Pharmacist work support system, pharmacist work support method, and pharmacist work support program

Abstract

Assists pharmacists in their work, such as creating medical histories. [Solution] An input unit 111 inputs conversation data indicating the contents of a conversation between a patient who receives a drug prescription and a pharmacist who serves the patient to a learning model 112. An output unit 113 outputs entry information generated by the learning model 112 in response to the conversation data input by the input unit 111, the entry information being at least about a subject in a SOAP (Subject Object Assessment Plan) medication history.

Description

[Technical field]

[0001] The present disclosure relates to a technology for supporting pharmacists in their work, such as creating medication histories. [Background technology]

[0002] Pharmacists record the prescribed medication as well as any complaints the patient may have had regarding the medication history for which they have prescribed medication. In doing so, pharmacists consider the various pieces of information they have obtained and decide what information should be recorded. When considering what information to record, differences in the experience or knowledge of pharmacists are likely to result in differences in quality. It is desirable to be able to record high-quality information as a medication history, regardless of the pharmacist in charge.

[0003] In addition, pharmacists spend a lot of time inputting information to be recorded as medical history into a computer. By reducing the time spent on this input work, they can spend more time on dispensing medicines and caring for patients. Therefore, it is desirable to be able to reduce the burden of input work.

[0004] Patent Document 1 describes a method in which medication instructions are given to patients using a questionnaire and prescription information while referring to a drug database, and information on the medication instructions is stored in a medical history database. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent Publication No. 2022-52905 Summary of the Invention [Problem to be solved by the invention]

[0006] Even if the technology described in Patent Document 1 is used, it is difficult to record high-quality information as a medical history regardless of the pharmacist in charge. The present disclosure aims to support pharmacists in their work, such as creating medical histories. [Means for solving the problem]

[0007] The pharmacist work support system according to the present disclosure includes: An input unit that inputs conversation data indicating the content of a conversation between a patient who is prescribed a drug and a pharmacist who is assigned to the patient into a learning model; an output unit that outputs entry information generated by the learning model in response to the conversation data input by the input unit, the entry information being about at least a subject in a SOAP (Subject Object Assessment Plan) medication history; Equipped with. Effect of the Invention

[0008] In the present disclosure, the learning model outputs information to be entered for at least the subject in the SOAP medication history, which supports the pharmacist's work of creating a medication history. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a configuration diagram of a pharmacist work support system 100 according to a first embodiment. [Diagram 2] 1 is a configuration diagram of a pharmacist work support device 10 according to a first embodiment. [Diagram 3] FIG. 2 is a configuration diagram of a pharmacy terminal 20 according to the first embodiment. [Figure 4] FIG. 4 is an explanatory diagram of patient data 131 according to the first embodiment. [Diagram 5] 3 is a flowchart of the process of the pharmacist work support system 100 according to the first embodiment. [Figure 6] FIG. 2 is an explanatory diagram of patient personal information according to the first embodiment. [Figure 7] FIG. 4 is an explanatory diagram of medical questionnaire information according to the first embodiment. [Figure 8] FIG. 2 is an explanatory diagram of prescription information according to the first embodiment. [Figure 9] 4 is a flowchart of a reception process according to the first embodiment. [Figure 10] FIG. 4 is an explanatory diagram of a prompt according to the first embodiment. [Figure 11] 11 is a flowchart of a reception process according to the second embodiment. [Figure 12] FIG. 11 is an explanatory diagram of a prompt according to the second embodiment. [Figure 13] 13 is a flowchart of a reception process according to the third embodiment. [Figure 14] FIG. 11 is an explanatory diagram of a prompt according to the third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Embodiment 1 ***Configuration Description*** The configuration of a pharmacist work support system 100 according to the first embodiment will be described with reference to FIG. Pharmacist work support system 100 includes pharmacist work support device 10 and one or more pharmacy terminals 20. Pharmacist work support device 10 and each pharmacy terminal 20 are connected via network 90. The pharmacist work support device 10 is a computer such as a cloud server. The pharmacy terminal 20 is a computer such as a PC that is installed in a pharmacy 30 and operated by a pharmacist. PC is an abbreviation for personal computer. The pharmacy 30 may be a chain store or an individual store that is not affiliated with a chain. A plurality of pharmacy terminals 20 may be installed in the same pharmacy 30. When a plurality of pharmacy terminals 20 are installed, different devices such as a PC and a tablet terminal may be combined and installed.

