Context

The CMS National Plan and Provider Enumeration System (NPPES) was developed as part of the Administrative Simplification provisions in the original HIPAA act. The primary purpose of NPPES was to develop a unique identifier for each physician that billed medicare and medicaid. This identifier is now known as the National Provider Identifier Standard (NPI) which is a required 10 digit number that is unique to an individual provider at the national level.

Once an NPI record is assigned to a healthcare provider, parts of the NPI record that have public relevance, including the provider’s name, speciality, and practice address are published in a searchable website as well as downloadable file of zipped data containing all of the FOIA disclosable health care provider data in NPPES and a separate PDF file of code values which documents and lists the descriptions for all of the codes found in the data file.

Content

The dataset contains the latest NPI downloadable file in an easy to query BigQuery table, npi_raw. In addition, there is a second table, npi_optimized which harnesses the power of Big Query’s next-generation columnar storage format to provide an analytical view of the NPI data containing description fields for the codes based on the mappings in Data Dissemination Public File - Code Values documentation as well as external lookups to the healthcare provider taxonomy codes . While this generates hundreds of columns, BigQuery makes it possible to process all this data effectively and have a convenient single lookup table for all provider information.

Fork this kernel to get started.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:nppes?_ga=2.117120578.-577194880.1523455401

https://console.cloud.google.com/marketplace/details/hhs/nppes?filter=category:science-research

Dataset Source: Center for Medicare and Medicaid Services. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @rawpixel from Unplash.

Inspiration

What are the top ten most common types of physicians in Mountain View?

What are the names and phone numbers of dentists in California who studied public health?

This dataset provides airborne LiDAR data collected over 90 sites totaling approximately 100,000 hectares of forested land in Kalimantan, Indonesia on the island of Borneo in late 2014. The data were collected as part of an effort to establish a national forest monitoring system for Indonesia that uses a combination of remote sensing and ground-based forest carbon inventory approaches.

Nursing Home Compare has detailed information about every Medicare and Medicaid nursing home in the country. A nursing home is a place for people who can’t be cared for at home and need 24-hour nursing care. These are the official datasets used on the Medicare.gov Nursing Home Compare Website provided by the Centers for Medicare & Medicaid Services. These data allow you to compare the quality of care at every Medicare and Medicaid-certified nursing home in the country, including over 15,000 nationwide.

The Covered Recipient Profile Supplement file contains information about physicians and non-physician practitioners who have been indicated as recipients of payments, other transfers of value, or ownership and investment interest in payment records, as well as physicians and non-physician practitioners who have been identified as principal investigators associated with research payment records published by Open Payments.

This file contains only those physicians that have at least one published payment record in this cycle of the publication as of January 30, 2025. The criteria used by the Centers for Medicare & Medicaid Services (CMS) to determine which payment records are eligible for publication is available in the Open Payments Methodology and Data Dictionary Document. This document can be found on the Resources page of the Open Payments website (https://www.cms.gov/OpenPayments/Resources). The Methodology and Data Dictionary Document also includes information on the data collection and reporting methodology, data fields included in the files, and any notes or special considerations that users should be aware of.

Context

Upon reviewing the CMS website (https://data.cms.gov/covid-19/covid-19-nursing-home-data), it was apparent a number of nursing home providers were missing from a map plot that was intended to show COVID-19 statistics. I wanted to take a deeper look into the data and just play around with a few visualizations via the CMS provided data set, however I noticed the provided set did not contain any long/lat values for the nursing homes. It could also be seen that certain providers were not being mapped on the CMS website due to string being mixed with numbers in Provider IDs assigned to each provider. New Provider IDs were assigned in rank order, alphabetically and by State. Each nursing home, along with their address was pulled and used to obtain a set of coordinates for their facility and can be joined to the original dataset via Provider ID for use.

Content

Original dataset was sourced from the cms.gov website, with Geocodio being used to geocode the coordinates for the nursing homes. Per the CMS, "The data posted by CMS is what nursing homes submitted through the Centers of Disease Control and Prevention (CDC) National Healthcare Safety Network (NHSN) system. CMS and CDC perform quality assurance checks on the data and may suppress data that appear to be erroneous. The data is not altered from what nursing homes report to the NHSN system. Data regarding numbers of new cases, suspected cases, or deaths are aggregated.". Nursing homes reported weekly COVID statistics spanning 05/24/20 - 08/05/2021, ranging from case, death, vaccination, equipment, etc. for both residents and staff. A separate table containing address information and coordinates for each individual provider is available for joining, in order to map each facility for visualization.

