Looking for a dataset on hospitals in the United States? Look no further! This dataset contains information on all of the hospitals registered with Medicare in the US, including their addresses, phone numbers, hospital type, and more. With such a large amount of data, this dataset is perfect for anyone interested in studying the US healthcare system.
This dataset can also be used to study hospital ownership, emergency services
If you want to study the US healthcare system, this dataset is perfect for you. It contains information on all of the hospitals registered with Medicare, including their addresses, phone numbers, hospital type, and more. With such a large amount of data, this dataset is perfect for anyone interested in studying the US healthcare system.
This dataset can also be used to study hospital ownership, emergency services, and EHR usage. In addition, the hospital overall rating and various comparisons are included for safety of care, readmission rates
This dataset was originally published by Centers for Medicare and Medicaid Services and has been modified for this project
File: Hospital_General_Information.csv | Column name | Description | |:-------------------------------------------------------|:----------------------------------------------------------------------------------------------------------| | Hospital Name | The name of the hospital. (String) | | Hospital Name | The name of the hospital. (String) | | Address | The address of the hospital. (String) | | Address | The address of the hospital. (String) | | City | The city in which the hospital is located. (String) | | City | The city in which the hospital is located. (String) | | State | The state in which the hospital is located. (String) | | State | The state in which the hospital is located. (String) | | ZIP Code | The ZIP code of the hospital. (Integer) | | ZIP Code | The ZIP code of the hospital. (Integer) | | County Name | The county in which the hospital is located. (String) | | County Name | The county in which the hospital is located. (String) | | Phone Number | The phone number of the hospital. (String) | | Phone Number | The phone number of the hospital. (String) | | Hospital Type | The type of hospital. (String) | | Hospital Type | The type of hospital. (String) | | Hospital Ownership | The ownership of the hospital. (String) | | Hospital Ownership | The ownership of the hospital. (String) | | Emergency Services | Whether or not the...
[IMPORTANT NOTE: Sample file posted on Datarade is not the complete dataset, as Datarade permits only a single CSV file. Visit https://www.careprecise.com/healthcare-provider-data-sample.htm for more complete samples.] Updated every month, CarePrecise developed the AHD to provide a comprehensive database of U.S. hospital information. Extracted from the CarePrecise master provider database with information all of the 6.3 million HIPAA-covered US healthcare providers and additional sources, the Authoritative Hospital Database (AHD) contains records for all HIPAA-covered hospitals. In this database of hospitals we include bed counts, patient satisfaction data, hospital system ownership, hospital charges and cases by Zip Code®, and more. Most records include a cabinet-level or director-level contact. A PlaceKey is provided where available.
The AHD includes bed counts for 95% of hospitals, full contact information on 85%, and fax numbers for 62%. We include detailed patient satisfaction data, employee counts, and medical procedure volumes.
The AHD integrates directly with our extended provider data product to bring you the physicians and practice groups affiliated with the hospitals. This combination of data is the only commercially available hospital dataset of this depth.
NEW: Hospital NPI to CCN Rollup A CarePrecise Exclusive. Using advanced record-linkage technology, the AHD now includes a new file that makes it possible to mine the vast hospital information available in the National Provider Identifier registry database. Hospitals may have dozens of NPI records, each with its own information about a unit, listing facility type and/or medical specialties practiced, as well as separate contact names. To wield the power of this new feature, you'll need the CarePrecise Master Bundle, which contains all of the publicly available NPI registry data. These data are available in other CarePrecise data products.
Counts are approximate due to ongoing updates. Please review the current AHD information here: https://www.careprecise.com/detail_authoritative_hospital_database.htm
The AHD is sold as-is and no warranty is offered regarding accuracy, timeliness, completeness, or fitness for any purpose.
