100+ datasets found
  1. E

    Minimum Hospital Data Set

    • healthinformationportal.eu
    html
    Updated Mar 4, 2022
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    Federal Public Service (FPS) Health, Food Chain Safety, and Environment (2022). Minimum Hospital Data Set [Dataset]. https://www.healthinformationportal.eu/health-information-sources/minimum-hospital-data-set
    Explore at:
    htmlAvailable download formats
    Dataset updated
    Mar 4, 2022
    Dataset authored and provided by
    Federal Public Service (FPS) Health, Food Chain Safety, and Environment
    License

    https://fair.healthdata.be/dataset/12d69eca-4449-47d2-943d-e4448a467292https://fair.healthdata.be/dataset/12d69eca-4449-47d2-943d-e4448a467292

    Variables measured
    sex, title, topics, acronym, country, language, data_owners, description, contact_name, geo_coverage, and 14 more
    Measurement technique
    Hospital resources & Healthcare administrative area resources
    Description

    The MZG is a registration with which all non-psychiatric hospitals in Belgium must make their (anonymised) administrative, medical and nursing data available to the Federal Public Service (FPS) Public Health. The aim of the MZG is to support the government's health policy by

    • Determining the needs for hospital facilities;
    • Describing the qualitative and quantitative accreditation standards of hospitals and their services;
    • Organising the financing of hospitals;
    • Determining policy for the practice of medicine;
    • To outline epidemiological policy.

    The MZG aims also to support the health policy of hospitals by providing national and individual feedback so that a hospital can compare itself with other hospitals and adapt its internal policy.

    All reports can be found here (in French/Dutch).

  2. G

    Open Database of Healthcare Facilities

    • open.canada.ca
    • catalogue.arctic-sdi.org
    • +1more
    csv, esri rest +4
    Updated Mar 2, 2022
    + more versions
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    Statistics Canada (2022). Open Database of Healthcare Facilities [Dataset]. https://open.canada.ca/data/en/dataset/a1bcd4ee-8e57-499b-9c6f-94f6902fdf32
    Explore at:
    fgdb/gdb, esri rest, csv, html, pdf, wmsAvailable download formats
    Dataset updated
    Mar 2, 2022
    Dataset provided by
    Statistics Canada
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Description

    The Open Database of Healthcare Facilities (ODHF) is a collection of open data containing the names, types, and locations of health facilities across Canada. It is released under the Open Government License - Canada. The ODHF compiles open, publicly available, and directly-provided data on health facilities across Canada. Data sources include regional health authorities, provincial, territorial and municipal governments, and public health and professional healthcare bodies. This database aims to provide enhanced access to a harmonized listing of health facilities across Canada by making them available as open data. This database is a component of the Linkable Open Data Environment (LODE).

  3. Database of Hospital Beds’ Utilization

    • healthinformationportal.eu
    html
    Updated Jan 4, 2023
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    Centre fo Disease Prevention and Control of Latvia (CDPC) - Slimību profilakses un kontroles centrs (SPKC) (2023). Database of Hospital Beds’ Utilization [Dataset]. https://www.healthinformationportal.eu/health-information-sources/database-hospital-beds-utilization
    Explore at:
    htmlAvailable download formats
    Dataset updated
    Jan 4, 2023
    Dataset provided by
    Centre for Disease Prevention and Control of Latviahttp://spkc.gov.lv/lv/
    Authors
    Centre fo Disease Prevention and Control of Latvia (CDPC) - Slimību profilakses un kontroles centrs (SPKC)
    Variables measured
    sex, title, topics, country, funding, language, data_owners, description, contact_name, geo_coverage, and 15 more
    Measurement technique
    Administrative data
    Dataset funded by
    <p>State funding</p>
    Description

    The Database of Hospital beds’ Utilisation is updated on the basis of information provided by inpatient treatment facilities. Inpatient information shall be provided on a monthly basis using form No. 016/u “Patient Movement and Bed Fund Accounting Summary Inpatient”.

  4. HCUP Kids' Inpatient Database (KID) - Restricted Access File

    • data.virginia.gov
    • healthdata.gov
    • +1more
    Updated Jul 25, 2023
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    Agency for Healthcare Research and Quality, Department of Health & Human Services (2023). HCUP Kids' Inpatient Database (KID) - Restricted Access File [Dataset]. https://data.virginia.gov/dataset/hcup-kids-inpatient-database-kid-restricted-access-file
    Explore at:
    Dataset updated
    Jul 25, 2023
    Description

    The Healthcare Cost and Utilization Project (HCUP) Kids' Inpatient Database (KID) is the largest publicly available all-payer pediatric inpatient care database in the United States, containing data from two to three million hospital stays each year. Its large sample size is ideal for developing national and regional estimates and enables analyses of rare conditions, such as congenital anomalies, as well as uncommon treatments, such as organ transplantation. 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 KID is a sample of pediatric discharges from 4,000 U.S. hospitals in the HCUP State Inpatient Databases yielding approximately two to three million unweighted hospital discharges for newborns, children, and adolescents per year. About 10 percent of normal newborns and 80 percent of other neonatal and pediatric stays are selected from each hospital that is sampled for patients younger than 21 years of age.

