Generative AI for Medication Counselling and Adherence in Community Pharmacies
9. Juni 2026 aktualisiert von: Derar H. Abdel-Qader, University of Petra
Human-AI Collaboration in the Pharmacy: A Cluster Randomized Controlled Trial of Generative AI for Medication Counselling and Adherence
Medication counseling within community pharmacies is crucial for managing chronic diseases, yet significant challenges regarding correctness and completeness remain in Jordan.
Although generative artificial intelligence (AI) can be utilized for patient education, there is a lack of research on clinical impact and safety of AI in medication counseling conducted by pharmacists in real-world practice.
The aim of this study is to evaluate the effect of pharmacist-supervised AI-assisted medication counseling on the correctness and completeness of counseling information and 30-day medication adherence among patients in Jordanian community pharmacies.
Studienübersicht
Status
Abgeschlossen
Bedingungen
Intervention / Behandlung
Detaillierte Beschreibung
Materials and Methods: This pragmatic, two-arm cluster randomized controlled trial enrolled 136 adult patients across 16 community pharmacies in Jordan (8 clusters per arm).
Pharmacists in the intervention arm used a standardized prompt strategy with ChatGPT® to generate counseling drafts, which were then verified and edited before delivery.
The control arm provided usual counseling.
Co-primary outcomes were correctness and completeness of counseling information (percentage scores based on blinded transcript analysis).
Secondary outcomes included 30-day medication adherence (General Medication Adherence Scale [GMAS]), immediate patient understanding, and satisfaction.
Data were analyzed using mixed-effects linear and logistic regression models.
Studientyp
Interventionell
Einschreibung (Tatsächlich)
136
Phase
- Unzutreffend
Kontakte und Standorte
Dieser Abschnitt enthält die Kontaktdaten derjenigen, die die Studie durchführen, und Informationen darüber, wo diese Studie durchgeführt wird.
Studienorte
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Amman, Jordanien
- Petra University
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Teilnahmekriterien
Forscher suchen nach Personen, die einer bestimmten Beschreibung entsprechen, die als Auswahlkriterien bezeichnet werden. Einige Beispiele für diese Kriterien sind der allgemeine Gesundheitszustand einer Person oder frühere Behandlungen.
Zulassungskriterien
Studienberechtigtes Alter
- Erwachsene
- Älterer Erwachsener
Akzeptiert gesunde Freiwillige
Ja
Beschreibung
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.
Studienplan
Dieser Abschnitt enthält Einzelheiten zum Studienplan, einschließlich des Studiendesigns und der Messung der Studieninhalte.
Wie ist die Studie aufgebaut?
Designdetails
- Hauptzweck: Sonstiges
- Zuteilung: Zufällig
- Interventionsmodell: Parallele Zuordnung
- Maskierung: Vervierfachen
Waffen und Interventionen
Teilnehmergruppe / Arm |
Intervention / Behandlung |
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Aktiver Komparator: 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
Andere Namen:
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Kein Eingriff: 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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Was misst die Studie?
Primäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
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Percentage of Applicable Counseling Domains Provided Correctly
Zeitfenster: 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
Zeitfenster: 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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Sekundäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
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Number of Counseling Deficiencies Categorized by Clinical Severity
Zeitfenster: 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)
Zeitfenster: 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
Zeitfenster: 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
Zeitfenster: 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
Zeitfenster: 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
Zeitfenster: 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
Zeitfenster: 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
Zeitfenster: 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
Zeitfenster: 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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Mitarbeiter und Ermittler
Hier finden Sie Personen und Organisationen, die an dieser Studie beteiligt sind.
Sponsor
Publikationen und hilfreiche Links
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Allgemeine Veröffentlichungen
- 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
Studienaufzeichnungsdaten
Diese Daten verfolgen den Fortschritt der Übermittlung von Studienaufzeichnungen und zusammenfassenden Ergebnissen an ClinicalTrials.gov. Studienaufzeichnungen und gemeldete Ergebnisse werden von der National Library of Medicine (NLM) überprüft, um sicherzustellen, dass sie bestimmten Qualitätskontrollstandards entsprechen, bevor sie auf der öffentlichen Website veröffentlicht werden.
Haupttermine studieren
Studienbeginn (Tatsächlich)
1. Januar 2026
Primärer Abschluss (Tatsächlich)
30. März 2026
Studienabschluss (Tatsächlich)
30. März 2026
Studienanmeldedaten
Zuerst eingereicht
3. Juni 2026
Zuerst eingereicht, das die QC-Kriterien erfüllt hat
9. Juni 2026
Zuerst gepostet (Tatsächlich)
16. Juni 2026
Studienaufzeichnungsaktualisierungen
Letztes Update gepostet (Tatsächlich)
16. Juni 2026
Letztes eingereichtes Update, das die QC-Kriterien erfüllt
9. Juni 2026
Zuletzt verifiziert
1. Juni 2026
Mehr Informationen
Begriffe im Zusammenhang mit dieser Studie
Zusätzliche relevante MeSH-Bedingungen
- Erkrankungen des endokrinen Systems
- Gefäßerkrankungen
- Pathologische Prozesse
- Krankheitsattribute
- Stoffwechselerkrankungen
- Erkrankungen des Immunsystems
- Erkrankungen der Atemwege
- Lungenkrankheit
- Störungen des Glukosestoffwechsels
- Bronchialerkrankungen
- Lungenerkrankungen, obstruktive
- Überempfindlichkeit der Atemwege
- Überempfindlichkeit, sofort
- Überempfindlichkeit
- Störungen des Fettstoffwechsels
- Pathologische Zustände, Anzeichen und Symptome
- Ernährungs- und Stoffwechselerkrankungen
- Lungenerkrankung, chronisch obstruktiv
- Hypertonie
- Asthma
- Herz-Kreislauf-Erkrankungen
- Diabetes Mellitus
- Dyslipidämien
- Chronische Erkrankung
Andere Studien-ID-Nummern
- Petrauniversity
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