This is a report on admitted patient care activity in English NHS hospitals and English NHS-commissioned activity in the independent sector. This annual publication covers the financial year ending March 2020. It contains final data and replaces the provisional data that are released each month. The data are taken from the Hospital Episodes Statistics (HES) data warehouse. HES contains records of all admissions, appointments and attendances for patients at NHS hospitals in England. The HES data used in this publication are called 'Finished Consultant Episodes', and each episode relates to a period of care for a patient under a single consultant at a single hospital. Therefore this report counts the number of episodes of care for admitted patients rather than the number of patients. This publication shows the number of episodes during the period, with a number of breakdowns including by patient's age, gender, diagnosis, procedure involved and by provider. Hospital Adult Critical Care (ACC) data are now included within this report, following the discontinuation of the 'Hospital Adult Critical Care Activity' publication. The ACC data tables are not a designated National Statistic and they remain separate from the APC data tables. The ACC data used in this publication draws on records submitted by providers as an attachment to the admitted patient care record. These data show the number of adult critical care records during the period, with a number of breakdowns including admission details, discharge details, patient demographics and clinical information. The purpose of this publication is to inform and support strategic and policy-led processes for the benefit of patient care. This document will also be of interest to researchers, journalists and members of the public interested in NHS hospital activity in England. Supplementary analysis has been produced, by NHS Digital, containing experimental statistics using the Paediatric Critical Care Minimum Data Set (PCCMDS) data, collected by NHS Digital, against activity published in NHS Reference Costs. This analysis seeks to assist users of the data in understanding the data quality of reported paediatric critical care data. Also included within this release, is supplementary analysis that has been produced in addition to the Retrospective Review of Surgery for Urogynaecological Prolapse and Stress Urinary Incontinence using Tape or Mesh: Hospital Episode Statistics (HES), Experimental Statistics, April 2008 - March 2017. It contains a count of Finished Consultant Episodes (FCEs) where a procedure for urogynaecological prolapse or stress urinary incontinence using tape or mesh has been recorded during the April 2019 to March 2020 period.

This dataset is being provided under creative commons License (Attribution-Non-Commercial-Share Alike 4.0 International (CC BY-NC-SA 4.0)) https://creativecommons.org/licenses/by-nc-sa/4.0/

Context

This data was collected from patients admitted over a period of two years (1 April 2017 to 31 March 2019) at Hero DMC Heart Institute, Unit of Dayanand Medical College and Hospital, Ludhiana, Punjab, India. This is a tertiary care medical college and hospital. During the study period, the cardiology unit had 14,845 admissions corresponding to 12,238 patients. 1921 patients who had multiple admissions.

Specifically, data were related to patients ; date of admission; date of discharge; demographics, such as age, sex, locality (rural or urban); type of admission (emergency or outpatient); patient history, including smoking, alcohol, diabetes mellitus (DM), hypertension (HTN), prior coronary artery disease (CAD), prior cardiomyopathy (CMP), and chronic kidney disease (CKD); and lab parameters corresponding to hemoglobin (HB), total lymphocyte count (TLC), platelets, glucose, urea, creatinine, brain natriuretic peptide (BNP), raised cardiac enzymes (RCE) and ejection fraction (EF). Other comorbidities and features (28 features), including heart failure, STEMI, and pulmonary embolism, were recorded and analyzed.

Shock was defined as systolic blood pressure < 90 mmHg, and when the cause for shock was any reason other than cardiac. Patients in shock due to cardiac reasons were classified into cardiogenic shock. Patients in shock due to multifactorial pathophysiology (cardiac and non-cardiac) were considered for both categories. The outcomes indicating whether the patient was discharged or expired in the hospital were also recorded.

Further details about this dataset can be found here: https://doi.org/10.3390/diagnostics12020241

If you use this dataset in academic research all publications arising out of it must cite the following paper: Bollepalli, S.C.; Sahani, A.K.; Aslam, N.; Mohan, B.; Kulkarni, K.; Goyal, A.; Singh, B.; Singh, G.; Mittal, A.; Tandon, R.; Chhabra, S.T.; Wander, G.S.; Armoundas, A.A. An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit. Diagnostics 2022, 12, 241. https://doi.org/10.3390/diagnostics12020241

If you intend to use this data for commercial purpose explicit written permission is required from data providers.

Content

table_headings.csv has explanatory names of all columns.

Acknowledgements

Data was collected from Hero Dayanand Medical College Heart Institute Unit of Dayanand Medical College and Hospital, Ludhiana, Punjab, India.