The CarePrecise U.S. HCP/HCO Collection Dataset includes deep data on all 6.7 million U.S. HIPAA-covered healthcare practitioners and organizations. Monthly full updates. Includes linkages between the individual practitioners and their practice groups, hospitals, and hospital systems. Licensing plans are available for basic (internal use), derivative products, and redistribution. Data updates are delivered quarterly or monthly to suit customer need; FTP push is available, standard delivery is via CDN. Single download for evaluation is available. CarePrecise is a leader in the fields of HCP/HCO data, supplying provider data to the industry since 2008. Note regarding pricing: The Collection price shown in Pricing is separate from email addresses. Email addresses are priced as low as $0.075 per, based on volume. Pricing shown is without derivative product (DP) licensing for use in web applications; DP license ranges in price from $1,900/year to $9,000/year on top of data purchase, based on application and overall exposure estimate. DP license is sold in two-year term and requires a license agreement.
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.
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 in a timeseries format dating back to January 1, 2020. These are derived from reports with facility-level granularity across three main sources: (1) HHS TeleTracking, (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities and (3) National Healthcare Safety Network (before July 15). 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 April 27, 2022 the following pediatric fields were added: all_pediatric_inpatient_bed_occupied all_pediatric_inpatient_bed_occupied_coverage all_pediatric_inpatient_beds all_pediatric_inpatient_beds_coverage previous_day_admission_pediatric_covid_confirmed_0_4 previous_day_admission_pediatric_covid_confirmed_0_4_coverage previous_day_admission_pediatric_covid_confirmed_12_17 previous_day_admission_pediatric_covid_confirmed_12_17_coverage previous_day_admission_pediatric_covid_confirmed_5_11 previous_day_admission_pediatric_covid_confirmed_5_11_coverage previous_day_admission_pediatric_covid_confirmed_unknown previous_day_admission_pediatric_covid_confirmed_unknown_coverage staffed_icu_pediatric_patients_confirmed_covid staffed_icu_pediatric_patients_confirmed_covid_coverage staffed_pediatric_icu_bed_occupancy staffed_pediatric_icu_bed_occupancy_coverage total_staffed_pediatric_icu_beds total_staffed_pediatric_icu_beds_coverage On January 19, 2022, the following fields have been added to this dataset: inpatient_beds_used_covid inpatient_beds_used_covid_coverage On September 17, 2021, this data set has had the following fields added: icu_patients_confirmed_influenza, icu_patients_confirmed_influenza_coverage, previous_day_admission_influenza_confirmed, previous_day_admission_influenza_confirmed_coverage, previous_day_deaths_covid_and_influenza, previous_day_deaths_covid_and_influenza_coverage, previous_day_deaths_influenza, previous_day_deaths_influenza_coverage, total_patients_hospitalized_confirmed_influenza, total_patients_hospitalized_confirmed_influenza_and_covid, total_patients_hospitalized_confirmed_influenza_and_covid_coverage, total_patients_hospitalized_confirmed_influenza_coverage On September 13, 2021, this data set has had the following fields added: on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses, on_hand_supply_therapeutic_b_bamlanivimab_courses, on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses, previous_week_therapeutic_a_casirivimab_imdevimab_courses_used, previous_week_therapeutic_b_bamlanivimab_courses_used, previous_week_therapeutic_c_bamlanivima
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:
Made available through Socrata COVID-19 Plugin via API.
From the source Web site: This dataset is intended to be used as a baseline for understanding the typical bed capacity and average yearly bed utilization of hospitals reporting such information. The date of last update received from each hospital may be varied. While the dataset is not updated in real-time, this information is critical for understanding the impact of a high utilization event, like COVID-19.
Definitive Healthcare is the leading provider of data, intelligence, and analytics on healthcare organizations and practitioners. In this service, Definitive Healthcare provides intelligence on the numbers of licensed beds, staffed beds, ICU beds, and the bed utilization rate for the hospitals in the United States.
The All CMS Data Feeds dataset is an expansive resource offering access to 118 unique report feeds, providing in-depth insights into various aspects of the U.S. healthcare system. With over 25.8 billion rows of data meticulously collected since 2007, this dataset is invaluable for healthcare professionals, analysts, researchers, and businesses seeking to understand and analyze healthcare trends, performance metrics, and demographic shifts over time. The dataset is updated monthly, ensuring that users always have access to the most current and relevant data available.
