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

Abstract

To support the work of a pharmacist, such as the preparation of a medication history.SOLUTION: A learning section 115 causes a learning model 112 to learn using learning data indicating examples of entries for at least Subject and Object in a SOAP (Subject Object Assessment Plan) medication history. An input section 111 inputs, to the learning model 112, patient personal information indicating an attribute of a patient who receives a prescription of medication, medical questionnaire information indicating a patient's response to a medical questionnaire, and prescription information indicating the content of a prescription for the patient. An output section 113 outputs entry information about at least Subject and Object in the SOAP medication history, which is entry information generated by the learning model 112 in correspondence with the patient personal information, the medical questionnaire information, and the prescription information input by the input section 111.SELECTED DRAWING: Figure 15

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 medication history of patients for whom they have prescribed medication, along with the medication and any complaints the patient may have. When doing so, pharmacists consider the various pieces of information they have obtained and decide what information to record. 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 entering information into a computer to record as medication history. By reducing the time spent on this input work, they can allocate more time to dispensing medications and caring for patients. Therefore, there is a need to reduce the burden of input work.

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

[0005] [Patent Document 1] Japanese 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 medication history regardless of the pharmacist in charge. The present disclosure aims to support pharmacists in their work, such as creating medication histories. [Means for solving the problem]

[0007] The pharmacist work support system according to the present disclosure includes: a learning unit that trains a learning model using learning data showing examples of entries for at least subjects and objects in a SOAP (Subject Object Assessment Plan) medication history; an input unit that inputs into the learning model patient personal information indicating the attributes of a patient who will receive a drug prescription, medical questionnaire information indicating the patient's responses to a medical questionnaire, and prescription information indicating the contents of a prescription for the patient; an output unit that outputs entry information generated by the learning model in response to the patient personal information, the medical questionnaire information, and the prescription information input by the input unit, the entry information being at least about a subject and an object in the SOAP medication history; Equipped with. [Effects 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, thereby supporting the pharmacist's work of creating a medication history. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a configuration diagram of a pharmacist work support system 100 according to a first embodiment. [Figure 2] 1 is a configuration diagram of a pharmacist work support device 10 according to a first embodiment. [Figure 3] FIG. 2 is a configuration diagram of a pharmacy terminal 20 according to the first embodiment. [Figure 4] FIG. 3 is an explanatory diagram of patient data 131 according to the first embodiment. [Figure 5] 3 is a flowchart of the processing 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. 3 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] 10 is a flowchart of a reception process according to the first embodiment. [Figure 10] FIG. 3 is an explanatory diagram of a prompt according to the first embodiment. [Figure 11] 10 is a flowchart of a reception process according to the second embodiment. [Figure 12] FIG. 10 is an explanatory diagram of a prompt according to the second embodiment. [Figure 13] 11 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. [Figure 15] FIG. 10 is a configuration diagram of a pharmacist work support device 10 according to a fourth embodiment. [Figure 16] FIG. 10 is an explanatory diagram of learning data A according to the fourth embodiment. [Figure 17] FIG. 10 is an explanatory diagram of learning data A according to the fourth embodiment. [Figure 18] FIG. 10 is an explanatory diagram of learning data B according to the fourth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[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 installed in a pharmacy 30 and operated by a pharmacist. PC stands 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 installed in combination.

[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 hardware components including 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 the 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 functional component 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. As a result, 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. If pharmacy terminal 20 is a PC, it may be connected to multiple monitors as hardware components.

[0015] Pharmacy terminal 20 includes, as functional components, reception unit 211, extraction unit 212, display unit 213, and editing unit 214. The functions of each functional component of pharmacy terminal 20 are realized by software. Storage 23 stores a program that realizes the function of each functional component of pharmacy terminal 20. This program is loaded into memory 22 by processor 21 and executed by processor 21. In this way, the function of each functional component of pharmacy terminal 20 is 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 stands for Static Random Access Memory. DRAM stands for Dynamic Random Access Memory.

[0018] The storages 13 and 23 are storage devices that store data. Specific examples of the storages 13 and 23 are HDDs. HDD stands for Hard Disk Drive. The storages 13 and 23 may also be portable recording media such as SD (registered trademark) memory cards, CompactFlash (registered trademark), NAND flash, flexible disks, optical disks, compact disks, Blu-ray (registered trademark) disks, and DVDs. SD stands for Secure Digital. DVD stands 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 Ethernet (registered trademark), USB, and HDMI (registered trademark) ports. USB is an abbreviation for Universal Serial Bus. HDMI is an abbreviation for High-Definition Multimedia Interface.

