Other variations In the embodiment, the medical institution has been described as a pharmacy, but it is not limited thereto. For example, it can also be applied to places that provide medicines installed in a hospital. In addition, it can also be applied to fields where it is necessary to create documents in a predetermined format. For example, it can be applied not only to places where prescriptions are written but also to hospital operations for creating SOAP in a hospital.

[0069] The text generation unit 21 may adopt a configuration having a dedicated neural network for generating medical texts. "Dedicated" means specialized for a specific field. For example, when applied to a pharmacy as in the embodiment, it is the field of medicine and healthcare. For example, the text generation unit 21 can be configured to perform operations based on a predefined learning model. Here, the learning model is a model that trains the model using a large number of teacher data to make future outputs predictable. As input data, for example, a large number of medical data (such as drug names) can be used, and as output data, for example, a large number of medical texts can be used. Thereby, the text generation unit 21 can generate medical texts based on a learning model that inputs medical data and outputs medical texts.

[0070] In the embodiment, it has been described as having the speech-to-text conversion unit 31 within the information processing system 100, but it is not limited thereto. That is, the speech-to-text conversion unit 31 exists outside the information processing system 100, and the information processing system 100 may be configured to adopt conversation data that has already been speech-to-text converted externally. In this case, the information processing device 1 can acquire the conversation data that has been speech-to-text converted externally and include this in the medical data DT3 to generate a text generation prompt.

[0071] In the above, the embodiments have been described, but these are presented as examples and are not intended to limit the scope of the invention. The novel embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made. The embodiments and their modifications are included in the scope and gist of the invention and are included in the invention described in the claims and its equivalent scope.

Explanation of Reference Numerals

[0072] 100: Information processing system 1: Information processing device 10: Communication unit 11: Storage unit 12: Control unit 121: Data acquisition unit 122: Medical Article Generation Unit 122a: Output Generation Unit 122b: Adjustment Unit 123: Encoding Unit 13: Output Unit 14: Input Unit 15: Communication Bus 2: Large Language Model Server 21: Article Generation Unit 3: Speech-to-Text Server 31: Speech Recognition Unit 4: Terminal 5: Audio Acquisition Device 6: Communication Network DT0: Stored Data DT1: Medical Data DT2: Medical Data DT3: Medical Data MT1: Medical Article MT2: Medical Article d1: Voice Conversation Data d2: Pre-Processed Character Data d3: Post-Processed Character Data

Claims

1. An information processing system for assisting in creating a medical article, comprising a data acquisition unit and a medical article generation unit, wherein the data acquisition unit is configured to be able to acquire conversation data and medical data, the conversation data includes the content of conversations between medical practitioners and patients, the medical data has pharmaceutical data, the pharmaceutical data has new pharmaceutical data and old pharmaceutical data, the new pharmaceutical data corresponds to the conversation in the conversation data, the old pharmaceutical data is data prior to the new pharmaceutical data, and the medical article generation unit is configured to generate the medical article based on the conversation data and the pharmaceutical data. An information processing system.

2. The information processing system according to claim 1, wherein the conversation data is voice conversation data, the data acquisition unit is configured to be able to acquire character conversation data from a speech recognition unit, the speech recognition unit is configured to generate the character conversation data which converts the content of the conversation into characters using the voice conversation data, and the medical article generation unit is configured to generate the medical article based on the character conversation data and the pharmaceutical data. An information processing system.

3. The information processing system according to claim 2, wherein the medical article generation unit has an output generation unit, the output generation unit generates an article generation prompt for output to a large language model, the article generation prompt has the character conversation data and the pharmaceutical data, and has an instruction to generate the medical article, and the data acquisition unit acquires the medical article as an output from the large language model for the article generation prompt. An information processing system.

4. The information processing system according to claim 3, wherein the output generation unit generates an accuracy improvement prompt for output to the large language model, the accuracy improvement prompt has pre - processing character data and the pharmaceutical data, and has an instruction to generate post - processing character data with reference to the pharmaceutical data, the pre - processing character data is data obtained by speech - recognizing the voice conversation data, the post - processing character data is the character conversation data used when the medical article generation unit generates the medical article, and the data acquisition unit acquires the post - processing character data as an output from the large language model for the accuracy improvement prompt. An information processing system.

5. The information processing system according to claim 4, wherein the accuracy improvement prompt has an instruction to generate the processed character data by distinguishing between the content spoken by the medical worker and the content spoken by the patient, the character conversation data is the information processing system in which the content spoken by the medical worker and the content spoken by the patient are distinguished.

6. The information processing system according to claim 1, wherein the conversation data is character data, and the content spoken by the medical worker and the content spoken by the patient are distinguished, the information processing system.

7. The information processing system according to any one of claims 1 to 6, wherein the medical data further has the basic data of the patient, the basic data has the name of the patient and the gender of the patient, the medical article generation unit generates the medical article based on the conversation data, the pharmaceutical data, and the basic data, the information processing system.

8. The information processing system according to any one of claims 1 to 6, wherein the medical data further has the medication history data of the patient, the medical article generation unit generates the medical article based on the conversation data, the pharmaceutical data, and the medication history data, the information processing system.

9. The information processing system according to any one of claims 1 to 6, wherein the medical article is generated by distinguishing between subjective information, objective information, evaluation, and plan, the information processing system.

10. An information processing method for assisting in creating a medical article, comprising an acquisition step and a generation step, in the acquisition step, conversation data and medical data are acquired, the conversation data includes the content of the conversation between the medical worker and the patient, the medical data has pharmaceutical data, the pharmaceutical data has new pharmaceutical data and old pharmaceutical data, the new pharmaceutical data corresponds to the conversation of the conversation data, the old pharmaceutical data is data prior to the new pharmaceutical data, in the generation step, the medical article is generated based on the conversation data and the pharmaceutical data, the information processing method.

11. A program for causing a computer to execute the information processing method according to claim 10.

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