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 reception section reads patient personal information, which is information indicating an attribute and the like of a patient from patient data, when there is information corresponding to a keyword in the patient data that is information about each patient, and receives an input of the patient personal information when there is no information corresponding to the keyword in the patient data. An input section 111 inputs, to a learning model 112, conversation data indicating the content of a conversation between the patient who receives a prescription of medication and a pharmacist corresponding to the patient. An output section 113 outputs entry information about at least Subject in a SOAP (Subject Object Assessment Plan) medication history, which is entry information generated by the learning model 112 in correspondence with the patient personal information and the conversation data input by the input section 111.SELECTED DRAWING: Figure 2

Description

Technical Field

[0006] , , , , ,

[0001] The present disclosure relates to a technology for supporting a pharmacist's work such as creating a medication history.

Background Art

[0002] A pharmacist records, as a medication history, the prescribed medications and other information such as what the patient has complained about for the patients who have been prescribed medications. At this time, the pharmacist considers the various information obtained and examines what should be recorded. The examination of the information to be recorded is likely to show a difference in quality due to differences in the experience or knowledge of the pharmacist. It is desired to be able to record high-quality information as a medication history regardless of the pharmacist in charge.

[0003] In addition, a pharmacist spends time inputting the information to be recorded as a medication history into a computer. By reducing the time for this input work, time can be allocated to dispensing and patient care. Therefore, it is desired to be able to reduce the burden of the input work.

[0004] Patent Document 1 describes performing medication guidance by referring to a drug database while using the information of a questionnaire conducted on a patient and a prescription, and storing the information of the performed medication guidance in a medication history database.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] Even by using the technology described in Patent Document 1, it is difficult to be able to record high-quality information as a medication history regardless of the pharmacist in charge. An object of the present disclosure is to support a pharmacist's work such as creating a medication history. [Means for solving the problem]

[0007] The pharmacist support system related to this disclosure is: If the patient data, which is information about each patient, contains information corresponding to the keyword, then patient personal information, which is information indicating the patient's attributes, is read from the patient data; if the patient data does not contain information corresponding to the keyword, then a reception unit accepts input of the patient personal information. An input unit that inputs conversation data showing the content of a conversation between a patient receiving a prescription for medication and the pharmacist assigned to that patient into a learning model, The input information generated by the learning model in correspondence with the patient's personal information and the conversation data input by the input unit, and the output unit outputs input information for at least the Subject in the SOAP (Subject Object Assessment Plan) medication history. It is equipped with. [Effects of the Invention]

[0008] This disclosure describes how a learning model outputs information about at least the subject in a SOAP-based patient record. This supports pharmacists in tasks such as creating patient records. [Brief explanation of the drawing]

[0009] [Figure 1] Configuration diagram of the pharmacist work support system 100 according to Embodiment 1. [Figure 2] Configuration diagram of the pharmacist work support device 10 according to Embodiment 1. [Figure 3] Configuration diagram of the pharmacy terminal 20 according to Embodiment 1. [Figure 4] An explanatory diagram of patient data 131 according to Embodiment 1. [Figure 5] A flowchart of the processing of the pharmacist work support system 100 according to Embodiment 1. [Figure 6] Diagram illustrating patient personal information according to Embodiment 1. [Figure 7] Explanatory drawing of the questionnaire information according to Embodiment 1. [Figure 8] Explanatory drawing of the prescription information according to Embodiment 1. [Figure 9] Flowchart of the reception process according to Embodiment 1. [Figure 10] Explanatory drawing of the prompt according to Embodiment 1. [Figure 11] Flowchart of the reception process according to Embodiment 2. [Figure 12] Explanatory drawing of the prompt according to Embodiment 2. [Figure 13] Flowchart of the reception process according to Embodiment 3. [Figure 14] Explanatory drawing of the prompt according to Embodiment 3.

MODE FOR CARRYING OUT THE INVENTION

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

[0011] Referring to FIG. 2, the configuration of the pharmacist work support device 10 according to Embodiment 1 will be described. The pharmacist work support device 10 is a computer. The pharmacist business support device 10 includes hardware such as a processor 11, a memory 12, a storage 13, and a communication interface 14. The processor 11 is connected to other hardware via signal lines and controls these other hardware.

