Improving safety and quality of emergency care using machine learning-based clinical decision support at triage

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Abstract Current emergency department (ED) triage systems across the U.S. lead to mis-triage in up to one third of patient encounters, worsening ED crowding and contributing to delays and disparities in care. Early studies showed triage prediction models outperformed the subjective triage systems used in most EDs by prioritizing sicker patients, although significant gaps remain. Early models did not: 1) predict outcomes other than hospital admission, even though 80-90% of ED visits do not result in hospital admission; 2) include pediatric patients, even though pediatric patients contribute up to 25% of visits; 3) consider health equity in design or evaluation of prediction models; or 4) study impacts on key patient safety and quality measures, such as timeliness of care. Our proposal addresses these unmet needs and responds to two AHRQ Special Emphasis Notices, HS- 21-014 (Health Services Research to Advance Health Equity) and HS-22-004 (Research on Digital Healthcare Safety). Our study team has completed significant preliminary analyses, including: 1) study cohort build of over 6 million ED encounters across the 21 EDs in our health system; 2) assessment of significant limitations of triage in study setting; and 3) development of machine-learning models to predict patient acuity and resource needs at triage. In Aim 1, we will refine triage models that predict: 1) critical illness; 2) hospital admission; and 3) fast-track eligibility (<2 resources needed, no hospital admission or critical outcomes). We will measure algorithm biases and explore strategies to improve equity in triage model predictions. In Aim 2, we will map probability thresholds for each outcome into clinically relevant triage category recommendations. We will use a human factors framework and significant stakeholder engagement to design, build, and evaluate clinician- facing triage clinical decision support (CDS). Lastly, we will build the CDS into our electronic health record to efficiently display personalized risk predictions for each outcome as part of standard triage workflows. In Aim 3, we will assess the impact of the CDS in real time in a pragmatic, step-wedged cluster randomized trial across 21 hospital-based EDs and one free-standing ED. Our primary outcomes will be: 1) timeliness of care for critically ill patients; 2) appropriate early identification of fast-track eligible patients; and 3) ED length of stay. In addition, to test the equity-driven calibrations in our models, we will assess for bias by race, gender, and socioeconomic status among primary outcomes. Our secondary outcomes will be CDS reach, adoption, and implementation. Upon successful completion of the proposed research, we expect to demonstrate the extent to which a novel point-of-care digital technology that uses advanced predictive analytics can lead to safer, higher quality, and more equitable care.