100+ datasets found
  1. Hospital Admissions Data

    • kaggle.com
    zip
    Updated Jan 21, 2022
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    Ashish Sahani (2022). Hospital Admissions Data [Dataset]. https://www.kaggle.com/datasets/ashishsahani/hospital-admissions-data
    Explore at:
    zip(522833 bytes)Available download formats
    Dataset updated
    Jan 21, 2022
    Authors
    Ashish Sahani
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    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

  2. German Hospital Statistics

    • healthinformationportal.eu
    html
    Updated Sep 28, 2022
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    Bundesamt für Statistik / Federal Statistical Office (2022). German Hospital Statistics [Dataset]. https://www.healthinformationportal.eu/health-information-sources/drg-statistics
    Explore at:
    htmlAvailable download formats
    Dataset updated
    Sep 28, 2022
    Dataset provided by
    Federal Statistical Officehttp://www.bfs.admin.ch/
    Authors
    Bundesamt für Statistik / Federal Statistical Office
    Variables measured
    title, topics, country, language, data_owners, description, contact_email, free_keywords, alternative_title, access_information, and 4 more
    Measurement technique
    Administrative data
    Description

    Since 2005, the Diagnosis Related Groups (DRG) statistics have provided annual information on morbidity events and morbidity trends in inpatient care, as well as on the volume and structure of demand for services, over and above the existing official hospital statistics. In particular, type of illness, case-flat-rate hospital statistic (DRGs), operations and procedures as well as length of stay and department are collected.

    The aggregated data are freely accessible.

  3. Number of hospitals in the United States 2014-2029

    • statista.com
    Updated Jul 22, 2025
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    Statista Research Department (2025). Number of hospitals in the United States 2014-2029 [Dataset]. https://www.statista.com/topics/1074/hospitals/
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    Dataset updated
    Jul 22, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    United States
    Description

    The number of hospitals in the United States was forecast to continuously decrease between 2024 and 2029 by in total 13 hospitals (-0.23 percent). According to this forecast, in 2029, the number of hospitals will have decreased for the twelfth consecutive year to 5,548 hospitals. Depicted is the number of hospitals in the country or region at hand. As the OECD states, the rules according to which an institution can be registered as a hospital vary across countries.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of hospitals in countries like Canada and Mexico.

  4. V

    American Hospital Directory Free National and State Statistics

    • data.virginia.gov
    html
    Updated Feb 3, 2024
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    Other (2024). American Hospital Directory Free National and State Statistics [Dataset]. https://data.virginia.gov/dataset/american-hospital-directory-free-national-and-state-statistics
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    htmlAvailable download formats
    Dataset updated
    Feb 3, 2024
    Dataset authored and provided by
    Other
    Description

    From the Web site: The American Hospital Directory® provides data, statistics, and analytics about more than 7,000 hospitals nationwide. AHD.com® hospital information includes both public and private sources such as Medicare claims data, hospital cost reports, and commercial licensors. AHD® is not affiliated with the American Hospital Association (AHA) and is not a source for AHA Data. Our data are evidence-based and derived from the most definitive sources.

  5. Australian hospital statistics public hospitals

    • researchdata.edu.au
    • data.gov.au
    Updated Jun 4, 2014
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    Australian Institute of Health and Welfare (2014). Australian hospital statistics public hospitals [Dataset]. https://researchdata.edu.au/australian-hospital-statistics-public-hospitals/2983840
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    Dataset updated
    Jun 4, 2014
    Dataset provided by
    Data.govhttps://data.gov/
    Authors
    Australian Institute of Health and Welfare
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Area covered
    Australia
    Description

    Publication Hospital resources 2016–17: Australian hospital statistics can be found on the AIHW Website.

