Generative AI for Medication Counselling and Adherence in Community Pharmacies
Human-AI Collaboration in the Pharmacy: A Cluster Randomized Controlled Trial of Generative AI for Medication Counselling and Adherence
연구 개요
상태
상세 설명
연구 유형
등록 (실제)
단계
- 해당 없음
연락처 및 위치
연구 장소
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Amman, 요르단
- Petra University
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참여기준
자격 기준
공부할 수 있는 나이
- 성인
- 고령자
건강한 자원 봉사자를 받아들입니다
설명
Patient Eligibility Criteria
Inclusion Criteria:
Adults aged 18 years or older. Presenting with a new prescription or a refill for a chronic medication requiring counseling within one of the following classes: antihypertensives, oral antidiabetics, lipid-lowering agents, anticoagulants, or inhaled maintenance therapies.
Willing and able to provide informed consent.
Exclusion Criteria:
Presence of acute infections. Diagnosis of psychiatric disorders or oncological conditions. Presence of severe acute illness requiring urgent medical referral. Cognitive impairment precluding informed consent. Hearing or communication barriers that prevent interview completion without the presence of a caregiver.
Inability to provide a follow-up phone number for the 30-day adherence assessment.
Pharmacy and Pharmacist (Cluster) Eligibility Criteria
Inclusion Criteria:
Pharmacies legally registered in Jordan, providing routine prescription dispensing services, having at least one licensed pharmacist available during recruitment hours, and agreeing to participate for the full trial period.
Licensed pharmacists with a minimum of 2 years of clinical experience, working in participating pharmacies, providing direct patient counseling, and consenting to take part in the study.
Exclusion Criteria:
Pharmacies that are already using structured AI-assisted counseling tools as part of their routine practice.
Pharmacists on temporary placement for less than one month. Pharmacists not involved in patient-facing counseling.
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
- 주 목적: 다른
- 할당: 무작위
- 중재 모델: 병렬 할당
- 마스킹: 네 배로
무기와 개입
참가자 그룹 / 팔 |
개입 / 치료 |
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활성 비교기: Intervention arm procedures
For all eligible patients in the intervention arm, the pharmacist performed the standard patient assessment and determined which medicine(s) needed counselling.
Then, the pharmacist input a prompt in a de-identified format into ChatGPT®.
The prompt was a request for an easy-to-understand counselling document with information regarding the indications for the medication, dosage, schedule, route, course, missed doses, possible side effects, important precautions, storage, and advice on taking the medicine as prescribed (Appendix A).
The pharmacist ensured that the content generated by the AI was accurate and clear, making corrections where necessary, and then gave verbal counselling to the patient.
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For all eligible patients in the intervention arm, the pharmacist performed the standard patient assessment and determined which medicine(s) needed counselling. Then, the pharmacist input a prompt in a de-identified format into ChatGPT®. The prompt was a request for an easy-to-understand counselling document with information regarding the indications for the medication, dosage, schedule, route, course, missed doses, possible side effects, important precautions, storage, and advice on taking the medicine as prescribed (Appendix A). The pharmacist ensured that the content generated by the AI was accurate and clear, making corrections where necessary, and then gave verbal counselling to the patient. The AI output was never provided to the patients without pharmacist evaluation. It is worth noting that pharmacists could also reject the AI output as inaccurate, insufficient, hazardous, and inappropriate altogether. Reproducibility was ensured through documenting the date and time, prompt te
다른 이름들:
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간섭 없음: Control arm procedures
Pharmacies randomized to the control arm continued to provide usual medication counselling according to their standard routine practice, without access to the AI prompt templates or study AI workflow.
Control pharmacists used their usual professional references, as would occur in routine care, but they were not trained in or asked to use ChatGPT® during the trial period.
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연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
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Percentage of Applicable Counseling Domains Provided Correctly
기간: day 0
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Defined as the proportion of clinically applicable counseling domains communicated accurately during the encounter, compared with a medication-specific reference sheet.
Scored on a 0-100 scale, calculated as (Number of applicable domains correctly informed / Total number of applicable domains) x 100.Correctness score= (Number of applicable domains
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day 0
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Percentage of Essential Counseling Domains Addressed
기간: Day 0
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Defined as the proportion of essential counseling domains that were addressed during the encounter.
Scored on a 0-100 scale, calculated as (Number of applicable domains addressed / Total number of applicable domains) x 100.
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Day 0
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2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
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Number of Counseling Deficiencies Categorized by Clinical Severity
기간: Day 0
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The frequency of omitted or incorrect counseling information, independently assessed by a panel of pharmacists using a 3-point scale: Low Severity (minor wording issues), Moderate Severity (errors leading to sub-therapeutic effects), and High Severity (errors with high potential for significant patient harm).