Inspiration

For any questions about the data or collaborations please contact ashish.sahani@iitrpr.ac.in

In 2023, there were ******* hospital admissions in Denmark, ******* of which were an emergency. Throughout the documented period, there was a higher number of emergency cases each year.

Directly age and sex standardised admission rate for emergency admissions for acute conditions that should not usually require hospital admission per 100,000 registered patients, 95% confidence intervals (CI). March 2022 - The coronavirus (COVID-19) pandemic began to have an impact on Hospital Episode Statistics (HES) data late in the 2019-20 financial year, which continued into the 2020-21 financial year. This means we are seeing different patterns in the submitted data, for example, fewer patients being admitted to hospital, and therefore statistics which contain data from this period should be interpreted with care. Further information is available in the annual HES publication: https://digital.nhs.uk/data-and-information/publications/statistical/hospital-admitted-patient-care-activity/2020-21/covid-19-impact As of the October 2020 release, the CCG OIS is now published on an annual basis, as a result provisional data periods will no longer be published. The annual update will be based on finalised data for the April to March reporting period each year. As of the March 2020 release, the data included in the December 2019 publication for the 2018/19, July 2018 to June 2019 (Provisional) and October 2018 to September 2019 (Provisional) data periods has been revised. This is due to a revision of a large proportion of records for East Sussex Healthcare NHS Trust (RXC) which had missing information for the condition the patient was in hospital for and other conditions the patients suffer from. The revised data for these reporting periods also differs from that originally published in December 2019 in that the HES database is routinely updated (overwritten) on a monthly basis for the year in progress. Data for the two provisional periods remain provisional, but is now more complete than it was when the December 2019 publication was released. This effect cannot be readily separated from the effect of the East Sussex Healthcare NHS Trust (RXC) resubmission which also took place after processing for the December 2019 publication. Legacy unique identifier: P01844

In 2019, almost ***** percent of people aged 18 to 44 years in the United States were hospitalized at least once. Unsurprisingly, hospitalization among people aged 65 years and older was higher, at almost ** percent. Hospital Stays Hospitalization in the U.S. has decreased since 1997. In 2019, a total of *** percent of people in the U.S. were hospitalized at least once. Hospitalization rates for females have been higher than males for the past two decades. In 2019, almost ***** percent of females were hospitalized, compared to only **** percent of males. The average length of stay in a hospital is currently *** days. However, this varies greatly by state. In Wyoming, for example, the average length of stay is *** days, which is more than twice the average length of stay in New York. Hospital Costs Community hospital expenses per inpatient stay in the United States have been constantly increasing. The average expenses for a community hospital per inpatient stay in 2019 was around ****** U.S. dollars. Expectedly, hospital care expenditure has also been increasing in the past two decades. In 2020, around **** trillion U.S. dollars were spent on hospital care.

This statistic displays the number of admissions to an NHS (National Health Service) hospital with a primary diagnosis of drug related mental health and behavioral disorders in England in 2019/20, by age. In this period there were 2,152 hospital admissions for individuals aged between 25 and 34 years old for primary drug related disorders.

The total number of admissions to private/independent hospitals or clinics in the United Kingdom has increased in 2024 for the ****** consecutive year to ******* episodes, despite the dip in numbers in 2020. Ireland saw the largest growth in terms of percentage increase, with an **** percent increase in 2024 compared to the previous year. England, of course, saw the largest absolute increase in number of admissions in the private sector.

A. SUMMARY This dataset includes weekly respiratory disease hospital admissions for Influenza, RSV, and COVID-19 into San Francisco hospitals. Columns in the dataset include a count and rate of hospital admissions per 100,000 people. The data are reported by week. B. HOW THE DATASET IS CREATED Hospital admission data is reported to the San Francisco Department of Public Health (SFDPH) from the United States Center for Disease Control’s (CDC) National Healthcare Safety Network (NHSN) program. San Francisco population estimates are pulled from a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2019-2023 5-year American Community Survey (ACS). C. UPDATE PROCESS The dataset is updated every Friday and includes data from the previous Sunday through Saturday. For example, the update on Friday, October 17th will include data through Saturday, October 11th. Data may change as more current information becomes available. D. HOW TO USE THIS DATASET Weekly data represent a count of confirmed admissions of Influenza, RSV, and COVID-19 patients to San Francisco hospitals by week. The admission rate per 100,000 is calculated by multiplying the count of admissions each week by 100,000 and dividing by the population estimate.