[0020] 2 shows only one processor 11. However, there may be multiple processors 11, and the multiple processors 11 may cooperate to execute programs that realize each function. Similarly, in FIG. 3, there is only one processor 21 shown. However, there may be multiple processors 21, and the multiple processors 21 may cooperate to execute programs that realize 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. Furthermore, 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] The patient data 131 according to the first embodiment will be described with reference to FIG. 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. Personal information is information about the patient, such as the insurer number, name, sex, date of birth, height, and weight. Prescription drug information is historical information about drugs prescribed to the patient. Contact information includes comments and previous instruction handovers. Comments are information that should be noted about the patient regarding dispensing or medication instructions, etc. Previous instruction handovers are items handed over from the pharmacist who provided the previous instruction. Basic confirmation information is information about items that need to be confirmed about the patient regarding dispensing or medication instructions, etc. Medication instruction history is historical information about medication instructions that have been given in the past. SOAP medication history is a medication history recorded divided into Subject, Object, Assessment, and Plan. Subject records subjective information about the patient. Object records objective information. Assessment records the pharmacist's analysis and opinion. Plan records the plan for the issue.

[0023] The process flow of the pharmacist work support system 100 according to the first embodiment will be described with reference to FIG. (Step S11: Reception process) Reception unit 211 of pharmacy terminal 20 receives reception information from a patient who is to receive a prescription for medicines. The reception information includes patient personal information, medical questionnaire information, and prescription information. The patient personal information is information that indicates the patient's attributes, etc. As shown in Fig. 6, the patient personal information includes information such as the patient's name, telephone number, address, sex, age, height, and weight. The medical questionnaire information is information indicating the patient's answers to the medical questionnaire. As shown in Fig. 7, the medical questionnaire information includes the patient's answers to questions about the name of the disease, medication status, and side effect occurrence status. Prescription information is information that indicates the contents of a prescription for a patient. As shown in Figure 8, prescription information includes the name, dosage, and usage of each prescribed drug. 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 personal patient information. The pharmacist inputs the filled-out patient personal information into the pharmacy terminal 20, and the reception unit 211 accepts the patient 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 some 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 some information from the medical interview information in the reception information and includes it in the input information. For example, the extraction unit 212 extracts pairs of questions and answers 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 some 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, quantity, 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 processing) 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, in addition to the input information, an order indicating assumptions and instructions, and constraints to the prompt. 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 constraints the way the SOAP medication history is written, the amount of information to be written, and the specification of 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 #instruction states that the pharmacist is an experienced pharmacist and that the instructions are to create a SOAP medication history based on the #constraints, #patient personal information, #questionnaire information, and #prescription information. #The constraints specify that the SOAP medication history should be written in bullet points, and that the length of the entries should be approximately five lines for each of Subject, Object, Assessment, and Plan. It also specifies that specific data such as numerical values ​​should be included. Furthermore, specific items to be included include medication status, etc. #Patient personal information contains information extracted from the patient personal information in step S12. #Questionnaire information contains information extracted from the questionnaire information in step S12. #Prescription information contains information extracted from the prescription information in step S12. #The output format is specified as 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 stands for artificial intelligence. Specific examples of the learning model 112 may be configured using algorithms such as BERT and GPT. BERT stands for Bidirectional Encoder Representations from Transformers. GPT stands for Generative Pretrained Transformer. The learning model 112 may be configured by combining multiple algorithms including these algorithms.

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

[0030] (Step S16: Display processing) Display unit 213 of pharmacy terminal 20 acquires the SOAP medication history entry information sent 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 that he or she has 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] ***Effects of the First Embodiment*** As described above, the pharmacist work support system 100 according to the first embodiment inputs input information including patient personal information, medical questionnaire information, and prescription information into the learning model 112 as a prompt, and generates information to be entered into the SOAP medication history. This reduces the workload of the pharmacist involved in creating the SOAP medication history. Furthermore, by performing appropriate learning on the learning model 112, it becomes possible to record high-quality information as a medication history regardless of the pharmacist in charge. The patient's personal information, medical questionnaire information, and prescription information are all information acquired during dispensing work. Therefore, acquiring the patient's personal information, medical questionnaire information, and prescription information does not increase the pharmacist's workload.

