Context

This official dataset from the Medicare.gov Nursing Home Compare website allows for comparison of over 15,000 Medicare and Medicaid-certified nursing homes in the country.

Content

Separate data collections include:

• Deficiencies, including fire safety, health, and inspection cycle types

• Ownership details, including ownership percentage

• Penalties, including filing date, fee, and payment date

• Provider details, including non or for profit status, staff ratings, and survey scores

• Quality MSR (Minimum Savings Rate) claims, including adjusted and observed scores

• MDS (Minimum Data Set) quality measures, scored on a quarterly basis

• State averages, including total number of quarterly deficiencies, and nurse staffing hours

• Survey summaries for each nursing home

Inspiration

• How would you determine what the top ten best nursing homes in the country are? The least?

• Which states have the best level of nursing home care? The least?

• In general, what are the most common types of complaints and deficiencies?

Acknowledgements

This dataset was collected by Medicare.gov, and the original files can be accessed here.

Minimum Data Set Frequency

  Description

The Minimum Data Set (MDS) Frequency data summarizes health status indicators for active residents currently in nursing homes. The MDS is part of the Federally-mandated process for clinical assessment of all residents in Medicare and Medicaid certified nursing homes. This process provides a comprehensive assessment of each resident's functional capabilities and helps nursing home staff identify health problems. Care Area Assessments… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/minimum-data-set-frequency.

Note: This web page provides data on health facilities only. To file a complaint against a facility, please see: https://www.cdph.ca.gov/Programs/CHCQ/LCP/Pages/FileAComplaint.aspx

The California Department of Public Health (CDPH), Center for Health Care Quality, Licensing and Certification (L&C) Program licenses and certifies more than 30 types of healthcare facilities. The Electronic Licensing Management System (ELMS) is a CDPH data system created to manage state licensing-related data and enforcement actions. This file includes California healthcare facilities that are operational and have a current license issued by the CDPH and/or a current U.S. Department of Health and Human Services’ Centers for Medicare and Medicaid Services (CMS) certification.

To link the CDPH facility IDs with those from other Departments, like HCAI, please reference the "Licensed Facility Cross-Walk" Open Data table at https://data.chhs.ca.gov/dataset/licensed-facility-crosswalk. Facility geographic variables are updated monthly, if latitude/longitude information is missing at any point in time, it should be available when the next time the Open Data facility file is refreshed.

Please note that the file contains the data from ELMS as of the 11th business day of the month. See DATA_DATE variable for the specific date of when the data was extracted.

Map of all Health Care Facilities in California: https://go.cdii.ca.gov/cdph-facilities

Centers for Medicare & Medicaid Services - Nursing Homes This feature layer, utilizing data from the Centers for Medicare & Medicaid Services (CMS), displays the locations of nursing homes in the U.S. Nursing homes provide a type of residential care. They are a place of residence for people who require constant nursing care and have significant deficiencies with activities of daily living. Per CMS, "Nursing homes, which include Skilled Nursing Facilities (SNFs) and Nursing Facilities (NFs), are required to be in compliance with Federal requirements to receive payment under the Medicare or Medicaid programs. The Secretary of the United States Department of Health & Human Services has delegated to the CMS and the State Medicaid Agency the authority to impose enforcement remedies against a nursing home that does not meet Federal requirements." This layer includes currently active nursing homes, including number of certified beds, address, and other information. Bridgepoint Sub-Acute and Rehab Capitol HillData downloaded: September 1, 2025Data source: Provider InformationData modification: This dataset includes only those facilities with addresses that were appropriately geocoded.For more information: Nursing homes including rehab servicesSupport documentation: Nursing Home Data DictionaryFor feedback, please contact: ArcGIScomNationalMaps@esri.com Centers for Medicare & Medicaid Services Per USA.gov, "The Centers for Medicare and Medicaid Services (CMS) provides health coverage to more than 100 million people through Medicare, Medicaid, the Children’s Health Insurance Program, and the Health Insurance Marketplace. The CMS seeks to strengthen and modernize the Nation’s health care system, to provide access to high quality care and improved health at lower costs."