Dataset Overview:
118 Report Feeds: - The dataset includes a wide array of report feeds, each providing unique insights into different dimensions of healthcare. These topics range from Medicare and Medicaid service metrics, patient demographics, provider information, financial data, and much more. The breadth of information ensures that users can find relevant data for nearly any healthcare-related analysis. - As CMS releases new report feeds, they are automatically added to this dataset, keeping it current and expanding its utility for users.
25.8 Billion Rows of Data:
Historical Data Since 2007: - The dataset spans from 2007 to the present, offering a rich historical perspective that is essential for tracking long-term trends and changes in healthcare delivery, policy impacts, and patient outcomes. This historical data is particularly valuable for conducting longitudinal studies and evaluating the effects of various healthcare interventions over time.
Monthly Updates:
Data Sourced from CMS:
Use Cases:
Market Analysis:
Healthcare Research:
Performance Tracking:
Compliance and Regulatory Reporting:
Data Quality and Reliability:
The All CMS Data Feeds dataset is designed with a strong emphasis on data quality and reliability. Each row of data is meticulously cleaned and aligned, ensuring that it is both accurate and consistent. This attention to detail makes the dataset a trusted resource for high-stakes applications, where data quality is critical.
Integration and Usability:
Ease of Integration:
The "COVID-19 Reported Patient Impact and Hospital Capacity by Facility" dataset from the U.S. Department of Health & Human Services, filtered for Connecticut. View the full dataset and detailed metadata here: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/anag-cw7u The following dataset provides facility-level data for hospital utilization aggregated on a weekly basis (Friday to Thursday). 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 2020-11-20 means the average/sum/coverage of the elements captured from that given facility starting and including Friday, November 20, 2020, and ending and including reports for Thursday, November 26, 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”. 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. 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_7_day_sum On May 8, 2021, this data set has been converted to a corrected data set. The corrections applied to this data set are to smooth out data anomalies caused by keyed in data errors. To help determine which records have had corrections made to it. An additional Boolean field called is_corrected has been added. To see the numbers as reported by the facilities, go to: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/uqq2-txqb On May 13, 2021 Changed vaccination fields from sum to max or min fields. This reflects the maximum or minimum number report
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Hospitals in the United States decreased to 18.36 per one million people in 2022 from 18.46 per one million people in 2021. This dataset includes a chart with historical data for the United States Hospitals.
http://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/
This dataset was created by Henry C. Wong
Released under Database: Open Database, Contents: Database Contents
This dataset contains Hospital General Information from the U.S. Department of Health & Human Services. This is the BigQuery COVID-19 public dataset. This data contains a list of all hospitals that have been registered with Medicare. This list includes addresses, phone numbers, hospital types and quality of care information. The quality of care data is provided for over 4,000 Medicare-certified hospitals, including over 130 Veterans Administration (VA) medical centers, across the country. You can use this data to find hospitals and compare the quality of their care
You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.cms_medicare.hospital_general_info.
How do the hospitals in Mountain View, CA compare to the average hospital in the US? With the hospital compare data you can quickly understand how hospitals in one geographic location compare to another location. In this example query we compare Google’s home in Mountain View, California, to the average hospital in the United States. You can also modify the query to learn how the hospitals in your city compare to the US national average.
“#standardSQL
SELECT
MTV_AVG_HOSPITAL_RATING,
US_AVG_HOSPITAL_RATING
FROM (
SELECT
ROUND(AVG(CAST(hospital_overall_rating AS int64)),2) AS MTV_AVG_HOSPITAL_RATING
FROM
bigquery-public-data.cms_medicare.hospital_general_info
WHERE
city = 'MOUNTAIN VIEW'
AND state = 'CA'
AND hospital_overall_rating <> 'Not Available') MTV
JOIN (
SELECT
ROUND(AVG(CAST(hospital_overall_rating AS int64)),2) AS US_AVG_HOSPITAL_RATING
FROM
bigquery-public-data.cms_medicare.hospital_general_info
WHERE
hospital_overall_rating <> 'Not Available')
ON
1 = 1”
What are the most common diseases treated at hospitals that do well in the category of patient readmissions?