This healthcare dataset, covering 55,500 patient records from 2019 to 2024, provides insights into patient demographics, medical conditions, hospital admissions, and billing trends. The average patient age is 51.54 years, with an equal gender distribution and O+ as the most common blood type. Obesity, Cancer, and Arthritis are the most frequent diagnoses, with Diabetes having the highest total billing amount. Emergency admissions are the most common, and the average billing amount is $25,539.32, ranging from - $2,008.49 (possible data error) to $52,764.28. Visualizations include histograms, pie charts, bar graphs, bubble charts, and treemaps, highlighting trends in admission types, medical conditions, and costs. The data can be used for predictive healthcare analytics, hospital resource planning, insurance cost analysis, and public health insights.

Provisional Monthly Hospital Episode Statistics for Admitted Patient Care, Outpatient and Accident and Emergency data April 2019 - December 2019

Note: After May 3, 2024, this dataset will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, hospital capacity, or occupancy data to HHS through CDC’s National Healthcare Safety Network (NHSN). The related CDC COVID Data Tracker site was revised or retired on May 10, 2023.

This dataset represents weekly COVID-19 hospitalization data and metrics aggregated to national, state/territory, and regional levels. COVID-19 hospitalization data are reported to CDC’s National Healthcare Safety Network, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN and included in this dataset represent aggregated counts and include metrics capturing information specific to COVID-19 hospital admissions, and inpatient and ICU bed capacity occupancy.

Reporting information:

• As of December 15, 2022, COVID-19 hospital data are required to be reported to NHSN, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Prior to December 15, 2022, hospitals reported data directly to the U.S. Department of Health and Human Services (HHS) or via a state submission for collection in the HHS Unified Hospital Data Surveillance System (UHDSS).
• While CDC reviews these data for errors and corrects those found, some reporting errors might still exist within the data. To minimize errors and inconsistencies in data reported, CDC removes outliers before calculating the metrics. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks.
• Many hospital subtypes, including acute care and critical access hospitals, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are included in the metric calculations provided in this report. Psychiatric, rehabilitation, and religious non-medical hospital types are excluded from calculations.
• Data are aggregated and displayed for hospitals with the same Centers for Medicare and Medicaid Services (CMS) Certification Number (CCN), which are assigned by CMS to counties based on the CMS Provider of Services files.
• Full details on COVID-19 hospital data reporting guidance can be found here: https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf

Metric details:

• Time Period: timeseries data will update weekly on Mondays as soon as they are reviewed and verified, usually before 8 pm ET. Updates will occur the following day when reporting coincides with a federal holiday. Note: Weekly updates might be delayed due to delays in reporting. All data are provisional. Because these provisional counts are subject to change, including updates to data reported previously, adjustments can occur. Data may be updated since original publication due to delays in reporting (to account for data received after a given Thursday publication) or data quality corrections.
• New COVID-19 Hospital Admissions (count): Number of new admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction.
• New COVID-19 Hospital Admissions (7-Day Average): 7-day average of new admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction.
• Cumulative COVID-19 Hospital Admissions: Cumulative total number of admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) in the entire jurisdiction since August 1, 2020.
• Cumulative COVID-19 Hospital Admissions Rate: Cumulative total number of admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) in the entire jurisdiction since August 1, 2020 divided by 2019 intercensal population estimate for that jurisdiction multiplied by 100,000.
• New COVID-19 Hospital Admissions Rate (7-day average) percent change from prior week: Percent change in the 7-day average new admissions of patients with laboratory-confirmed COVID-19 per 100,000 population compared with the prior week.
• New COVID-19 Hospital Admissions (7-Day Total): 7-day total number of new admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) in the entire jurisdiction.
• New COVID-19 Hospital Admissions Rate (7-Day Total): 7-day total number of new admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) for the entire jurisdiction divided by 2019 intercensal population estimate for that jurisdiction multiplied by 100,000.
• Total Hospitalized COVID-19 Patients: 7-day total number of patients currently hospitalized with laboratory-confirmed COVID-19 (including both adult and pediatric patients) for the entire jurisdiction.
• Total Hospitalized COVID-19 Patients (7-Day Average): 7-day average of the number of patients currently hospitalized with laboratory-confirmed COVID-19 (including both adult and pediatric patients) for the entire jurisdiction.
• COVID-19 Inpatient Bed Occupancy (7-Day Average): Percentage of all staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 (including both adult and pediatric patients) within the entire jurisdiction is calculated as an average of valid daily values within the past 7 days (e.g., if only three valid values, the average of those three is taken). Averages are separately calculated for the daily numerators (patients hospitalized with confirmed COVID-19) and denominators (staffed inpatient beds). The average percentage can then be taken as the ratio of these two values for the entire jurisdiction.
• COVID-19 Inpatient Bed Occupancy absolute change from prior week: The absolute change in the percent of staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 represents the week-over-week absolute difference between the 7-day average occupancy of patients with confirmed COVID-19 in staffed inpatient beds in the past 7 days, compared with the prior week, in the entire jurisdiction.
• COVID-19 ICU Bed Occupancy (7-Day Average): Percentage of all staffed inpatient beds occupied by adult patients with confirmed COVID-19 within the entire jurisdiction is calculated as a 7-day average of valid daily values within the past 7 days (e.g., if only three valid values, the average of those three is taken). Averages are separately calculated for the daily numerators (adult patients hospitalized with confirmed COVID-19) and denominators (staffed adult ICU beds). The average percentage can then be taken as the ratio of these two values for the entire jurisdiction.
• COVID-19 ICU Bed Occupancy absolute change from prior week: The absolute change in the percent of staffed ICU beds occupied by patients with laboratory-confirmed COVID-19 represents the week-over-week absolute difference between the average occupancy of patients with confirmed COVID-19 in staffed adult ICU beds for the past 7 days, compared with the prior week, in the in the entire jurisdiction.

