Triage Tool for Accurate Disposition of Patients in Disaster Response 


Scott Levin’s project seeks to develop a decision support tool to improve surge capacity in mass casualty incidents.



To improve hospital surge capacity by developing a rapid evidence-based tool to predict appropriate level of in-hospital care using emergency department information.

Completed Project Overview

Research activities for the PACER funded project "Triage Tool for Accurate Disposition of Emergency Patients in Disaster Response" has beendirected toward developing 'HopScore' a novel, electronic, outcomes-based triage tool. The tool, 'HopScore' (developed at Johns Hopkins) aims to support objective triage decisions and improve patient differentiation based on time-sensitive and critical patient outcomes.

HopScore uses easily obtained patient demographic and clinical information commonly collected at ED triage to predict patients risk for time-sensitive outcomes(i.e., in-hospital mortality, intensive care unit admission, emergent surgery or cardiac catheterization) and general inpatient admission. Inputinformation includes basic demographics, chief complaint, mode of arrival, and vital signs (temperature, heart rate, systolic blood pressure,respiratory rate, and oxygen saturation). HopScore was originally derived on a sample of 25,198 (97 million weighted) ED visits for adult (>18years) patients from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a nationally representative probability sample collectedby the Center for Disease Control and Prevention (CDC).

The tool was later refined to include over a 100,000 patients that were seen at the JohnsHopkins emergency department (ED). HopScore addresses deficiencies in the current standard, Emergency Severity Index (ESI) triage tool used by 72% of EDs across the United States (US). Deficiencies of ESI include: (1) no link to critical patient outcomes, (2) a strong reliance on triageevaluator's subjective judgment which results in variable application, (3) poor distribution (i.e., discrimination) of patients across the 5-levelindex; almost half of all patients nationally are undifferentiated, classified as acuity level 3. HopScore uses machine learning (i.e., decision tree)algorithm to render predictions (i.e., probability estimates) and then stratifies patients to a 5-level index based on risk of critical outcomes. Much of our analyses compares HopScore to the current standard ESI.


Principal Investigator(s) and Researchers(s)

Scott Levin, Ph.D.

Principal Investigator, PACER