Acknowledgements

Original Data Source: https://data.cms.gov/covid-19/covid-19-nursing-home-data

Geocode Source: https://www.geocod.io

We are releasing data that compares the HHS Provider Relief Fund and the CMS Accelerated and Advance Payments by State and provider as of May 15, 2020. This data is already available on other websites, but this chart brings the information together into one view for comparison. You can find additional information on the Accelerated and Advance Payments at the following links:

Fact Sheet: https://www.cms.gov/files/document/Accelerated-and-Advanced-Payments-Fact-Sheet.pdf;

Zip file on providers in each state: https://www.cms.gov/files/zip/accelerated-payment-provider-details-state.zip

Medicare Accelerated and Advance Payments State-by-State information and by Provider Type: https://www.cms.gov/files/document/covid-accelerated-and-advance-payments-state.pdf.

This file was assembled by HHS via CMS, HRSA and reviewed by leadership and compares the HHS Provider Relief Fund and the CMS Accelerated and Advance Payments by State and provider as of December 4, 2020.

HHS Provider Relief Fund President Trump is providing support to healthcare providers fighting the coronavirus disease 2019 (COVID-19) pandemic through the bipartisan Coronavirus Aid, Relief, & Economic Security Act and the Paycheck Protection Program and Health Care Enhancement Act, which provide a total of $175 billion for relief funds to hospitals and other healthcare providers on the front lines of the COVID-19 response. This funding supports healthcare-related expenses or lost revenue attributable to COVID-19 and ensures uninsured Americans can get treatment for COVID-19. HHS is distributing this Provider Relief Fund money and these payments do not need to be repaid. The Department allocated $50 billion of the Provider Relief Fund for general distribution to Medicare facilities and providers impacted by COVID-19, based on eligible providers' net reimbursement. It allocated another $22 billion to providers in areas particularly impacted by the COVID-19 outbreak, rural providers, and providers who serve low-income populations and uninsured Americans. HHS will be allocating the remaining funds in the near future.

As part of the Provider Relief Fund distribution, all providers have 45 days to attest that they meet certain criteria to keep the funding they received, including public disclosure. As of May 15, 2020, there has been a total of $34 billion in attested payments. The chart only includes those providers that have attested to the payments by that date. We will continue to update this information and add the additional providers and payments once their attestation is complete.

CMS Accelerated and Advance Payments Program On March 28, 2020, to increase cash flow to providers of services and suppliers impacted by the coronavirus disease 2019 (COVID-19) pandemic, the Centers for Medicare & Medicaid Services (CMS) expanded the Accelerated and Advance Payment Program to a broader group of Medicare Part A providers and Part B suppliers. Beginning on April 26, 2020, CMS stopped accepting new applications for the Advance Payment Program, and CMS began reevaluating all pending and new applications for Accelerated Payments in light of the availability of direct payments made through HHS’s Provider Relief Fund.

Since expanding the AAP program on March 28, 2020, CMS approved over 21,000 applications totaling $59.6 billion in payments to Part A providers, which includes hospitals, through May 18, 2020. For Part B suppliers—including doctors, non-physician practitioners and durable medical equipment suppliers— during the same time period, CMS approved almost 24,000 applications advancing $40.4 billion in payments. The AAP program is not a grant, and providers and suppliers are required to repay the loan.

CMS has published AAP data, as required by the Continuing Appropriations and Other Extensions Act of 2021, on this website: https://www.cms.gov/files/document/covid-medicare-accelerated-and-advance-payments-program-covid-19-public-health-emergency-payment.pdf

These data consist of high-resolution maps of aboveground biomass at four forested sites in the US: Garcia River Tract in California, Anne Arundel and Howard Counties in Maryland, Parker Tract in North Carolina, and Hubbard Brook Experimental Forest in New Hampshire. Biomass maps were generated using a combination of field data (forest inventory and Lidar) and modeling approaches. Estimates of uncertainty are also provided for the Maryland site using two different modeling methodologies.These data provide estimates of aboveground biomass for the nominal year of 2011 at 20-50 meter resolution in units of megagrams of carbon per hectare (or acre for the Garcia Tract site).The data are presented as a series of 11 GeoTIFF (*.tif) files.

The Medicare Demonstrations dataset provides information on demonstrations conducted in the CMS Innovation Center. It includes the demonstration project name, type, description, year, and website for the demonstration page.