Note: October 27, 2023: Due to a data processing error, reported values for avg_percent_inpatient_beds_occupied_covid_confirmed will appear lower than previously reported values by an average difference of less than 1%. Therefore, previously reported values for avg_percent_inpatient_beds_occupied_covid_confirmed may have been overestimated and should be interpreted with caution.

October 27, 2023: Due to a data processing error, reported values for abs_chg_avg_percent_inpatient_beds_occupied_covid_confirmed will differ from previously reported values by an average absolute difference of less than 1%. Therefore, previously reported values for abs_chg_avg_percent_inpatient_beds_occupied_covid_confirmed should be interpreted with caution.

December 29, 2023: Hospitalization data reported to CDC’s National Healthcare Safety Network (NHSN) through December 23, 2023, should be interpreted with caution due to potential reporting delays that are impacted by Christmas and New Years holidays. As a result, metrics including new hospital admissions for COVID-19 and influenza and hospital occupancy may be underestimated for the week ending December 23, 2023.

January 5, 2024: Hospitalization data reported to CDC’s National Healthcare Safety Network (NHSN) through December 30, 2023 should be interpreted with caution due to potential reporting delays that are impacted by Christmas and New Years holidays. As a result, metrics including new hospital admissions for COVID-19 and influenza and hospital occupancy may be underestimated for the week ending December 30, 2023.

Count and rate of hospital admission related to incident common infections in patients not prescribed antibiotics (using data from 1 January 2019 to 31 August 2022).

Percentage change in the hospital admission rates from 1998–2019 in Australia.

In 2019, around *** percent of all males aged 1-64 years in the United States had one or more hospital stays. This statistic shows the percentage of persons with one or more hospital stays in the past year from 1997 to 2019 in the United States, by gender.

This dataset describes the age-adjusted rate of hospital admissions for asthma in each county of Tennessee.

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 2020. 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 two 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 Hospital Episode Statistics (HES) and A&E Attendances and Emergency Admissions Monthly Situation Reports (MSitAE). This publication releases some high level analyses of both 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 Additional exploratory analyses have also been included as part of this release that seek to review reported data quality to inform future uses of the data.

The Connected Bradford ICNARC : Flexible Data Model (FDM) Contains routinely collected data for approximately 13,000 patients. The dataset has been fully anonymised, but can link to other Connected Bradford FDM's.

The Bradford Royal Infirmary FDM last build date was 2023-05-15 and contains data up to : 2019-10-22. The observation period for this data is: 2002-07-21 to 2019-10-22

The FDM is made up of 2source table using routinely collected data for 13,936 individuals.

The tables are as supplied with minimal reformatting.

The source tables are largely populated by fields with the tbl_ where there is a person and a start and end date, and cb_ where there is no identifiable person (these are typically lookup tables)

It includes basic patient demographics, information about consultation events, medical history including diagnoses and investigations, laboratory results, medications, theatre, spells, pharmacy etc as per the ICNARC standard.

Age groups that have the highest rate of admissions stratified by cause.