  6. CA Hospital Dataset – Q1 2025

    • kaggle.com
    Updated Aug 9, 2025
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    Rajkumar K P (2025). CA Hospital Dataset – Q1 2025 [Dataset]. https://www.kaggle.com/datasets/rajkumarpadmanabhan/ca-hospital-dataset-q1-2025
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 9, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Rajkumar K P
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    This synthetic dataset simulates the end-to-end operations of a California-based hospital for Q1 2025. It includes over 126,000 rows across 9 fully integrated tables that capture patient visits, clinical procedures, diagnoses, lab tests, medication prescriptions, provider details, billing, claims, and denials — designed for data analytics, machine learning, and healthcare research.

    📦 Tables Included: patients.csv – Patient demographics, insurance, DOB, gender

    encounters.csv – Admission/discharge details, visit types, departments

    diagnoses.csv – ICD-10 diagnosis codes linked to encounters

    procedures.csv – CPT/ICD-10-PCS procedure codes per patient

    medications.csv – Drug names, dosages, prescription data

    lab_tests.csv – Test names, result values, normal ranges

    claims_and_billing.csv – Financial charges, insurance claims, payments

    providers.csv – Doctors, specializations, provider roles

    denials.csv – Reasons for claim denial, status, appeal info

    This dataset was custom-built to reflect real-world healthcare challenges including:

    Messy and missing data (for cleaning exercises)

    Insurance claim workflows and denial patterns

    Analysis of repeat admissions and chronic disease trends

    Medication brand usage, cost patterns, and outcomes

    🧠 Ideal For: Healthcare Data Science Projects

    Revenue Cycle Management (RCM) analytics

    Power BI & Tableau Dashboards

    Machine Learning modeling (readmission, denial prediction, etc.)

    Python/SQL Data Cleaning Practice

    This dataset is completely synthetic and safe for public use. It was generated using custom rules, distributions, and logic reflective of real hospital operations.

  7. Total hospital admissions in the United States 1946-2023

    • statista.com
    Updated Jun 27, 2025
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    Statista (2025). Total hospital admissions in the United States 1946-2023 [Dataset]. https://www.statista.com/statistics/459718/total-hospital-admission-number-in-the-us/
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    Dataset updated
    Jun 27, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    In 2023, there were over **** million hospital admissions in the United States. The number of hospitals in the U.S. has decreased in recent years, although the country faces an increasing elder population. Predictably, the elderly account for the largest share of hospital admissions in the U.S. Hospital stays Stays in hospitals are more common among females than males, with around *** percent of females reporting one or more hospital stays in the past year, compared to *** percent of males. Furthermore, **** percent of those aged 65 years and older had a hospitalization in the past year, compared to just *** percent of those aged 18 to 44 years. The average length of a stay in a U.S. hospital is *** days. Hospital beds In 2022, there were ******* hospital beds in the U.S. In the past few years, there has been a decrease in the number of hospital beds available. This is unsurprising given the decrease in the number of overall hospitals. In 2021, the occupancy rate of hospitals in the U.S. was ** percent.

  8. COVID-19 Hospital Data Coverage for Hospital in Suspense

    • catalog.data.gov
    • healthdata.gov
    • +1more
    Updated Jul 4, 2025
    + more versions
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    U.S. Department of Health and Human Services (2025). COVID-19 Hospital Data Coverage for Hospital in Suspense [Dataset]. https://catalog.data.gov/dataset/covid-19-hospital-data-coverage-for-hospital-in-suspense
    Explore at:
    Dataset updated
    Jul 4, 2025
    Dataset provided by
    United States Department of Health and Human Serviceshttp://www.hhs.gov/
    Description