[0011] The configuration of the pharmacist work support device 10 according to the first embodiment will be described with reference to FIG. The pharmacist work support device 10 is a computer. The pharmacist work support device 10 includes the following hardware components: a processor 11, a memory 12, a storage 13, and a communication interface 14. The processor 11 is connected to other hardware components via signal lines and controls these other hardware components.

[0012] Pharmacist work support device 10 includes, as functional components, input unit 111, learning model 112, output unit 113, and recording unit 114. The functions of each of the functional components of pharmacist work support device 10 are realized by software. Storage 13 stores programs that realize the functions of each functional component of pharmacist work support device 10. These programs are loaded into memory 12 by processor 11 and executed by processor 11. In this way, the functions of each functional component of pharmacist work support device 10 are realized.

[0013] The storage 13 stores patient data 131.

[0014] The configuration of pharmacy terminal 20 according to the first embodiment will be described with reference to FIG. Pharmacy terminal 20 is a computer. Pharmacy terminal 20 includes the following hardware components: processor 21, memory 22, storage 23, and communication interface 24. Processor 21 is connected to other hardware components via signal lines and controls the other hardware components. When pharmacy terminal 20 is a PC, it may be connected to multiple monitors as hardware components.

[0015] Pharmacy terminal 20 includes, as functional components, a reception unit 211, an extraction unit 212, a display unit 213, and an editing unit 214. The functions of the functional components of pharmacy terminal 20 are realized by software. Storage 23 stores programs that realize the functions of each functional component of pharmacy terminal 20. These programs are loaded into memory 22 by processor 21 and executed by processor 21. In this way, the functions of each functional component of pharmacy terminal 20 are realized.

[0016] The processors 11 and 21 are ICs that perform processing. IC stands for Integrated Circuit. Specific examples of the processors 11 and 21 are a CPU, a DSP, and a GPU. CPU stands for Central Processing Unit. DSP stands for Digital Signal Processor. GPU stands for Graphics Processing Unit.

[0017] The memories 12 and 22 are storage devices that temporarily store data. Specific examples of the memories 12 and 22 are SRAM and DRAM. SRAM is an abbreviation for Static Random Access Memory. DRAM is an abbreviation for Dynamic Random Access Memory.

[0018] The storages 13 and 23 are storage devices that store data. A specific example of the storages 13 and 23 is an HDD. HDD is an abbreviation for Hard Disk Drive. The storages 13 and 23 may also be portable recording media such as an SD (registered trademark) memory card, CompactFlash (registered trademark), NAND flash, a flexible disk, an optical disk, a compact disk, a Blu-ray (registered trademark) disk, or a DVD. SD is an abbreviation for Secure Digital. DVD is an abbreviation for Digital Versatile Disk.

[0019] The communication interfaces 14 and 24 are interfaces for communicating with external devices. Specific examples of the communication interfaces 14 and 24 are ports for Ethernet (registered trademark), USB, and HDMI (registered trademark). USB is an abbreviation for Universal Serial Bus. HDMI is an abbreviation for High-Definition Multimedia Interface.

[0020] In Fig. 2, only one processor 11 is shown. However, there may be a plurality of processors 11, and the plurality of processors 11 may cooperate to execute a program that realizes each function. Similarly, in Fig. 3, only one processor 21 is shown. However, there may be a plurality of processors 21, and the plurality of processors 21 may cooperate to execute a program that realizes each function.

[0021] ***Explanation of Operation*** The operation of the pharmacist work support system 100 according to the first embodiment will be described with reference to FIGS. The operation procedure of pharmacist work support system 100 according to embodiment 1 corresponds to the pharmacist work support method according to embodiment 1. Moreover, the program that realizes the operation of pharmacist work support system 100 according to embodiment 1 corresponds to the pharmacist work support program according to embodiment 1.