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Day 0
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Score on the General Medication Adherence Scale (GMAS)
기간: 30 Days Post-Encounter
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Medication adherence assessed via telephone follow-up using the continuous total score from the General Medication Adherence Scale (GMAS).
Higher scores indicate better medication adherence.
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30 Days Post-Encounter
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Number of Participants Achieving Good Adherence
기간: 30 Days Post-Encounter
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The number of participants meeting the validated threshold for "good adherence" based on their GMAS survey responses.
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30 Days Post-Encounter
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Total Score on the Immediate Patient Understanding (Teach-Back) Assessment
기간: Day 0
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A brief interviewer-administered understanding assessment based on teach-back principles.
Scores range from 0 to 4, with higher scores indicating a better understanding of the medication.
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Day 0
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Total Score on the Patient Satisfaction Questionnaire
기간: Day 0
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A questionnaire covering clarity, usefulness, confidence, and overall satisfaction.
Total scores range from 5 to 25, with higher scores indicating greater patient satisfaction.
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Day 0
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Time Spent on Face-to-Face Counseling
기간: Day 0
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Total face-to-face counseling time measured in minutes using audio timestamps from the start of counseling to completion.
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Day 0
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Number of Encounters Based on AI Output Acceptance Level
기간: Day 0
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The proportion of encounters in which the AI-generated counseling output was fully accepted, edited before delivery, or rejected outright by the pharmacist.
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Day 0
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Number of AI-Related Discrepancies Identified
기간: Day 0
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The frequency of detected AI inaccuracies prior to counseling, such as omitted counseling points, overly technical wording, or incomplete missed-dose advice.
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Day 0
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Number of Clinical Near Misses and Safety Incidents
기간: Day 0
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The number of encounters featuring a "near miss" (an AI error identified and corrected by the pharmacist before reaching the patient) or an "incident" (inaccurate information that actually reached the patient).
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Day 0
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공동 작업자 및 조사자
간행물 및 유용한 링크
일반 간행물
- Abdel-Qader, D. H., Al Meslamani, A. Z., Lewis, P. J., & Hamadi, S. (2021). Incidence, nature, severity, and causes of dispensing errors in community pharmacies in Jordan. International journal of clinical pharmacy, 43(1), 165-173. https://doi.org/10.1007/s11096-020-01126-w Abdel-Qader, D. H., et al. (2024). A comprehensive analysis of public satisfaction: Community pharmacists' pandemic preparedness in Jordan. Journal of Applied Pharmaceutical Science, 14(8), 160-168. Abdel-Qader, D. H., et al. (2025). Drug-Drug interaction management among pharmacists in Jordan: A national comparative survey. Pharmacy, 137. https://doi.org/10.3390/pharmacy13050137 Abu Hammour, K., et al. (2023). ChatGPT in pharmacy practice: A cross-sectional exploration of Jordanian pharmacists' perception, practice, and concerns. Journal of Pharmaceutical Policy and Practice, 16(1), 115. Ali, S., Shimels, T., & Bilal, A. I. (2019). Assessment of patient counseling on dispensing of medicines in outpatient pharmacy of Tikur-Anbessa Specialized Hospital, Ethiopia. Ethiopian journal of health sciences, 29(6), 727. Campbell, M. K., et al. (2012). Consort 2010 statement: Extension to cluster randomised trials. BMJ, 345. Chan, A.-W., et al. (2015). SPIRIT 2013 Statement: Defining standard protocol items for clinical trials. Revista Panamericana de Salud Pública, 38, 506-514. Elayeh, E. R., et al. (2019). Use of secret simulated patient followed by workshop based education to assess and improve inhaler counseling in community pharmacy in Jordan. Pharmacy Practice (Granada), 17(4). Fattah, F. H., et al. (2025). Comparative analysis of ChatGPT and Gemini (Bard) in medical inquiry: A scoping review. Frontiers in digital health, 7, 1482712. FIP, I. P. F. (2021). Medication review and medicines use review: A toolkit for pharmacists Colophon. FIP, I. P. F. (2025). An artificial intelligence toolkit for pharmacy: An introduction and resource guide for pharmacists. (March). Hammad, E. A., et al. (2022). Feasibi
연구 기록 날짜
연구 주요 날짜
연구 시작 (실제)
기본 완료 (실제)
연구 완료 (실제)
연구 등록 날짜
최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
QC 기준을 충족하는 마지막 업데이트 제출
마지막으로 확인됨
추가 정보
이 연구와 관련된 용어
추가 관련 MeSH 약관
기타 연구 ID 번호
- Petrauniversity
개별 참가자 데이터(IPD) 계획
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심혈관 질환에 대한 임상 시험
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University of Pennsylvania완전한Intrntl Classification of Diseases, 9th Revision, (ICD-9-CM) 410의 주진단 또는 이차진단 코드가 있는 환자(5번째 숫자가 2인 경우 제외)미국