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

Abstract

This will enable high-quality medication instructions to be provided regardless of the pharmacist in charge. [Solution] An input unit 111 inputs patient personal information indicating the attributes of a patient who will receive a prescription for a drug, prescription information indicating the contents of the prescription for the patient, and at least information on precautions and side effects included in the information contained in the package insert of the drug to a learning model 112. An output unit 113 outputs advice information for providing medication instructions to a patient, generated by the learning model 112 in correspondence with the patient personal information, prescription information, and information contained in the package insert.

Description

[Technical Field]

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

[0002] Pharmacists not only perform the physical task of dispensing medicines according to prescriptions, but also the interpersonal task of providing medication instructions to patients. Differences in the experience and knowledge of pharmacists can easily result in differences in the quality of medication instructions. It is desirable to be able to provide high-quality medication instructions regardless of the pharmacist in charge.

[0003] Patent Document 1 describes extracting and displaying a list of medication instruction items according to the type of medication and the number of times the medication has been provided, thereby enabling pharmacists to provide medication instruction without omission. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2014-059675 Summary of the Invention [Problem to be solved by the invention]

[0005] The technology described in Patent Document 1 only indicates guidance items according to the type of medication and the number of times the medication has been provided, and there are cases where appropriate medication guidance items are not indicated. The present disclosure aims to enable high-quality medication instructions to be provided regardless of the pharmacist in charge. [Means for solving the problem]

[0006] The pharmacist work support system according to the present disclosure includes: an input unit that inputs into a learning model patient personal information indicating the attributes of a patient who will receive a prescription for a drug, prescription information indicating the contents of the prescription for the patient, and at least information on precautions and side effects included in the package insert of the drug; an output unit that outputs advice information generated by the learning model in response to the patient personal information, the prescription information, and the information included in the package insert input by the input unit when providing medication guidance to the patient; Equipped with. [Effects of the Invention]

[0007] In this disclosure, the system inputs patient personal information, prescription information, and information contained in the package insert, and outputs advice information generated by a learning model. By referencing the advice information, it becomes possible to understand what medication instructions should be given to patients, enabling high-quality medication instructions to be given regardless of the pharmacist in charge. [Brief explanation of the drawings]

[0008] [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]10 is a flowchart of a reception process according to the second 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 first to eighth embodiments. [Figure 13] FIG. 10 is an explanatory diagram of a prompt according to the first to eighth embodiments. [Figure 14] FIG. 10 is an explanatory diagram of a prompt according to the first to eighth embodiments. [Figure 15] FIG. 10 is an explanatory diagram of a prompt according to the first to eighth embodiments. [Figure 16] FIG. 10 is an explanatory diagram of a prompt according to the first to eighth embodiments. [Figure 17] FIG. 10 is an explanatory diagram of a prompt according to the first to eighth embodiments. [Figure 18] FIG. 10 is an explanatory diagram of a prompt according to the first to eighth embodiments. [Figure 19] FIG. 10 is an explanatory diagram of a prompt according to the first to eighth embodiments. [Figure 20] FIG. 10 is an explanatory diagram of a prompt according to the first to eighth embodiments. [Figure 21] FIG. 10 is an explanatory diagram of a prompt according to the first to eighth embodiments. [Figure 22] FIG. 10 is an explanatory diagram of a prompt according to the first to eighth embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] 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.

[0011] 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.

[0012] The storage 13 stores patient data 131.

[0013] 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.

[0014] Pharmacy terminal 20 includes, as functional components, reception unit 211, extraction unit 212, and display unit 213. 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.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 2, 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.