    After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations. This report shows facilities currently in suspense regarding CoP requirements due to being in a work plan or other related reasons is shown if any facilities are currently in suspense. These CCNs will not be included in the tab listing all other hospitals or included in any summary counts while in suspense. 01/05/2024 – As of FAQ 6, the following optional fields have been added to this report: total_adult_patients_hospitalized_confirmed_influenza total_pediatric_patients_hospitalized_confirmed_influenza previous_day_admission_adult_influenza_confirmed previous_day_admission_pediatric_influenza_confirmed staffed_icu_adult_patients_confirmed_influenza staffed_icu_pediatric_patients_confirmed_influenza total_adult_patients_hospitalized_confirmed_rsv total_pediatric_patients_hospitalized_confirmed_rsv previous_day_admission_adult_rsv_confirmed previous_day_admission_pediatric_rsv_confirmed staffed_icu_adult_patients_confirmed_rsv staffed_icu_pediatric_patients_confirmed_rsv 6/17/2023 - With the new 28-day compliance reporting period, CoP reports will be posted every 4 weeks. 9/12/2021 - To view other COVID-19 Hospital Data Coverage datasets, follow this link to view summary page: https://healthdata.gov/stories/s/ws49-ddj5 As of FAQ3, the following field are federally inactive and will no longer be included in this report: previous_week_personnel_covid_vaccinated_doses_administered total_personnel_covid_vaccinated_doses_none total_personnel_covid_vaccinated_doses_one total_personnel_covid_vaccinated_doses_all total_personnel previous_week_patients_covid_vaccinated_doses_one previous_week_patients_covid_vaccinated_doses_all

  9. COVID-19 Hospital Data Coverage Report

    • healthdata.gov
    • data.virginia.gov
    • +1more
    csv, xlsx, xml
    Updated Dec 15, 2020
    + more versions
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    U.S. Department of Health & Human Services (2020). COVID-19 Hospital Data Coverage Report [Dataset]. https://healthdata.gov/Hospital/COVID-19-Hospital-Data-Coverage-Report/v4wn-auj8
    Explore at:
    xlsx, xml, csvAvailable download formats
    Dataset updated
    Dec 15, 2020
    Dataset authored and provided by
    U.S. Department of Health & Human Services
    License

    Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
    License information was derived automatically

    Description

    After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

    This report shows data completeness information on data submitted by hospitals for the previous week, from Friday to Thursday. The U.S. Department of Health and Human Services requires all hospitals licensed to provide 24-hour care to report certain data necessary to the all-of-America COVID-19 response. The report includes the following information for each hospital:

    • The percentage of mandatory fields reported.
    • The number of days in the preceding week where 100% of the fields were completed.
    • Whether a hospital is required to report on Wednesdays only.
    • A cell for each required field with the number of days that specific field was reported for the week.
    Hospitals are key partners in the Federal response to COVID-19, and this report is published to increase transparency into the type and amount of data being successfully reported to the U.S. Government.
  10. 9/12/2021 - Added a Summary page and broke out the attached Excel, tabbed spreadsheet into its own reports. You can access the Summary page with this link: https://healthdata.gov/stories/s/ws49-ddj5
  11. 6/17/2023 - With the new 28-day compliance reporting period, CoP reports will be posted every 4 weeks.

  12. Source: HHS Protect, U.S. Department of Health & Human Services

  • C

    Hospital Emergency Department - Characteristics by Facility (Pivot Profile)

    • data.chhs.ca.gov
    • data.ca.gov
    • +2more
    .xlsx, xlsm, xlsx +1
    Updated Nov 7, 2025
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    Department of Health Care Access and Information (2025). Hospital Emergency Department - Characteristics by Facility (Pivot Profile) [Dataset]. https://data.chhs.ca.gov/dataset/hospital-emergency-department-characteristics-by-facility-pivot-profile
    Explore at:
    zip, xlsx, xlsx(556712), xlsx(561869), xlsx(1341306), xlsx(1351305), xlsx(592486), xlsx(558673), xlsx(1377749), xlsx(551027), xlsx(1333357), xlsx(1347217), xlsm(1346583), xlsx(572109), xlsx(585517), xlsx(1301355), .xlsx(1305598)Available download formats
    Dataset updated
    Nov 7, 2025
    Dataset authored and provided by
    Department of Health Care Access and Information
    Description

    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.

