Advisor(s)
Nancy Borkowski
Tapan Mehta
Committee Member(s)
Erin Delaney
Kala Dixon
Larry Hearld
School
School of Health Professions
Document Type
Dissertation
Department (new version)
Administration/Health Services
Date of Award
9-11-2025
Abstract
The rising prevalence of obesity, diabetes, and cardiovascular disease (CVD) places increasing strain on healthcare systems and primary care providers (PCPs). In response, the University of Alabama at Birmingham (UAB) adapted the Cardiometabolic Disease Staging System (CMDS), a validated clinical decision support tool, designed to stratify patient risk and guide evidence-based treatment. Embedded within the electronic health record (EHR) as a Care Pathway, the CMDS was designed for real-time use during primary care visits. This dissertation examines utilization of the CMDS in a two-phase case study, guided by the Adult Learning Theory, Group Dynamics Theory, Force Field Theory and Change As Three Steps (CATS) model. Phase I examined whether the delivery method of training, synchronous (live Clinical Conference) versus asynchronous (recorded session), influenced adoption. Among 120 UAB PCPs, synchronous attendees demonstrated significantly higher tool utilization (38.9%) compared to asynchronous learners (6.9%) (p = 0.001, Fisher’s Exact Test), and used the tool more frequently (mean 2.71 vs. 1.00 interactions; p = 0.030, Mann-Whitney U Test). Those attending synchronously also reported high usability (SUS = 80.9) and strong scores for acceptability (AIM = 4.52), appropriateness (IAM = 4.57), and feasibility (FIM = 4.43). Phase II investigated the underlying causes of overall low adoption (14 users of 120) through nine semi-structured interviews. Thematic analysis revealed eight factors acting as driving and restraining forces. Facilitators included reduced cognitive load, peer modeling, low click burden, and patient-facing features. Barriers included lack of visibility, poor workflow fit, tool redundancy, and time constraints. Another embedded decision support system tool, the ASCVD Risk Calculator, emerged as a model of clinical tool success. Overall, findings suggest that even well-designed tools may fail without thoughtful implementation. Early training, visible placement in the EHR, and peer-led modeling emerged as key strategies to drive behavior change. Conversely, tools poorly embedded in existing workflows face significant resistance. Future work should evaluate scalable implementation models and conduct formal force field analyses to better quantify the strength of adoption barriers and facilitators.
ProQuest ID
Recommended Citation
Park, Khushbu Khatri, "Understanding Clinical Tool Adoption In Primary Care: A Case Study Of The Cmds Care Pathway" (2025). All ETDs from UAB. 7371.
https://digitalcommons.library.uab.edu/etd-collection/7371