[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 a portion of information from the patient's personal information in the reception information and includes it in the input information. For example, the extraction unit 212 extracts information such as gender, age, height, and weight from the patient's personal information in the reception information and includes it in the input information. The extraction unit 212 also extracts at least a portion of information from the medical questionnaire 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 questionnaire information in the reception information and includes it in the input information. The extraction unit 212 also extracts at least a portion of information from the prescription information in the reception information and includes it in the input information. For example, the extraction unit 212 extracts the name, 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 and information contained in the package insert of the drug included in the input information as prompts to the learning model 112. The package insert of the drug is the package insert of a medical drug. The package insert is stored in advance in the storage 13. Here, the input unit 111 inputs at least information on precautions and side effects as information contained in the package insert to the learning model 112. The input unit 111 may further input information on efficacy and effect as information contained in the package insert to the learning model 112. At this time, the input unit 111 adds to the prompt, in addition to the input information and the information contained in the attached document, an order indicating assumptions and instructions, and constraints. Here, the input unit 111 writes, in the order, the assumption that the person is a pharmacist and an instruction to generate advice information for providing medication instruction. The input unit 111 writes, in the constraints, how to write the advice information, the amount of information to be written, etc. The advice information for providing medication instruction is information indicating what kind of medication instruction should be given to the patient. The input unit 111 may describe, as the instruction content, advice information for providing medication instruction from information included in the package insert. This allows advice information for providing medication instruction to be extracted from information included in the package insert, rather than extracted from information on the Internet.

[0027] (Step S14: Generation process) The learning model 112 of the pharmacist work support device 10 generates advice information for providing medication instruction based on the prompt input in step S13. The learning model 112 is a so-called generative AI. AI stands for artificial intelligence. The learning model 112 may be configured using algorithms such as BERT and GPT, for example. BERT is a bidirectional It is an abbreviation for Encoder Representations from Transformers. GPT is an abbreviation for Generative Pretrained Transformer. The learning model 112 may be configured by combining multiple algorithms including these algorithms.

[0028] (Step S15: Output process) The output unit 113 of the pharmacist work support device 10 acquires the advice information for providing medication instruction generated in step S14. Then, the output unit 113 transmits the acquired advice information for providing medication instruction to the pharmacy terminal 20.

[0029] (Step S16: Display processing) Display unit 213 of pharmacy terminal 20 acquires the advice information for providing medication instruction transmitted in step S15. Display unit 213 displays the acquired advice information for providing medication instruction on a display device connected to pharmacy terminal 20 via communication interface 24.

[0030] ***Effects of the First Embodiment*** As described above, the pharmacist work support system 100 according to the first embodiment receives input information and information contained in the attached document as input, and outputs advice information generated by the learning model 112. By referring to the advice information, it becomes possible to know what kind of medication instruction should be given to the patient, and high-quality medication instruction can be given regardless of the pharmacist in charge. Furthermore, by outputting the questions as sample questions that can be asked directly to patients, the advice information becomes easy to use regardless of the pharmacist's experience.

[0031] In the pharmacist work support system 100 of the first embodiment, information contained in the package insert is provided as input to the learning model 112. This makes it possible to prevent hallucination and obtain appropriate advice information corresponding to the prescribed drug. In particular, by providing instructions to extract advice information for providing medication instructions from the information contained in the package insert, hallucination is appropriately prevented.

[0032] ***Other Configurations*** <Variation 1> In the first embodiment, the input information includes information extracted from the medical questionnaire information. However, the medical questionnaire information is not essential. If the input information includes information extracted from the medical questionnaire information, it is possible to obtain more appropriate advice information. However, even if the input information does not include information extracted from the medical questionnaire information, it is possible to obtain generally appropriate advice information.

[0033] Embodiment 2 The second embodiment differs from the first embodiment in that prescription difference information indicating the difference between the contents of the current prescription and the contents of the previous prescription is input. In the second embodiment, this difference will be explained, and explanations of the same points will be omitted.

[0034] 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.

[0035] ***Explanation of Operation*** The operation of the pharmacist work support system 100 according to the second embodiment will be described with reference to FIGS.

[0036] The processing of the pharmacist work support system 100 according to the second embodiment will be described with reference to Fig. 5. The processing from step S11 to step S13 differs from that of 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.

[0037] 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.

[0038] 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.

[0039] 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).

[0040] (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 to 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. For example, the extraction unit 212 extracts the drug name, quantity, and usage of the prescribed drug from each of the most recent prescription information and the past prescription information. Then, the extraction unit 212 extracts prescription difference information indicating the difference between the information extracted from the most recent prescription information and the information extracted from the past prescription information. In other words, the extraction unit 212 extracts prescription difference information indicating the difference between the contents of the current prescription and the contents of the previous prescription. Then, the extraction unit 212 includes the most recent prescription information and the prescription difference information in the input information. Note that the extraction unit 212 may also include past prescription information in the input information.

