About this dataset

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

How to use the dataset

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

Research Ideas

1. Predicting readmission rates for different hospital conditions
2. Analyzing relationships between hospital ownership and quality of care
3. Studying the relationship between hospital type and patient experience

Acknowledgements

This dataset was originally published by Centers for Medicare and Medicaid Services and has been modified for this project

Columns

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...

The number of hospitals in the United States has steadily declined over the past five decades, dropping from ***** in 1975 to ***** in 2022. This significant reduction reflects broader changes in the healthcare landscape, including consolidation, technological advancements, and shifts in patient care delivery models. Hospital types and ownership As of 2023, the U.S. healthcare system comprises ***** community hospitals, which are primarily non-profit institutions. For-profit hospitals make up about ** percent of these facilities, and their numbers have increased over the past two decades. The healthcare landscape also includes *** federal hospitals and ***** nonfederal hospitals. This diversity in ownership and management structures reflects the complex nature of the U.S. healthcare system and its various funding sources. Hospital capacity and utilization The decline in hospital numbers has been accompanied by a reduction in available hospital beds, decreasing from about *** million in 1975 to ******* in 2023. Despite this reduction, hospital admissions have remained relatively stable, with over **** million admissions recorded in 2023. Interestingly, hospital occupancy rates have generally decreased compared to 1975, although recent figures are showing signs of increase again.

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.

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

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

Reporting information:

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

Metric details:

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

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

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

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

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

This statistic displays the percent change in the number of U.S. hospitals from 2010 to 2017. From 2013 to 2014, there was a *** percent increase in the number of hospitals in the country. The Affordable Care Act impacted the way hospitals function, for example, by decreasing the necessity for hospital readmissions and hospital acquired infections.

Forecast: Number of Hospital in the US 2022 - 2026 Discover more data with ReportLinker!

Trends indicate that the overall number of hospital beds in the U.S. is decreasing. In 1975, there were about *** million hospital beds in the country. Despite fluctuations, by 2023 there were just ******* hospital beds in the U.S. There is a growing trend towards consumer use of outpatient services, which tend to be less costly for patients. This may be only one reason why hospital bed numbers are decreasing in the United States. Hospital occupancy Despite seeing a decrease in the number of hospital beds in the U.S., hospital occupancy rate has also generally decreased compared to 1975. The number of hospital admissions, on the other hand, has been fluctuating. Hospital costs Costs also may be an important factor in the reduction of number of hospital beds in the U.S., however, costs do not appear to be on the decline. Inpatient stays in U.S. community hospitals has been steadily increasing. In fact, the United States has the highest daily hospital costs in the world. While hospital costs depend heavily on the condition that is being treated, the U.S. had consistently the highest costs for inpatient treatments such as a hip replacement, or a coronary bypass surgery.

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.

Context

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

Querying BigQuery tables

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.

Sample Query

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;”

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

• 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

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.

Forecast: Number of Hospital Beds in the US 2023 - 2027 Discover more data with ReportLinker!

Forecast: Number of Hospital Beds in Publicly Owned Hospitals in the US 2023 - 2027 Discover more data with ReportLinker!

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.