For hospitals that achieved “Above the national average” in the category of patient readmissions, it might be interesting to review the types of diagnoses that are treated at those inpatient facilities. While this query won’t provide the granular detail that went into the readmission calculation, it gives us a quick glimpse into the top disease related groups (DRG)
, or classification of inpatient stays that are found at those hospitals. By joining the general hospital information to the inpatient charge data, also provided by CMS, you could quickly identify DRGs that may warrant additional research. You can also modify the query to review the top diagnosis related groups for hospital metrics you might be interested in.
“#standardSQL
SELECT
drg_definition,
SUM(total_discharges) total_discharge_per_drg
FROM
bigquery-public-data.cms_medicare.hospital_general_info
gi
INNER JOIN
bigquery-public-data.cms_medicare.inpatient_charges_2015
ic
ON
gi.provider_id = ic.provider_id
WHERE
readmission_national_comparison = 'Above the national average'
GROUP BY
drg_definition
ORDER BY
total_discharge_per_drg DESC
LIMIT
10;”
This dataset contains electronic health records used to study associations between PFAS occurrence and multimorbidity in a random sample of UNC Healthcare system patients. The dataset contains the medical record number to uniquely identify each individual as well as information on PFAS occurrence at the zip code level, the zip code of residence for each individual, chronic disease diagnoses, patient demographics, and neighborhood socioeconomic information from the 2010 US Census. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Because this data has PII from electronic health records the data can only be accessed with an approved IRB application. Project analytic code is available at L:/PRIV/EPHD_CRB/Cavin/CARES/Project Analytic Code/Cavin Ward/PFAS Chronic Disease and Multimorbidity. Format: This data is formatted as a R dataframe and associated comma-delimited flat text file. The data has the medical record number to uniquely identify each individual (which also serves as the primary key for the dataset), as well as information on the occurrence of PFAS contamination at the zip code level, socioeconomic data at the census tract level from the 2010 US Census, demographics, and the presence of chronic disease as well as multimorbidity (the presence of two or more chronic diseases). This dataset is associated with the following publication: Ward-Caviness, C., J. Moyer, A. Weaver, R. Devlin, and D. Diazsanchez. Associations between PFAS occurrence and multimorbidity as observed in an electronic health record cohort. Environmental Epidemiology. Wolters Kluwer, Alphen aan den Rijn, NETHERLANDS, 6(4): p e217, (2022).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Health Care Payrolls in the United States decreased to 30.60 Thousand in August from 51.30 Thousand in July of 2025. This dataset includes a chart with historical data for the United States Health Care Payrolls.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Comprehensive dataset containing 2,622 verified General hospital businesses in United States with complete contact information, ratings, reviews, and location data.
This database is part of the National Medical Information System (NMIS). The National Health Care Practitioner Database (NHCPD) supports Veterans Health Administration Privacy Act requirements by segregating personal information about health care practitioners such as name and social security number from patient information recorded in the National Patient Care Database for Ambulatory Care Reporting and Primary Care Management Module.
https://www.icpsr.umich.edu/web/ICPSR/studies/34644/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/34644/terms
Overview: The goal of the project was to develop a unique database linking chronic disease clinical data from an electronic medical record (EMR) of a large academic healthcare system to multi-payer claims data. The longitudinal relational database can be used to study clinical effectiveness of many diagnostic and treatment interventions. The population of patients used consisted of those patients who were attributed to the University of Michigan Health System (UMHS) as continuing care patients, who are also in adjudicated and validated chronic disease registries. Data Access: These data are not available from ICPSR. The data are restricted to use by the principal investigator and cannot be shared.
