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Generative AI for Medication Counselling and Adherence in Community Pharmacies

9 de junho de 2026 atualizado por: 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.

Visão geral do estudo

Status

Concluído

Condições

Intervenção / Tratamento

Descrição detalhada

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.

Tipo de estudo

Intervencional

Inscrição (Real)

136

Estágio

  • Não aplicável

Contactos e Locais

Esta seção fornece os detalhes de contato para aqueles que conduzem o estudo e informações sobre onde este estudo está sendo realizado.

Locais de estudo

  • Jordânia
      • Amman, Jordânia
        • Petra University

Critérios de participação

Os pesquisadores procuram pessoas que se encaixem em uma determinada descrição, chamada de critérios de elegibilidade. Alguns exemplos desses critérios são a condição geral de saúde de uma pessoa ou tratamentos anteriores.

Critérios de elegibilidade

Idades elegíveis para estudo

  • Adulto
  • Adulto mais velho

Aceita Voluntários Saudáveis

Sim

Descrição

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.

Plano de estudo

Esta seção fornece detalhes do plano de estudo, incluindo como o estudo é projetado e o que o estudo está medindo.

Como o estudo é projetado?

Detalhes do projeto

  • Finalidade Principal: Outro
  • Alocação: Randomizado
  • Modelo Intervencional: Atribuição Paralela
  • Mascaramento: Quadruplicar

Armas e Intervenções

Grupo de Participantes / Braço
Intervenção / Tratamento
Comparador Ativo: 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.
Outro: pharmacist-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

Outros nomes:
  • Intervention arm procedures
Sem intervenção: 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.

O que o estudo está medindo?

Medidas de resultados primários

Medida de resultado
Descrição da medida
Prazo
Percentage of Applicable Counseling Domains Provided Correctly
Prazo: 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
day 0
Percentage of Essential Counseling Domains Addressed
Prazo: 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.
Day 0

Medidas de resultados secundários

Medida de resultado
Descrição da medida
Prazo
Number of Counseling Deficiencies Categorized by Clinical Severity
Prazo: 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).
Day 0
Score on the General Medication Adherence Scale (GMAS)
Prazo: 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.
30 Days Post-Encounter
Number of Participants Achieving Good Adherence
Prazo: 30 Days Post-Encounter
The number of participants meeting the validated threshold for "good adherence" based on their GMAS survey responses.
30 Days Post-Encounter
Total Score on the Immediate Patient Understanding (Teach-Back) Assessment
Prazo: 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.
Day 0
Total Score on the Patient Satisfaction Questionnaire
Prazo: 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.
Day 0
Time Spent on Face-to-Face Counseling
Prazo: Day 0
Total face-to-face counseling time measured in minutes using audio timestamps from the start of counseling to completion.
Day 0
Number of Encounters Based on AI Output Acceptance Level
Prazo: 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.
Day 0
Number of AI-Related Discrepancies Identified
Prazo: Day 0
The frequency of detected AI inaccuracies prior to counseling, such as omitted counseling points, overly technical wording, or incomplete missed-dose advice.
Day 0
Number of Clinical Near Misses and Safety Incidents
Prazo: 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).
Day 0

Colaboradores e Investigadores

É aqui que você encontrará pessoas e organizações envolvidas com este estudo.

Patrocinador

University of Petra

Publicações e links úteis

A pessoa responsável por inserir informações sobre o estudo fornece voluntariamente essas publicações. Estes podem ser sobre qualquer coisa relacionada ao estudo.

Publicações Gerais

  • 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

Datas de registro do estudo

Essas datas acompanham o progresso do registro do estudo e os envios de resumo dos resultados para ClinicalTrials.gov. Os registros do estudo e os resultados relatados são revisados ​​pela National Library of Medicine (NLM) para garantir que atendam aos padrões específicos de controle de qualidade antes de serem publicados no site público.

Datas Principais do Estudo

Início do estudo (Real)

1 de janeiro de 2026

Conclusão Primária (Real)

30 de março de 2026

Conclusão do estudo (Real)

30 de março de 2026

Datas de inscrição no estudo

Enviado pela primeira vez

3 de junho de 2026

Enviado pela primeira vez que atendeu aos critérios de CQ

9 de junho de 2026

Primeira postagem (Real)

16 de junho de 2026

Atualizações de registro de estudo

Última Atualização Postada (Real)

16 de junho de 2026

Última atualização enviada que atendeu aos critérios de controle de qualidade

9 de junho de 2026

Última verificação

1 de junho de 2026

Mais Informações

Termos relacionados a este estudo

Termos MeSH relevantes adicionais

Outros números de identificação do estudo

  • Petrauniversity

Plano para dados de participantes individuais (IPD)

Planeja compartilhar dados de participantes individuais (IPD)?

NÃO

Informações sobre medicamentos e dispositivos, documentos de estudo

Estuda um medicamento regulamentado pela FDA dos EUA

Não

Estuda um produto de dispositivo regulamentado pela FDA dos EUA

Não

Essas informações foram obtidas diretamente do site clinicaltrials.gov sem nenhuma alteração. Se você tiver alguma solicitação para alterar, remover ou atualizar os detalhes do seu estudo, entre em contato com register@clinicaltrials.gov. Assim que uma alteração for implementada em clinicaltrials.gov, ela também será atualizada automaticamente em nosso site .

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