# Health Inequality and a Machine Learning-Based Tool for Emergency Department Triage: A Mixed Methods Approach

> **NIH NIH F31** · UNIVERSITY OF PENNSYLVANIA · 2021 · $33,463

## Abstract

Project Summary
There is growing evidence that artificial intelligence (AI) technologies like machine learning (ML) can
perpetuate or even worsen social inequalities when deployed into real-world settings. This has been
demonstrated in many realms, including policing, the court system, banking, social services provision, and
there is growing concern the same is true in medicine. At the same time, there has been an outpouring of new
AI-based interventions, with a ten-fold increase in the number of Food and Drug Administration (FDA)
approvals for AI-based technologies since 2017. However, little research empirically examines the health
equity implications of ML-based clinical decision-making tools. One clinical arena in which ML-based tools are
already in use is emergency department (ED) triage, as an alternative to the common Emergency Severity
Index (ESI) system. Despite its widespread popularity, evidence has shown that ESI-based triage has many
problems, including poor acuity discrimination, with up to 50% of patients triaged at the midpoint of the scale,
and is associated with racial inequalities, with African-American patients experiencing longer wait-times and
lower triage levels controlling for illness severity. This study will use an ML-based ED triage tool that is already
in use at a major academic medical center in the United States to explore the extent to which several factors
are associated with inequality in predictive performance across patient racial/ethnic groups. This research will
take a mixed methods approach to concurrently examine both human and ‘machine’ elements that affect the
triage tool’s final impact on patients. Aim 1 will be a qualitative study involving ethnographic observation and
semi-structured interviewing of triage nurses, to develop a conceptual framework for clinicians’ understanding
of and interaction with an ML-based tool. Aim 2 will examine ‘label bias’, a type of measurement bias. The
Applicant will use synthetic and real electronic health record (EHR) data and simulate different levels of label
bias, then examine predictive performance of the triage tool across patient racial/ethnic groups. Aim 3 will
explore different methods for imputing missing EHR data. The Applicant will deploy common, simplistic
deletion-based methods as well as a promising new ML-based imputation method called an autoencoder,
apply the triage model to generate predictions and examine performance across patient racial/ethnic groups.
This project is innovative because it contributes to the development of a ‘life cycle’ model of ML-based tools
and their health equity implications using a mixed methods approach that integrates both human and
computational elements, while also providing a rigorous training plan for the Applicant, an MD-PhD student in
epidemiology. This training plan is rigorous, synergistic yet diverse, and will include advanced coursework,
dedicated 1-on-1 and group mentoring with experts in the field, attendance at se...

## Key facts

- **NIH application ID:** 10248299
- **Project number:** 5F31LM013403-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Stephanie Teeple
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $33,463
- **Award type:** 5
- **Project period:** 2020-08-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10248299

## Citation

> US National Institutes of Health, RePORTER application 10248299, Health Inequality and a Machine Learning-Based Tool for Emergency Department Triage: A Mixed Methods Approach (5F31LM013403-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10248299. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*