[0012] As functional components, the pharmacist business support device 10 includes an input unit 111, a learning model 112, an output unit 113, and a recording unit 114. The functions of each functional component of the pharmacist business support device 10 are realized by software. The storage 13 stores programs that realize the functions of each functional component of the pharmacist business support device 10. These programs are read into the memory 12 by the processor 11 and executed by the processor 11. Thereby, the functions of each functional component of the pharmacist business support device 10 are realized.

[0013] The storage 13 stores patient data 131.

[0014] Referring to FIG. 3, the configuration of the pharmacy terminal 20 according to Embodiment 1 will be described. The pharmacy terminal 20 is a computer. The pharmacy terminal 20 includes hardware such as a processor 21, a memory 22, a storage 23, and a communication interface 24. The processor 21 is connected to other hardware via signal lines and controls these other hardware. When the pharmacy terminal 20 is a PC, it may be connected to a plurality of monitors as hardware.

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

[0016] Processors 11 and 21 are integrated circuits (ICs) that perform processing. IC stands for Integrated Circuit. Specific examples of processors 11 and 21 include CPUs, DSPs, and GPUs. CPU stands for Central Processing Unit. DSP stands for Digital Signal Processor. GPU stands for Graphics Processing Unit.

[0017] Memory 12 and 22 are memory devices that temporarily store data. Specific examples of memory 12 and 22 are SRAM and DRAM. SRAM stands for Static Random Access Memory. DRAM stands for Dynamic Random Access Memory.

[0018] Storage 13 and 23 are storage devices for storing data. A concrete example of storage 13 and 23 is an HDD. HDD stands for Hard Disk Drive. Alternatively, storage 13 and 23 may be portable recording media such as SD® memory cards, CompactFlash®, NAND flash, flexible disks, optical disks, compact disks, Blu-ray® discs, and DVDs. SD stands for Secure Digital. DVD stands for Digital Versatile Disk.

[0019] Communication interfaces 14 and 24 are interfaces for communicating with external devices. Specific examples of communication interfaces 14 and 24 include Ethernet®, USB, and HDMI® ports. USB stands for Universal Serial Bus. HDMI stands for High-Definition Multimedia Interface.

[0020] In Figure 2, only one processor 11 was shown. However, there may be multiple processors 11, and multiple processors 11 may work together to execute programs that implement each function. Similarly, in Figure 3, only one processor 21 was shown. However, there may be multiple processors 21, and multiple processors 21 may work together to execute programs that implement each function.

[0021] ***Explanation of operation*** Referring to Figures 4 to 10, the operation of the pharmacist work support system 100 according to Embodiment 1 will be explained. The operating procedure of the pharmacist support system 100 according to Embodiment 1 corresponds to the pharmacist support method according to Embodiment 1. Furthermore, the program that implements the operation of the pharmacist support system 100 according to Embodiment 1 corresponds to the pharmacist support program according to Embodiment 1.

[0022] Referring to Figure 4, the patient data 131 according to Embodiment 1 will be explained. Patient data 131 contains information about each patient. For each patient, patient data 131 includes personal information, prescribed medication information, contact information, basic confirmation information, medication guidance history, and SOAP medication history. Personal information includes the patient's personal information such as the insurer number, name, gender, date of birth, height, and weight. Prescription drug information is the history of medications prescribed to the patient. Contact information includes comments and previous guidance handover. Comments are information that should be noted regarding the patient in relation to dispensing or medication guidance. Previous guidance handover is information that was passed on from the pharmacist who provided the previous guidance. Basic confirmation information is information that needs to be confirmed regarding the patient in relation to dispensing or medication guidance. Medication guidance history is the history of medication guidance provided in the past. SOAP medication history is a medication history recorded in four parts: Subject, Object, Assessment, and Plan. Subject records subjective information of the patient. Object records objective information. Assessment records the pharmacist's analysis and opinion. Plan records the plan for addressing the issues.