[0022] With reference to FIG. 4, the patient data 131 according to the first embodiment will be described. The patient data 131 is information about each patient. The patient data 131 includes information about each patient, such as personal information, prescription drug information, contact information, basic confirmation information, medication instruction history, and SOAP medication history. The personal information is information about the patient, such as the insurer number, name, sex, date of birth, height, and weight. The prescription drug information is historical information about the drugs prescribed to the patient. The contact information includes comments and previous instruction handover. The comments are information that should be noted about the patient regarding dispensing or medication instruction, etc. The previous instruction handover is information handed over from the pharmacist who provided the previous instruction. The basic confirmation information is information about matters that require confirmation about the patient regarding dispensing or medication instruction, etc. The medication instruction history is historical information about medication instructions that have been provided in the past. The SOAP medication history is a medication history recorded divided into Subject, Object, Assessment, and Plan. The subjective information of the patient is recorded in Subject. The objective information is recorded in Object. The pharmacist's analysis and opinion is recorded in Assessment. The plan for the task is recorded in Plan.

[0023] The flow of processing in the pharmacist work support system 100 according to the first embodiment will be described with reference to FIG. (Step S11: Reception process) The reception unit 211 of the pharmacy terminal 20 receives reception information from a patient who is to receive a prescription for medicine. The reception information includes the patient's personal information, medical questionnaire information, and prescription information. The patient personal information is information indicating the attributes of the patient, etc. As shown in Fig. 6, the patient personal information includes information such as the patient's name, the patient's telephone number, address, sex, age, height, and weight. The medical interview information is information indicating the patient's answers to the medical interview. As shown in Fig. 7, the medical interview information includes the patient's answers to questions about the name of the disease, the medication status, and the occurrence of side effects. The prescription information is information that indicates the contents of a prescription for a patient. As shown in Fig. 8, the prescription information includes the name, quantity, and usage of each prescribed drug. The prescription information also includes the name of the hospital that issued the prescription and the date of issuance.

[0024] The reception process (step S11 in FIG. 5) according to the first embodiment will be described with reference to FIG. When a patient visits the pharmacy 30, the pharmacist has the patient fill out the patient's personal information. The pharmacist inputs the filled-out patient's personal information into the pharmacy terminal 20, and the reception unit 211 accepts the patient's personal information (step S111). Next, the pharmacist has the patient fill out a medical questionnaire. The pharmacist inputs the answers to the medical questionnaire into the pharmacy terminal 20, and the reception unit 211 accepts the medical questionnaire information (step S112). Next, the pharmacist receives a prescription from the patient. The pharmacist inputs the prescription information into the pharmacy terminal 20, and the reception unit 211 accepts the prescription information (step S113). At least some of the processes from step S111 to step S113 may be performed in parallel.

[0025] (Step S12: Extraction process) The extraction unit 212 of the pharmacy terminal 20 extracts input information from the reception information received in step S11, and transmits the input information to the pharmacist work support device 10. Note that the extraction unit 212 may extract all of the reception information as input information. Specifically, the extraction unit 212 extracts at least a part of information from the patient's personal information in the reception information and includes it in the input information. For example, the extraction unit 212 extracts information such as gender, age, height, and weight from the patient's personal information in the reception information and includes it in the input information. The extraction unit 212 also extracts at least a part of information from the medical interview information in the reception information and includes it in the input information. For example, the extraction unit 212 extracts a pair of a question and an answer for each question item from the medical interview information in the reception information and includes it in the input information. The extraction unit 212 also extracts at least a part of information from the prescription information in the reception information and includes it in the input information. For example, the extraction unit 212 extracts the name, amount, and usage of the prescribed drug from the prescription information in the reception information and includes it in the input information.

[0026] (Step S13: Input process) The input unit 111 of the pharmacist work support device 10 receives the input information transmitted in step S12. The input unit 111 inputs the input information to the learning model 112 as a prompt. At this time, the input unit 111 adds an order indicating the assumption and instructions, and constraint conditions to the prompt in addition to the input information. Here, the input unit 111 writes in the order the assumption that the person is a pharmacist and the instruction to create a SOAP medication history. Note that the input unit 111 may also write in the order the instruction to create at least the subject of the SOAP medication history, or at least the subject and object. The input unit 111 writes in the constraint conditions the way to write the SOAP medication history, the amount to be written, and the specification of the specific items to be written.