This dataset shows the the America Best Nursing Homes in 2023 issued by the Newsweek and Statista.

Skilled Nursing Facility Cost Report

  Description

The Skilled Nursing Facility (SNF) Cost Report dataset is a public use file that provides select measures from the skilled nursing facility annual cost report. This data includes provider information such as facility characteristics, utilization data, cost and charges by cost center (in total and for Medicare), Medicare settlement data, and financial statement data organized by CMS Certification Number.

  Dataset… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/skilled-nursing-facility-cost-report.

Context

This dataset contains locations of Hospitals for 50 US states, Washington D.C., US territories of Puerto Rico, Guam, American Samoa, Northern Mariana Islands, Palau, and Virgin Islands.

Content

This feature class/shapefile contains locations of Hospitals for 50 US states, Washington D.C., US territories of Puerto Rico, Guam, American Samoa, Northern Mariana Islands, Palau, and Virgin Islands. The dataset only includes hospital facilities based on data acquired from various state departments or federal sources which has been referenced in the SOURCE field. Hospital facilities which do not occur in these sources will be not present in the database. The source data was available in a variety of formats (pdfs, tables, webpages, etc.) which was cleaned and geocoded and then converted into a spatial database. The database does not contain nursing homes or health centers. Hospitals have been categorized into children, chronic disease, critical access, general acute care, long term care, military, psychiatric, rehabilitation, special, and women based on the range of the available values from the various sources after removing similarities.In this version any information contained in ADDRESS2 field found in earlier versions of this dataset has been merged with the ADDRESS field and the ADDRESS2 field has been deleted.In this update 75 additional records were added and the TRAUMA field was populated for 574 additional hospitals.

Acknowledgements

This dataset was downloaded on March 23, 2019 from: https://hifld-geoplatform.opendata.arcgis.com/datasets/a2817bf9632a43f5ad1c6b0c153b0fab_0

This dataset is provided by the Homeland Infrastructure Foundation-Level Data (HIFLD) without a license and for Public Use.

HIFLD Open GP - Public Health Shared By: jrayer_geoplatform Data Source: services1.arcgis.com

Users are advised to read the data set's metadata thoroughly to understand appropriate use and data limitations.

This dataset contains quarterly data on non-nurse staffing levels in nursing homes, as reported through the Payroll Based Journal (PBJ) system. It includes daily hours that non-nurse staff are paid to work, along with the facility's daily census derived from Minimum Data Set (MDS) submissions. The dataset is designed to provide verifiable staffing information for non-nurse roles in nursing facilities. It's important to note that this dataset is very large and requires database or statistical software to handle effectively.

Source Organization: Centers for Medicare & Medicaid Services (CMS)

Data Collection System: Payroll Based Journal (PBJ) system

Purpose of Data Collection:
To obtain verifiable and standardized daily non-nurse staffing information from Medicare and Medicaid-certified nursing homes. This data is used for:

• Comprehensive staffing analysis in nursing homes, covering roles beyond nursing staff.
• Contributing to quality assessments and potentially value-based purchasing programs.
• Enhancing oversight and enforcement of staffing standards across all roles.
• Public transparency and research into the broader spectrum of nursing home staffing.

Data Scope:
Daily staffing levels reported by nursing homes for non-nurse staff, including:

• Hours paid for various non-nurse staff roles (e.g., therapists, social workers, activities staff, etc.).
• Employee and contract staff hours for non-nurse roles.
• Daily facility census (derived from MDS submissions).
• Facility characteristics (location, Medicare Provider Number, etc.).

Geographic Coverage: United States

Time Period:
Quarterly data, updated approximately four times per year. Check the CMS Data website for the latest data quarters.

Accessibility:
Publicly available on the CMS Data website.