    The KID 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 discharge status, diagnoses, procedures, patient demographics (e.g., sex, age), expected source of primary payment (e.g., Medicare, Medicaid, private insurance, self-pay, and other insurance types), and hospital charges and cost.

    Restricted access data files are available with a data use agreement and brief online security training.

  5. HCUP Nationwide Readmissions Database (NRD)- Restricted Access Files

    • catalog.data.gov
    • data.virginia.gov
    • +1more
    Updated Jul 26, 2023
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    Agency for Healthcare Research and Quality, Department of Health & Human Services (2023). HCUP Nationwide Readmissions Database (NRD)- Restricted Access Files [Dataset]. https://catalog.data.gov/dataset/healthcare-cost-and-utilization-project-nationwide-readmissions-database-nrd
    Explore at:
    Dataset updated
    Jul 26, 2023
    Description

    The Healthcare Cost and Utilization Project (HCUP) Nationwide Readmissions Database (NRD) is a unique and powerful database designed to support various types of analyses of national readmission rates for all payers and the uninsured. The NRD includes discharges for patients with and without repeat hospital visits in a year and those who have died in the hospital. Repeat stays may or may not be related. The criteria to determine the relationship between hospital admissions is left to the analyst using the NRD. This database addresses a large gap in health care data - the lack of nationally representative information on hospital readmissions for all ages. Outcomes of interest include national readmission rates, reasons for returning to the hospital for care, and the hospital costs for discharges with and without readmissions. Unweighted, the NRD contains data from approximately 18 million discharges each year. Weighted, it estimates roughly 35 million discharges. 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 NRD is drawn from HCUP State Inpatient Databases (SID) containing verified patient linkage numbers that can be used to track a person across hospitals within a State, while adhering to strict privacy guidelines. The NRD is not designed to support regional, State-, or hospital-specific readmission analyses. The NRD contains more than 100 clinical and non-clinical data elements provided in a hospital discharge abstract. Data elements include but are not limited to: diagnoses, procedures, patient demographics (e.g., sex, age), expected source of payer, regardless of expected payer, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge, discharge month, quarter, and year, total charges, length of stay, and data elements essential to readmission analyses. The NIS excludes data elements that could directly or indirectly identify individuals. Restricted access data files are available with a data use agreement and brief online security training.

  6. American Hospital Association (AHA) Annual Survey Database - 2012

    • archive.ciser.cornell.edu
    Updated May 31, 2025
    + more versions
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    American Hospital Association (2025). American Hospital Association (AHA) Annual Survey Database - 2012 [Dataset]. https://archive.ciser.cornell.edu/studies/2765/related-articles
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    Dataset updated
    May 31, 2025
    Dataset authored and provided by
    American Hospital Associationhttp://www.aha.org/
    Variables measured
    Organization
    Description

    AHA Annual Survey Database for Fiscal Year 2012 is a comprehensive hospital database for health services research and market analysis. It is derived primarily from the AHA Annual Survey of Hospitals, which has been conducted by the American Hospital Association (AHA) or its subsidiary, Health Forum, since 1946. The survey responses are supplemented by data drawn from the American Hospital Association registration database, the US Census Bureau, hospital accrediting bodies, and other organizations. The database maintains hospital characteristics across time to allow researchers to conduct time-series analyses.

  7. COVID-19 Reported Patient Impact and Hospital Capacity by State (RAW)

    • healthdata.gov
    • datahub.hhs.gov
    • +2more
    Updated Dec 14, 2020
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    U.S. Department of Health & Human Services (2020). COVID-19 Reported Patient Impact and Hospital Capacity by State (RAW) [Dataset]. https://healthdata.gov/dataset/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/6xf2-c3ie
    Explore at:
    xml, csv, application/rssxml, application/rdfxml, tsv, application/geo+json, kml, kmzAvailable download formats
    Dataset updated
    Dec 14, 2020
    Dataset provided by
    United States Department of Health and Human Serviceshttp://www.hhs.gov/
    Authors
    U.S. Department of Health & Human Services
    License

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

    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.

    The following dataset provides state-aggregated data for hospital utilization. These are derived from reports with facility-level granularity across two main sources: (1) HHS TeleTracking, and (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities.

    The file will be updated regularly and provides the latest values reported by each facility within the last four days for all time. This allows for a more comprehensive picture of the hospital utilization within a state by ensuring a hospital is represented, even if they miss a single day of reporting.

    No statistical analysis is applied to account for non-response and/or to account for missing data.

    The below table displays one value for each field (i.e., column). Sometimes, reports for a given facility will be provided to more than one reporting source: HHS TeleTracking, NHSN, and HHS Protect. When this occurs, to ensure that there are not duplicate reports, prioritization is applied to the numbers for each facility.

