# Understanding and Addressing Disparities in Triage and Disposition Decisions in the Emergency Department

> **NIH AHRQ R03** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2022 · $99,854

## Abstract

Abstract
There are nearly 150 million emergency department (ED) visits in the United States each year. Patients
present to EDs for a wide range of health problems and acuities from life-threatening emergencies to
ambulatory conditions. Further, the ED patient population is highly diverse with respect to demographics,
cultural, and socioeconomic factors. Providing equitable care in the high-volume, time-constrained, and diverse
ED setting has proven to be a challenge with growing evidence of disparities in clinical decision making and
health care delivery for racial and ethnic minorities and women. Triage and disposition decisions involve some
subjectivity and are especially prone to bias. Our preliminary analyses of electronic health record (EHR) data
from a single academic ED found evidence of disparities in ED triage, prioritization for rooming, and hospital
admission decisions. Our long-term research goal is to fully characterize gender, racial, and ethnic disparities
in ED triage and disposition decisions in the United States and to apply statistical and machine learning
methods to develop and evaluate innovative solutions to mitigate these disparities. Our research will be
embedded within a Learning Health System that integrates scientific evidence, internal data, and stakeholder
engagement to improve equity of healthcare delivery in the ED. As an initial step, we will obtain and analyze
retrospective EHR data from 10 diverse EDs across a large health system. Our aims are to: (1) identify patient
gender, racial, and ethnic disparities in ED decisions (triage level assignment, rooming priority, and hospital
admission) and determine whether ED operating conditions (e.g., volumes, wait times) exacerbate these
disparities; and (2) develop a prototype machine learning model that integrates patient- and ED-level data to
predict potentially inequitable decision making in the ED. Upon successful completion of this pilot project, we
will have obtained essential preliminary evidence to fully develop a novel machine learning prediction model
and validate the model in multiple Learning Health Systems. In future research, we also intend to investigate
potential applications of the machine learning model of inequitable deicison making, such as a point-of-care
tool to alert ED providers and a data monitoring and reporting feedback system for ED providers and
administrators and health system leaders. Findings from this research has the potential to lead to innovative
data-driven solutions to promote equitable patient-centered care for the millions who present to EDs each year.

## Key facts

- **NIH application ID:** 10510091
- **Project number:** 1R03HS029078-01
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Mehul D. Patel
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2022
- **Award amount:** $99,854
- **Award type:** 1
- **Project period:** 2022-08-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10510091

## Citation

> US National Institutes of Health, RePORTER application 10510091, Understanding and Addressing Disparities in Triage and Disposition Decisions in the Emergency Department (1R03HS029078-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10510091. Licensed CC0.

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