Intended Use:
This dataset is valuable for understanding the roles of non-nurse staff in nursing homes. It can be used for research on:

• Analyzing non-nurse staffing trends.
• Investigating the impact of non-nurse staffing on quality of care and resident outcomes.
• Developing staffing models that consider the contributions of all staff roles.
• Informing policy related to comprehensive staffing in nursing facilities.

Nursing home facility data were downloaded from the NYS Dept. of Health website. For more information on nursing home facilities, visit http://www.health.state.ny.us/facilities/nursing/index.htm. The nursing home facility data were last updated April, 2019.

Payroll Based Journal Employee Detail Nursing Home Staffing

  Description

The Payroll Based Journal (PBJ) Employee Detail Nursing Home Staffing dataset provides information submitted by nursing homes including rehabilitation services on a quarterly basis. The data include a system generated employee identification number, work date, job type and employment status, and hours worked for each nursing home employee. Note: This full dataset contains more records than most… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/payroll-based-journal-employee-detail-nursing-home.

Note: This web page provides data on health facilities only. To file a complaint against a facility, please see: https://www.cdph.ca.gov/Programs/CHCQ/LCP/Pages/FileAComplaint.aspx

The California Department of Public Health (CDPH), Center for Health Care Quality, Licensing and Certification (L&C) Program licenses more than 30 types of healthcare facilities. The Electronic Licensing Management System (ELMS) is a California Department of Public Health data system created to manage state licensing-related data. This file lists the bed types and bed type capacities that are associated with California healthcare facilities that are operational and have a current license issued by the CDPH and/or a current U.S. Department of Health and Human Services’ Centers for Medicare and Medicaid Services (CMS) certification. This file can be linked by FACID to the Healthcare Facility Locations (Detailed) Open Data file for facility-related attributes, including geo-coding. The L&C Open Data facility beds file is updated monthly. To link the CDPH facility IDs with those from other Departments, like HCAI, please reference the "Licensed Facility Cross-Walk" Open Data table at https://data.chhs.ca.gov/dataset/licensed-facility-crosswalk. A list of healthcare facilities with addresses can be found at: https://data.chhs.ca.gov/dataset/healthcare-facility-locations.

Large healthcare administrative databases, like Medicare claims, are a common means to evaluate drug policies. However, administrative data often have a lag time of months to years before they are available to researchers and decision-makers. Therefore, administrative data are not always ideal for timely policy evaluations. Other sources of data are needed to rapidly evaluate policy changes and inform subsequent studies that utilize large administrative data once available. An emerging area of interest in both pharmacoepidemiology and drug policy research that can benefit from rapid data availability is biosimilar uptake, due to the potential for substantial cost savings. To respond to the need for such a data source, we established a public-private partnership to create a near-real-time database of over 1,000 nursing homes’ electronic health records to describe and quantify the effects of recent policies related to COVID-19 and medications. In this article, we first describe the components and infrastructure used to create our EHR database. Then, we provide an example that illustrates the use of this database by describing the uptake of insulin glargine-yfgn, a new exchangeable biosimilar for insulin glargine, in US nursing homes. We also examine the uptake of all biosimilars in nursing homes before and after the onset of the COVID-19 pandemic. We conclude with potential directions for future research and database infrastructure.

This feature layer contains locations of Hospitals for 50 US states, Washington D.C., US territories of Puerto Rico, Guam, American Samoa, Northern Mariana Islands, Palau, and Virgin Islands. The dataset only includes hospital facilities based on data acquired from various state departments or federal sources which has been referenced in the SOURCE field. Hospital facilities which do not occur in these sources will be not present in the database. The source data was available in a variety of formats (pdfs, tables, webpages, etc.) which was cleaned and geocoded and then converted into a spatial database. The database does not contain nursing homes or health centers. Hospitals have been categorized into children, chronic disease, critical access, general acute care, long term care, military, psychiatric, rehabilitation, special, and women based on the range of the available values from the various sources after removing similarities. In this update the TRAUMA field was populated for 172 additional hospitals and helipad presence were verified for all hospitals.

