Transparency in Admissions and Personalized Learning Through Resident Patient Selection.

Area of Science:

• Medical Education
• Healthcare Administration

Background:

• Adult learning principles (andragogy) emphasize self-directed learning for enhanced educational experiences.
• Traditional patient assignment models often rely on faculty perception, not learner needs.
• Optimizing patient allocation between teaching and non-teaching services is institution-dependent.

Purpose of the Study:

• To evaluate a novel patient assignment model prioritizing resident-identified learning needs.
• To assess the impact of a learner-driven model on resident education and hospitalist workflow.
• To determine if this new model affects patient care metrics like case mix index and length of stay.

Main Methods:

• Implemented a new patient assignment model centered on residents selecting cases aligned with their learning goals.
• Conducted a 1-month pilot followed by a 5-month implementation with senior residents and hospitalists.
• Surveyed residents and hospitalists to gather feedback on the new process and its impact.

Main Results:

• 100% of surveyed residents confirmed the model met their learning objectives and supported its continuation.
• 81% of hospitalists reported a positive impact on workflow, with 76% favoring the new process.
• The model demonstrated no adverse effects on case mix index or patient length of stay.

Conclusions:

• Restructuring patient assignment based on educational theory can enhance resident training.
• A learner-centered approach may improve hospitalist workflow efficiency.
• Integrating andragogy principles into patient allocation offers a viable model for medical education.