Emergency room visit rates across the United States show significant variation, with a national average of 422 visits per 1,000 population in 2023. This average masks considerable differences between states, ranging from 596 visits per 1,000 population in West Virginia to just 226 in Nevada. Wait times in emergency rooms While ER visit rates provide insight into utilization, wait times offer a glimpse into the efficiency of emergency care delivery. In 2022, ER patients waited an average of 38.1 minutes to see a healthcare provider in emergency departments nationwide. Interestingly, the COVID-19 pandemic temporarily reduced wait times in 2020, but they rebounded to pre-pandemic levels by 2021. Most patients, roughly 70 percent, spend less than an hour in the emergency department before being seen by a medical professional. These figures suggest that despite high utilization in some areas, many emergency departments manage to process patients relatively quickly. Demographic disparities in emergency care Emergency department usage varies significantly across different demographic groups, revealing important healthcare access disparities. Infants under one-year-old and adults 75 years and over have the highest ED visit rates among all age groups. Additionally, racial disparities in ED rates are evident, with non-Hispanic Black individuals having double the ED visit rate of non-Hispanic White individuals. These patterns underscore the need for targeted healthcare interventions and improved access to acute care for vulnerable populations.

The National Hospital Ambulatory Medical Care Survey (NHAMCS), conducted by the National Center for Health Statistics (NCHS), collects annual data on visits to emergency departments to describe patterns of utilization and provision of ambulatory care delivery in the United States. Data are collected from nonfederal, general, and short-stay hospitals from all 50 U.S. states and the District of Columbia, and are used to develop nationally representative estimates. The data include counts and rates of emergency department visits from 2016-2022 for the 10 leading primary diagnoses and reasons for visit, stratified by selected patient and hospital characteristics. Rankings for the 10 leading categories were identified using weighted data from 2022 and were then assessed in prior years.

In 2023, there were, on average, 422 hospital emergency room (ER) visits per 1,000 population in the United States. ER visit rates had been steadily increasing from 365 visits per 1,000 population in 1999 till a peak of 445 visits in 2017. There was a drop in ER visit rates during the pandemic, but numbers are slowly increasing to pre-pandemic levels again.

In 2022, there were over *** million hospital emergency department visits in the United States. While the number of ED visits has fluctuated in the past years, numbers have been steadily increasing since 2000. This statistic represents the number of hospital emergency department visits in the United States from 2000 to 2022.

This dataset contains annual Excel pivot tables that display summaries of the patients treated in each Emergency Department (ED). The Emergency Department data is sourced from two databases, the ED Treat-and-Release Database and the Inpatient Database (i.e. patients treated in the ED and then formally admitted to the hospital). The summary data include number of visits, expected payer, discharge disposition, age groups, sex, preferred language spoken, race groups, principal diagnosis groups, and principal external cause of injury/morbidity groups. The data can also be summarized statewide or for a specific hospital county, ED service level, teaching/rural status, and/or type of control.

In 2022, emergency department visit rate was highest among infants under the age of one. Adults 75 years and over had the second-highest ED visit rate, while the average for all ages was 47 visits per 100 people in 2022.

To statistics of emergency room consultation rates - by gender and age group division

Emergency department visits in U.S. hospitals continue to surge, with *********************************** in Dallas leading the pack in 2024. The facility recorded ******* ED visits, followed closely by ******************************** in Florida with ******* visits. This trend highlights the growing demand for emergency medical services across the country, particularly in large urban centers. Evolving healthcare landscape While emergency departments are busier than ever, the overall number of hospitals in the U.S. has been decreasing since the 1970s. Meanwhile, there is a rise of large health systems. The Hospital Corporation of America, based in Nashville, Tennessee, stands as the largest health system in the country, operating *** hospitals as of February 2025. This consolidation trend reflects the changing dynamics of healthcare delivery and management in the United States. Specialization and capacity challenges As hospitals face increasing pressure on their emergency departments, many are also focusing on specialized services to meet diverse patient needs. For instance, the ****************************************************** performed ****** organ transplants between January 1988 and March 2025, making it the nation's ******* transplant center. Meanwhile, ******************** in Florida holds the title of the largest U.S. hospital with ***** beds.

