HypertensionNCT07649577
Generative AI for Medication Counselling and Adherence in Community Pharmacies
A study testing pharmacist-supervised AI-assisted medication counseling in people with Hypertension and Diabetes Mellitus, run by University of Petra at 1 site in Jordan.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Hypertension, Diabetes Mellitus, Dyslipidemias
- What is being tested?
- pharmacist-supervised AI-assisted medication counseling
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 136 participants
- Where
- 1 siteJordan
- Study dates
- 1 Jan 2026to 30 Mar 2026
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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.
- 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.
Who cannot join
- 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 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.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: 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.
Read the 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.
- Conditions
- Hypertension, Diabetes Mellitus, Dyslipidemias, Cardiovascular Diseases, Pulmonary Disease, Chronic Obstructive, Asthma
- Being tested
- pharmacist-supervised AI-assisted medication counseling
- Medical subject headings
- Hypertension, Diabetes Mellitus, Dyslipidemias, Cardiovascular Diseases, Pulmonary Disease, Chronic Obstructive, Asthma, Chronic Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
pharmacist-supervised AI-assisted medication counselingOther
Also called 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.
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
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 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.
Measured: Day 0
Secondary outcomes (9)
Number of Counseling Deficiencies Categorized by Clinical Severity
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).
Measured: Day 0
Score on the General Medication Adherence Scale (GMAS)
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.
Measured: 30 Days Post-Encounter
Number of Participants Achieving Good Adherence
The number of participants meeting the validated threshold for "good adherence" based on their GMAS survey responses.
Measured: 30 Days Post-Encounter
Total Score on the Immediate Patient Understanding (Teach-Back) Assessment
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.
Measured: Day 0
Total Score on the Patient Satisfaction Questionnaire
A questionnaire covering clarity, usefulness, confidence, and overall satisfaction. Total scores range from 5 to 25, with higher scores indicating greater patient satisfaction.
Measured: Day 0
Time Spent on Face-to-Face Counseling
Total face-to-face counseling time measured in minutes using audio timestamps from the start of counseling to completion.
Measured: Day 0
Number of Encounters Based on AI Output Acceptance Level
The proportion of encounters in which the AI-generated counseling output was fully accepted, edited before delivery, or rejected outright by the pharmacist.
Measured: Day 0
Number of AI-Related Discrepancies Identified
The frequency of detected AI inaccuracies prior to counseling, such as omitted counseling points, overly technical wording, or incomplete missed-dose advice.
Measured: Day 0
Number of Clinical Near Misses and Safety Incidents
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).
Measured: Day 0
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Jun 2026
- Study start1 Jan 2026
- Primary completion30 Mar 2026
- Study completion30 Mar 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for HypertensionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- 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.
- Masking
- Quadruple blind
- Who is masked
- Participant, Care provider, Investigator, Outcomes assessor
- Enrolment
- 136 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Petra
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Derar H. Abdel-Qader, University of Petra
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07649577
- Sponsor's protocol ID
- Petrauniversity
- Status last verified
- Jun 2026
- First submitted
- 3 Jun 2026
- Last updated
- 16 Jun 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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
Study at a glance
From the registry record, last updated by the sponsor on 16 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Human-AI Collaboration in the Pharmacy: A Cluster Randomized Controlled Trial of Generative AI for Medication Counselling and Adherence
- ClinicalTrials.gov ID
- NCT07649577
- Acronym / sponsor ID
- Petrauniversity
- Conditions
- Hypertension, Diabetes Mellitus, Dyslipidemias, Cardiovascular Diseases, Pulmonary Disease, Chronic Obstructive, Asthma
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 136 participants (actual)
- Lead sponsor
- University of PetraUniversity, hospital or other
- Study start
- 1 Jan 2026 (actual)
- Primary completion
- 30 Mar 2026 (actual)
- Study completion
- 30 Mar 2026 (actual)
- First posted
- 16 Jun 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Quadruple blind (participant, care provider, investigator, outcomes assessor)
- Number of arms
- 2
- 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.
| Arm | Type | Receives |
|---|---|---|
| Intervention arm procedures | Active comparator | Other: pharmacist-supervised AI-assisted medication counselingFor 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. |
| 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- 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.
- 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.
Key exclusion criteria (5 listed)
- 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 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| pharmacist-supervised AI-assisted medication counselingAlso: Intervention arm proceduresFor 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 | Intervention arm procedures |
Primary outcome
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
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.
9 secondary outcomes
Number of Counseling Deficiencies Categorized by Clinical Severity
Time frame: Day 0
Score on the General Medication Adherence Scale (GMAS)
Time frame: 30 Days Post-Encounter
Number of Participants Achieving Good Adherence
Time frame: 30 Days Post-Encounter
Total Score on the Immediate Patient Understanding (Teach-Back) Assessment
Time frame: Day 0
Total Score on the Patient Satisfaction Questionnaire
Time frame: Day 0
Time Spent on Face-to-Face Counseling
Time frame: Day 0
Number of Encounters Based on AI Output Acceptance Level
Time frame: Day 0
Number of AI-Related Discrepancies Identified
Time frame: Day 0
Number of Clinical Near Misses and Safety Incidents
Time frame: Day 0
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Jun 2026.
- Results publicationAbdel-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
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing pharmacist-supervised AI-assisted medication counseling in people with Hypertension and Diabetes Mellitus, run by University of Petra at 1 site in Jordan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 16 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Jordan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Percentage of Applicable Counseling Domains Provided Correctly (time frame: day 0). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07649577, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 16 Jun 2026.