[0027] As a specific example, the input unit 111 generates a prompt as shown in Fig. 10. The prompt shown in Fig. 10 is based on the patient personal information shown in Fig. 6, the medical questionnaire information shown in Fig. 7, and the prescription information shown in Fig. 8. #The order states that the pharmacist is an experienced pharmacist and instructs the pharmacist to create a SOAP medication history based on #constraints, #patient personal information, #medical questionnaire information, and #prescription information. #The constraints specify that the SOAP medication history should be written in bullet points, and that the amount of information to be written should be about five lines for each of Subject, Object, Assessment, and Plan. It also specifies that specific data such as numerical values ​​should be shown. Furthermore, items such as medication status are specified as items that should be specifically written. #The patient personal information includes information extracted from the patient personal information in step S12. #The medical questionnaire information includes information extracted from the medical questionnaire information in step S12. #The prescription information includes information extracted from the prescription information in step S12. #The output format is specified as the SOAP medication history output format.

[0028] (Step S14: Generation process) The learning model 112 of the pharmacist work support device 10 generates information to be written in the SOAP medication history based on the prompt input in step S13. The information to be written in the SOAP medication history may include information to be written about a problem in addition to the Subject, Object, Assessment, and Plan. A problem is an issue that a patient has. The learning model 112 is a so-called generative AI. AI is Artificial Intelligence. The learning model 112 may be configured using algorithms such as BERT and GPT, as specific examples. BERT is an abbreviation for Bidirectional Encoder Representations from Transformers. GPT is an abbreviation for Generative Pretrained Transformer. The learning model 112 may be configured by combining a plurality of algorithms including these algorithms.

[0029] (Step S15: Output process) The output unit 113 of the pharmacist work support device 10 acquires the entry information of the SOAP medication history generated in step S14. Then, the output unit 113 transmits the acquired entry information of the SOAP medication history to the pharmacy terminal 20.

[0030] (Step S16: Display process) Display unit 213 of pharmacy terminal 20 acquires the SOAP medication history entry information transmitted in step S15. Display unit 213 displays the acquired SOAP medication history entry information on a display device connected to pharmacy terminal 20 via communication interface 24.

[0031] (Step S17: Editing process) Editing unit 214 of pharmacy terminal 20 accepts edits to the SOAP medication history entry information displayed in step S16. Specifically, if there is content to be corrected or added to the displayed SOAP medication history entry information, the pharmacist makes edits. In addition, if the pharmacist has provided medication instructions to the patient, the pharmacist adds the content of the medication instructions provided.

[0032] (Step S18: Transmission process) The editing unit 214 of the pharmacy terminal 20 transmits the final SOAP medication history information edited in step S17 to the pharmacist work support device 10.

[0033] (Step S19: Recording process) The recording unit 114 of the pharmacist work support device 10 records the SOAP medication history information transmitted in step S18 in the patient data 131.

[0034] ***Advantages of the First Embodiment*** As described above, the pharmacist work support system 100 according to the first embodiment inputs input information including the patient's personal information, medical questionnaire information, and prescription information as a prompt into the learning model 112, and generates information to be entered into the SOAP medical history. This reduces the workload of the pharmacist in creating the SOAP medical history. In addition, by performing appropriate learning on the learning model 112, it becomes possible to record high-quality information as the medical history, regardless of the pharmacist in charge. The patient personal information, the medical history form information, and the prescription information are acquired in the dispensing process. Therefore, acquiring the patient personal information, the medical history form information, and the prescription information does not increase the workload of the pharmacist.

[0035] ***Other configurations*** <Variation 1> In the first embodiment, the pharmacist work support device 10 includes the learning model 112. However, the learning model 112 may be provided outside the pharmacist work support device 10. In this case, in step S13 of Fig. 5, the input unit 111 inputs a prompt to the learning model 112 via the transmission line. Then, in step S15 of Fig. 5, the output unit 113 acquires the information to be entered in the SOAP medication history via the transmission line.