[0023] Referring to Figure 5, the processing flow of the pharmacist work support system 100 according to Embodiment 1 will be explained. (Step S11: Reception Processing) The reception unit 211 of the pharmacy terminal 20 receives reception information from patients receiving prescriptions for medication. This reception information includes patient personal information, medical questionnaire information, and prescription information. Patient personal information refers to information that indicates the patient's attributes, etc. As shown in Figure 6, patient personal information includes the patient's name, telephone number and address, gender, age, height, and weight. Questionnaire information is information that shows the patient's answers to the questionnaire. As shown in Figure 7, questionnaire information includes the patient's answers to questions about the name of the illness, medication status, and the occurrence of side effects. Prescription information is information that shows the contents of a prescription for a patient. As shown in Figure 8, prescription information includes the name of the drug, the quantity, and the instructions for use for each prescribed medication. In addition, prescription information includes the name of the hospital that issued the prescription and the date of issue.

[0024] Referring to Figure 9, the reception process according to Embodiment 1 (step S11 in Figure 5) will be described. When a patient visits pharmacy 30, the pharmacist has the patient fill out their personal information. The reception desk 211 receives the patient's personal information by having the pharmacist input it into the pharmacy terminal 20 (step S111). Next, the pharmacist has the patient answer a medical questionnaire. The reception desk 211 receives the questionnaire information by having the pharmacist input the answers into the pharmacy terminal 20 (step S112). Next, the pharmacist receives a prescription from the patient. The reception desk 211 receives the prescription information by having the pharmacist input the prescription information into the pharmacy terminal 20 (step S113). Furthermore, at least a portion of the processing 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. The extraction unit 212 may also 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 question and answer pairs for each question item from the medical interview information in the reception information and includes them 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 drug name, quantity, and usage of the prescribed medication 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 as a prompt into the learning model 112. In this case, the input unit 111 adds an instruction sheet indicating assumptions and instructions, as well as constraints, to the prompt, in addition to the input information. Here, the input unit 111 includes in the instruction sheet the assumption that the user is a pharmacist and the instruction to create a SOAP medical history. The input unit 111 may also include an instruction in the instruction sheet to create at least the Subject, or at least the Subject and Object, of the SOAP medical history. The constraints of the input unit 111 include the writing style and amount of information to be included in the SOAP medical history, as well as the specification of the items to be specifically included.

[0027] As a specific example, the input unit 111 generates a prompt as shown in Figure 10. The prompt shown in Figure 10 is based on the patient personal information shown in Figure 6, the medical questionnaire information shown in Figure 7, and the prescription information shown in Figure 8. The instruction document includes the assumption that the pharmacist is an experienced professional, and instructs them to create a SOAP patient history based on the constraints, patient personal information, medical questionnaire information, and prescription information. The constraints specify that the SOAP medication history should be written using bullet points, with approximately five lines per section for 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 adherence status. #Patient personal information contains information extracted from patient personal information in step S12. #Medical questionnaire information contains information extracted from medical questionnaire information in step S12. #Prescription information contains information extracted from 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 SOAP medication history information based on the prompt entered in step S13. The SOAP medication history information may include information about the problem, in addition to the Subject, Object, Assessment, and Plan. The problem is the issue the patient is facing. The learning model 112 is a so-called generative AI. AI stands for Artificial Intelligence. The learning model 112 may be constructed 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 constructed by combining multiple algorithms, including these algorithms.

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

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

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

[0032] (Step S18: Sending process) The editing unit 214 of the pharmacy terminal 20 transmits the final SOAP patient record information, which has been 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 into the patient data 131.

[0034] ***Effects of Embodiment 1*** As described above, the pharmacist support system 100 according to Embodiment 1 inputs input information, including patient personal information, medical questionnaire information, and prescription information, as prompts into the learning model 112 to generate SOAP medication history information. This reduces the workload of pharmacists involved in creating SOAP medication history. Furthermore, by appropriately training the learning model 112, it becomes possible to record high-quality information as medication history regardless of the pharmacist in charge. Furthermore, patient personal information, medical questionnaire information, and prescription information are all information obtained during the dispensing process. Therefore, obtaining patient personal information, medical questionnaire information, and prescription information will not increase the workload of pharmacists.

