SHM Converge Scientific Abstract ePoster Gallery-Quantifying Urgency in Hospital Medicine Consults: A Novel Consultation Signal Index (Csi) for Residency Education

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This abstract describes the development and pilot testing of a new educational tool called the Consultation Signal Index (CSI) for internal medicine residency training. The problem identified is that resident consultation teaching is inconsistent and often emphasizes which specialty to consult rather than when consultation is urgent. At the authors’ program, only 12% of residents rated consultation teaching as excellent, and limited faculty time was a major barrier.  <br /><br />To address this gap, the team created a quantitative 0–100% scoring system using the MNEMORAI teaching platform. Over 3 months, 5 attending hospitalists led 12 teaching sessions for 20 PGY-2/3 residents. CSI scenarios used a hybrid algorithm combining rule-based clinical guidelines with AI contextual analysis. Inputs included demographics, symptoms, comorbidities, vital signs, and diagnostics. The system classified consult urgency into five levels: primary team workup, routine consultation, time-sensitive, urgent, and STAT.  <br /><br />CSI also provided educational support, including specialty recommendations with evidence citations, missing-data prompts, initial workup guidance, red flags, and secondary specialty considerations. The tool underwent a 5-stage quality assurance process covering evidence, guideline currency, accuracy, reliability, and error correction.  <br /><br />Results showed 100% agreement between CSI scores and expert faculty judgment during post-session review. Residents improved in their understanding of consultation urgency and were better able to explain the rationale behind timing decisions rather than treating consultation as a binary choice. Faculty feedback was strongly positive.  <br /><br />The authors conclude that CSI is the first quantitative framework aimed at teaching consultation timing, not just specialty selection. They suggest it could help meet ACGME consultation competency goals and be scalable across institutions and specialties. They propose future prospective studies comparing CSI-trained residents with those taught using traditional curricula.

Asset Subtitle

Luis Daniel Lugo

Meta Tag

Author List Anas Bizanti, Chia-Hung Sze, Daniela Carralero-Somoza, Luis Daniel Lugo, Omar Zuhdi, Zoya Khan

Category Innovations

Concept Consultation Signal Index

Concept Consultation Urgency

Concept Five-Tier Spectrum

Concept STAT Consult

Concept Emergent Consult

Distinguished Non-Finalist

Presenter Organization Lakeland Regional Health

Presenting Author Luis Daniel Lugo

Track Consultative Medicine

Keywords

Consultation Signal Index

internal medicine residency

consultation urgency

resident education

MNEMORAI platform

AI clinical decision support

quantitative scoring system

consultation timing

hospitalist teaching

ACGME competency

Consultation Signal Index

Consultation Urgency

Five-Tier Spectrum

STAT Consult

Emergent Consult