    On June 26, 2023 the field "reporting_cutoff_start" was replaced by the field "date".

    On April 27, 2022 the following pediatric fields were added:

  8. all_pediatric_inpatient_bed_occupied
  9. all_pediatric_inpatient_bed_occupied_coverage
  10. all_pediatric_inpatient_beds
  11. all_pediatric_inpatient_beds_coverage
  12. previous_day_admission_pediatric_covid_confirmed_0_4
  13. previous_day_admission_pediatric_covid_confirmed_0_4_coverage
  14. previous_day_admission_pediatric_covid_confirmed_12_17
  15. previous_day_admission_pediatric_covid_confirmed_12_17_coverage
  16. previous_day_admission_pediatric_covid_confirmed_5_11
  17. previous_day_admission_pediatric_covid_confirmed_5_11_coverage
  18. previous_day_admission_pediatric_covid_confirmed_unknown
  19. previous_day_admission_pediatric_covid_confirmed_unknown_coverage
  20. staffed_icu_pediatric_patients_confirmed_covid
  21. staffed_icu_pediatric_patients_confirmed_covid_coverage
  22. staffed_pediatric_icu_bed_occupancy
  23. staffed_pediatric_icu_bed_occupancy_coverage
  24. total_staffed_pediatric_icu_beds
  25. total_staffed_pediatric_icu_beds_coverage

    On January 19, 2022, the following fields have been added to this dataset:
  26. inpatient_beds_used_covid
  27. inpatient_beds_used_covid_coverage

    On September 17, 2021, this data set has had the following fields added:
  28. icu_patients_confirmed_influenza,
  29. icu_patients_confirmed_influenza_coverage,
  30. previous_day_admission_influenza_confirmed,
  31. previous_day_admission_influenza_confirmed_coverage,
  32. previous_day_deaths_covid_and_influenza,
  33. previous_day_deaths_covid_and_influenza_coverage,
  34. previous_day_deaths_influenza,
  35. previous_day_deaths_influenza_coverage,
  36. total_patients_hospitalized_confirmed_influenza,
  37. total_patients_hospitalized_confirmed_influenza_and_covid,
  38. total_patients_hospitalized_confirmed_influenza_and_covid_coverage,
  39. total_patients_hospitalized_confirmed_influenza_coverage

    On September 13, 2021, this data set has had the following fields added:
  40. on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses,
  41. on_hand_supply_therapeutic_b_bamlanivimab_courses,
  42. on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses,
  43. previous_week_therapeutic_a_casirivimab_imdevimab_courses_used,
  44. previous_week_therapeutic_b_bamlanivimab_courses_used,
  45. previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used

    On June 30, 2021, this data set has had the following fields added:
  46. deaths_covid
  47. deaths_covid_coverage

    On April 30, 2021, this data set has had the following fields added:
  48. previous_day_admission_adult_covid_confirmed_18-19
  49. previous_day_admission_adult_covid_confirmed_18-19_coverage
  50. previous_day_admission_adult_covid_confirmed_20-29_coverage
  51. previous_day_admission_adult_covid_confirmed_30-39
  52. previous_day_admission_adult_covid_confirmed_30-39_coverage
  53. previous_day_admission_adult_covid_confirmed_40-49
  54. previous_day_admission_adult_covid_confirmed_40-49_coverage
  55. previous_day_admission_adult_covid_confirmed_40-49_coverage
  56. previous_day_admission_adult_covid_confirmed_50-59
  57. previous_day_admission_adult_covid_confirmed_50-59_coverage
  58. previous_day_admission_adult_covid_confirmed_60-69
  59. previous_day_admission_adult_covid_confirmed_60-69_coverage
  60. previous_day_admission_adult_covid_confirmed_70-79
  61. previous_day_admission_adult_covid_confirmed_70-79_coverage
  62. previous_day_admission_adult_covid_confirmed_80+
  63. previous_day_admission_adult_covid_confirmed_80+_coverage
  64. previous_day_admission_adult_covid_confirmed_unknown
  65. previous_day_admission_adult_covid_confirmed_unknown_coverage
  66. previous_day_admission_adult_covid_suspected_18-19
  67. previous_day_admission_adult_covid_suspected_18-19_coverage
  68. previous_day_admission_adult_covid_suspected_20-29
  69. previous_day_admission_adult_covid_suspected_20-29_coverage
  70. previous_day_admission_adult_covid_suspected_30-39
  71. previous_day_admission_adult_covid_suspected_30-39_coverage
  72. previous_day_admission_adult_covid_suspected_40-49
  73. previous_day_admission_adult_covid_suspected_40-49_coverage
  74. previous_day_admission_adult_covid_suspected_50-59
  75. previous_day_admission_adult_covid_suspected_50-59_coverage
  76. previous_day_admission_adult_covid_suspected_60-69
  77. previous_day_admission_adult_covid_suspected_60-69_coverage
  78. previous_day_admission_adult_covid_suspected_70-79
  79. previous_day_admission_adult_covid_suspected_70-79_coverage
  80. previous_day_admission_adult_covid_suspected_80+
  81. previous_day_admission_adult_covid_suspected_80+_coverage
  82. previous_day_admission_adult_covid_suspected_unknown
  83. previous_day_admission_adult_covid_suspected_unknown_coverage