[0035] ***Other configurations*** <Example 1> In Embodiment 1, the pharmacist support device 10 is equipped with a learning model 112. However, the learning model 112 may be located outside the pharmacist support device 10. In this case, in step S13 of Figure 5, the input unit 111 inputs a prompt to the learning model 112 via the transmission line. Then, in step S15 of Figure 5, the output unit 113 acquires SOAP medication history entry information via the transmission line.

[0036] <Modification 2> In step S13 of Figure 5, the input unit 111 may input example entries, which are options for the information to be entered for each item of the SOAP medication history, and have the learning model 112 select only a specified number (for example, 3) of those that are most likely to be applicable. For example, regarding the Subject, the pharmacist could narrow down the options, add the narrowed-down options to the prompt, and provide them to the learning model 112, which could then select a specified number of the options that are most likely to be relevant.

[0037] <Variation 3> In Embodiment 1, each functional component was implemented in software. However, in Modification 3, each functional component may be implemented in hardware. The differences between this Modification 3 and Embodiment 1 will be explained below.

[0038] When each functional component is implemented in hardware, the pharmacist work support device 10 includes an electronic circuit 15 instead of a processor 11, memory 12, and storage 13. The electronic circuit 15 is a dedicated circuit that implements the functions of each functional component, as well as the functions of the memory 12 and storage 13.

[0039] Electronic circuits 15 can include single circuits, complex circuits, programmed processors, parallel programmed processors, logic ICs, GAs, ASICs, and FPGAs. GA stands for Gate Array. ASIC stands for Application Specific Integrated Circuit. FPGA stands for Field-Programmable Gate Array. Each functional component may be implemented in a single electronic circuit 15, or each functional component may be implemented by distributing them across multiple electronic circuits 15.

[0040] <Modification 4> As a fourth variation, some of the functional components may be implemented in hardware, while others may be implemented in software.

[0041] The processor 11, memory 12, storage 13, and electronic circuit 15 are collectively referred to as the processing circuit. In other words, the function of each functional component is realized by the processing circuit.

[0042] Embodiment 2. Embodiment 2 differs from Embodiment 1 in that it generates prompts using previously recorded information about the patient. Embodiment 2 will explain this difference, while the same points will not be explained.

[0043] Patients may return to pharmacy 30. In this case, information about the patient from their previous visit is recorded. The pharmacist support system 100 then uses the recorded information about the patient to generate prompts.

[0044] ***Explanation of operation*** The operation of the pharmacist work support system 100 according to Embodiment 2 will be explained with reference to Figures 5, 11, and 12.

[0045] Referring to Figure 5, the processing of the pharmacist work support system 100 in this case will be explained. The processing from step S11 to step S13 differs from that of Embodiment 1. (Step S11: Reception Processing) The reception unit 211 of the pharmacy terminal 20 receives reception information from patients receiving prescriptions, similar to Embodiment 1. The reception information includes patient personal information, medical questionnaire information, and prescription information. In the case of returning patients, the patient personal information and medical questionnaire information include information on changes since their last visit. In addition, the prescription information includes not only the most recent prescription information, which is the prescription for the current visit, but also past prescription information, which is the prescription for the previous visit.

[0046] Referring to Figure 11, the reception process according to Embodiment 2 (step S11 in Figure 5) will be described. The process from step S111 to step S113 is the same as in Figure 9.

[0047] The reception unit 211 identifies whether the patient is a returning patient or not based on the patient's name, telephone number, and address (step S110). Specifically, the reception unit 211 searches the patient's 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 identifies the patient as a first-time visitor. On the other hand, if information corresponding to the keywords is found, the reception unit 211 identifies the patient as a returning visitor. The reception unit 211 proceeds to step S111 if the patient is visiting for the first time. On the other hand, the reception unit 211 proceeds to step S114 if the patient is visiting for the second time.

