Automated analysis of unstructured clinical assessments improves emergency department triage performance: A retrospective deep learning analysis

Author:

Sax Dana R.1ORCID,Warton E. Margaret2ORCID,Sofrygin Oleg3,Mark Dustin G.1ORCID,Ballard Dustin W.4ORCID,Kene Mamata V.4ORCID,Vinson David R.5ORCID,Reed Mary E.2ORCID

Affiliation:

1. Department of Emergency Medicine Kaiser East Bay and Kaiser Permanente Northern California Division of Research Oakland California USA

2. Kaiser Permanente Northern California Division of Research Oakland California USA

3. Uber San Francisco California USA

4. Department of Emergency Medicine Kaiser San Rafael and Kaiser Permanente Northern California Division of Research Oakland California USA

5. Department of Emergency Medicine Roseville, and Kaiser Permanente Northern California Division of Research Oakland California USA

Abstract

AbstractObjectivesEfficient and accurate emergency department (ED) triage is critical to prioritize the sickest patients and manage department flow. We explored the use of electronic health record data and advanced predictive analytics to improve triage performance.MethodsUsing a data set of over 5 million ED encounters of patients 18 years and older across 21 EDs from 2016 to 2020, we derived triage models using deep learning to predict 2 outcomes: hospitalization (primary outcome) and fast‐track eligibility (exploratory outcome), defined as ED discharge with <2 resource types used (eg, laboratory or imaging studies) and no critical events (eg, resuscitative medications use or intensive care unit [ICU] admission). We report area under the receiver operator characteristic curve (AUC) and 95% confidence intervals (CI) for models using (1) triage variables alone (demographics and vital signs), (2) triage nurse clinical assessment alone (unstructured notes), and (3) triage variables plus clinical assessment for each prediction target.ResultsWe found 12.7% of patients were hospitalized (n = 673,659) and 37.0% were fast‐track eligible (n = 1,966,615). The AUC was lowest for models using triage variables alone: AUC 0.77 (95% CI 0.77–0.78) and 0.70 (95% CI 0.70–0.71) for hospitalization and fast‐track eligibility, respectively, and highest for models incorporating clinical assessment with triage variables for both hospitalization and fast‐track eligibility: AUC 0.87 (95% CI 0.87–0.87) for both prediction targets.ConclusionOur findings highlight the potential to use advanced predictive analytics to accurately predict key ED triage outcomes. Predictive accuracy was optimized when clinical assessments were added to models using simple structured variables alone.

Publisher

Wiley

Subject

Emergency Medicine

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