  • E

    Hospital Discharge Records database

    • www-acc.healthinformationportal.eu
    • healthinformationportal.eu
    html
    Updated Jan 10, 2023
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    Ministero della Salute Italiano (2023). Hospital Discharge Records database [Dataset]. https://www-acc.healthinformationportal.eu/services/find-data?page=26
    Explore at:
    htmlAvailable download formats
    Dataset updated
    Jan 10, 2023
    Dataset authored and provided by
    Ministero della Salute Italiano
    Variables measured
    sex, title, topics, acronym, country, funding, language, data_owners, description, contact_name, and 16 more
    Measurement technique
    Hospitalization statistics of the hospitals of the National Health System
    Dataset funded by
    <p>Public funding</p>
    Description

    The information flow of the Hospital Discharge database (SDO flow) is the tool for collecting information relating to all hospitalization episodes provided in public and private hospitals throughout the national territory.

    Born for purely administrative purposes of the hospital setting, the SDO, thanks to the wealth of information contained, not only of an administrative but also of a clinical nature, has become an indispensable tool for a wide range of analyzes and elaborations, ranging from areas to support of health planning activities for monitoring the provision of hospital assistance and the Essential Levels of Assistance, for use for proxy analyzes of other levels of assistance as well as for more strictly clinical-epidemiological and outcome analyzes. In this regard, the SDO database is a fundamental element of the National Outcomes Program (PNE).

    The information collected includes the patient's personal characteristics (including age, sex, residence, level of education), characteristics of the hospitalization (for example institution and discharge discipline, hospitalization regime, method of discharge, booking date, priority class of hospitalization) and clinical features (e.g. main diagnosis, concomitant diagnoses, diagnostic or therapeutic procedures)

    Information relating to drugs administered during hospitalization or adverse reactions to them (subject to other specific information flows) is excluded from the discharge form.

  • Healthcare Management System

    • kaggle.com
    Updated Dec 23, 2023
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    Anouska Abhisikta (2023). Healthcare Management System [Dataset]. https://www.kaggle.com/datasets/anouskaabhisikta/healthcare-management-system
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 23, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Anouska Abhisikta
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    Patients Table:

    • PatientID: Unique identifier for each patient.
    • firstname: First name of the patient.
    • lastname: Last name of the patient.
    • email: Email address of the patient.

    This table stores information about individual patients, including their names and contact details.

    Doctors Table:

    • DoctorID: Unique identifier for each doctor.
    • DoctorName: Full name of the doctor.
    • Specialization: Area of medical specialization.
    • DoctorContact: Contact details of the doctor.

    This table contains details about healthcare providers, including their names, specializations, and contact information.

    Appointments Table:

    • AppointmentID: Unique identifier for each appointment.
    • Date: Date of the appointment.
    • Time: Time of the appointment.
    • PatientID: Foreign key referencing the Patients table, indicating the patient for the appointment.
    • DoctorID: Foreign key referencing the Doctors table, indicating the doctor for the appointment.

    This table records scheduled appointments, linking patients to doctors.

    MedicalProcedure Table:

    • ProcedureID: Unique identifier for each medical procedure.
    • ProcedureName: Name or description of the medical procedure.
    • AppointmentID: Foreign key referencing the Appointments table, indicating the appointment associated with the procedure.

    This table stores details about medical procedures associated with specific appointments.

    Billing Table:

    • InvoiceID: Unique identifier for each billing transaction.
    • PatientID: Foreign key referencing the Patients table, indicating the patient for the billing transaction.
    • Items: Description of items or services billed.
    • Amount: Amount charged for the billing transaction.

    This table maintains records of billing transactions, associating them with specific patients.

    demo Table:

    • ID: Primary key, serves as a unique identifier for each record.
    • Name: Name of the entity.
    • Hint: Additional information or hint about the entity.

    This table appears to be a demonstration or testing table, possibly unrelated to the healthcare management system.

    This dataset schema is designed to capture comprehensive information about patients, doctors, appointments, medical procedures, and billing transactions in a healthcare management system. Adjustments can be made based on specific requirements, and additional attributes can be included as needed.

