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Research Snapshot Theater: Quality and Patient Safety, Adult II

Advancing Trauma ICU Onboarding and Protocol Adherence via AI Retrieval-Augmented Generation

Tuesday, March 24, 2026
11:15 AM - 12:15 PM Central Time
Location: Connections Central - RST 10

  • Celestino Castanon, MD (he/him/his)

    Louisiana

    Disclosure(s): No relevant financial relationship(s) to disclose.

  • JB

    Jennifer Brewer, MD

    Trauma and Surgical Critical Care Fellow
    Louisiana State University Health Sciences Center

    Disclosure information not submitted.

  • MS

    McKenzie Schwarze, MD

    Med–Peds Resident (PGY4) - LSU Health Sciences Center, Louisiana State University Health
    LSUHSC

    Disclosure information not submitted.

  • PG

    Patrick Greiffenstein, MD, FACS

    Trauma Intensive Care Unit Medical Director at LSU Health Sciences Center
    Louisiana State University Health Sciences Center

    Disclosure information not submitted.

  • AS

    Alison Smith, MD, PhD, FACS

    Trauma Medical Director, McSwain Level 1 Trauma Center
    Louisiana State University Health Sciences Center

    Disclosure information not submitted.

Introduction: Retrieval-Augmented Generation (RAG), an emerging AI architecture for large language models (LLMs), has been shown to improve model performance and reliability in medical applications by grounding responses in external, verified sources such as clinical practice guidelines. Our Level I trauma intensive care unit, which trains over 50 rotating residents from multiple specialties annually, exemplifies a clinical environment where rapid access to standardized information is critical. While institutional protocols and onboarding materials are accessible, they are housed as extensive documents within departmental intranet sites, rendering them cumbersome and impractical for point-of-care decision-making. Our goal was to evaluate the feasibility of deploying a centralized RAG platform to provide residents with immediate, verifiable answers to clinical queries sourced directly from our institutional protocols.


Methods: Following a Plan-Do-Study-Act methodology, 26 institutional protocols were integrated into a free-to-use RAG platform to create a proof-of-concept LLM tool grounded exclusively in our uploaded source protocols. This prototype was then tested for retrieval speed and accuracy on 10 clinical queries representing a wide variety of clinical scenarios. Of note, AI was used to aid with grammar, spelling, and clarity in the preparation of this abstract.


Results: The prototype demonstrated scalability by successfully indexing all 26 institutional protocols with significant capacity for additional source material. Validation queries (n=10) were answered in a mean of 6.1 seconds with 100% citation accuracy. Each answer was fully verifiable, providing a direct hyperlink to the specific source text within the original protocol.


Conclusions: This study demonstrates the feasibility of developing a RAG-based AI platform to improve protocol accessibility for residents in a critical care setting. By providing rapid, verifiable answers, this tool addresses knowledge retrieval challenges in time-critical situations. Future iterations will use query analytics to identify knowledge gaps and inform targeted educational interventions relevant to the most queried topics. The platform is scalable as a reproducible framework for AI-augmented clinical decision support in other clinical environments.