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.

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 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.

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).

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 case counts of emergency room visits for chronic obstructive pulmonary disease (COPD) among Tennessee state residents, in 2020.

This dataset describes the age-adjusted rate of emergency department visits for asthma in each county of Tennessee.

Number of Asthma ED visits for the years 2010-2020. These data include emergency room visits of individuals who are seen in an emergency department. These data are based only on principal diagnosis codes.

These data do not include individuals who are admitted to an inpatient hospital facility. Veterans Affairs, Indian Health Services and institutionalized (prison) populations are excluded. No attempt has been made to remove records resulting from transfer among emergency departments. As a result there may be duplicate records for a single event.

Effective October 1, 2015, hospital record data transitioned to a new coding system called the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM). Differences between counts and rates in years prior to 2015 compared with 2015 and subsequent years could be a result of this coding change and not an actual difference in the number of events.

The Nationwide Emergency Department Sample (NEDS) was created to enable analyses of emergency department (ED) utilization patterns and support public health professionals, administrators, policymakers, and clinicians in their decision-making regarding this critical source of care. The NEDS can be weighted to produce national estimates. The NEDS is the largest all-payer ED database in the United States. It was constructed using records from both the HCUP State Emergency Department Databases (SEDD) and the State Inpatient Databases (SID), both also described in healthdata.gov. 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). The SID contain information on patients initially seen in the emergency room and then admitted to the same hospital. The NEDS contains 25-30 million (unweighted) records for ED visits for over 950 hospitals and approximates a 20-percent stratified sample of U.S. hospital-based EDs. The NEDS contains 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 75% of patients, regardless of payer, including patients covered by Medicaid, private insurance, and the uninsured. The NEDS excludes data elements that could directly or indirectly identify individuals, hospitals, or states.

In fiscal year 2021-2022, the number of emergency department (ED) visits for pneumonia in Canada totaled 63,244, up from 45,776 visits a year prior. The country reported the highest number of ED visits due to this respiratory disease in 2020, with 139,805 cases. This statistic shows the number of emergency department visits for pneumonia in Canada from 2003 to 2022.

ABSTRACT Purpose: The emergency medical service is a fundamental part of healthcare, albeit crowded emergency rooms lead to delayed and low-quality assistance in actual urgent cases. Machine-learning algorithms can provide a smart and effective estimation of emergency patients’ volume, which was previously restricted to artificial intelligence (AI) experts in coding and computer science but is now feasible by anyone without any coding experience through auto machine learning. This study aimed to create a machine-learning model designed by an ophthalmologist without any coding experience using AutoML to predict the influx in the emergency department and trauma cases. Methods: A dataset of 356,611 visits at Hospital da Universidade Federal de São Paulo from January 01, 2014 to December 31, 2019 was included in the model training, which included visits/day and the international classification disease code. The training and prediction were made with the Amazon Forecast by 2 ophthalmologists with no prior coding experience. Results: The forecast period predicted a mean emergency patient volume of 216.27/day in p90, 180.75/day in p50, and 140.35/day in p10, and a mean of 7.42 trauma cases/ day in p90, 3.99/day in p50, and 0.56/day in p10. In January of 2020, there were a total of 6,604 patient visits and a mean of 206.37 patients/day, which is 13.5% less than the p50 prediction. This period involved a total of 199 trauma cases and a mean of 6.21 cases/day, which is 55.77% more traumas than that by the p50 prediction. Conclusions: The development of models was previously restricted to data scientists’ experts in coding and computer science, but transfer learning autoML has enabled AI development by any person with no code experience mandatory. This study model showed a close value to the actual 2020 January visits, and the only factors that may have influenced the results between the two approaches are holidays and dataset size. This is the first study to apply AutoML in hospital visits forecast, showing a close prediction of the actual hospital influx.

Total emergency department visits, and visits and admissions for influenza-like and/or pneumonia illness by modified ZIP code tabulation area of patient residence.

This is a publication on Accident and Emergency (A&E) activity in English NHS hospitals and English NHS-commissioned activity in the independent sector. This annual publication covers the financial year ending March 2021. It contains final data and replaces the provisional data that are published each month. This is a joint publication between NHS Digital and NHS England. This collaboration enables data to be brought together from different sources enabling inclusion of a wider set of breakdowns and measures and a more complete picture to be presented. The data sources for this publication are the Emergency Care Data Set (ECDS) for 2020-21, HES A&E for activity prior to 2020-21 and the A&E Attendances and Emergency Admissions Monthly Situation Reports (MSitAE). This is the first year this report has been produced using ECDS in its submitted format, replacing the use of Hospital Episode Statistics (HES). Further information is available in the Data Quality Statement. The ECDS data set contains several new and additional reporting fields not previously available in HES A&E enabling new insights to be identified from data. Reported information based on these new splits and metrics presented within the report are presented as Experimental Statistics and should be used with caution. Experimental statistics are new official statistics undergoing evaluation. They are published in order to involve users and stakeholders in their development and as a means to build in quality at an early stage. More information about experimental statistics can be found on the UK Statistics Authority website. This publication releases some high level analyses of both ECDS/HES and MSitAE data relating to A&E attendances in NHS hospitals, minor injury units and walk-in centres. It includes analysis by patient demographics, time spent in A&E, distributions by time of arrival and day of week, arriving by ambulance, performance times, waits for admission and re-attendances to A&E within 7 days. The following additional analyses are also included in this report: • Comparison of 4 hour and 12 hour waits between the four home nations, England, Scotland, Northern Ireland and Wales • A&E attendances by Index of Multiple Deprivation (IMD) • A&E attendances by ethnicity • Weekly variation in attendance activity during the pandemic, by department type