  • Hospital Annual Utilization Report & Pivot Tables

    • data.chhs.ca.gov
    • data.ca.gov
    • +3more
    aspx, csv, docx, html +3
    Updated May 30, 2025
    + more versions
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    Department of Health Care Access and Information (2025). Hospital Annual Utilization Report & Pivot Tables [Dataset]. https://data.chhs.ca.gov/dataset/hospital-annual-utilization-report
    Explore at:
    pdf, xlsx, xlsx(1080890), xlsx(605638), xlsx(572310), pdf(315089), xlsx(637002), pdf(358211), xlsx(1116716), csv(108533621), pdf(682851), xlsx(915800), pdf(536270), pdf(293988), pdf(972079), pdf(368791), pdf(301252), xlsx(657042), pdf(532200), xlsx(1108403), pdf(386430), xlsx(982162), pdf(294518), docx, html, pdf(380270), xlsx(586048), aspx, xlsx(598028), pdf(383225), xlsx(602836), zip, xlsx(607287), pdf(302833), xlsx(1107998), xlsx(1073059)Available download formats
    Dataset updated
    May 30, 2025
    Dataset authored and provided by
    Department of Health Care Access and Information
    Description

    The complete data set of annual utilization data reported by hospitals contains basic licensing information including bed classifications; patient demographics including occupancy rates, the number of discharges and patient days by bed classification, and the number of live births; as well as information on the type of services provided including the number of surgical operating rooms, number of surgeries performed (both inpatient and outpatient), the number of cardiovascular procedures performed, and licensed emergency medical services provided.

  • E

    Hospital Billing Data

    • healthinformationportal.eu
    html
    Updated Mar 2, 2022
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    Federal Public Service (FPS) Social Security (2022). Hospital Billing Data [Dataset]. https://www.healthinformationportal.eu/health-information-sources/hospital-billing-data
    Explore at:
    htmlAvailable download formats
    Dataset updated
    Mar 2, 2022
    Dataset authored and provided by
    Federal Public Service (FPS) Social Security
    License

    https://socialsecurity.belgium.be/fr/chiffres-de-la-protection-sociale/statistiques-de-la-protection-sociale/comptes-de-la-santehttps://socialsecurity.belgium.be/fr/chiffres-de-la-protection-sociale/statistiques-de-la-protection-sociale/comptes-de-la-sante

    Variables measured
    sex, title, topics, acronym, country, language, data_owners, description, contact_name, geo_coverage, and 11 more
    Measurement technique
    Hospital resources & Healthcare administrative area resources
    Description

    The System of Health Accounts (SHA) establishes a methodological framework within which countries can produce internationally comparable estimates of their population's consumption of goods and services for health and long-term care. The compilation of these 'Health Accounts' is mandatory for the Member States of the European Union. The standardised framework allows making comparisons on how these services are provided, for what purpose and who bears part of the financing burden.

  • HCUP California

    • redivis.com
    application/jsonl +7
    Updated May 20, 2020
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    Stanford Center for Population Health Sciences (2020). HCUP California [Dataset]. http://doi.org/10.57761/krfh-m184
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    stata, application/jsonl, parquet, arrow, sas, spss, avro, csvAvailable download formats
    Dataset updated
    May 20, 2020
    Dataset provided by
    Redivis Inc.
    Authors
    Stanford Center for Population Health Sciences
    Time period covered
    Jan 1, 2008 - Dec 31, 2011
    Area covered
    California
    Description

    Abstract

    The State Ambulatory Surgery Databases (SASD), State Inpatient Databases (SID), and State Emergency Department Databases (SEDD) are part of a family of databases and software tools developed for the Healthcare Cost and Utilization Project (HCUP).

    HCUP's state-specific databases can be used to investigate state-specific and multi-state trends in health care utilization, access, charges, quality, and outcomes. PHS has several years (2008-2011) and datasets (SASSD, SED and SIDD) for HCUP California available.

    Usage

    The State Ambulatory Surgery and Services Databases (SASD) are State-specific files that include data for ambulatory surgery and other outpatient services from hospital-owned facilities. In addition, some States provide ambulatory surgery and outpatient services from nonhospital-owned facilities. The uniform format of the SASD helps facilitate cross-State comparisons. The SASD are well suited for research that requires complete enumeration of hospital-based ambulatory surgeries within geographic areas or States.

    The State Inpatient Databases (SID) are State-specific files that contain all inpatient care records in participating states. Together, the SID encompass more than 95 percent of all U.S. hospital discharges. The uniform format of the SID helps facilitate cross-state comparisons. In addition, the SID are well suited for research that requires complete enumeration of hospitals and discharges within geographic areas or states.

    The State Emergency Department Databases (SEDD) are a set of longitudinal State-specific emergency department (ED) databases included in the HCUP family. The SEDD capture discharge information on all emergency department visits that do not result in an admission. Information on patients seen in the emergency room and then admitted to the hospital is included in the State Inpatient Databases (SID)

    SASD, SID, and SEDD each have **Documentation **which includes:

    • Description of the Database
    • Restrictions on Use
    • File Specifications and Load Program
    • Data Elements
    • Additional Resources for Data Elements
    • ICD-10-CM/PCS Data Included in the Dataset Starting with 2015
    • Known Data Issues
    • HCUP Tools: Labels and Formats
    • HCUP Supplemental Files
    • Obtaining HCUP Data

    %3C!-- --%3E

    Before Manuscript Submission

    All manuscripts (and other items you'd like to publish) must be submitted to

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    Documentation

    The HCUP California inpatient files were constructed from the confidential files received from the Office of Statewide Health Planning and Development (OSHPD). OSHPD excluded inpatient stays that, after processing by OSHPD, did not contain a complete and “in-range” admission date or discharge date. California also excluded inpatient stays that had an unknown or missing date of birth. OSHPD removes ICD-9-CM and ICD-10-CM diagnoses codes for HIV test results. Beginning with 2009 data, OSHPD changed regulations to require hospitals to report all external cause of injury diagnosis codes including those specific to medical misadventures. Prior to 2009, OSHPD did not require collection of diagnosis codes identifying medical misadventures.