This dataset provides gridded average annual wetland salinity concentrations in practical salinity units (PSU) at 30-meter resolution within 24 coastal estuary sites in the United States predicted for 2020. Salinity in estuaries can serve as a proxy for sulfate concentration, which can inhibit methanogenesis. Data were derived from a hybrid approach to mapping salinity as a continuous variable using a combination of physical watershed and stream characteristics, optical remote sensing based on vegetation characteristics, and climate variables. Data are provided in cloud-optimized GeoTIFF format covering 33 Hydrologic Unit Code 8-digit (HUC8) watersheds to the extent of palustrine and estuarine wetlands as defined by NOAA's 2016 Coastal Change Analysis Program (C-CAP) Coastal Land Cover layer. Additionally, model outputs are provided in comma separated values (CSV) files, and code scripts are provided in a compressed (*.zip) file.

This dataset provides canopy height and elevation data products derived from airborne LiDAR data collected over 90 sites on the island of Borneo in late 2014. The sites cover approximately 100,000 hectares of forested land in Kalimantan, Indonesia. The data were produced as part of an effort to improve a national forest monitoring system for Indonesia that uses a combination of remote sensing and ground-based forest carbon inventory approaches.

This data set provides the results of (1) monthly measurements of soil CO2 efflux, volumetric water content, and temperature, and (2) seasonal measurements of soil (porosity, bulk density, nitrogen (N) and carbon (C) content) and vegetation (leaf area index (LAI), litter and fine root biomass) properties in a water-limited ecosystem in Baja California, Mexico. Measurements and samples were collected from August 2011 to August 2012.

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_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.

On May 13, 2021 Changed vaccination fields from sum to max or min fields. This reflects the maximum or minimum number reported for that metric in a given week.

On June 7, 2021 Changed vaccination fields from max or min fields to Wednesday reported only. This reflects that the number reported for that metric is only reported on Wednesdays in a given week.

On September 20, 2021, the following has been updated: The use of analytic dataset as a source.

On January 19, 2022, the following fields have been added to this dataset:

• inpatient_beds_used_covid_7_day_avg
• inpatient_beds_used_covid_7_day_sum
• inpatient_beds_used_covid_7_day_coverage

On April 28, 2022, the following pediatric fields have been added to this dataset:

• all_pediatric_inpatient_bed_occupied_7_day_avg
• all_pediatric_inpatient_bed_occupied_7_day_coverage
• all_pediatric_inpatient_bed_occupied_7_day_sum
• all_pediatric_inpatient_beds_7_day_avg
• all_pediatric_inpatient_beds_7_day_coverage
• all_pediatric_inpatient_beds_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum
• staffed_icu_pediatric_patients_confirmed_covid_7_day_avg
• staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage
• staffed_icu_pediatric_patients_confirmed_covid_7_day_sum
• staffed_pediatric_icu_bed_occupancy_7_day_avg
• staffed_pediatric_icu_bed_occupancy_7_day_coverage
• staffed_pediatric_icu_bed_occupancy_7_day_sum
• total_staffed_pediatric_icu_beds_7_day_avg
• total_staffed_pediatric_icu_beds_7_day_coverage
• total_staffed_pediatric_icu_beds_7_day_sum

On October 24, 2022, the data includes more analytical calculations in efforts to provide a cleaner dataset. For a raw version of this dataset, please follow this link: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/uqq2-txqb

Due to changes in reporting requirements, after June 19, 2023, a collection week is defined as starting on a Sunday and ending on the next Saturday.

The Content Management System (CMS) market is experiencing robust growth, driven by the increasing demand for user-friendly website development tools across diverse sectors. The market, estimated at $15 billion in 2025, is projected to expand significantly over the next decade, fueled by a Compound Annual Growth Rate (CAGR) of approximately 12%. This growth is propelled by several key factors: the rising adoption of digital strategies by businesses of all sizes (SMEs, large enterprises, and even personal users), the proliferation of mobile-first website designs, and the increasing need for advanced functionalities such as e-commerce integration and improved SEO capabilities. The web-based CMS segment holds a significant market share, primarily due to its accessibility, cost-effectiveness, and ease of scalability. However, the on-premises CMS segment retains a substantial presence, particularly among organizations with stringent data security and control requirements. Competition within the CMS landscape is fierce, with established players like WordPress, Joomla, and Drupal facing challenges from newer, more specialized platforms. The market is segmented by application (personal use, SMEs, large enterprises, and others) and type (web-based and on-premises). While WordPress dominates the personal and SME segments due to its open-source nature and extensive community support, enterprise-grade solutions like Adobe Experience Manager and Microsoft SharePoint cater to the needs of larger organizations demanding advanced features and robust security protocols. Future growth will be influenced by advancements in artificial intelligence (AI) for content automation, the increasing integration of headless CMS architectures, and the growing adoption of cloud-based solutions. Continued innovation and the ability to adapt to evolving user needs will be crucial for CMS vendors to maintain their competitiveness in this dynamic market.