[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 path. 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 path.

[0036] <Variation 2> In step S13 of Figure 5, the input unit 111 inputs example entries as options for information to be entered for each item of the SOAP medication history, and the learning model 112 may select a specified number (e.g., three) of those that are most likely to be relevant. 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 relevant.

[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 differences between the first embodiment and the third modification.

[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 stands for Gate Array. ASIC stands for Application Specific Integrated Circuit. FPGA stands for Field-Programmable Gate Array. Each functional component may be realized by one electronic circuit 15, or each functional component may be realized by distributing it among a plurality of 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, memory 12, storage 13, and 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 it generates prompts using previously recorded information about the patient. In the second embodiment, this difference will be explained, and explanations of the same points will be omitted.

[0043] There are 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 the pharmacist work support system 100 according to the second embodiment will be described with reference to FIGS.

[0045] The processing of the pharmacist work support system 100 in this case will be described with reference to Fig. 5. The processing 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 medication. The reception information includes patient personal information, medical questionnaire information, and prescription information. In the case of a patient who is visiting the pharmacy again, the patient personal information and medical questionnaire information include information on changes since the patient's previous visit. Furthermore, the prescription information includes not only the most recent prescription information, which is information on the prescription at the current visit, but also past prescription information, which is information on the prescription at the previous visit.

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

[0047] The reception unit 211 determines whether the patient is a returning patient based on the patient's 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's name, telephone number, and address as keywords. If no information corresponding to the keywords is found, the reception unit 211 determines that the patient is a first-time patient. On the other hand, if information corresponding to the keywords is found, the reception unit 211 determines that the patient is a returning patient. If the patient is visiting the store for the first time, the reception unit 211 advances the process to step S111. On the other hand, if the patient is visiting the store for the second time, 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 in the pharmacist work support device 10. The pharmacist confirms with the patient any 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 identified changes (step S114). Note that the reception unit 211 may also include information before the changes. Next, the pharmacist receives the 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. The reception unit 211 then sets the most recent prescription information and the past prescription information together as the prescription information (step S115).

[0049] (Step S12: Extraction process) As in the first embodiment, 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. However, the extraction unit 212 also includes information indicating changes in the patient personal information and medical questionnaire information in the input information. Furthermore, with regard to 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 processing) 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. Furthermore, the input unit 111 generates a prompt that includes past prescription information in addition to 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 have been changes to the patient personal information and medical questionnaire information, and shows the information before and after the change. Furthermore, 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 using previously recorded information about a patient. This enables the learning model 112 to generate more appropriate information to be entered in the SOAP medication history.

[0053] Embodiment 3 The third embodiment differs from the first and second embodiments in that a prompt is generated using conversation data indicating the content of a conversation between a patient and a pharmacist. In the third embodiment, this difference will be explained, and explanations 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 the pharmacist work support system 100 according to the third embodiment will be described with reference to FIGS.

[0055] The processing of the pharmacist work support system 100 in this case will be described with reference to Fig. 5. The processing 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 prescribed a drug, as in embodiment 1. The reception information includes the patient's personal information, medical questionnaire information, prescription information, and conversation data indicating the content of the conversation between the patient and the pharmacist.

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

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

[0058] (Step S12: Extraction process) As in the first embodiment, 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. 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 portion 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 status of taking the previously prescribed medication. The extraction unit 212 also extracts instruction information indicating the content of the instruction given to the patient by the pharmacist. The instruction information includes how to take the medication, advice for improving symptoms, and the like.

[0059] (Step S13: Input processing) 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, the input unit 111 generates a prompt as shown in Fig. 14. The prompt shown in Fig. 14 also includes hearing information and instruction information, which are information extracted from conversation data. Note that in Fig. 14, the #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 prompts by using conversation data, which enables 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 conversation data in addition to the patient's personal information, medical questionnaire information, and prescription information. However, the prompt may be generated without using at least some of the patient's personal information, medical questionnaire information, and prescription information. For example, for the Subject of the SOAP medication history, it may be possible to generate information to be entered using only the conversation data. Therefore, the prompt may be generated using only the conversation data, without using the patient's personal information, medical questionnaire information, and prescription information.