[0041] (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. As a result, for the patient personal information and medical questionnaire information, information indicating changes is input as a prompt. For the prescription information, in addition to the most recent prescription information, prescription difference information is input as a prompt.

[0042] ***Effects of the Second Embodiment*** As described above, the pharmacist work support system 100 according to the second embodiment generates prompts by using previously recorded information about the patient, which enables the learning model 112 to generate more appropriate advice information.

[0043] Embodiment 3 The third embodiment differs from the second embodiment in that information indicating the content of medication instructions given to the patient in the past is input to the learning model 112. In the third embodiment, this difference will be explained, and explanation of the same points will be omitted.

[0044] The processing of the pharmacist work support system 100 according to the third embodiment will be described with reference to Fig. 5. The processing from step S11 to step S13 differs from that of the second embodiment. (Step S11: Reception process) In the case of a returning patient, reception unit 211 of pharmacy terminal 20 reads out information indicating the contents of medication instructions previously given to the patient.

[0045] The reception process (step S11 in FIG. 5) according to the third embodiment will be described with reference to FIG. The processing from step S110 to step S115 is the same as in Fig. 10. In the case of a returning patient, following the processing of step S115, reception unit 211 reads out information indicating the content of medication instructions given to the patient in the past and adds it to the reception information (step S116). Specifically, reception unit 211 reads out information registered in the patient's past SOAP medication history as information indicating the content of medication instructions given in the past. At this time, reception unit 211 may read out all of the patient's past SOAP medication history, or may read out only the SOAP medication history for a certain period of time in the past.

[0046] (Step S12: Extraction process) As in the second 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 includes at least a part of the information indicating the contents of medication instructions given in the past in the input information.

[0047] (Step S13: Input processing) As in the second embodiment, the input unit 111 of the pharmacist work support device 10 inputs input information as a prompt to the learning model 112. As a result, information indicating the content of medication instructions given to the patient in the past is input as a prompt.

[0048] ***Effects of the Third Embodiment*** As described above, the pharmacist work support system 100 according to the third embodiment inputs information indicating the content of medication instructions given to patients in the past into the learning model 112. This enables the learning model 112 to identify items that should have been instructed but have not been given. As a result, the learning model 112 can generate more appropriate advice information.

[0049] ***Other Configurations*** <Variation 2> In the third embodiment, a case where a function is added to the second embodiment has been described. That is, in the third embodiment, in addition to the prescription difference information etc. described in the second embodiment, information indicating the content of medication instructions given to the patient in the past is additionally input. However, it is also possible to additionally input only the information indicating the content of medication instructions given to the patient in the past without inputting the prescription difference information etc. described in the second embodiment. Even in this case, a certain degree of effect can be obtained.

[0050] Embodiment 4 The fourth embodiment differs from the first to third embodiments in that information indicating that a hearing has not been conducted is input to the learning model 112. 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.

[0051] ***Explanation of Operation*** The processing of the pharmacist work support system 100 according to the fourth embodiment will be described with reference to Fig. 5. The processing of step S13 differs from that of the first embodiment.

[0052] (Step S13: Input processing) As in the first embodiment, the input unit 111 of the pharmacist work support device 10 inputs the input information as a prompt to the learning model 112. At this time, the input unit 111 adds information indicating that the pharmacist has not yet interviewed the patient to the prompt, and then inputs the information to the learning model 112. Here, it is assumed that medication instructions will be given to patients and that patient interviews will be conducted by referring to the advice information generated by the learning model 112. By inputting information indicating that no interviews have been conducted into the learning model 112, it becomes possible to properly convey this assumption to the learning model 112.

[0053] ***Effects of the Fourth Embodiment*** As described above, the pharmacist work support system 100 according to the fourth embodiment inputs information indicating that no interview has been conducted into the learning model 112. This allows the learning model 112 to be informed of the assumption that medication instructions will be provided to the patient and that an interview will be conducted with the patient, by referring to the advice information generated by the learning model 112. As a result, it becomes possible for the learning model 112 to generate more appropriate advice information.

[0054] Embodiment 5. The fifth embodiment differs from the first to fourth embodiments in that statistical information indicating the onset time of side effects of drugs is input to the learning model 112. In the fifth embodiment, this difference will be explained, and explanation of the same points will be omitted. In the fifth embodiment, a case where a modification is made to the first embodiment will be described. However, modifications can also be made to the second to fourth embodiments.

