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

点击翻译按钮获取中文摘要

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.