[0036] <Variation 2> In step S13 of FIG. 5, the input unit 111 may input example entries as options for information to be entered for each item of the SOAP medication history, and have the learning model 112 select a specified number (e.g., three) of those that are most likely to be applicable. For example, for a subject, a pharmacist may narrow down the general options, add the narrowed down options to a prompt, and provide the prompt to the learning model 112, which may then select a specified number of options that are most likely to be applicable.

[0037] <Variation 3> In the first embodiment, each functional component is realized by software. However, as a third modification, each functional component may be realized by hardware. The following describes the third modification, focusing on the differences from the first embodiment.

[0038] When each functional component is realized by hardware, the pharmacist work support device 10 includes an electronic circuit 15 instead of the processor 11, the memory 12, and the storage 13. The electronic circuit 15 is a dedicated circuit for realizing the functions of each functional component, the memory 12, and the storage 13.

[0039] The electronic circuit 15 may be a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, a logic IC, a GA, an ASIC, or an FPGA. GA is an abbreviation for Gate Array. ASIC is an abbreviation for Application Specific Integrated Circuit. FPGA is an abbreviation for Field-Programmable Gate Array. Each functional component may be realized by one electronic circuit 15, or each functional component may be distributed across multiple electronic circuits 15.

[0040] <Variation 4> As a fourth modification, some of the functional components may be realized by hardware, and other functional components may be realized by software.

[0041] The processor 11, the memory 12, the storage 13, and the electronic circuit 15 are collectively referred to as a processing circuit. In other words, the functions of the respective functional components are realized by the processing circuit.

[0042] Embodiment 2 The second embodiment differs from the first embodiment in that a prompt is generated using information previously recorded about the patient. In the second embodiment, this difference will be described, and a description of the same points will be omitted.

[0043] There may be cases where a patient revisits the pharmacy 30. In such cases, information about the patient from previous visits has been recorded. Therefore, the pharmacist work support system 100 generates a prompt using the information recorded about the patient.

[0044] ***Explanation of Operation*** The operation of pharmacist work support system 100 according to the second embodiment will be described with reference to FIGS.

[0045] The process of pharmacist work support system 100 in this case will be described with reference to Fig. 5. The process from step S11 to step S13 differs from that in the first embodiment. (Step S11: Reception process) As in the first embodiment, reception unit 211 of pharmacy terminal 20 receives reception information from a patient who is prescribed medicines. The reception information includes patient personal information, medical questionnaire information, and prescription information. In the case of a returning patient, the patient personal information and medical questionnaire information include information on changes since the patient's previous visit. Furthermore, the prescription information includes past prescription information, which is information on the prescription at the previous visit, in addition to most recent prescription information, which is information on the prescription at the current visit.

[0046] The reception process (step S11 in FIG. 5) according to the second embodiment will be described with reference to FIG. The processes from step S111 to step S113 are the same as those in FIG.

[0047] The reception unit 211 identifies whether the patient is a returning patient based on the patient name, telephone number, address, etc. (step S110). Specifically, the reception unit 211 searches personal information in the patient data 131 of the pharmacist work support device 10 using the patient name, telephone number, and address as keywords. If no information corresponding to the keywords is found, the reception unit 211 identifies the patient as a first-time patient. On the other hand, if information corresponding to the keywords is found, the reception unit 211 identifies the patient as a returning patient. If the patient is a first-time visitor, the reception unit 211 advances the process to step S111. On the other hand, if the patient is a repeat visitor, the reception unit 211 advances the process to step S114.

[0048] In the case of a returning patient, the reception unit 211 reads out the patient's personal information and medical questionnaire information from the patient data 131 of the pharmacist work support device 10. The pharmacist confirms with the patient the changes to the patient's personal information and medical questionnaire information. When the pharmacist inputs the changes into the pharmacy terminal 20, the reception unit 211 accepts the patient's personal information and medical questionnaire information with the changes identified (step S114). Note that the reception unit 211 may also include information before the changes. Next, the pharmacist receives a prescription from the patient. The pharmacist inputs the prescription information into the pharmacy terminal 20, and the reception unit 211 receives the most recent prescription information. Furthermore, the reception unit 211 reads out the patient's previous prescription information from the patient data 131 of the pharmacist work support device 10 as past prescription information. Then, the reception unit 211 sets the most recent prescription information and the past prescription information together as the prescription information (step S115).

