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.