[0055] ***Explanation of Operation*** The processing of the pharmacist work support system 100 according to the fifth embodiment will be described with reference to Fig. 5. The processing of step S13 differs from that of the first to fourth embodiments.

[0056] (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 adds statistical information indicating the timing of the occurrence of side effects of the drug currently prescribed to the patient to the prompt, and then inputs the information to the learning model 112. Statistical information indicating the timing of side effects is information that indicates, for example, that there is an A% chance that side effect X will occur within x days of starting to take the drug, and that there is a B% chance that side effect Y will occur between x and x days.

[0057] ***Effects of the Fifth Embodiment*** As described above, the pharmacist work support system 100 according to the fifth embodiment inputs statistical information indicating the timing of the onset of side effects into the learning model 112. This enables the learning model 112 to identify what side effects are likely to occur in the current patient, and to generate advice information regarding the side effects that may occur. As a result, the learning model 112 can generate more appropriate advice information.

[0058] Embodiment 6 The sixth embodiment differs from the first to fifth embodiments in that advice information is output with a priority assigned to it. In the sixth embodiment, this difference will be explained, and explanation of the same points will be omitted. In the sixth embodiment, a case where a modification is made to the first embodiment will be described. However, modifications can also be made to the second to fifth embodiments.

[0059] ***Explanation of Operation*** The processing of the pharmacist work support system 100 according to the sixth embodiment will be described with reference to Fig. 5. The processing of step S13 differs from that of the first to fifth embodiments. (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 writes an instruction to prioritize advice information in the instruction form for generating the prompt. The input unit 111 writes the instruction so that advice information indicating content that should be given priority to the patient is given a higher priority.

[0060] ***Effects of the Sixth Embodiment*** As described above, the pharmacist work support system 100 according to the sixth embodiment prioritizes advice information. This allows the pharmacist to refer to the priority order when providing medication instruction and decide which advice information to refer to. As a result, medication instruction can be provided smoothly.

[0061] ***Other Configurations*** <Variation 3> The input unit 111 may write in the command or constraint conditions for generating a prompt that a specified number of pieces of advice information are to be output in descending order of priority. A plurality of modes may be prepared as operation modes of pharmacy terminal 20, and the designated number may be changed depending on the selected operation mode. For example, a normal mode and a simple mode may be prepared, and when the simple mode is selected, the designated number may be reduced compared to when the normal mode is selected, so that advice information is displayed in a simpler manner.

[0062] Embodiment 7 The seventh embodiment differs from the first to sixth embodiments in that in addition to generating advice information, generation of a reason for selecting the advice information is instructed. In the seventh embodiment, this difference will be explained, and explanation of the same points will be omitted. In the seventh embodiment, a case where a modification is made to the first embodiment will be described. However, modifications can also be made to the second to sixth embodiments.

[0063] ***Explanation of Operation*** The processing of the pharmacist work support system 100 according to the sixth embodiment will be described with reference to Fig. 5. The processing from step S13 to step S16 differs from that of the first embodiment. (Step S13: Input processing) As in the first embodiment, the input unit 111 of the pharmacist work support device 10 inputs the input information as a prompt to the learning model 112. At this time, the input unit 111 writes an instruction to generate a reason for selecting the advice information in the instruction for generating the prompt.

[0064] (Step S14: Generation process) The learning model 112 of the pharmacist work support device 10 generates advice information for providing medication instruction based on the prompt input in step S13, and also generates a reason for selecting the generated advice information. (Step S15: Output process) The output unit 113 of the pharmacist work support device 10 acquires the advice information and the selection reason for providing medication instruction generated in step S14. Then, the output unit 113 transmits the acquired advice information and the selection reason for providing medication instruction to the pharmacy terminal 20.

[0065] (Step S16: Display processing) Display unit 213 of pharmacy terminal 20 acquires the advice information and the selection reason for providing medication instruction transmitted in step S15. Display unit 213 displays the acquired advice information and the selection reason for providing medication instruction on a display device connected to pharmacy terminal 20 via communication interface 24.