https://www.usa.gov/government-workshttps://www.usa.gov/government-works
This dataset represents weekly hospital respiratory data and metrics aggregated to national and state/territory levels reported to CDC’s National Health Safety Network (NHSN) beginning August 2020. Data for reporting dates through April 30, 2024 represent data reported during a previous mandated reporting period as specified by the HHS Secretary. Data for reporting dates May 1, 2024 – October 31, 2024 represent voluntarily reported data in the absence of a mandate. Data for reporting dates beginning November 1, 2024 represent data reported during a current mandated reporting period. All data and metrics capturing information on respiratory syncytial virus (RSV) were voluntarily reported until November 1, 2024. All data included in this dataset represent aggregated counts, and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and new hospital admissions with corresponding metrics indicating reporting coverage for a given reporting week. NHSN monitors national and local trends in healthcare system stress and capacity for all acute care and critical access hospitals in the United States.
For more information on the reporting mandate per the Centers for Medicare and Medicaid Services (CMS) requirements, visit: Updates to the Condition of Participation (CoP) Requirements for Hospitals and Critical Access Hospitals (CAHs) To Report Acute Respiratory Illnesses.
For more information regarding NHSN’s collection of these data, including full reporting guidance, visit: NHSN Hospital Respiratory Data.
Source: CDC National Healthcare Safety Network (NHSN).
Archived datasets updated during the mandatory hospital reporting period from August 1, 2020, to April 30, 2024:
Archived datasets updated during the voluntary hospital reporting period from May 1, 2024, to October 31, 2024:
Note: The following columns were added to this dataset as of September 26,
The average number of hospital beds available per 1,000 people in the United States was forecast to continuously decrease between 2024 and 2029 by in total 0.1 beds (-3.7 percent). After the eighth consecutive decreasing year, the number of available beds per 1,000 people is estimated to reach 2.63 beds and therefore a new minimum in 2029. Depicted is the number of hospital beds per capita in the country or region at hand. As defined by World Bank this includes inpatient beds in general, specialized, public and private hospitals as well as rehabilitation centers.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 average number of hospital beds available per 1,000 people in countries like Canada and Mexico.
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. 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:
Looking for a dataset on hospitals in the United States? Look no further! This dataset contains information on all of the hospitals registered with Medicare in the US, including their addresses, phone numbers, hospital type, and more. With such a large amount of data, this dataset is perfect for anyone interested in studying the US healthcare system.
This dataset can also be used to study hospital ownership, emergency services
If you want to study the US healthcare system, this dataset is perfect for you. It contains information on all of the hospitals registered with Medicare, including their addresses, phone numbers, hospital type, and more. With such a large amount of data, this dataset is perfect for anyone interested in studying the US healthcare system.
This dataset can also be used to study hospital ownership, emergency services, and EHR usage. In addition, the hospital overall rating and various comparisons are included for safety of care, readmission rates
This dataset was originally published by Centers for Medicare and Medicaid Services and has been modified for this project
File: Hospital_General_Information.csv | Column name | Description | |:-------------------------------------------------------|:----------------------------------------------------------------------------------------------------------| | Hospital Name | The name of the hospital. (String) | | Hospital Name | The name of the hospital. (String) | | Address | The address of the hospital. (String) | | Address | The address of the hospital. (String) | | City | The city in which the hospital is located. (String) | | City | The city in which the hospital is located. (String) | | State | The state in which the hospital is located. (String) | | State | The state in which the hospital is located. (String) | | ZIP Code | The ZIP code of the hospital. (Integer) | | ZIP Code | The ZIP code of the hospital. (Integer) | | County Name | The county in which the hospital is located. (String) | | County Name | The county in which the hospital is located. (String) | | Phone Number | The phone number of the hospital. (String) | | Phone Number | The phone number of the hospital. (String) | | Hospital Type | The type of hospital. (String) | | Hospital Type | The type of hospital. (String) | | Hospital Ownership | The ownership of the hospital. (String) | | Hospital Ownership | The ownership of the hospital. (String) | | Emergency Services | Whether or not the...