This dataset identifies health care spending at medical services such as hospitals, physicians, clinics, and nursing homes etc. as well as for medical products such as medicine, prescription glasses and hearing aids. This dataset pertains to personal health care spending in general. Other datasets in this series include Medicaid personal health care spending and Medicare personal health care spending.

Skilled Nursing Facility Change of Ownership

  Description

The Skilled Nursing Facility (SNF) Change of Ownership (CHOW) dataset provides information on the SNF ownership changes that occurred on or after January 1, 2016. This data includes information on the buyer and seller organization’s legal business name, provider type, change of ownership type (CHOW, Acquisition/Merger, or Consolidation) and the effective date of the change.

  Dataset Details

Publisher:… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/skilled-nursing-facility-change-of-ownership.

A. SUMMARY This archived dataset includes data for population characteristics that are no longer being reported publicly. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.

B. HOW THE DATASET IS CREATED Data on the population characteristics of COVID-19 cases are from:  * Case interviews  * Laboratories  * Medical providers    These multiple streams of data are merged, deduplicated, and undergo data verification processes.  

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases. * The population estimates for the "Other" or “Multi-racial” groups should be considered with caution. The Census definition is likely not exactly aligned with how the City collects this data. For that reason, we do not recommend calculating population rates for these groups.

Gender * The City collects information on gender identity using these guidelines.

Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing Facility (SNF) is a type of long-term care facility that provides care to individuals, generally in their 60s and older, who need functional assistance in their daily lives.  * This dataset includes data for COVID-19 cases reported in Skilled Nursing Facilities (SNFs) through 12/31/2022, archived on 1/5/2023. These data were identified where “Characteristic_Type” = ‘Skilled Nursing Facility Occupancy’.

Sexual orientation * The City began asking adults 18 years old or older for their sexual orientation identification during case interviews as of April 28, 2020. Sexual orientation data prior to this date is unavailable. * The City doesn’t collect or report information about sexual orientation for persons under 12 years of age. * Case investigation interviews transitioned to the California Department of Public Health, Virtual Assistant information gathering beginning December 2021. The Virtual Assistant is only sent to adults who are 18+ years old. https://www.sfdph.org/dph/files/PoliciesProcedures/COM9_SexualOrientationGuidelines.pdf">Learn more about our data collection guidelines pertaining to sexual orientation.

Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death.

Homelessness Persons are identified as homeless based on several data sources: * self-reported living situation * the location at the time of testing * Department of Public Health homelessness and health databases * Residents in Single-Room Occupancy hotels are not included in these figures. These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions.

Single Room Occupancy (SRO) tenancy * SRO buildings are defined by the San Francisco Housing Code as having six or more "residential guest rooms" which may be attached to shared bathrooms, kitchens, and living spaces. * The details of a person's living arrangements are verified during case interviews.

Transmission Type * Information on transmission of COVID-19 is based on case interviews with individuals who have a confirmed positive test. Individuals are asked if they have been in close contact with a known COVID-19 case. If they answer yes, transmission category is recorded as contact with a known case. If they report no contact with a known case, transmission category is recorded as community transmission. If the case is not interviewed or was not asked the question, they are counted as unknown.

C. UPDATE PROCESS This dataset has been archived and will no longer update as of 9/11/2023.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of cases on each date.

New cases are the count of cases within that characteristic group where the positive tests were collected on that specific specimen collection date. Cumulative cases are the running total of all San Francisco cases in that characteristic group up to the specimen collection date listed.

This data may not be immediately available for recently reported cases. Data updates as more information becomes available.

To explore data on the total number of cases, use the ARCHIVED: COVID-19 Cases Over Time dataset.

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases by population characteristics over time are no longer being updated. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.
• 6/6/2023 - data on cases by transmission type have been removed. See section ARCHIVED DATA for more detail.
• 5/16/2023 - data on cases by sexual orientation, comorbidities, homelessness, and single room occupancy have been removed. See section ARCHIVED DATA for more detail.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 1/31/2023 - updated “population_estimate” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/5/2023 - data on SNF cases removed. See section ARCHIVED DATA for more detail.
• 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.