[0048] For returning patients, the reception unit 211 reads the patient's personal information and medical questionnaire information from the patient data 131 of the pharmacist support device 10. The pharmacist confirms with the patient any changes to the patient's personal information and medical questionnaire information. When the pharmacist enters 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). The reception unit 211 may also include the information before the changes when considering 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 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 combines the most recent prescription information and the past prescription information and sets it as prescription information (step S115).

[0049] (Step S12: Extraction process) The extraction unit 212 of the pharmacy terminal 20 extracts input information from the reception information received in step S11, similar to the first embodiment, and transmits the input information to the pharmacist work support device 10. However, the extraction unit 212 also includes information indicating changes in patient personal information and medical questionnaire information as input information. Furthermore, the extraction unit 212 extracts at least some information from both recent and past prescription information and includes it as input information. For example, the extraction unit 212 extracts the drug name, quantity, and usage instructions of the prescribed medication from both recent and past prescription information and includes them as input information.

[0050] (Step S13: Input Processing) The input unit 111 of the pharmacist work support device 10 inputs the input information as a prompt to the learning model 112, similar to the first embodiment. However, the input unit 111 includes information indicating changes in the input information for patient personal information and medical questionnaire information. In addition, the input unit 111 generates prompts that include not only the most recent prescription information but also past prescription information as prescription information.

[0051] As a specific example, the input unit 111 generates a prompt as shown in Figure 12. Note that the instruction sheet is omitted in Figure 12. The prompt shown in Figure 12 indicates that there have been changes to the patient's personal information and medical questionnaire information, and shows both the information before and after the change. In addition, the prompt shown in Figure 12 includes past prescription information in addition to the most recent prescription information.

[0052] ***Effects of Embodiment 2*** As described above, the pharmacist support system 100 according to Embodiment 2 generates prompts using previously recorded information about patients. This makes it possible for the learning model 112 to generate more appropriate SOAP medication history entry information.

[0053] Embodiment 3. Embodiment 3 differs from Embodiments 1 and 2 in that it generates prompts using conversation data that shows the content of the conversation between the patient and the pharmacist. Embodiment 3 explains this difference, and omits the explanation of the same points. Embodiment 3 describes a case in which a modification has been made to Embodiment 1. However, it is also possible to modify Embodiment 2.

[0054] ***Explanation of operation*** The operation of the pharmacist work support system 100 according to Embodiment 3 will be explained with reference to Figures 5, 13, and 14.

[0055] Referring to Figure 5, the processing of the pharmacist work support system 100 in this case will be explained. The processing from step S11 to step S13 differs from that of Embodiment 1. (Step S11: Reception Processing) The reception unit 211 of the pharmacy terminal 20 receives reception information from patients receiving prescriptions, similar to the first embodiment. The reception information includes patient personal information, medical questionnaire information, prescription information, and conversation data showing the content of the conversation between the patient and the pharmacist.

[0056] Referring to Figure 13, the reception process according to Embodiment 3 (step S11 in Figure 5) will be described. The process from step S111 to step S113 is the same as in Figure 9.

[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 entering prescription information. For example, the pharmacist converses with the patient to confirm symptoms or disease name, physical condition, and how the patient has taken previously prescribed medications. The pharmacist also provides the patient with instructions on how to take the medication and advice on how to improve symptoms. The reception unit 211 receives voice data of the conversation between the patient and the pharmacist via a microphone connected to the pharmacy terminal 20 through the communication interface 24.

[0058] (Step S12: Extraction process) The extraction unit 212 of the pharmacy terminal 20 extracts input information from the reception information received in step S11, similar to the first embodiment, and transmits the input information to the pharmacist work support device 10. In this process, the extraction unit 212 converts the audio data, which is conversation data, into text data. The extraction unit 212 then extracts at least some 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. Hearing information includes symptoms or disease names, physical condition, and how the patient has taken previously prescribed medications. The extraction unit 212 also extracts guidance information, which shows what the pharmacist instructed the patient on. Guidance information includes how to take medication and advice for symptom improvement.

[0059] (Step S13: Input Processing) The input unit 111 of the pharmacist work support device 10 inputs the input information as a prompt to the learning model 112, similar to the first embodiment. In this case, the input unit 111 generates the prompt including information extracted from conversation data.