[0063] <Variation 6> In the third embodiment, the learning model 112 generates information to be entered for each item in the SOAP medication 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 medication history. Therefore, generating the patient's chief complaint is basically the same as generating information to be entered for the Subject of the SOAP medication history. However, changing the wording in the prompt may change the content to be generated. 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 that instructs the system to generate a specified number of candidates for the chief complaint, rather than information to be entered for each item in the SOAP medication history. Specifically, the #instruction part 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] Alternatively, the input unit 111 may provide possible options for the chief complaint as a prompt, and have the learning model 112 select a specified number of options that are likely to be relevant.

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

[0066] Embodiment 4 The fourth embodiment differs from the first to third embodiments in that the learning model 112 is trained using training data. In the fourth embodiment, this difference will be explained, and explanation of the same points will be omitted. In the fourth embodiment, a case where a modification is made to the first embodiment will be described. However, modifications can also be made to the second and third embodiments.

[0067] ***Configuration Description*** The configuration of a pharmacist work support device 10 according to the fourth embodiment will be described with reference to FIG. 2 in that the pharmacist work support device 10 includes a learning unit 115 as a functional component. The function of the learning unit 115 is realized by software or hardware, similar to the other functional components.

[0068] ***Explanation of Operation*** The operation of the pharmacist work support system 100 according to the fourth embodiment will be described with reference to FIGS.

[0069] 5, a learning process is executed by the learning unit 115. In the learning process, the learning unit 115 trains the learning model 112 using learning data showing entry examples for at least Subject and Object in the SOAP medication history. Specifically, the learning unit 115 trains the learning model 112 using at least one of the following learning data A and learning data B.

[0070] The learning data A according to the fourth embodiment will be described with reference to FIGS. The learning unit 115 uses, for each drug, example data showing an explanation of the problem and the content to be taught, and an example of how to fill in each item in the SOAP medication history, as learning data A. The learning data A is configured such that multiple problems are set for each drug, and when the drug and the problem are identified, an example of how to fill in each item in the SOAP medication history is identified. Depending on the drug, there may be content that is likely to be entered for at least some items in the SOAP medication history. Therefore, by training the learning model 112 using the training data A, the learning model 112 can generate appropriate information to be entered. Here, the problem is the issue that the patient has, and the explanation is the content that should be taught in response to the problem. Note that, depending on the drug, there may be no example of filling out some items. Also, Figures 16 and 17 show examples of filling out the Plan, divided into EP and OP. EP stands for Educational Plan. The information and instructions given to the patient are entered in the EP. OP stands for Observational Plan. The next step for the pharmacist in charge of the patient is entered in the OP. Here, in addition to EP and OP, the Plan also includes CP. CP stands for Care Plan. The CP describes inquiries about medication and changes to dispensing methods. However, since there is no set CP for each drug, CP is omitted here.

[0071] Learning data B according to the fourth embodiment will be described with reference to FIG. The learning unit 115 uses medication history data indicating medications prescribed in the past and the entry content for each item in the SOAP medication history at that time as learning data B. In other words, learning data B is the entry content for each item in the SOAP medication history that a pharmacist previously entered according to the prescribed medication. Therefore, by having the learning model 112 learn using learning data B, the learning model 112 can generate appropriate entry information. In Figure 18, the lines #3, #4, and #5 show problems that the pharmacist inferred from the patient's chief complaint and interview information. For example, the display of #4 shows an example of a problem that the pharmacist inferred for drug C: lifestyle precautions. In addition, the learning data B not only shows the entry content of each item in the SOAP medication history for each drug, but also shows the entry content of the SOAP medication history object for each prescription change. Therefore, as described in the second embodiment, when the latest prescription information and past prescription information are input and prescription changes can be identified, more appropriate entry information can be generated by having the learning model 112 learn using the learning data B.

[0072] ***Effects of the Fourth Embodiment*** As described above, the pharmacist work support system 100 according to the fourth embodiment allows the learning model 112 to learn in advance, thereby making it possible to generate more appropriate entry information.

[0073] ***Other Configurations*** <Variation 8> In the above embodiment, functional components are distributed between pharmacist work support device 10 and pharmacy terminal 20. However, the functional components of pharmacist work support system 100 only need to be provided by pharmacist work support system 100, and either pharmacist work support device 10 or pharmacy terminal 20 may have the functional components.

[0074] 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 considered 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 simply serves as an interface with pharmacists.

[0075] 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.

[0076] Furthermore, the term "unit" in the above description may be read as a "circuit," "step," "procedure," "process," or "processing circuit."