    **Types of Facilities Included in the Files Provided to HCUP by the Partner **

    California supplied discharge data for inpatient stays in general acute care hospitals, acute psychiatric hospitals, chemical dependency recovery hospitals, psychiatric health facilities, and state operated hospitals. A comparison of the number of hospitals included in the SID and the number of hospitals reported in the AHA Annual Survey is available starting in data year 2010. Hospitals do not always report data for a full calendar year. Some hospitals open or close during the year; other hospitals have technical problems that prevent them from reporting data for all months in a year.

    **Inclusion of Stays in Special Units **

    Included with the general acute care stays are stays in skilled nursing, intermediate care, rehabilitation, alcohol/chemical dependency treatment, and psychiatric units of hospitals in California. How the stays in these different types of units can be identified differs by data year. Beginning in 2006, the information is retained in the HCUP variable HOSPITALUNIT. Reliability of this indicator for the level of care depends on how it was assigned by the hospital. For data years 1998-2006, the information was retained in the HCUP variable LEVELCARE. Prior to 1998, the first

  • Weekly United States COVID-19 Hospitalization Metrics by County (Historical)...

    • healthdata.gov
    • data.virginia.gov
    • +1more
    application/rdfxml +5
    Updated Jun 16, 2023
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    data.cdc.gov (2023). Weekly United States COVID-19 Hospitalization Metrics by County (Historical) – ARCHIVED [Dataset]. https://healthdata.gov/dataset/Weekly-United-States-COVID-19-Hospitalization-Metr/n48a-vb2r
    Explore at:
    csv, tsv, application/rssxml, application/rdfxml, xml, jsonAvailable download formats
    Dataset updated
    Jun 16, 2023
    Dataset provided by
    data.cdc.gov
    Area covered
    United States
    Description

    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.

    Note: May 3,2024: Due to incomplete or missing hospital data received for the April 21,2024 through April 27, 2024 reporting period, the COVID-19 Hospital Admissions Level could not be calculated for CNMI and will be reported as “NA” or “Not Available” in the COVID-19 Hospital Admissions Level data released on May 3, 2024.

    This dataset represents COVID-19 hospitalization data and metrics aggregated to county or county-equivalent, for all counties or county-equivalents (including territories) in the United States as of the initial date of reporting for each weekly metric. 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
    Calculation of county-level hospital metrics:
    • County-level hospital data are derived using calculations performed at the Health Service Area (HSA) level. An HSA is defined by CDC’s National Center for Health Statistics as a geographic area containing at least one county which is self-contained with respect to the population’s provision of routine hospital care. Every county in the United States is assigned to an HSA, and each HSA must contain at least one hospital. Therefore, use of HSAs in the calculation of local hospital metrics allows for more accurate characterization of the relationship between health care utilization and health status at the local level.
    • Data presented at the county-level represent admissions, hosp

  • C

    Hospital Annual Financial Disclosure Report – Complete Data Set

    • data.chhs.ca.gov
    • data.ca.gov
    • +2more
    csv, docx, html, pdf +2
    Updated Apr 16, 2025
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    Department of Health Care Access and Information (2025). Hospital Annual Financial Disclosure Report – Complete Data Set [Dataset]. https://data.chhs.ca.gov/dataset/hospital-annual-financial-disclosure-report-complete-data-set
    Explore at:
    pdf, xlsx, html, xlsx(11764965), docx, xlsx(12480917), xlsx(12204483), xlsx(12202181), xlsx(11925012), csv, zip, xlsx(12183715), xlsx(12399572)Available download formats
    Dataset updated
    Apr 16, 2025
    Dataset authored and provided by
    Department of Health Care Access and Information
    Description

    On an annual basis (individual hospital fiscal year), individual hospitals and hospital systems report detailed facility-level data on services capacity, inpatient/outpatient utilization, patients, revenues and expenses by type and payer, balance sheet and income statement.

  • S

    Hospital

    • data.sanjoseca.gov
    • gisdata-csj.opendata.arcgis.com
    Updated Apr 28, 2025
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    Enterprise GIS (2025). Hospital [Dataset]. https://data.sanjoseca.gov/dataset/hospital
    Explore at:
    zip, arcgis geoservices rest api, csv, kml, html, geojsonAvailable download formats
    Dataset updated
    Apr 28, 2025
    Dataset provided by
    City of San José
    Authors
    Enterprise GIS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The representation of Hospitals in San Jose.

