Asthma, Cardiovascular Diseases, Chronic Disease, Diabetes Mellitus, Dyslipidemias, Hypertension, Pulmonary Disease, Chronic Obstructive
Conditions
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.
Detailed description
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.
Interventions
OTHERpharmacist-supervised AI-assisted medication counseling
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
Sponsors
University of Petra
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
OTHER
Masking
QUADRUPLE (Subject, Caregiver, Investigator, Outcomes Assessor)
Masking description
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).
Intervention model description
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.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Percentage of Applicable Counseling Domains Provided Correctly | 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 |
| Percentage of Essential Counseling Domains Addressed | 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
| Measure | Time frame | Description |
|---|---|---|
| Number of Counseling Deficiencies Categorized by Clinical Severity | 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). |
| Score on the General Medication Adherence Scale (GMAS) | 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. |
| Number of Participants Achieving Good Adherence | 30 Days Post-Encounter | The number of participants meeting the validated threshold for "good adherence" based on their GMAS survey responses. |
| Total Score on the Immediate Patient Understanding (Teach-Back) Assessment | 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. |
| Total Score on the Patient Satisfaction Questionnaire | 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. |
| Time Spent on Face-to-Face Counseling | Day 0 | Total face-to-face counseling time measured in minutes using audio timestamps from the start of counseling to completion. |
| Number of Encounters Based on AI Output Acceptance Level | 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. |
| Number of AI-Related Discrepancies Identified | Day 0 | The frequency of detected AI inaccuracies prior to counseling, such as omitted counseling points, overly technical wording, or incomplete missed-dose advice. |
| Number of Clinical Near Misses and Safety Incidents | 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). |
Countries
Jordan
Outcome results
None listed