[0066] ***Effects of the Seventh Embodiment*** As described above, the pharmacist work support system 100 according to the seventh embodiment instructs the generation of reasons for selecting the advice information in addition to generating advice information. As a result, the learning model 112 generates reasons for selecting the advice information when generating the advice information. When an experienced pharmacist provides medication instructions, the learning model 112 determines the content of the instructions after understanding the reasons for selecting the content of the instructions. By generating reasons for selecting the advice information when generating the advice information, the learning model 112 is able to think in a similar way to an experienced pharmacist when providing medication instructions, making it possible to generate more appropriate advice information.

[0067] The pharmacist work support system 100 according to the seventh embodiment displays the reason for selecting the advice information together with the advice information. By referring to the selection reason, the pharmacist can easily determine the advice information to refer to.

[0068] When prioritizing advice information, it is useful to refer to the reasons for selecting the advice information. Therefore, by adding the functions of embodiment 7 to embodiment 6 and generating the reasons for selecting the advice information while prioritizing the advice information, it becomes possible to appropriately prioritize the advice information.

[0069] Embodiment 8 The eighth embodiment differs from the first to seventh embodiments in that in addition to generating advice information, it instructs the generation of an OAP in the SOAP medication history. In the eighth embodiment, this difference will be explained, and explanation of the same points will be omitted. In the eighth embodiment, a case where a modification is made to the first embodiment will be described. However, modifications can also be made to the second to seventh embodiments.

[0070] ***Explanation of Operation*** The processing of the pharmacist work support system 100 according to the sixth embodiment will be described with reference to Fig. 5. The processing from step S13 to step S16 differs from that of the first embodiment. (Step S13: Input processing) As in the first embodiment, the input unit 111 of the pharmacist work support device 10 inputs the input information as a prompt to the learning model 112. At this time, the input unit 111 writes an instruction to generate an OAP for the SOAP medication history in the instruction form for generating the prompt.

[0071] (Step S14: Generation process) The learning model 112 of the pharmacist work support device 10 generates advice information for providing medication instruction based on the prompt input in step S13, and also generates an OAP for the SOAP medication history. (Step S15: Output process) The output unit 113 of the pharmacist work support device 10 acquires the advice information for providing medication instruction and the OAP of the SOAP medication history generated in step S14. Then, the output unit 113 transmits the acquired advice information for providing medication instruction and the OAP of the SOAP medication history to the pharmacy terminal 20.

[0072] (Step S16: Display processing) Display unit 213 of pharmacy terminal 20 acquires the advice information for providing medication instruction and the OAP of the SOAP medication history transmitted in step S15. Display unit 213 displays the acquired advice information for providing medication instruction and the OAP of the SOAP medication history on a display device connected to pharmacy terminal 20 via communication interface 24.

[0073] ***Effects of the eighth embodiment*** As described above, the pharmacist work support system 100 according to the seventh embodiment instructs the generation of an OAP of a SOAP medication history in addition to the generation of advice information. As a result, the learning model 112 generates an OAP of a SOAP medication history when generating advice information. When an experienced pharmacist provides medication instructions, the content of the instructions is determined after considering what the OAP of the SOAP medication history will be. By generating an OAP of a SOAP medication history when generating advice information, the learning model 112 is able to think in a similar way to an experienced pharmacist when providing medication instructions, making it possible to generate more appropriate advice information.

[0074] Here, when an experienced pharmacist provides medication instructions, he or she considers what the OAP of the SOAP medication history will be, imagines the reasons for selecting the instruction content, and then decides on the instruction content. Therefore, it is more desirable to add the functions of embodiment 8 to embodiment 7, so that in addition to generating advice information, the system instructs the reasons for selecting the advice information and the generation of the OAP of the SOAP medication history. This allows the system to think more similarly to how an experienced pharmacist would when providing medication instructions, making it possible to generate more appropriate advice information.

[0075] The pharmacist work support system 100 according to the eighth embodiment displays the OAP of the SOAP medication history together with advice information. By referring to the OAP, the pharmacist can provide medication instructions while imagining the items to be written in the OAP of the SOAP medication history.

[0076] When prioritizing advice information, it is useful to refer to the OAP of the SOAP medication history. Therefore, by adding the functions of the eighth embodiment to the sixth embodiment and generating the OAP of the SOAP medication history while prioritizing the advice information, it becomes possible to appropriately prioritize the advice information.