  • d

    Hospital Admitted Patient Care Activity

    • digital.nhs.uk
    • production-like.nhsd.io
    Updated Sep 25, 2025
    + more versions
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    (2025). Hospital Admitted Patient Care Activity [Dataset]. https://digital.nhs.uk/data-and-information/publications/statistical/hospital-admitted-patient-care-activity
    Explore at:
    Dataset updated
    Sep 25, 2025
    License

    https://digital.nhs.uk/about-nhs-digital/terms-and-conditionshttps://digital.nhs.uk/about-nhs-digital/terms-and-conditions

    Time period covered
    Apr 1, 2024 - Mar 31, 2025
    Description

    This publication reports on Admitted Patient Care activity in England for the financial year 2024-25 This report includes but is not limited to analysis of hospital episodes by patient demographics, diagnoses, external causes/injuries, operations, bed days, admission method, time waited, specialty, provider level analysis and Adult Critical Care (ACC). It describes NHS Admitted Patient Care Activity, Adult Critical Care activity and performance in hospitals in England. The purpose of this publication is to inform and support strategic and policy-led processes for the benefit of patient care and may also be of interest to researchers, journalists and members of the public interested in NHS hospital activity in England. The data source for this publication is Hospital Episode Statistics (HES). It contains final data and replaces the provisional data that are released each month. HES contains records of all admissions, appointments and attendances at NHS-commissioned hospital services 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 breakdowns including by patient's age, gender, diagnosis, procedure involved and by provider. Please send queries or feedback via email to enquiries@nhsdigital.nhs.uk. Author: Secondary Care Open Data and Publications, NHS England. Lead Analyst: Karl Eichler

  • COVID-19 Hospital Admissions Over Time

    • healthdata.gov
    csv, xlsx, xml
    Updated Apr 8, 2025
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    data.sfgov.org (2025). COVID-19 Hospital Admissions Over Time [Dataset]. https://healthdata.gov/dataset/COVID-19-Hospital-Admissions-Over-Time/ydyb-je5g
    Explore at:
    xlsx, csv, xmlAvailable download formats
    Dataset updated
    Apr 8, 2025
    Dataset provided by
    data.sfgov.org
    Description

    As of 9/12/2024, we have resumed reporting on COVID-19 hospitalization data using a San Francisco specific dataset. These new data differ slightly from previous hospitalization data sources but the overall patterns and trends in hospitalizations remain consistent. You can access the previous data here.

    A. SUMMARY This dataset includes information on COVID+ hospital admissions for San Francisco residents into San Francisco hospitals. Specifically, the dataset includes the count and rate of COVID+ hospital admissions per 100,000. 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) via the COVID Hospital Data Repository (CHDR), a system created via health officer order C19-16. The data includes all San Francisco hospitals except for the San Francisco VA Medical Center.

    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 2018-2022 5-year American Community Survey (ACS).

    C. UPDATE PROCESS Data updates weekly on Wednesday with data for the past Wednesday-Tuesday (one week lag). Data may change as more current information becomes available.

    D. HOW TO USE THIS DATASET New admissions are the count of COVID+ hospital admissions among San Francisco residents 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.

    E. CHANGE LOG

  • r

    MyHospitals Profile Data - Number of Beds

    • researchdata.edu.au
    • data.gov.au
    null
    Updated Jun 28, 2023
    + more versions
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    National Health Performance Authority (2023). MyHospitals Profile Data - Number of Beds [Dataset]. https://researchdata.edu.au/myhospitals-profile-data-number-beds/2737683
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    nullAvailable download formats
    Dataset updated
    Jun 28, 2023
    Dataset provided by
    Australian Urban Research Infrastructure Network (AURIN)
    Authors
    National Health Performance Authority
    License

    Attribution-NonCommercial-NoDerivs 3.0 (CC BY-NC-ND 3.0)https://creativecommons.org/licenses/by-nc-nd/3.0/
    License information was derived automatically

    Area covered
    Description

    MyHospitals provides performance information for public and private hospitals in Australia. You can also compare the performance of these hospitals and find information about hospitals near you.