[0049] (Step S12: Extraction process) Extraction unit 212 of pharmacy terminal 20 extracts input information from the reception information received in step S11, and transmits the input information to pharmacist work support device 10, as in the first embodiment. However, the extraction unit 212 also includes information indicating changes in the patient personal information and medical questionnaire information in the input information. Also, for prescription information, the extraction unit 212 extracts at least a portion of information from each of the most recent prescription information and the past prescription information and includes it in the input information. For example, the extraction unit 212 extracts the drug name, amount, and usage of the prescribed drug from each of the most recent prescription information and the past prescription information and includes it in the input information.

[0050] (Step S13: Input process) As in the first embodiment, the input unit 111 of the pharmacist work support device 10 inputs input information as a prompt to the learning model 112. However, the input unit 111 includes information indicating changes in the patient personal information and medical questionnaire information in the input information. Also, the input unit 111 generates a prompt including past prescription information as well as the most recent prescription information as prescription information.

[0051] As a specific example, the input unit 111 generates a prompt as shown in Fig. 12. Note that the order form is omitted in Fig. 12. The prompt shown in Fig. 12 states that there are changes in the patient personal information and the medical questionnaire information, and shows the information before and after the change. Also, the prompt shown in Fig. 12 includes past prescription information in addition to the most recent prescription information in #Prescription information.

[0052] ***Effects of the second embodiment*** As described above, the pharmacist work support system 100 according to the second embodiment generates a prompt by using information recorded in the past about a patient. This makes it possible to generate more appropriate information to be written in the SOAP medication history by the learning model 112.

[0053] Embodiment 3 The third embodiment differs from the first and second embodiments in that a prompt is generated using conversation data indicating the contents of a conversation between a patient and a pharmacist. In the third embodiment, this difference will be described, and a description of the same points will be omitted. In the third embodiment, a case where a modification is made to the first embodiment will be described. However, it is also possible to make modifications to the second embodiment.

[0054] ***Explanation of Operation*** The operation of pharmacist work support system 100 according to the third embodiment will be described with reference to FIGS.

[0055] The process of pharmacist work support system 100 in this case will be described with reference to Fig. 5. The process from step S11 to step S13 differs from that in the first embodiment. (Step S11: Reception process) Reception unit 211 of pharmacy terminal 20 receives reception information from a patient who is to receive a prescription for medicine, similar to embodiment 1. The reception information includes conversation data indicating the contents of the conversation between the patient and the pharmacist, in addition to the patient's personal information, medical questionnaire information, and prescription information.

[0056] The reception process (step S11 in FIG. 5) according to the third embodiment will be described with reference to FIG. The processes from step S111 to step S113 are the same as those in FIG.

[0057] The reception unit 211 receives voice data as conversation data of a conversation between a patient and a pharmacist (S114). Specifically, the pharmacist converses with the patient when inputting prescription information. For example, the pharmacist converses with the patient to confirm the symptoms or disease name, physical condition, and the taking status of the previously prescribed medicine. The pharmacist also provides the patient with guidance on how to take the medicine and advice on improving symptoms. The reception unit 211 receives the voice data of the conversation between the patient and the pharmacist by a microphone connected to the pharmacy terminal 20 via the communication interface 24.

[0058] (Step S12: Extraction process) Extraction unit 212 of pharmacy terminal 20 extracts input information from the reception information received in step S11, and transmits the input information to pharmacist work support device 10, as in the first embodiment. At this time, the extraction unit 212 converts the voice data, which is conversation data, into text data. Then, the extraction unit 212 extracts at least a part of information from the text data and includes it in the input information. For example, the extraction unit 212 extracts hearing information, which is information heard from the patient. The hearing information includes symptoms or disease name, physical condition, and the taking status of the medicine prescribed last time. In addition, the extraction unit 212 extracts instruction information indicating the contents of the instruction given to the patient by the pharmacist. The instruction information includes how to take the medicine, advice for improving symptoms, and the like.

[0059] (Step S13: Input process) As in the first embodiment, the input unit 111 of the pharmacist work support device 10 inputs input information as a prompt to the learning model 112. At this time, the input unit 111 generates the prompt including information extracted from the conversation data.

