Generative AI for Medication Counselling and Adherence in Community Pharmacies

NCT07649577 | Completed

• 1 other identifier: Petrauniversity
• Study Type: interventional
• Target: 136
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Hypertension; Diabetes Mellitus; Dyslipidemias; Cardiovascular Diseases; Pulmonary Disease, Chronic Obstructive; Asthma; Chronic Disease

Outcome Measures

Primary Outcomes (2)

• Percentage of Applicable Counseling Domains Provided Correctly

  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

  day 0

• Percentage of Essential Counseling Domains Addressed

  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.

  Day 0

Secondary Outcomes (9)

• Number of Counseling Deficiencies Categorized by Clinical Severity

  Day 0

• Score on the General Medication Adherence Scale (GMAS)

  30 Days Post-Encounter

• Number of Participants Achieving Good Adherence

  30 Days Post-Encounter

• Total Score on the Immediate Patient Understanding (Teach-Back) Assessment

  Day 0

• Total Score on the Patient Satisfaction Questionnaire

  Day 0

Study Arms (2)

Intervention arm procedures

ACTIVE COMPARATOR

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.

Other: pharmacist-supervised AI-assisted medication counseling

Control arm procedures

NO INTERVENTION

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.

Interventions

pharmacist-supervised AI-assisted medication counseling OTHER

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

Also known as: Intervention arm procedures

Intervention arm procedures

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.

You may not qualify if:

• 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
• 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.
• 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.

Sponsors & Collaborators

• University of Petra: lead

Study Sites (1)

Petra University

Amman, Jordan

Location

Related Publications (1)

• 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

  RESULT

MeSH Terms

Conditions

Hypertension; Diabetes Mellitus; Dyslipidemias; Cardiovascular Diseases; Pulmonary Disease, Chronic Obstructive; Asthma; Chronic Disease

Condition Hierarchy (Ancestors)

Vascular Diseases; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases; Lipid Metabolism Disorders; Lung Diseases, Obstructive; Lung Diseases; Respiratory Tract Diseases; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Bronchial Diseases; Respiratory Hypersensitivity; Hypersensitivity, Immediate; Hypersensitivity; Immune System Diseases

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: QUADRUPLE
• Who Masked: PARTICIPANT, CARE PROVIDER, INVESTIGATOR, OUTCOMES ASSESSOR
• Masking Details: Blinding of pharmacists was not possible because they knew whether they were using the AI-assisted workflow. However, the following layers of blinding were implemented: transcript scorers for correctness and completeness were blinded to group allocation; the statistician analyzed a masked dataset with anonymized arm labels where feasible; patients were not explicitly told the trial hypothesis comparing AI-assisted with usual counselling, only that the study evaluated medication-counselling processes. These procedures are important because cluster trials involving provider behavior are particularly vulnerable to performance and detection biases if blinding is not addressed carefully (Campbell et al., 2012; Hemming et al., 2017).
• Purpose: OTHER
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinical Professor

Model Details: This study was a pragmatic, parallel, two-arm cluster randomized controlled trial design, with the community pharmacy as the unit of randomization and the patient encounter as the unit of analysis.

Study Record Dates

• First Submitted: June 3, 2026
• First Posted: June 16, 2026
• Study Start: January 1, 2026
• Primary Completion: March 30, 2026
• Study Completion: March 30, 2026
• Last Updated: June 16, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will not share

Locations

JO (1)