# Making Computerized Trauma Triage Decision Support Accurate and Trustworthy

> **NIH NIH R15** · TENNESSEE TECHNOLOGICAL UNIVERSITY · 2022 · $373,481

## Abstract

Abstract/Summary
 Trauma triage frequently occurs in high stress environments characterized by time and information
constraints that are suboptimal for making consequential decisions. Such conditions have made it necessary to
rely on decision-making rulesets that are simple and straightforward enough for emergency medical personnel
to execute quickly while providing urgently needed patient care. To date, published triage studies have not
achieved the goals for trauma triage system performance despite efforts to optimize the trauma triage process.
Current triage systems may not be able to achieve these goals. Our prior work demonstrated that allowing more
complex rules with more detailed data can achieve a significant step toward those goals. Our long-term aim is
to build an intelligent, learning computerized trauma triage decision support (CTDS) system, that, aided by an
information-rich environment, collects and processes prehospital data and effectively communicates accurate
and understandable triage recommendations that improve patient outcomes. The proposed step toward this goal
will validate and extend our preliminary results and assess the complexity of AI-generated explanations intended
to improve the trustworthiness of such a CTDS system. We propose using a large demographically and
geographically diverse data set to first build and quantitatively assess the performance of multiple complex
models. We propose to then assess the group fairness of these complex models and evaluate multiple bias
mitigation strategies, and lastly, we propose working with paramedics to both design algorithmically generated,
EMS-oriented explanations and assess the trustworthiness of those explanations. The proposed project is
innovative, first, because it embraces the complexity that appears to be required to approach published accuracy
goals while simultaneously assessing practical techniques to address the challenges associated with that
complexity. Second, it will help define a path forward for trauma triage by addressing opportunities and
challenges that emerging technologies (e.g., low-cost, Internet-connected sensors) create for prehospital
decision making. The proposed project is significant because reducing the number of mistriaged patients can
result in substantial cost-savings and mortality reduction, but current triage systems may not be able to achieve
sensitivity and specificity goals or even significantly reduce current mistriage rates. Improving accuracy through
complex models, however, might not be enough to result in the impactful change we envision. The acceptance
of such recommendations from such models is likely to improve if bias known to be mitigated and if
recommendation explanations are seen as trustworthy.

## Key facts

- **NIH application ID:** 10515204
- **Project number:** 1R15LM013824-01A1
- **Recipient organization:** TENNESSEE TECHNOLOGICAL UNIVERSITY
- **Principal Investigator:** Douglas Alan Talbert
- **Activity code:** R15 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $373,481
- **Award type:** 1
- **Project period:** 2022-09-16 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10515204

## Citation

> US National Institutes of Health, RePORTER application 10515204, Making Computerized Trauma Triage Decision Support Accurate and Trustworthy (1R15LM013824-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10515204. Licensed CC0.

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