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

[0061] ***Effects of Embodiment 3*** As described above, the pharmacist support system 100 according to Embodiment 3 generates prompts using conversational data. This makes it possible for the learning model 112 to generate more appropriate SOAP medication history entry information.

[0062] ***Other configurations*** <Modification 5> In Embodiment 3, prompts were generated using conversation data in addition to patient personal information, medical questionnaire information, and prescription information. However, prompts may be generated without using at least some of the patient personal information, medical questionnaire information, and prescription information. For example, for the Subject of SOAP medication history, it may be possible to generate the information to be entered using only conversation data. Therefore, prompts may be generated using only conversation data, without using patient personal information, medical questionnaire information, or prescription information.

[0063] <Variation 6> In Embodiment 3, the learning model 112 was made to generate the information to be entered for each item of the SOAP medical history. Alternatively, the learning model 112 may be made to 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 the information to be entered for the Subject of the SOAP medical history. However, the generated content may change by changing the wording in the prompt. For example, a specified number (e.g., 3) of candidate chief complaints for the patient may be generated from the conversation data. In this case, in step S13 of Figure 5, the input unit 111 should generate a prompt that instructs the learning model 112 to generate a specified number of candidate chief complaints instead of the information to be entered for each item of the SOAP medical history. Specifically, the #command section should be rewritten to generate a specified number of candidate chief complaints. Furthermore, at this stage, a priority ranking indicating the likelihood of each candidate chief complaint being the actual chief complaint may also be generated.

[0064] Alternatively, the input unit 111 may be configured to prompt the learning model 112 with a specified number of options that are likely to be the main complaint, and to select the ones that are most likely to be applicable.

[0065] <Example 7> In Embodiment 3, in step S12 of Figure 5, the extraction unit 212 converts the audio data into text data and then extracts at least some information from the text data. The extraction unit 212 may also extract at least some information from the audio data without converting it into text data. In this case, in step S13 of Figure 5, the input unit 111 inputs the audio data to the learning model 112 as part of a prompt.

[0066] <Differentiation Example 8> In the above embodiment, the pharmacist support device 10 and the pharmacy terminal 20 are equipped with functional components in a distributed manner. However, regarding the functional components of the pharmacist support system 100, it is sufficient for the pharmacist support system 100 to be equipped with them, and either the pharmacist support device 10 or the pharmacy terminal 20 may be equipped with the functional components.

[0067] For example, the pharmacy terminal 20 may have all 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 (generating AI). Furthermore, for example, the pharmacist support device 10 may include all functional components. In this configuration, the pharmacy terminal 20 can be considered solely as a terminal that handles the interface with the pharmacist.

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

[0069] Furthermore, the term "part" in the above explanation may be replaced with "circuit," "process," "procedure," "processing," or "processing circuit."

[0070] The various aspects of this disclosure are summarized below as an appendix. (Note 1) An input unit that inputs conversation data showing the content of a conversation between a patient receiving a prescription for medication and the pharmacist attending to the patient into a learning model, An output unit that outputs information generated by the learning model in response to the conversation data input by the input unit, and which outputs information about at least the subject in the SOAP (Subject Object Assessment Plan) medication history. A pharmacist work support system equipped with the following features. (Note 2) The input unit inputs audio data of the conversation between the patient and the pharmacist as conversation data. The pharmacist work support system described in Appendix 1. (Note 3) The input unit receives as conversation data text data obtained by converting audio data of a conversation between the patient and the pharmacist into text. The pharmacist work support system described in Appendix 1. (Note 4) The aforementioned conversation data includes hearing information, which is information obtained from the patient, and instructional information, which shows the content of the instructions given to the patient by the pharmacist. A pharmacist work support system described in any one of the items 1 to 3 in the appendix. (Note 5) The input unit further inputs patient personal information indicating the patient's attributes, questionnaire information indicating the patient's responses to the questionnaire, and prescription information indicating the contents of the prescription for the patient. A pharmacist work support system described in any one of the items 1 through 4 in the appendix. (Note 6) The computer inputs conversation data, which represents the content of the conversation between a patient receiving a prescription and the pharmacist attending to that patient, into a learning model. A pharmacist work support method in which a computer outputs information generated by the learning model in response to the conversation data, which includes information about at least the subject in a SOAP (Subject Object Assessment Plan) medication history. (Note 7) Input processing involves inputting conversation data, which represents the content of the conversation between a patient receiving a prescription for medication and the pharmacist attending to the patient, into a learning model. The input process generates information in the conversation data input by the input process, which is then generated by the learning model, and outputs information in the SOAP (Subject Object Assessment Plan) medication history, which includes information about the subject. A pharmacist work support program that enables a computer to function as a pharmacist work support system.