[0077] Various aspects of the present disclosure are summarized below as appendices. (Appendix 1) a learning unit that trains a learning model using learning data showing examples of entries for at least subjects and objects in a SOAP (Subject Object Assessment Plan) medication history; an input unit that inputs into the learning model patient personal information indicating the attributes of a patient who will receive a drug prescription, medical questionnaire information indicating the patient's responses to a medical questionnaire, and prescription information indicating the contents of a prescription for the patient; an output unit that outputs entry information generated by the learning model in response to the patient personal information, the medical questionnaire information, and the prescription information input by the input unit, the entry information being at least about a subject and an object in the SOAP medication history; A pharmacist work support system equipped with the following. (Appendix 2) The learning unit uses, as the learning data, entry example data showing entry examples for at least the subject and the object in the SOAP medication history for each drug. A pharmacist work support system as described in Appendix 1. (Appendix 3) The example data indicates problems that patients have for each of the drugs. A pharmacist work support system as described in Appendix 2. (Appendix 4) The learning unit uses medication history data indicating prescribed medications and entry contents for at least Subject and Object in the SOAP medication history as the learning data. A pharmacist work support system described in any one of appendices 1 to 3. (Appendix 5) The medication history data indicates the contents of the instruction given to the patient by the pharmacist. A pharmacist work support system as described in Appendix 4. (Appendix 6) The input unit inputs, as the prescription information, past prescription information indicating the contents of a past prescription in addition to most recent prescription information indicating the contents of a most recent prescription. A pharmacist work support system described in any one of appendices 1 to 5. (Appendix 7) The pharmacist work support system according to any one of appendices 1 to 6, wherein the input unit further inputs hearing information, which is information obtained from the patient. (Appendix 8) The input unit further inputs instruction information indicating the content of the instruction given to the patient by the pharmacist. A pharmacist work support system described in any one of appendices 1 to 7. (Appendix 9) The pharmacist work support system further comprises: an extraction unit that extracts the hearing information from conversation data indicating the content of a conversation between the patient and a pharmacist who is attending to the patient; Equipped with The input unit inputs the hearing information extracted by the extraction unit. A pharmacist work support system as described in Appendix 7. (Appendix 10) The pharmacist work support system further comprises: an extraction unit that extracts the guidance information from conversation data indicating the content of a conversation between the patient and a pharmacist who is attending to the patient; Equipped with The input unit inputs the guidance information extracted by the extraction unit. A pharmacist work support system as described in Appendix 8. (Appendix 11) The input unit inputs a plurality of examples of entries for the subject. A pharmacist work support system described in any one of appendices 1 to 10. (Appendix 12) The learning model selects at least one example from the plurality of example entries, and outputs the entry information about the subject. A pharmacist work support system as described in Appendix 11. (Appendix 13) The output unit outputs a problem, which is a challenge that the patient has, generated by the learning model. A pharmacist work support system described in any one of appendices 1 to 12. (Appendix 14) The learning unit uses, as the learning data, example data showing examples of how to fill in the Subject, Object, Assessment, Plan, and Problem that the patient has in the SOAP medication history for each medication; The output unit outputs entry information generated by the learning model, which is entry information about a subject, an object, an assessment, a plan, and a problem in a SOAP medication history. A pharmacist work support system as described in Appendix 1. (Appendix 15) The computer trains a learning model using learning data showing examples of entries for at least the subject and the object in a SOAP (Subject Object Assessment Plan) medication history; The computer inputs into the learning model patient personal information indicating the attributes of a patient who will receive a drug prescription, medical questionnaire information indicating the patient's responses to a medical questionnaire, and prescription information indicating the contents of a prescription for the patient; A pharmacist work support method in which a computer outputs entry information generated by the learning model in correspondence with the patient's personal information, the questionnaire information, and the prescription information, and which outputs entry information for at least the subject and object in the SOAP medication history. (Appendix 16) A learning process for training a learning model using learning data showing examples of entries for at least subjects and objects in a SOAP (Subject Object Assessment Plan) medication history; an input process for inputting into the learning model patient personal information indicating the attributes of a patient who will receive a prescription for a drug, medical questionnaire information indicating the patient's responses to a medical questionnaire, and prescription information indicating the contents of a prescription for the patient; A pharmacist work support program that causes a computer to function as a pharmacist work support system that performs an output process that outputs the entry information for at least the subject and object in the SOAP medication history, which is the entry information generated by the learning model in response to the patient personal information, the questionnaire information, and the prescription information input by the input process.