This is a dataset created for the Medicaid Scorecard website (https://www.medicaid.gov/state-overviews/scorecard/index.html), and is not intended for use outside that application.

The Content Management System (CMS) market is experiencing robust growth, driven by the increasing demand for user-friendly website development and management tools across various industries. The market's expansion is fueled by several key factors, including the rising adoption of digital marketing strategies, the proliferation of e-commerce platforms, and the increasing need for businesses to maintain a strong online presence. This necessitates efficient tools capable of managing content, enhancing user experience, and driving business growth. The market is segmented based on deployment (cloud-based, on-premise), pricing models (open-source, licensed), and industry verticals (e-commerce, healthcare, education, etc.). While precise market sizing data is unavailable, analysts estimate the market size to be approximately $15 billion in 2025, with a Compound Annual Growth Rate (CAGR) of 12% projected from 2025 to 2033. This growth is anticipated to be fueled by increased adoption of headless CMS architectures and the growing demand for personalized user experiences. Key players like WordPress, Wix, Shopify, and Adobe contribute significantly to the market's competitive landscape, while emerging players constantly innovate with AI-powered content creation and improved scalability. The market faces challenges such as security concerns, the complexity of integrating with other systems, and the ongoing need for skilled developers to manage and maintain CMS platforms. Despite these challenges, the future of the CMS market appears bright. Continuous technological advancements, including improved security features, enhanced integrations, and AI-powered content generation tools, are expected to further propel market growth. The increasing demand for omnichannel strategies and the need for businesses to maintain consistency across all digital touchpoints also contributes to the market's expansion. The rise of mobile-first design and the ever-increasing focus on user experience will continue to influence the development and adoption of new CMS functionalities, creating new opportunities for innovation and growth within the industry. Market players will need to focus on scalability, security, and ease of use to cater to the diverse needs of businesses and individuals.

This dataset contains half-hourly ground solar-induced chlorophyll fluorescence (SIF) and vegetation indices including NDVI, EVI, Red edge chlorophyll index, green chlorophyll index, and photochemical reflectance index at seven crop sites in Nebraska and Illinois for the period 2016-2021. Four sites were located at Eddy Covariance (EC) tower sites (sites US-Ne2, US-Ne3, US-UiB, and US-UiC), and three sites were located on private farms (sites Reifsteck, Rund, and Reinhart). The sites were either miscanthus, corn-soybean rotation or corn-corn-soybean rotation. The spectral data for SIF retrieval and hyperspectral reflectance for vegetation index calculation were collected by the FluoSpec2 system, installed near planting, and uninstalled after harvest to collect whole growing-season data. Raw nadir SIF at 760 nm from different algorithms (sFLD, 3FLD, iFLD, SFM) are included. SFM_nonlinear and SFM_linear represent the Spectral fitting method (SFM) with the assumption that fluorescence and reflectance change with wavelength non-linearly and linearly, respectively. Additional data include two SIF correction factors including calibration coefficient adjustment factor (f_cal_corr_QEPRO) and upscaling nadir SIF to eddy covariance footprint factor (ratio_EC footprint, SIF pixel), and measured FPAR from quantum sensors and Rededge NDVI calculated FPAR. The data are provided in comma-separated values (CSV) format.

The data used in this project cannot be shared publicly. The data were purchased from the Centers for Medicaid and Medicare Services (CMS) with specific Data Use Agreement (DUA) requirements in terms of safeguarding the confidentiality of CMS data. Researchers may download the data directly from the CMS website after completing individual requests. Information regarding data requests and download are available at: https://www.cms.gov/Research-Statistics-Data-and-Systems/Computer-Data-and-Systems/MedicaidDataSourcesGenInfo/MAXGeneralInformation

The Nursing Homes Profiles quality data provides a consumer-friendly product that allows patients and their families to understand how the New York State Nursing Homes perform within five specific domains of care and overall. The domains (Preventive Care, Quality of Care, Quality of Life, Resident Safety and Resident Status) encompass twenty-four different quality measures. A Domain Rating assesses performance over all the measures within that domain, with 5 stars indicating the highest performance and 1 star the lowest performance. The Overall Rating is a normalized star rating based on the Nursing Homes' performance across the five domains. The normalization of the Overall Rating resets the distribution, with the highest performing Nursing Homes across all the domains having 5 stars and the lowest performing Nursing Homes across the five domains having 1 star. New York’s Nursing Home Domain Rating differs from CMS’ 5-star rating in data reporting period and in methodology.

This is a dataset created for the Medicaid Scorecard website (https://www.medicaid.gov/state-overviews/scorecard/index.html), and is not intended for use outside that application.

Brand performance data collected from AI search platforms for the query "headless CMS options for retail sites".