Emergency Department Access

The goal of this analysis is to figure out how long people have to wait to be seen and how many decide to leave before being seen at different hospitals.

Overcrowding and long wait times in emergency departments (EDs) are a problem in the U.S. health care system. Studies have consistently found that hospitals dealing with more crowding and longer wait times tend to deliver lower-quality care and outcomes (Jones et al., 2021, Pearce et al., 2023). Long wait times to be seen in the ED are associated with longer lengths of stay, increased morbidity and mortality, patients leaving the ED without being seen, unhappy patients, and higher bills (Darraj et al., 2023).

There is growing emphasis on adopting interventions that have proven effective in decreasing ED wait times and crowding. However, only a small number of hospitals have been successful in implementing strategies for decreasing ED wait times (Manning et al., 2023).

This analysis compares how long people have to wait to be seen in the ED and how this impacts decisions to leave before being seen at different hospitals in the U.S. The data can help identify the factors that contribute to ED overcrowding and wait times and suggest effective interventions to improve ED efficiency and quality of care.

This data is also available as an Excel spreadsheet.

ED2024.xlsx (1.1 MB)

Data Sources
Data Definitions

This data represents the most current publicly available information sources that are commonly used to study health care trends. The data is derived from the data sources listed below and combined to support the analysis.

Care Compare - Hospital General Information (data released July 2024)
Care Compare - Timely and Effective Care Measures (data released July 2024)

A blank entry indicates unreported data. A value of zero is a defined value and does not represent unreported data.

Facility ID: This number is a unique identifier for the hospital.

FIPS: The federal information processing standard (FIPS) code for the geographic location

% Rural: The percentage of the county population living in a census-defined rural area.

RUCA: The Rural-Urban Commuting Area Code for the associated hospital.

County: The name of the county where the hospital is located. County names are listed as provided on the US Census Bureau's list of 2020 FIPS Codes for Counties and County Equivalent Entities.

County Population Size: The total number of individuals residing in the county. The data is derived from the Census Population Estimates from 2022.

Facility Name: The name of the hospital.

Address: The address for the hospital.

City: The city where the hospital is located.

State: The abbreviated name of the state where the hospital is located.

ZIP Code: The zip code for the hospital.

Hospital Type: The type of hospital, which includes acute care hospitals, acute care -Department of Defense hospitals, critical access hospitals, and children's hospitals.

Hospital Ownership: The ownership type for the hospital, which includes: Voluntary non-profit - Private, Voluntary non-profit - Other, Voluntary non-profit - Church, Tribal, Proprietary, Physician, Government - State, Government - Local, Government - Hospital District of Authority, Government - Federal, Department of Defense.

Measure Name: The name of the timely and effective care measure. The measures include the following:

OP_18b: Average (median) time patients spent in the emergency department before leaving from the visit. A lower value is desired.
OP_18c: Average (median) time patients spent in the emergency department before leaving from the visit- Psychiatric/Mental Health Patients. A lower number of minutes is better.
OP_22: Percentage of patients who left the emergency department before being seen. A lower value is desired.

Measure Score: The numeric score for the measure which may represent the average time in minutes or a proportion depending on the measure. See Measure Name for more information on how to interpret the score for each measure. The reporting period for the OP_18b and OP_18c measures is July 1, 2022 through June 30, 2023 and for OP_22 measure is January 1, 2022 through December 31, 2022.

Denominator: The sample size used to determine the score for the measure.

County Uninsured Rate: The estimated number of individuals in the county under age 65 without health insurance. Data is from the Small Area Health Insurance Estimates (SAHIE) Program from 2021.

Population Type: The population type is determined based on the population size of a specific county. The population types include metro, nonmetro cities, and nonmetro towns. These types are adapted from the rural-urban commuting area codes (RUCA) and core-based statistical areas (CBSA) definitions of rural and urban. The population types for counties are defined as follows:

Metro - A population of 50,000 or more
Nonmetro cities - A population between 2,500 and less than 50,000
Nonmetro towns - A population of less than 2,500

Tutorial Video

In this tutorial video, we look at ED Access. The video guides you through how to use Tableau data analysis to identify wait times and leave percentages across different types of hospitals.