[0077] ***Other Configurations*** <Variation 4> When generating advice information, it is considered that pharmacists place importance on P in the OAP. Therefore, the input unit 111 may enter an instruction to generate only P in the SOAP medication history in the instruction for generating the prompt. It is also considered that pharmacists place importance on A after P. Therefore, the input unit 111 may enter an instruction to generate only AP from the SOAP medication history in the instruction form for generating the prompt.

[0078] <Variation 5> In the first embodiment, each functional component is realized by software. However, as a fifth modification, each functional component may be realized by hardware. The differences between the fifth modification and the first embodiment will be described below.

[0079] 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.

[0080] 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.

[0081] <Variation 6> As a sixth modification, some of the functional components may be realized by hardware, and other functional components may be realized by software.

[0082] 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.

[0083] <Example prompt> Examples of prompts are described with reference to Figures 12 through 19. Figures 12 to 14 show the system prompt. The system prompt defines Role, Status, Goal, Variables, Constraints, OutputFormat, and keys. Role, Status, and Goal correspond to the above-mentioned instruction. Constraints correspond to the above-mentioned constraints. OutputFormat is information that determines the output format. Keys is a term definition. Here, the Goal is instructed to generate questions to be asked in medication guidance corresponding to the content described in embodiment 1, as well as the reason for generation (reason for selection) corresponding to the content described in embodiment 7, and a SOAP medication history corresponding to the content described in embodiment 8. Note that here, the generation of S in the SOAP medication history is also instructed, but since the hearing has not yet taken place, it is also possible to instruct the generation of only the OAP, excluding S.

[0084] 15 to 19 show user prompts. A user prompt is information that defines a patient and corresponds to the input in a system prompt. The user prompt defines the gender and age extracted from the patient's personal information, the symptoms (current illness name) extracted from the medical questionnaire information, and the name and amount of the drug extracted from the prescription information. The user prompt also defines information contained in the drug's package insert (such as precautions, side effect information, and efficacy).

[0085] An example of a response obtained from the learning model 112 will be described with reference to Figures 20 to 22. The response includes an example of a SOAP medication history entry, the reason for the generated information, and advice information (interview). Note that Figures 15 to 19 show only information about a newly prescribed atorvastatin tablet 5 mg among the prescribed medications, but in reality, it is assumed that the prescription of Zetia tablets 10 mg and Lotriga granular capsules 2 g has been continued and the prescription of Livalo tablets 2 mg has been discontinued. Normally, the fact that the prescription of Zetia tablets 10 mg and Lotriga granular capsules 2 g has been continued and the prescription of Livalo tablets 2 mg has been discontinued would also be stated in the user prompt.

[0086] Aspects of the present disclosure Various aspects of the present disclosure are summarized below as appendices. (Appendix 1) an input unit that inputs into a learning model patient personal information indicating the attributes of a patient who will receive a prescription for a drug, prescription information indicating the contents of the prescription for the patient, and at least information on precautions and side effects included in the package insert of the drug; an output unit that outputs advice information generated by the learning model in response to the patient personal information, the prescription information, and the information included in the package insert input by the input unit when providing medication guidance to the patient; A pharmacist work support system equipped with the following. (Appendix 2) The input unit inputs, as the prescription information, current prescription information indicating the content of the current prescription and prescription difference information indicating the difference between the content of the current prescription and the content of the previous prescription. A pharmacist work support system as described in Appendix 1. (Appendix 3) The input unit further inputs medical questionnaire information indicating the patient's answers to a medical questionnaire. A pharmacist work support system as described in Appendix 1 or 2. (Appendix 4) The input unit further inputs information indicating the content of medication instructions given to the patient in the past. A pharmacist work support system described in any one of appendices 1 to 3. (Appendix 5) The input unit further inputs information indicating that a hearing from the patient has not been conducted. A pharmacist work support system described in any one of appendices 1 to 4. (Appendix 6) The input unit further inputs statistical information indicating the timing of occurrence of side effects of the drug. A pharmacist work support system described in any one of appendices 1 to 5. (Appendix 7) the input unit instructs the learning model to generate the advice information and also to generate a reason for selecting the advice information; The output unit further outputs a reason for selecting the advice information generated by the learning model. A pharmacist work support system described in any one of appendices 1 to 6. (Appendix 8) The input unit instructs the learning model to generate at least a plan in a SOAP (Subject Object Assessment Plan) medication history in addition to generating the advice information, The output unit further outputs the plan generated by the learning model. A pharmacist work support system described in any one of appendices 1 to 7. (Appendix 9) the input unit instructs the learning model to generate a specified number of pieces of advice information having high priority from among the pieces of advice information; The output unit outputs the specified number of pieces of advice information generated by the learning model. A pharmacist work support system described in any one of appendices 1 to 8. (Appendix 10) A computer inputs into a learning model patient personal information indicating the attributes of a patient who will receive a prescription for a drug, prescription information indicating the contents of the prescription for the patient, and at least information on precautions and side effects included in the package insert of the drug; A pharmacist work support method in which a computer outputs advice information for providing medication instructions to the patient, generated by the learning model in accordance with the patient's personal information, the prescription information, and the information contained in the attached document. (Appendix 11) an input process for inputting into a learning model patient personal information indicating the attributes of a patient who will receive a prescription for a drug, prescription information indicating the contents of the prescription for the patient, and at least information on precautions and side effects included in the package insert of the drug; an output process for outputting advice information for providing medication guidance to the patient, which advice information is generated by the learning model in response to the patient personal information, the prescription information, and the information included in the package insert input by the input process; A pharmacist work support program that enables a computer to function as a pharmacist work support system.

