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Published on: August 12, 2016
Training clinicians to facilitate diabetes group visits
Moe Ameri1, Layla Abdul Jabbar1, Stephanie Kim2
1Department of Internal Medicine, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555, USA.
Introduction:
Managing diabetes in community clinics often presents substantial challenges. Diabetes group visits, shared medical appointments involving a clinician, offer a potential solution to these challenges. However, there is a need for training clinicians to effectively lead and facilitate these group visits.
Aim:
The aim of this study is to develop and assess a training program designed to equip clinician learners with the skills to facilitate diabetes group visits.
Methods:
Using the ADDIE model (Analysis, Design, Development, Implementation, Evaluation) as a guiding framework, we developed a 3-hour virtual training program for clinician learners at two community clinics. The training covered group visit facilitation and the logistics of 1:1 clinician-patient encounters. For the patient encounter component, we applied evidence-based guidelines, such as those from the American Diabetes Association, to create novel algorithms specifying low-cost medications for diabetes, hypertension, and hyperlipidemia. Training effectiveness was evaluated through learner participation (6-10 learners per site), knowledge improvement measured via pre- and post-tests, case studies, and learner feedback.
Results:
Clinics successfully met their clinician recruitment targets. Learners showed proficiency in applying the medication algorithms through five case studies. Knowledge improved significantly from the pretest (46.36%) to the posttest (92.95%) (p < 0.001). Learner feedback indicated high satisfaction with the training's structure, content, and relevance, particularly in relation to using the algorithms to manage diabetes in low-income settings.
Discussion:
This study demonstrates the successful development of a diabetes group visit training for clinicians, as evidenced by recruitment success, knowledge improvement, and positive feedback. The low-cost medication algorithms served as a valuable resource for clinicians.
Clinical Trial:
NCT04835493.
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