[0060] As a specific example, input unit 111 generates a prompt as shown in Fig. 14. The prompt shown in Fig. 14 also includes hearing information and instruction information that are information extracted from conversation data. Note that in Fig. 14, #instruction and #output format are omitted.

[0061] ***Effects of the Third Embodiment*** As described above, the pharmacist work support system 100 according to the third embodiment generates a prompt by using conversation data. This makes it possible for the learning model 112 to generate more appropriate information to be written in the SOAP medication history.

[0062] ***Other configurations*** <Variation 5> In the third embodiment, the prompt is generated using the conversation data in addition to the patient's personal information, the medical questionnaire information, and the prescription information. However, the prompt may be generated without using at least a part of the patient's personal information, the medical questionnaire information, and the prescription information. For example, for the Subject of the SOAP medication history, there are cases where the information to be entered can be generated only from the conversation data. Therefore, the prompt may be generated using only the conversation data, without using the patient's personal information, the medical questionnaire information, and the prescription information.

[0063] <Variation 6> In the third embodiment, the learning model 112 generates information to be entered for each item of the SOAP medical history. Alternatively, the learning model 112 may generate the patient's chief complaint. The patient's chief complaint corresponds to the Subject of the SOAP medical history. Therefore, generating the patient's chief complaint is basically the same as generating information to be entered for the Subject of the SOAP medical history. However, by changing the wording in the prompt, the generated content may change. For example, a specified number (e.g., three) of candidates for the patient's chief complaint may be generated from the conversation data. In this case, in step S13 of FIG. 5, the input unit 111 may generate a prompt to instruct the generation of a specified number of candidates for the chief complaint, instead of the information to be entered for each item of the SOAP medical history. Specifically, the #instruction form portion may be rewritten to generate a specified number of candidates for the chief complaint. At this time, a priority indicating the likelihood of each candidate chief complaint may also be generated.

[0064] Furthermore, the input unit 111 may provide possible options for the chief complaint as a prompt, and have the learning model 112 select a designated number of options that are likely to be relevant.

[0065] <Variation 7> In the third embodiment, in step S12 in Fig. 5, the extraction unit 212 converts the voice data into text data and extracts at least a portion of information from the text data. The extraction unit 212 may extract at least a portion of information from the voice data without converting the voice data into text data. In this case, in step S13 in Fig. 5, the input unit 111 inputs the voice data to the learning model 112 as part of the prompt.

[0066] <Variation 8> In the above embodiment, functional components are distributed between pharmacist work support device 10 and pharmacy terminal 20. However, as long as pharmacist work support system 100 is provided with the functional components, either pharmacist work support device 10 or pharmacy terminal 20 may have the functional components.

[0067] For example, the pharmacy terminal 20 may include all of the functional components except for the learning model 112. In this configuration, the pharmacist work support device 10 can be regarded as the learning model 112 (generative AI). Also, for example, all of the functional components may be provided in pharmacist work support device 10. In this configuration, pharmacy terminal 20 can be regarded as a terminal that only serves as an interface with pharmacists.

[0068] Furthermore, pharmacist work support system 100 may be configured as a single computer. In other words, the functions of both pharmacist work support device 10 and pharmacy terminal 20 may be provided in a single computer.

[0069] Furthermore, the term "part" in the above description may be read as a "circuit," "step," "procedure," "processing," or "processing circuit."

