Making Computerized Trauma Triage Decision Support Accurate and Trustworthy

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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.