Age-adjusted rate of asthma ED Visits per 10,000 for the years 2010-2020. These data include emergency room visits of individuals who are seen in an emergency department. These data are based only on principal diagnosis codes.

These data do not include individuals who are admitted to an inpatient hospital facility. Veterans Affairs, Indian Health Services and institutionalized (prison) populations are excluded. No attempt has been made to remove records resulting from transfer among emergency departments. As a result there may be duplicate records for a single event.

Effective October 1, 2015, hospital record data transitioned to a new coding system called the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM). Differences between counts and rates in years prior to 2015 compared with 2015 and subsequent years could be a result of this coding change and not an actual difference in the number of events.

Emergency department visits in the United States for myocardial infarction, stroke and hyperglycemic crisis all decreased from January 5 to May 23, 2020 due to the COVID-19 pandemic. This statistic shows the percentage decrease in emergency department visits for select diagnoses immediately before and during the early COVID-19 pandemic in the U.S. by gender.

This dataset contains counts of inpatient hospitalizations and emergency department visits for persons experiencing homelessness.

Asthma crude rate of Emergency Department visits per 10,000 for years 2010-2020. These data include emergency room visits of individuals who are seen in an emergency department.

These data are based only on principal diagnosis codes. These data do not include individuals who are admitted to an inpatient hospital facility. Veterans Affairs, Indian Health Services and institutionalized (prison) populations are excluded. No attempt has been made to remove records resulting from transfer among emergency departments. As a result there may be duplicate records for a single event.

Effective October 1, 2015, hospital record data transitioned to a new coding system called the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM). Differences between counts and rates in years prior to 2015 compared with 2015 and subsequent years could be a result of this coding change and not an actual difference in the number of events.

Table contains count and age-adjusted rate of asthma-related emergency department (ED) visits among children ages less than 18 years. Data represent ED visits among county residents. Data are presented at county and zip code of residence level. Data are masked when the number of events is 15 or fewer. Data are presented for zip codes (ZCTAs) fully within the county. Source: Santa Clara County Public Health Department, Department of Health Care Access and Information (HCAI) (previously Office of Statewide Health Planning and Development (OSHPD)), Emergency Department Database, 2016-2020METADATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographyGEOID (Numeric): Geography IDyear (String): Year of emergency department visitcount (Numeric): Number of asthma-related emergency department visitAA rate (Numeric): Age-adjusted rate of asthma-related emergency department visit

This dataset includes counts and percentages of Emergency Department visits for Respiratory Viruses – COVID-19, Influenza, and RSV by Weeks, Geography (Health District and Health Region) and Facility Type starting from March 2020 to Present

In 2022, emergency department (ED) visitors waited on average **** minutes to see a physician, advanced practice registered nurse (APRN), or physician assistant in the United States. The median wait time was much lower at just ** minutes. That means ** percent of ED visits were seen to in less than ** minutes. This significant difference between median and mean times comes from a small number of patients who experience much longer waits, skewing the average upward. Pandemic impact on emergency department visits The year 2020 saw notably shorter wait times, with a median of ** minutes and a mean of **** minutes. This decrease likely resulted from reduced emergency department visits during the height of the COVID-19 pandemic. However, by 2021, wait times had rebounded to levels similar to those seen in 2019. Despite these fluctuations, the majority of emergency department visits were attended to within an hour in 2022, with ** percent of patients waiting ** minutes or less. While wait times are an important metric, the total time spent in emergency departments is also crucial. In 2022, most patients spent less than **** hours in the emergency department, with ** percent staying between *** and **** hours. Demographic disparities in ED utilization Emergency department usage varies significantly across different demographic groups. In 2022, ************************** had the highest ED visit rate among all age groups, followed closely by adults 75 years and over. Notably, there are also substantial racial disparities in ED utilization. ****************** individuals had an ED visit rate which was double the rate of non-Hispanic White individuals. These disparities point to broader issues of healthcare access and socioeconomic factors influencing emergency care utilization.