This dataset contains counts and rates (per 10,000 residents) of asthma emergency department (ED) visits among Californians. The table “Asthma Emergency Department Visit Rates by County” contains statewide and county-level data stratified by age group (all ages, 0-17, 18+, 0-4, 5-17, 18-64, 65+) and race/ethnicity (white, black, Hispanic, Asian/Pacific Islander, American Indian/Alaskan Native). The table “Asthma Emergency Department Visit Rates by ZIP Code” contains zip-code level data stratified by age group (all ages, 0-17, 18+). The data are derived from the Department of Health Care Access and Information emergency department database. These data include emergency department visits from all licensed hospitals in California. These data are based only on primary discharge diagnosis codes. On October 1, 2015, diagnostic coding for asthma transitioned from ICD9-CM (493) to ICD10-CM (J45). Because of this change, CDPH and CDC do not recommend comparing data from 2015 (or earlier) to 2016 (or later). NOTE: Rates are calculated from the total number of asthma emergency department visits (not the unique number of individuals).

This dataset provides the Emergency Department ratio of encounters and treatment stations to represent the ED Burden. Smaller ratios indicate fewer ED visits per available treatment station and less burden. Larger ratios of ED visits per available treatment station indicate greater burden. The encounters are broken down by health-related conditions: Active COVID-19, Asthma, Cancer, Cardiac, COPD, COVID-19 History, Diabetes, Homeless, Hypertension, Mental Health, Obesity, Pneumonia, Respiratory, Sepsis, Stroke, and Substance Abuse.

In emergency departments across the United States, visit rate was highest among patients who paid through Medicaid, CHIP, or another state-based program in the year 2022. On the other hand, those with private insurance had a much lower ED visit rate.

In the fall of 2020, DC Health's Center for Policy, Planning and Evaluation (CPPE) was awarded one of ten Centers for Disease Control and Prevention (CDC), Firearm Injury Surveillance Through Emergency Rooms (FASTER) grants. This grant has allowed the District to begin regular surveillance of firearm injury visits to the city’s seven emergency departments. DC-FASTER helps address important gaps in timely data availability for firearm injuries. Timely reporting at the city level through FASTER, allows the District to detect surges in gun violence, monitor the victimization of at-risk groups, and understand trends in firearm injury. Data provided in this dashboard are updated monthly and represent the number of emergency department visits for firearm injury. All seven of the District’s Emergency rooms report to DC Health daily.

The Healthcare Cost and Utilization Project (HCUP) Nationwide Emergency Department Sample (NEDS) is the largest all-payer emergency department (ED) database in the United States. yielding national estimates of hospital-owned ED visits. Unweighted, it contains data from over 30 million ED visits each year. Weighted, it estimates roughly 145 million ED visits nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels.

Sampled from the HCUP State Inpatient Databases (SID) and State Emergency Department Databases (SEDD), the HCUP NEDS can be used to create national and regional estimates of ED care. The SID contain information on patients initially seen in the ED and subsequently admitted to the same hospital. The SEDD capture information on ED visits that do not result in an admission (i.e., treat-and-release visits and transfers to another hospital). Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels.

The NEDS contain information about geographic characteristics, hospital characteristics, patient characteristics, and the nature of visits (e.g., common reasons for ED visits, including injuries). The NEDS contains clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). It includes ED charge information for over 85% of patients, regardless of expected payer, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. The NEDS excludes data elements that could directly or indirectly identify individuals, hospitals, or states.Restricted access data files are available with a data use agreement and brief online security training.

The data includes a broader range of geographic units of analysis than have previously been made available in HPD public reports, in addition to other measures. - Geography: County, Covered California Region, Public Health Region, California Assembly District, California Senate District, California Congressional District - Demographics: Age Band, Sex - Payer Type: Commercial, Medi-Cal, Medicare - Measures: Prevalence of 23 chronic conditions, total emergency department (ED) visits, avoidable ED visits