[0087] 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]

[0088] 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, 30 Pharmacy.

Claims

1. an input unit that inputs into a learning model patient personal information indicating the attributes of a patient who is prescribed a drug, prescription information indicating the contents of the prescription for the patient, at least information on precautions and side effects included in the information included in the package insert of the drug, and information indicating that the patient has not been interviewed; an output unit that outputs advice information generated by the learning model when providing medication guidance to the patient in response to at least information on precautions and side effects among the patient personal information, the prescription information, and the information included in the package insert input by the input unit, and information indicating that a hearing has not been conducted with the patient; and A pharmacist work support system equipped with the following.

2. The input unit inputs, as the prescription information, current prescription information indicating the content of the current prescription and prescription difference information indicating the difference between the content of the current prescription and the content of the previous prescription. The pharmacist work support system according to claim 1.

3. The input unit further inputs medical questionnaire information indicating the patient's answers to a medical questionnaire. The pharmacist work support system according to claim 1.

4. The input unit further inputs information indicating the content of medication instructions given to the patient in the past. The pharmacist work support system according to claim 1.

5. The input unit further inputs statistical information indicating the timing of occurrence of side effects of the drug. The pharmacist work support system according to claim 1.

6. the input unit instructs the learning model to generate the advice information and also to generate a reason for selecting the advice information; The output unit further outputs a reason for selecting the advice information generated by the learning model. The pharmacist work support system according to claim 1.

7. The input unit instructs the learning model to generate at least a Plan in a SOAP (Subject Object Assessment Plan) medication history in addition to generating the advice information, The output unit further outputs the plan generated by the learning model. The pharmacist work support system according to claim 1.

8. the input unit instructs the learning model to generate a specified number of pieces of advice information having high priority from among the pieces of advice information; The output unit outputs the specified number of pieces of advice information generated by the learning model. The pharmacist work support system according to claim 1.

9. A computer inputs into a learning model patient personal information indicating the attributes of a patient who is prescribed a drug, prescription information indicating the contents of the prescription for the patient, at least information on precautions and side effects included in the information included in the package insert of the drug, and information indicating that a hearing has not been conducted with the patient; A method for supporting pharmacist work, in which a computer outputs advice information for providing medication instructions to a patient, generated by the learning model, in response to at least the precautions and side effect information contained in the patient's personal information, the prescription information, and the information contained in the package insert, and information indicating that the patient has not been interviewed.

10. an input process for inputting into a learning model patient personal information indicating the attributes of a patient who will receive a prescription for a drug, prescription information indicating the contents of the prescription for the patient, at least information on precautions and side effects included in the package insert of the drug, and information indicating that a hearing has not been conducted with the patient; an output process for outputting advice information generated by the learning model when providing medication guidance to the patient, in response to at least information on precautions and side effects among the patient personal information, the prescription information, and the information included in the package insert input by the input process, and information indicating that a hearing has not been conducted with the patient; and A pharmacist work support program that enables a computer to function as a pharmacist work support system.

Images