    The annual average number of beds available to be used by an admitted patient was grouped into the following categories: fewer than 50, 50-100, 100-200, 200-500 and more than 500. These data are as reported by states and territories to the NPHED, and are referred to in statistical publications (including Australian hospital statistics) as 'average available beds'. The average number of available beds presented may differ from counts published elsewhere. For example, counts based on bed numbers at a specified date such as 30 June may differ from the average available beds over the reporting period. Comparability of bed numbers can be affected by the range and types of patients treated by a hospital. For example, hospitals may have different proportions of beds available for general versus special purposes (such as beds or cots used exclusively for intensive care). Bed counts also include chairs for same-day admissions.

    Data is current as of December 2015. Data sourced from: http://www.myhospitals.gov.au/about-the-data/download-data

  • d

    Provisional Monthly Hospital Episode Statistics for Admitted Patient Care,...

    • digital.nhs.uk
    Updated Jun 27, 2024
    + more versions
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    (2024). Provisional Monthly Hospital Episode Statistics for Admitted Patient Care, Outpatient and Accident and Emergency data [Dataset]. https://digital.nhs.uk/data-and-information/publications/statistical/provisional-monthly-hospital-episode-statistics-for-admitted-patient-care-outpatient-and-accident-and-emergency-data
    Explore at:
    Dataset updated
    Jun 27, 2024
    License

    https://digital.nhs.uk/about-nhs-digital/terms-and-conditionshttps://digital.nhs.uk/about-nhs-digital/terms-and-conditions

    Time period covered
    Apr 1, 2024 - Apr 30, 2024
    Description

    Hospital Episodes Statistics (HES) is a data warehouse containing records of all patients admitted to NHS hospitals in England. It contains details of inpatient care and outpatient appointments. Hospital episode statistics (HES) statistics are produced and published on a monthly basis. The data are provisional and should therefore be treated as an estimate until the final National Statistics annual publications.

  • d

    Hospital Admitted Patient Care Activity

    • digital.nhs.uk
    Updated Sep 17, 2020
    + more versions
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    (2020). Hospital Admitted Patient Care Activity [Dataset]. https://digital.nhs.uk/data-and-information/publications/statistical/hospital-admitted-patient-care-activity
    Explore at:
    Dataset updated
    Sep 17, 2020
    License

    https://digital.nhs.uk/about-nhs-digital/terms-and-conditionshttps://digital.nhs.uk/about-nhs-digital/terms-and-conditions

    Time period covered
    Apr 1, 2019 - Mar 31, 2020
    Description

    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.

  • w

    Hospital Statistics

    • data.wu.ac.at
    html
    Updated Mar 1, 2014
    + more versions
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    Department of Health, Social Services and Public Safety (2014). Hospital Statistics [Dataset]. https://data.wu.ac.at/odso/data_gov_uk/Y2YyYmFhMzgtNDJmOC00MDZhLTkwOGUtZDViNzRiNjA2NTQ3
    Explore at:
    htmlAvailable download formats
    Dataset updated
    Mar 1, 2014
    Dataset provided by
    Department of Health, Social Services and Public Safety
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    The Hospital Statistics publication shows activity data analysed by Programme of Care. Specialty tables covering both inpatients and outpatients along with a Key Points document showing comparisons over the previous five years are also published.

    Source agency: Health, Social Service and Public Safety (Northern Ireland)

    Designation: National Statistics

    Language: English

    Alternative title: Hospital Statistics

  • Hospital Statistics - Dataset - data.gov.uk

    • ckan.publishing.service.gov.uk
    Updated Dec 10, 2011
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    ckan.publishing.service.gov.uk (2011). Hospital Statistics - Dataset - data.gov.uk [Dataset]. https://ckan.publishing.service.gov.uk/dataset/hospital_statistics
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    Dataset updated
    Dec 10, 2011
    Dataset provided by
    CKANhttps://ckan.org/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    The Hospital Statistics publication shows activity data analysed by Programme of Care. Specialty tables covering both inpatients and outpatients along with a Key Points document showing comparisons over the previous five years are also published. Source agency: Health, Social Service and Public Safety (Northern Ireland) Designation: National Statistics Language: English Alternative title: Hospital Statistics

  • D

    DQS Hospital admission, average length of stay, outpatient visits, and...

