Skip to main content
OpenTrials
Completed

NCT Number: NCT07649577

Generative AI for Medication Counselling and Adherence in Community Pharmacies

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.

Completed

Looking for future studies?

Notify Me

Key information

Conditions

Hypertension Asthma Bronchial Diseases Cardiovascular Diseases Chronic Disease Diabetes Mellitus Disease Attributes Dyslipidemias Endocrine System Diseases Glucose Metabolism Disorders Hypersensitivity Hypersensitivity, Immediate Immune System Diseases Lipid Metabolism Disorders Lung Diseases Lung Diseases, Obstructive Metabolic Diseases Nutritional and Metabolic Diseases Pathologic Processes Pathological Conditions, Signs and Symptoms Pulmonary Disease, Chronic Obstructive Respiratory Hypersensitivity Respiratory Tract Diseases Vascular Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Petra University

Amman, Jordan

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

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

Other names: Intervention arm procedures

Primary outcomes

  1. Percentage of Applicable Counseling Domains Provided Correctly

    Time frame: day 0

    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

  2. Percentage of Essential Counseling Domains Addressed

    Time frame: Day 0

    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.

Secondary outcomes

  1. Number of Counseling Deficiencies Categorized by Clinical Severity

    Time frame: Day 0

    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).

  2. Score on the General Medication Adherence Scale (GMAS)

    Time frame: 30 Days Post-Encounter

    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.

  3. Number of Participants Achieving Good Adherence

    Time frame: 30 Days Post-Encounter

    The number of participants meeting the validated threshold for "good adherence" based on their GMAS survey responses.

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

    Time frame: Day 0

    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.

  5. Total Score on the Patient Satisfaction Questionnaire

    Time frame: Day 0

    A questionnaire covering clarity, usefulness, confidence, and overall satisfaction. Total scores range from 5 to 25, with higher scores indicating greater patient satisfaction.

  6. Time Spent on Face-to-Face Counseling

    Time frame: Day 0

    Total face-to-face counseling time measured in minutes using audio timestamps from the start of counseling to completion.

  7. Number of Encounters Based on AI Output Acceptance Level

    Time frame: Day 0

    The proportion of encounters in which the AI-generated counseling output was fully accepted, edited before delivery, or rejected outright by the pharmacist.

  8. Number of AI-Related Discrepancies Identified

    Time frame: Day 0

    The frequency of detected AI inaccuracies prior to counseling, such as omitted counseling points, overly technical wording, or incomplete missed-dose advice.

  9. Number of Clinical Near Misses and Safety Incidents

    Time frame: Day 0

    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).

Sponsors and collaborators

Lead sponsor

University of Petra

Other

Registry information

Official study title

Human-AI Collaboration in the Pharmacy: A Cluster Randomized Controlled Trial of Generative AI for Medication Counselling and Adherence

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Jun 16, 2026
Registry last updated
Jun 16, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Related clinical trials

Published trials that share one or more normalized conditions with this study.