    Data is published on Mondays on a weekly basis.

  • O

    Koala Hospital Data

    • data.qld.gov.au
    • data.wu.ac.at
    csv
    Updated Jul 6, 2023
    + more versions
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    Environment, Tourism, Science and Innovation (2023). Koala Hospital Data [Dataset]. https://www.data.qld.gov.au/dataset/koala-hospital-data
    Explore at:
    csv(2 MiB), csv(292.6 KiB), csv(6 MiB)Available download formats
    Dataset updated
    Jul 6, 2023
    Dataset authored and provided by
    Environment, Tourism, Science and Innovation
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset contains raw data extracted from the Department Of Environment and Science's koala records database (KoalaBase) between July 1996 – July 2022.

    Please note that a small number of duplicate records may exist

  • HCUP Nationwide Emergency Department Database (NEDS) Restricted Access File

    • catalog.data.gov
    • data.virginia.gov
    • +2more
    Updated Jul 26, 2023
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    Agency for Healthcare Research and Quality, Department of Health & Human Services (2023). HCUP Nationwide Emergency Department Database (NEDS) Restricted Access File [Dataset]. https://catalog.data.gov/dataset/hcup-nationwide-emergency-department-database-neds-restricted-access-file
    Explore at:
    Dataset updated
    Jul 26, 2023
    Description

    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.

  • d

    Health Care Provider (HCP) Data | Physicians Data, Hospital Data | Global...

    • datarade.ai
    Updated May 9, 2022
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    Grepsr (2022). Health Care Provider (HCP) Data | Physicians Data, Hospital Data | Global Coverage | Pharmaceutical Sales Targeting [Dataset]. https://datarade.ai/data-products/healthcare-provider-professional-data-grepsr-grepsr-6c13
    Explore at:
    .bin, .json, .xml, .csv, .xls, .sql, .txtAvailable download formats
    Dataset updated
    May 9, 2022
    Dataset authored and provided by
    Grepsr
    Area covered
    Mexico, United States of America, Samoa, Rwanda, Kenya, Uruguay, Cayman Islands, United Arab Emirates, Virgin Islands (U.S.), Central African Republic
    Description

    Healthcare Provider/Professional Data contains the data of individual providers and facilities, including their information about opening hours, insurance networks, specialties, NPI, etcetera. In addition to discovering data sources, merging data, running analytics, and receiving decision-making guidance, the bigger problem is responding to marketplace business and patient care demands in a timely manner. Pharmacy contains the location details of pharmacies and has attributes such as addresses, opening hours, facilities, etcetera.

    A. Usecase/Applications possible with the data:

    a. Provider network data systems (PNDS) - The primary goal of the PNDS is to collect data needed to evaluate provider networks, which include physicians, hospitals, labs, home health agencies, durable medical equipment providers, and so on, for all types of Health Insurers. Such information can be used to:

    b. Find health care providers in my network - Use this directory to easily find other providers in my network.

    c. Comprehensive services assessment - Determine whether insurers have contracted with a sufficient number of primary care practitioners, clinical specialists, and service facilities (hospitals, labs, etc.) within the insurer's service area.

    d. Capacity analysis - Calculate the potential capacity of a managed care plan’s primary care providers.

    e. Locate pharmacies in your local areas.

    f. Support Employee Benefits Decisions - Having access to network data can help you make better decisions about which providers to use for Employee Medical Benefits.

    g. Know about the facilities available across different pharmacies.

    How does it work?

    • Analyze sample data
    • Customize parameters to suit your needs
    • Add to your projects
    • Contact support for further customization
  • HCUP State Inpatient Databases (SID) - Restricted Access File

    • catalog.data.gov
    • healthdata.gov
    • +2more
    Updated Feb 22, 2025
    + more versions
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    Agency for Healthcare Research and Quality, Department of Health & Human Services (2025). HCUP State Inpatient Databases (SID) - Restricted Access File [Dataset]. https://catalog.data.gov/dataset/hcup-state-inpatient-databases-sid-restricted-access-file
    Explore at:
    Dataset updated
    Feb 22, 2025
    Description

    The Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases (SID) are a set of hospital databases that contain the universe of hospital inpatient discharge abstracts from data organizations in participating States. The data are translated into a uniform format to facilitate multi-State comparisons and analyses. The SID are based on data from short term, acute care, nonfederal hospitals. Some States include discharges from specialty facilities, such as acute psychiatric hospitals. The SID include all patients, regardless of payer and contain 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). Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels. The SID contain clinical and resource-use information that is included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, admission and discharge status, patient demographics (e.g., sex, age), total charges, length of stay, and expected payment source, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. In addition to the core set of uniform data elements common to all SID, some include State-specific data elements. The SID exclude data elements that could directly or indirectly identify individuals. For some States, hospital and county identifiers are included that permit linkage to the American Hospital Association Annual Survey File and county-level data from the Bureau of Health Professions' Area Resource File except in States that do not allow the release of hospital identifiers. Restricted access data files are available with a data use agreement and brief online security training.

