Background: Internal medicine residents receive inconsistent consultation training, focusing on which specialists to consult rather than urgency timing. At our residency program, only 12% of residents rated consultation teaching as excellent quality, with inadequate faculty preparation time as the primary barrier.1 No quantitative frameworks exist for systematically teaching consultation urgency assessment,2 leaving residents to develop this critical skill through trial-and-error.
Purpose: To develop and pilot a Consultation Signal Index (CSI) quantifying consultation urgency using a 0-100% scoring system with integrated educational rationale for Internal Medicine residency teaching.
Description: CSI Algorithm Architecture: We developed a hybrid consultation urgency scoring system combining rule-based clinical guidelines with AI-powered contextual analysis. The algorithm processes patient demographics, symptoms, comorbidities, vitals, and diagnostics to generate urgency scores (0-100%) mapped to five tiers: 0-20% (primary team workup), 21-50% (routine consultation), 51-70% (time-sensitive), 71-85% (urgent), 86-100% (STAT).Educational Components: CSI generates specialty recommendations with evidence citations, missing data identification, initial workup guidance, red flag indicators, and secondary specialty considerations.Quality Assurance: CSI employs five-stage validation: Evidence Verification, Guideline Currency (2020+), Clinical Accuracy Review, Inter-rater Reliability Testing, and Error Correction Protocol.3Implementation: CSI was integrated into the MNEMORAI teaching platform and piloted across 12 Internal Medicine residency afternoon teaching sessions over three months. Five attending hospitalists used CSI-generated consultation scenarios with 20 PGY-2/3 residents.Preliminary Data: Faculty reported 100% agreement between CSI scores and expert judgment during post-session reviews. Residents demonstrated improved understanding of consultation timing, articulating urgency rationale in discussions. Faculty feedback: “CSI transformed how I teach consultation timing—residents now understand urgency gradients, not binary decisions.”
Conclusions: CSI successfully operationalized consultation urgency assessment for graduate medical education, representing the first quantitative framework for teaching consultation timing rather than specialty selection alone. Faculty validation confirmed clinical accuracy and educational utility. By providing structured decision scaffolding, CSI addresses gaps in residency curricula identified by ACGME Milestones (Systems-Based Practice: Consultation competency).4 The innovation is scalable across specialties and institutions. Future work includes prospective validation comparing CSI-trained versus traditional-curriculum residents on consultation appropriateness metrics. CSI demonstrates how AI-augmented education can systematize expert tacit knowledge, democratizing consultation expertise regardless of faculty availability.
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