Abstract
Identifying critically ill patients is a key challenge in emergency department (ED) triage. Mis-triage errors are still widespread in triage systems around the world. Here, we present a machine learning system (MLS) to assist ED triage officers better recognize critically ill patients and provide a text-based explanation of the MLS recommendation. To derive the MLS, an existing dataset of 22,272 patient encounters from 2012 to 2019 from our institution’s electronic emergency triage system (EETS) was used for algorithm training and validation. The area under the receiver operating characteristic curve (AUC) was 0.875 ± 0.006 (CI:95%) in retrospective dataset using fivefold cross validation, higher than that of reference model (0.843 ± 0.005 (CI:95%)). In the prospective cohort study, compared to the traditional triage system’s 1.2% mis-triage rate, the mis-triage rate in the MLS-assisted group was 0.9%. This MLS method with a real-time explanation for triage officers was able to lower the mis-triage rate of critically ill ED patients.
Highlights
Identifying critically ill patients is a key challenge in emergency department (ED) triage
There are still some critical patients triaged into emergency triage scale/standard (ETS) levels 3 or 4 that need to wait in line with other non-critical patients until they are identified in subsequent medical encounters, sometimes several hours after the initial triage screening
This study aimed to derive and validate a machine learning method to support the identification of potentially life-threatening mis-triage and offer at triage a real-time, detailed explanation showing why the algorithm scored a patient as high risk in the hope of improving the detection of ED patients in need of critical care
Summary
In the prospective cohort study, compared to the traditional triage system’s 1.2% mis-triage rate, the mis-triage rate in the MLS-assisted group was 0.9% This MLS method with a real-time explanation for triage officers was able to lower the mistriage rate of critically ill ED patients. There are still some critical patients triaged into ETS levels 3 or 4 that need to wait in line with other non-critical patients until they are identified in subsequent medical encounters, sometimes several hours after the initial triage screening. This study aimed to derive and validate a machine learning method to support the identification of potentially life-threatening mis-triage and offer at triage a real-time, detailed explanation showing why the algorithm scored a patient as high risk in the hope of improving the detection of ED patients in need of critical care
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