  • COVID-19 Reported Patient Impact and Hospital Capacity by Facility -- RAW

    • catalog.data.gov
    • healthdata.gov
    • +4more
    Updated Jul 4, 2025
    + more versions
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    U.S. Department of Health & Human Services (2025). COVID-19 Reported Patient Impact and Hospital Capacity by Facility -- RAW [Dataset]. https://catalog.data.gov/dataset/covid-19-reported-patient-impact-and-hospital-capacity-by-facility-raw
    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. The following dataset provides facility-level data for hospital utilization aggregated on a weekly basis (Sunday to Saturday). These are derived from reports with facility-level granularity across two main sources: (1) HHS TeleTracking, and (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities. The hospital population includes all hospitals registered with Centers for Medicare & Medicaid Services (CMS) as of June 1, 2020. It includes non-CMS hospitals that have reported since July 15, 2020. It does not include psychiatric, rehabilitation, Indian Health Service (IHS) facilities, U.S. Department of Veterans Affairs (VA) facilities, Defense Health Agency (DHA) facilities, and religious non-medical facilities. For a given entry, the term “collection_week” signifies the start of the period that is aggregated. For example, a “collection_week” of 2020-11-15 means the average/sum/coverage of the elements captured from that given facility starting and including Sunday, November 15, 2020, and ending and including reports for Saturday, November 21, 2020. Reported elements include an append of either “_coverage”, “_sum”, or “_avg”. A “_coverage” append denotes how many times the facility reported that element during that collection week. A “_sum” append denotes the sum of the reports provided for that facility for that element during that collection week. A “_avg” append is the average of the reports provided for that facility for that element during that collection week. The file will be updated weekly. No statistical analysis is applied to impute non-response. For averages, calculations are based on the number of values collected for a given hospital in that collection week. Suppression is applied to the file for sums and averages less than four (4). In these cases, the field will be replaced with “-999,999”. A story page was created to display both corrected and raw datasets and can be accessed at this link: https://healthdata.gov/stories/s/nhgk-5gpv This data is preliminary and subject to change as more data become available. Data is available starting on July 31, 2020. Sometimes, reports for a given facility will be provided to both HHS TeleTracking and HHS Protect. When this occurs, to ensure that there are not duplicate reports, deduplication is applied according to prioritization rules within HHS Protect. For influenza fields listed in the file, the current HHS guidance marks these fields as optional. As a result, coverage of these elements are varied. For recent updates to the dataset, scroll to the bottom of the dataset description. On May 3, 2021, the following fields have been added to this data set. hhs_ids previous_day_admission_adult_covid_confirmed_7_day_coverage previous_day_admission_pediatric_covid_confirmed_7_day_coverage previous_day_admission_adult_covid_suspected_7_day_coverage previous_day_admission_pediatric_covid_suspected_7_day_coverage previous_week_personnel_covid_vaccinated_doses_administered_7_day_sum total_personnel_covid_vaccinated_doses_none_7_day_sum total_personnel_covid_vaccinated_doses_one_7_day_sum total_personnel_covid_vaccinated_doses_all_7_day_sum previous_week_patients_covid_vaccinated_doses_one_7_day_sum previous_week_patients_covid_vaccinated_doses_all_

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    Federal Public Service (FPS) Health, Food Chain Safety, and Environment (2022). Minimum Hospital Data Set [Dataset]. https://www.healthinformationportal.eu/health-information-sources/minimum-hospital-data-set

    Minimum Hospital Data Set

    Explore at:
    htmlAvailable download formats
    Dataset updated
    Mar 4, 2022
    Dataset authored and provided by
    Federal Public Service (FPS) Health, Food Chain Safety, and Environment
    License

    https://fair.healthdata.be/dataset/12d69eca-4449-47d2-943d-e4448a467292https://fair.healthdata.be/dataset/12d69eca-4449-47d2-943d-e4448a467292

    Variables measured
    sex, title, topics, acronym, country, language, data_owners, description, contact_name, geo_coverage, and 14 more
    Measurement technique
    Hospital resources & Healthcare administrative area resources
    Description

    The MZG is a registration with which all non-psychiatric hospitals in Belgium must make their (anonymised) administrative, medical and nursing data available to the Federal Public Service (FPS) Public Health. The aim of the MZG is to support the government's health policy by

    • Determining the needs for hospital facilities;
    • Describing the qualitative and quantitative accreditation standards of hospitals and their services;
    • Organising the financing of hospitals;
    • Determining policy for the practice of medicine;
    • To outline epidemiological policy.

    The MZG aims also to support the health policy of hospitals by providing national and individual feedback so that a hospital can compare itself with other hospitals and adapt its internal policy.

    All reports can be found here (in French/Dutch).

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