This dataset includes aggregated weekly data on the percent of emergency department visits and the percent of hospital inpatient admissions due to influenza-like illness (ILI), COVID-19, influenza, RSV, and acute respiratory illness. The Illinois Department of Public Health (IDPH) collects data for Emergency Department visits to all 185 acute care hospitals in Illinois. The data are submitted from IDPH to the CDC’s BioSense Platform for access and analysis by health departments via the ESSENCE system. The CDC National Syndromic Surveillance Program (NSSP) utilizes diagnostic codes and clinical terms to create definitions for diagnosed COVID-19, influenza, RSV, and acute respiratory illness. For more information on diagnostic codes and clinical terms used, visit: https://www.cdc.gov/nssp/php/onboarding-resources/companion-guide-ed-data-respiratory-illness.html The data is characterized by selected demographic groups including age group and race/ethnicity. The dataset also includes percent of weekly outpatient visits due to ILI as reported by several outpatient clinics throughout Chicago that participate in CDC’s Influenza-like Illness Surveillance Network (ILINet). For more information on ESSENCE, see https://www.dph.illinois.gov/data-statistics/syndromic-surveillance For more information on ILINet, see https://www.cdc.gov/fluview/overview/index.html#cdc_generic_section_3-outpatient-illness-surveillance All data are provisional and subject to change. Information is updated as additional details are received. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources.

List of footnotes, notes, and source information for The National Hospital Ambulatory Medical Care Survey (NHAMCS). Each row of this dataset contains the accompanying text for a footnote found in NHAMCS dataset. The footnote lookup can be merged onto any NHAMCS dataset using, DATASET_SHORT_NAME, FN_ID, FN_TYPE, and FN_TEXT.

SOURCE: National Center for Health Statistics CDC, The National Hospital Ambulatory Medical Care Survey (NHAMCS)

Data on visits to physician offices, hospital outpatient departments and hospital emergency departments by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. Note that the data file available here has more recent years of data than what is shown in the PDF or Excel version. Data for 2017 physician office visits are not available.

SOURCE: NCHS, National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey. For more information on the National Ambulatory Medical Care Survey and the National Hospital Ambulatory Medical Care Survey, see the corresponding Appendix entries at https://www.cdc.gov/nchs/data/hus/hus17_appendix.pdf.

This dataset captures data maintained by the Record Research Request Service (R3) at the University of Pittsburgh. R3 is a service of the Department of Biomedical Informatics (DBMI), provisioning UPMC clinical data and authorizing additional UPMC data sources for research.

Data captures emergency department (ED) visits and primary care visits for the pediatric population (ages 0-17) served by UPMC in Allegheny County by Census Block Group geography (using 2010 U.S. Census geographies).

ED Data captures total visits, along with visits related to asthma, injury, low-acuity visits, and respiratory tract infections. Low acuity visits are often defined as visits by patients that didn't arrive by ambulance and visits that were less-serious or less-urgent where the patient was discharged to their normal place of residence.

The primary care visit data includes total visits, well child visits, and asthma diagnoses.

Each dataset includes the estimated population in each blockgroup. This estimate was obtained from the U.S. Census Bureau's 2015-19 5-year American Community Survey. Pediatric populations are defined as those under age 18.

UPMC data captures a large share of ED visits due to the health system's role with UPMC Children's Hospital of Pittsburgh.

The Regional Data Center teamed with colleagues at the University of Pittsburgh Center for Social Research to geocode data to blockgroup, and the totals were computed by R3.

Special thanks to partners at The Pittsburgh Study for helping us make this data available, and the Data Across Sectors for Health program for providing financial support for this work.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

Data source: Medical service point declaration data from insurance medical service providersNumerator: Number of cases returning to the same hospital emergency room within three daysDenominator: Number of discharged casesCalculation formula: (Numerator / Denominator) X 100%

Emergency Department Visits Related To Mental Health Conditions - This indicator shows the rate of emergency department visits related to mental health disorders (per 100,000 population). Mental health problems can place a heavy burden on the healthcare system, particularly when persons in crisis utilize emergency departments instead of other sources of care when available. In Maryland, there were 207,650 mental health disorder-related emergency department visits in 2014.Diagnoses include adjustment disorders, anxiety disorders, attention deficit disorders, disruptive behavior disorders, mood disorders, personality disorders, schizophrenia, and other psychotic disorders, suicide and intentional self-inflicted injury and miscellaneous mental disorders.