[0070] Various aspects of the present disclosure are summarized below as appendices. (Appendix 1) An input unit that inputs conversation data indicating the content of a conversation between a patient who is prescribed a drug and a pharmacist who is assigned to the patient into a learning model; an output unit that outputs entry information generated by the learning model in response to the conversation data input by the input unit, the entry information being about at least a subject in a SOAP (Subject Object Assessment Plan) medication history; A pharmacist work support system equipped with: (Appendix 2) The input unit inputs, as the conversation data, voice data of a conversation between the patient and the pharmacist. A pharmacist work support system as described in Appendix 1. (Appendix 3) The input unit inputs, as the conversation data, text data obtained by converting voice data of the conversation between the patient and the pharmacist into text. A pharmacist work support system as described in Appendix 1. (Appendix 4) The conversation data includes hearing information, which is information heard from the patient, and instruction information, which indicates the content of the instruction given by the pharmacist to the patient. A pharmacist work support system as described in any one of appendix 1 to 3. (Appendix 5) The input unit further inputs patient personal information indicating attributes of the patient, medical questionnaire information indicating the patient's answers to a medical questionnaire, and prescription information indicating the contents of a prescription for the patient. A pharmacist work support system as described in any one of appendix 1 to 4. (Appendix 6) The computer inputs conversation data indicating the content of a conversation between a patient who receives a drug prescription and a pharmacist who serves the patient into a learning model; A pharmacist work support method in which a computer outputs entry information generated by the learning model in response to the conversation data, the entry information being about at least a subject in a SOAP (Subject Object Assessment Plan) medication history. (Appendix 7) An input process of inputting conversation data indicating the content of a conversation between a patient who is prescribed a drug and a pharmacist who is assigned to the patient into a learning model; an output process for outputting entry information generated by the learning model in response to the conversation data input by the input process, the entry information being about at least a subject in a SOAP (Subject Object Assessment Plan) medication history; A pharmacist work support program that enables a computer to function as a pharmacist work support system.

[0071] The above describes the embodiments and modifications of the present disclosure. Some of these embodiments and modifications may be combined and implemented. Also, one or some of them may be partially implemented. Note that the present disclosure is not limited to the above embodiments and modifications, and various modifications are possible as necessary. [Explanation of symbols]

[0072] 100 pharmacist work support system, 10 pharmacist work support device, 11 processor, 12 memory, 13 storage, 14 communication interface, 111 input unit, 112 learning model, 113 output unit, 114 recording unit, 131 patient data, 20 pharmacy terminal, 21 processor, 22 memory, 23 storage, 24 communication interface, 211 reception unit, 212 extraction unit, 213 display unit, 214 editing unit, 30 pharmacy.

Claims

1. an input unit that inputs into a learning model at least one of the following information: conversation data indicating the content of a conversation between a patient who is prescribed a drug and a pharmacist who is assigned to the patient, patient personal information indicating the attributes of the patient, questionnaire information indicating the patient's responses to a questionnaire, and prescription information indicating the content of a prescription for the patient; an output unit that outputs entry information generated by the learning model in response to the conversation data and at least any of the information input by the input unit, the entry information being about at least a subject and an object in a SOAP (Subject Object Assessment Plan) medication history; A pharmacist work support system equipped with:

2. The input unit inputs, as the conversation data, voice data of a conversation between the patient and the pharmacist. The pharmacist work support system according to claim 1.

3. The input unit inputs, as the conversation data, text data obtained by converting voice data of the conversation between the patient and the pharmacist into text. The pharmacist work support system according to claim 1.

4. The conversation data includes hearing information, which is information heard from the patient, and instruction information, which indicates the content of the instruction given by the pharmacist to the patient. The pharmacist work support system according to claim 1.

5. The input unit inputs all of the patient personal information, the medical questionnaire information, and the prescription information. The pharmacist work support system according to claim 1.

6. The computer inputs into a learning model at least one of the following information: patient personal information indicating attributes of the patient, questionnaire information indicating the patient's responses to a questionnaire, and prescription information indicating the contents of a prescription for the patient, in addition to conversation data indicating the contents of a conversation between a patient who is prescribed a drug and a pharmacist who is assigned to the patient; A pharmacist work support method in which a computer outputs entry information generated by the learning model in response to the conversation data and at least any of the information, the entry information being about at least a Subject and an Object in a SOAP (Subject Object Assessment Plan) medication history.

7. an input process for inputting into a learning model at least one of the following information: conversation data indicating the content of a conversation between a patient who is prescribed a drug and a pharmacist who is assigned to the patient, patient personal information indicating the attributes of the patient, medical questionnaire information indicating the patient's responses to a medical questionnaire, and prescription information indicating the content of a prescription for the patient; an output process for outputting entry information generated by the learning model in response to the conversation data and at least any of the information input by the input process, the entry information being about at least a subject and an object in a SOAP (Subject Object Assessment Plan) medical history; A pharmacist work support program that enables a computer to function as a pharmacist work support system.

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