    • data.cdc.gov
    csv, xlsx, xml
    Updated Sep 29, 2025
    + more versions
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    NCHS/Division of Analysis and Epidemiology (2025). DQS Hospital admission, average length of stay, outpatient visits, and outpatient surgery by type of ownership and size of hospital: United States [Dataset]. https://data.cdc.gov/National-Center-for-Health-Statistics/DQS-Hospital-admission-average-length-of-stay-outp/4q35-rqzk
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    csv, xml, xlsxAvailable download formats
    Dataset updated
    Sep 29, 2025
    Dataset authored and provided by
    NCHS/Division of Analysis and Epidemiology
    License

    https://www.usa.gov/government-workshttps://www.usa.gov/government-works

    Area covered
    United States
    Description

    Data on hospital admission, average length of stay, outpatient visits, and outpatient surgery in the United States, by type of ownership and size of hospital. Data are from Health, United States. SOURCE: American Hospital Association (AHA) Annual Survey of Hospitals, Hospital Statistics. Search, visualize, and download these and other estimates from a wide range of health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.

  • C

    Hospital Inpatient - Characteristics by Facility (Pivot Profile)

    • data.chhs.ca.gov
    • data.ca.gov
    • +2more
    .xlsx, xls, xlsx, zip
    Updated Nov 7, 2025
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    Department of Health Care Access and Information (2025). Hospital Inpatient - Characteristics by Facility (Pivot Profile) [Dataset]. https://data.chhs.ca.gov/dataset/hospital-inpatient-characteristics-by-facility-pivot-profile
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    xls, xlsx, xlsx(1778842), xlsx(1736211), xlsx(1736990), xlsx(1762190), xlsx(1740830), xlsx(1730937), .xlsx(1724148), zipAvailable download formats
    Dataset updated
    Nov 7, 2025
    Dataset authored and provided by
    Department of Health Care Access and Information
    Description

    This dataset contains annual Excel pivot tables that display summaries of the inpatients treated in each hospital. The summary data include discharges, discharge days, average length of stay, age groups, race groups, sex, expected payer, type of care, do not resuscitate orders, admission source, admission type, discharge disposition, principal diagnosis groups, principal procedure groups, and principal external cause of injury/morbidity groups. The data can also be summarized statewide or for a specific hospital county, bed size grouping, and/or type of control.

  • a

    AIHW - National Hospital Statistics (Point) 2012-2013 - Dataset - AURIN

    • data.aurin.org.au
    Updated Mar 6, 2025
    + more versions
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    (2025). AIHW - National Hospital Statistics (Point) 2012-2013 - Dataset - AURIN [Dataset]. https://data.aurin.org.au/dataset/au-govt-aihw-aihw-hospital-statistics-2012-2013-na
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    Dataset updated
    Mar 6, 2025
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    This dataset presents the location and detailed overview of Australia's public and private hospitals. In 2012–13, there were about 9.4 million separations from hospitals, including: 5.2 million same-day acute separations; 3.7 million overnight acute separations; about 450,000 sub-acute and non-acute separations. There were also 7.9 million non-admitted patient emergency services and more than 46 million outpatient services provided by public hospitals. For further information about this dataset, visit the data source:Australian Institute of Health and Welfare.

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    Ashish Sahani (2022). Hospital Admissions Data [Dataset]. https://www.kaggle.com/datasets/ashishsahani/hospital-admissions-data
    Organization logo

    Hospital Admissions Data

    Two Year Hospital Admissions and Discharge Data from Hero DMC Heart Institute

    Explore at:
    zip(522833 bytes)Available download formats
    Dataset updated
    Jan 21, 2022
    Authors
    Ashish Sahani
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    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

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