Transparency in Admissions and Personalized Learning Through Resident Patient Selection.
Background: Adult learning (andragogy) posits that adult learners have an improved educational experience when engaged in self-directed learning. The decision to allocate patients to the teaching service vs a nonresident service varies according to institution. Previously, our institution focused on faculty perception of learning value as the deciding factor in patient assignment. We hypothesized that transitioning to a process in which adult learners (residents) select patients for their teams based on their own identified learning needs could improve the educational experience without adversely impacting the workflow for nonteaching teams. Methods: A new patient assignment model focused on learner-driven identification of patients for their own inpatient service, consistent with the principle of andragogy, was created. This patient assignment strategy was tested during a 1-month pilot period followed by a 5-month implementation period with 20 senior residents and 31 hospitalists. Both residents and hospitalists were surveyed after the intervention. Results: Sixteen of 20 residents completed the paper survey, and 100% of the respondents indicated "yes" when asked if they were able to direct cases to their team that were in line with their learning goals and if the new process should continue. Twenty-one of 31 hospitalists responded to the electronic survey; 81% of responding hospitalists reported a slightly positive to very positive impact on the hospitalist workflow, and 76% felt the new process should continue. The new patient assignment model had no negative impact on case mix index or length of stay. Conclusion: Restructuring patient assignment processes based on educational theory may improve resident education and improve hospitalist workflow.
Archibald, A; Zimmerman, P; Seay, W; Verma, L; Wilson, J; Sharma, P
Volume / Issue
Start / End Page
Pubmed Central ID
International Standard Serial Number (ISSN)
Digital Object Identifier (DOI)