[0078] The embodiments and modifications of the present disclosure have been described above. Some of these embodiments and modifications may be combined and implemented. Also, one or more of them may be implemented partially. Note that the present disclosure is not limited to the above embodiments and modifications, and various modifications are possible as needed. [Explanation of symbols]

[0079] 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, 115 Learning 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. a learning unit that trains a learning model using learning data showing examples of entries for at least the subject and the object in a SOAP (Subject Object Assessment Plan) medication history; an input unit that inputs into the learning model patient personal information indicating the attributes of a patient who will receive a drug prescription, medical questionnaire information indicating the patient's responses to a medical questionnaire, and prescription information indicating the contents of a prescription for the patient; an output unit that outputs entry information generated by the learning model in response to the patient personal information, the medical questionnaire information, and the prescription information input by the input unit, the entry information being about at least a subject and an object in a SOAP medication history; A pharmacist work support system equipped with the following.

2. The learning unit uses, as the learning data, entry example data showing entry examples for at least the subject and the object in the SOAP medication history for each drug. The pharmacist work support system according to claim 1.

3. The example data indicates problems that patients have for each of the drugs. The pharmacist work support system according to claim 2.

4. The learning unit uses medication history data indicating the prescribed medication and the entry contents for at least the subject and object in the SOAP medication history as the learning data. The pharmacist work support system according to claim 1.

5. The medication history data indicates the contents of the instruction given to the patient by the pharmacist. The pharmacist work support system according to claim 4.

6. The input unit inputs, as the prescription information, past prescription information indicating the contents of a past prescription in addition to most recent prescription information indicating the contents of a most recent prescription. The pharmacist work support system according to claim 1.

7. The pharmacist work support system according to claim 1 , wherein the input unit further inputs hearing information obtained from the patient.

8. The input unit further inputs instruction information indicating the content of the instruction given to the patient by the pharmacist. The pharmacist work support system according to claim 1.

9. The pharmacist work support system further comprises: an extraction unit that extracts the hearing information from conversation data indicating the content of a conversation between the patient and a pharmacist who is attending to the patient; Equipped with The input unit inputs the hearing information extracted by the extraction unit. The pharmacist work support system according to claim 7.

10. The pharmacist work support system further comprises: an extraction unit that extracts the guidance information from conversation data indicating the content of a conversation between the patient and a pharmacist who is attending to the patient; Equipped with The input unit inputs the guidance information extracted by the extraction unit. The pharmacist work support system according to claim 8.

11. The input unit inputs a plurality of examples of entries for the subject. The pharmacist work support system according to claim 1.

12. The learning model selects at least one example from the plurality of example entries to output the entry information for the subject. The pharmacist work support system according to claim 11.

13. The output unit outputs a problem, which is a challenge that the patient has, generated by the learning model. The pharmacist work support system according to claim 1.

14. The learning unit uses, as the learning data, example data showing examples of how to fill out the Subject, Object, Assessment, Plan, and problems that the patient has in the SOAP medication history for each drug, The output unit outputs entry information generated by the learning model, which is entry information about a subject, an object, an assessment, a plan, and a problem in a SOAP medication history. The pharmacist work support system according to claim 1.

15. The computer trains a learning model using learning data showing examples of entries for at least the subject and the object in a SOAP (Subject Object Assessment Plan) medication history; The computer inputs into the learning model patient personal information indicating the attributes of a patient who will receive a drug prescription, medical questionnaire information indicating the patient's responses to a medical questionnaire, and prescription information indicating the contents of a prescription for the patient; A pharmacist work support method in which a computer outputs entry information generated by the learning model in correspondence with the patient's personal information, the questionnaire information, and the prescription information, and which outputs entry information for at least the subject and object in the SOAP medication history.

16. a learning process for training a learning model using learning data showing examples of entries for at least the subject and the object in a SOAP (Subject Object Assessment Plan) medication history; an input process for inputting into the learning model patient personal information indicating the attributes of a patient who will receive a prescription for a drug, medical questionnaire information indicating the patient's responses to a medical questionnaire, and prescription information indicating the contents of a prescription for the patient; A pharmacist work support program that causes a computer to function as a pharmacist work support system that performs output processing to output the entry information for at least the Subject and Object in the SOAP medication history, which is entry information generated by the learning model in response to the patient personal information, the questionnaire information, and the prescription information input by the input processing.

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