No description provided

This table contains features that represent healthcare facilities associated with nine NAICS codes. Establishment-specific information for all hospitals and some nursing homes, except the annual excess food estimate, was obtained from the Homeland Infrastructure Foundation-Level Data (https://hifld-geoplatform.opendata.arcgis.com/) and reflects 2020 data. For the remaining nursing homes, establishment-specific information except the annual excess food estimate was licensed to the EPA from D&B Hoovers in 2021 (https://www.dnb.com/). Calculations used to estimate annual excess food estimates are described in EPA’s 2023 publication: EPA Excess Food Opportunities Map Version 3 - Technical Methodology. The dataset contains 57,521 facilities.

This dataset provides restaurant inspections, violations, grades and adjudication information

Hospital Care Quality Measures

Timely & Effective Care Across the U.S

By Health [source]

About this dataset

This dataset includes provider-level data revealing the quality of timely and effective care from hospitals across the United States. It allows us to analyze heart attack, heart failure, pneumonia, surgical, emergency department, preventive care for children's asthma and stroke prevention and treatment data for pregnancy and delivery care courtesy of the Centers for Medicare & Medicaid Services. With this dataset you can analyze hospital's performance on all these areas using Hospital Name, Addresss , City , State , ZIP Code , County Name , Phone Number as well as scores creditable to Measure Name , Sample size from which it was derived a Footnote explanation based on location. Dig deep into each provider's level of care with this dataset to understand their performance on providing timely effective care

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How to use the dataset

To get the most out of this dataset, it is important to understand each column in the dataset: Hospital Name identifies the health care facility; Address provides the address of the hospital; City identifies the city where it is located; State specifies which state it belongs to; ZIP Code denotes its specific zip code; County Name mentions what county it belongs to; Phone Number connects you with an immediate contact at the facility if needed; Condition categorizes types of tests/treatments being monitored in that case study; Measure Name outlines all related measures under said condition umbrella or metric(s) studied as part of that investigative research project/condition category (i.e., infection prevention); Score grades out how well that measure was doing compared against expectations or goals for quality & safe patient protections (higher scores are indicative of better performance on those surveyed & tracked items); Sample details how many patients were involved in this particular study topic component and involved participant sample size selection & unit evaluation criteria definition considerations during research recruitment and retention efforts associated with a particular area of specialty treatment/testing cluster system activity factors reviewed directionally by researchers via cohort based review activities over time [note: matching non-patients or control subject population reference points also sometimes may be used depending on written scope descriptions outlined by investigators]; Footnotes can amplify additional evaluations/CAVEATS sometimes noted regarding high-lighted findings(-such as improvement yet still not meeting standards), etc.; Measure Start Date defines when all test students were allowed entry into their respective study groups associated with one another for convergence analysis purposes within a defined subject patient group prospectively selected category designation feature component selection batch cases (new patients added mid-project have crossed design frontiers at random intervals sometimes necessary). Lastly, Measure End Date reflects terminal endpoint lead review periods cut off times when no new data entries can be accepted post-data collection stopped official time period specifications if designated by protocol order via institutional clinical trial board IRB approved advanced notification statements issued throughout any official project undertaking design process stages at its multiplex points).

Understanding each column's features will assist you in selecting relevant variables from this dataset according to your research needs. Additionally, using Location can help narrow down search results geographically. With this information researchers can gain valuable insight into overall trends regarding timely and effective care in different hospitals across different states

Research Ideas

• Create an interactive heatmap to visualize provider-level data across different states. This can allow researchers, consumers and policy makers to identify areas of excellence as well as opportunities for improvement in timely and effective care measures.
• Develop a web app that allows users to locate hospitals in their area based on any given health condition, measure name, score or timeframe data provided by this dataset. This could give patients access to quality care options and help them make informed decisions while seeking medical attention.
• Utilizing the geographic coordinates data included in the Location column, create a virtual tour function that lets people virtually explore the interior of hospital facilities associated with this dataset...