Second, as highlighted by Sax et al., in their Conclusion, we must embrace a more data-driven and objective approach to ED triage.¹³ Widespread adoption of the EHR has generated continuously growing pools of clinical data with potential to inform and improve ED care delivery. Artificial intelligence (AI) applications that leverage these data to provide easily accessible (i.e., embedded within EHR workflow) decision support are a promising means to achieve more accurate triage.^11,20,21 AI algorithms can use historical data to rapidly estimate clinical risk for individual patients in real-time and can provide decision rationale. These algorithms can be adapted to each ED site to account for differences in patient populations, resource availability, and operational objectives.

AI-driven approaches also generate opportunities for increasing triage equity. AI in medicine has been met with justified concern for perpetuation of bias and exacerbation of social inequities.^22–23 However, most risk for algorithmic bias is conferred from datasets used for algorithm development; datasets that were created by human-based structures and systems. The same data science methods that empower AI can provide a means to interrogate, expose, and understand existing bias. Once uncovered, AI-based methods can be used to mitigate bias.^24,25 This includes the power to intervene on potential biases directly at the point-of-care. The need for such an approach to ED triage is clear.

Nearly a decade ago, through a federally funded collaboration between data scientists, emergency nurses and physicians, our institution developed a CDS tool that leverages AI to generate risk-driven triage acuity recommendations embedded into the EHR workflow.^11,12 In 2017, we implemented this tool in place of ESI. Using it, we have been able to more reliably identify patients with critical illness and reduce the time these patients wait for care. We have decreased the proportion of patients allocated to mid-point Level 3 by increasing our usage of Levels 4 and 5—without increasing risk or length of stay for this low-acuity group.^26,27 Our data-driven approach has also generated outcome-rich data streams that inform quality and nursing leadership and facilitate practice-based learning. In 2018, the tool became the cornerstone for our department’s Nursing Magnet Designation. With support from the National Science Foundation, it has since been commercialized and is now being introduced to other EDs worldwide.

Dr. Hinson is an associate professor of emergency medicine and co-director of the Center for Data Science in emergency medicine at The Johns Hopkins University.

Dr. Levin is the senior director for Research and Innovation for the Clinical Decision Support Solutions Unit at Beckman Coulter Diagnostics.