[0071] The embodiments and variations of this disclosure have been described above. Some of these embodiments and variations may be implemented in combination. Alternatively, some or all of them may be implemented in part. However, this disclosure is not limited to the embodiments and variations described above, and various modifications are possible as needed. [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. A reception unit receives patient data, which is information about each patient, containing keywords that can identify individual patients receiving prescriptions for medication. If there is information corresponding to the keywords, it identifies the patient as a returning patient and reads the patient's personal information from the patient data. If there is no information corresponding to the keywords in the patient data, it identifies the patient as a first-time visitor and accepts input of the patient's personal information. It also receives conversation data showing the content of conversations between patients receiving prescriptions and pharmacists. An input unit that generates a prompt including at least some of the patient personal information and the conversation data, which indicates a prompt indicating a generation instruction for the Subject in the SOAP (Subject Object Assessment Plan) medication history, and inputs the prompt into a learning model, An output unit that outputs information generated by the learning model in response to the prompt input by the input unit, and information about the Subject in the SOAP medical history. Equipped with Pharmacist work support system.

2. If the reception unit finds information corresponding to the keyword in the patient data, it reads the patient's personal information from the patient data and then accepts any changes to the patient's personal information. The pharmacist work support system according to claim 1.

3. The editorial department accepts corrections or additions to the aforementioned information displayed on the display unit. The pharmacist work support system according to claim 1, further comprising:

4. The aforementioned keywords are the patient's name, the patient's telephone number, and their address. The pharmacist work support system according to claim 1.

5. The reception desk further receives prescription information indicating the contents of the prescription for the patient, The input unit further inputs the prescription information, The prescription information includes, if the patient data contains information corresponding to the keyword, the most recent prescription information (previous prescription information) as well as past prescription information (prescription information from the previous visit). The pharmacist work support system according to claim 1.

6. The computer receives patient data, which is information about each patient, containing keywords that can identify an individual patient receiving a prescription for medication. If there is information corresponding to the keywords, it identifies the patient as a returning patient and reads the patient's personal information from the patient data. If there is no information corresponding to the keywords in the patient data, it identifies the patient as a first-time patient and accepts input of the patient's personal information. It also accepts conversation data showing the content of the conversation between the patient receiving the prescription and the pharmacist. The computer generates a prompt that includes at least some of the patient's personal information and the conversation data, which indicates a prompt indicating a generation instruction for a Subject in a SOAP (Subject Object Assessment Plan) medical history, and inputs the prompt into a learning model. A pharmacist work support method in which a computer outputs information generated by the learning model in response to the prompt, which includes information about at least the Subject in the SOAP medication history.

7. The system accepts patient data containing information about each patient, including keywords that can identify individual patients receiving prescriptions for medication. If information corresponding to the keywords is found, the system identifies the patient as a returning patient and reads the patient's personal information from the patient data. If no information corresponding to the keywords is found in the patient data, the system identifies the patient as a first-time visitor and accepts input of the patient's personal information. The system also accepts conversation data indicating the content of the conversation between the patient receiving the prescription and the pharmacist. An input process that generates a prompt including at least some of the patient personal information and the conversation data, which indicates a prompt indicating a generation instruction for a Subject in a SOAP (Subject Object Assessment Plan) medication history, and inputs the prompt into a learning model. An output process that outputs information generated by the learning model in response to the prompt input by the input process, wherein the output process outputs information for at least the Subject in the SOAP medical history. A pharmacist work support program that enables a computer to function as a pharmacist work support system.

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