Abstract for: Learning from the Adoption of a Readmissions Clinical Decision Support Tool with Group Model Building

Computerized clinical decision support (CDS) can improve care, reduce unwanted variations, and lower overall healthcare costs. Many tools have been developed, however, there exists no unified framework for implementation to identify conditions important for successful adoption. We conducted workshops with case managers, physicians, and physical and occupational therapists. Facilitators guided participants to identify and connect variables in causal loop diagrams. We coded workshop transcripts in DynamicVu software to identify themes, aggregated them into a single casual loop diagram, and reviewed with participants to converge on a common model. Simulation of the loops identified conditions leading to full, limited, or no adoption of a tool. We identified key balancing loops around responses to external pressure that drive initial adoption and reinforcing loops around internal perceived benefits that sustain the effort. The simulation model clarified conditions under which the balancing loops would only lead to limited long-term tool adoption, and situations in which the reinforcing loops will lead to more complete and sustained adoption External pressure to improve can be a strong motivator for initial adoption, but in the face of conflicting demands on attention it can fall short of sustained long-term tool use. Tools are more likely to have extensive and sustained use when those using the tools able to perceive internal benefits. Only to summarize our notes