This reference provides significant summary information about health expenditures and the Centers for Medicare & Medicaid Services' (CMS) programs. The information presented was the most current available at the time of publication. Significant time lags may occur between the end of a data year and aggregation of data for that year.

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;”

This dataset tracks the updates made on the dataset "NCHS Survey Data Linked to Centers for Medicare & Medicaid Services (CMS) Medicare Data Files" as a repository for previous versions of the data and metadata.

Verify the accuracy of SSNs of all individual Medicare providers, owners, managing/directing employees, authorized representatives, ambulance service medical directors, ambulance crew members, technicians, chain organization administrators, Independent Diagnostic Test Facility (IDTF), supervising/directing physicians, and IDTF interpretation service providers. Also included in this Agreement are individual health care providers who apply for a National Provider Identification Number (NPI).

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset hosted by the Centers for Medicare & Medicaid Services (CMS). The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore CMS's Data using Kaggle and all of the data sources available through the CMS organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

This dataset is distributed under the following licenses: Public Domain, NA

Content

************************PLEASE NOTE**************************

This dataset will no longer be updated effective 2/25/2019

Context

This is a dataset hosted by the Centers for Medicare & Medicaid Services (CMS). The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore CMS's Data using Kaggle and all of the data sources available through the CMS organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by rawpixel on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This dataset is distributed under NA

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?

The Nursing Home COVID-19 Public File from the Centers for Medicare & Medicaid Services, filtered for Connecticut. View the full dataset and detailed metadata here. The Nursing Home COVID-19 Public File includes data reported by nursing homes to the CDC’s National Healthcare Safety Network (NHSN) system COVID-19 Long Term Care Facility Module, including Resident Impact, Facility Capacity, Staff & Personnel, and Supplies & Personal Protective Equipment, and Ventilator Capacity and Supplies Data Elements.

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:

• With over 25.8 billion rows of data, this dataset provides a comprehensive view of the U.S. healthcare system. This extensive volume of data allows for granular analysis, enabling users to uncover insights that might be missed in smaller datasets. The data is also meticulously cleaned and aligned, ensuring accuracy and ease of use.

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:

• To ensure that users have access to the most current information, the dataset is updated monthly. These updates include new reports as well as revisions to existing data, making the dataset a continuously evolving resource that stays relevant and accurate.

Data Sourced from CMS:

• The data in this dataset is sourced directly from the Centers for Medicare & Medicaid Services (CMS). After collection, the data is meticulously cleaned and its attributes are aligned, ensuring consistency, accuracy, and ease of use for any application. Furthermore, any new updates or releases from CMS are automatically integrated into the dataset, keeping it comprehensive and current.

Use Cases:

Market Analysis:

• The dataset is ideal for market analysts who need to understand the dynamics of the healthcare industry. The extensive historical data allows for detailed segmentation and analysis, helping users identify trends, market shifts, and growth opportunities. The comprehensive nature of the data enables users to perform in-depth analyses of specific market segments, making it a valuable tool for strategic decision-making.

Healthcare Research:

• Researchers will find the All CMS Data Feeds dataset to be a robust foundation for academic and commercial research. The historical data, combined with the breadth of coverage across various healthcare metrics, supports rigorous, in-depth analysis. Researchers can explore the effects of healthcare policies, study patient outcomes, analyze provider performance, and more, all within a single, comprehensive dataset.

Performance Tracking:

• Healthcare providers and organizations can use the dataset to track performance metrics over time. By comparing data across different periods, organizations can identify areas for improvement, monitor the effectiveness of initiatives, and ensure compliance with regulatory standards. The dataset provides the detailed, reliable data needed to track and analyze key performance indicators.

Compliance and Regulatory Reporting:

• The dataset is also an essential tool for compliance officers and those involved in regulatory reporting. With detailed data on provider performance, patient outcomes, and healthcare utilization, the dataset helps organizations meet regulatory requirements, prepare for audits, and ensure adherence to best practices. The accuracy and comprehensiveness of the data make it a trusted resource for regulatory compliance.

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 dataset is provided in a CSV format, which is widely compatible with most data analysis tools and platforms. This ensures that users can easily integrate the data into their existing wo...

NCHS has linked data from various surveys with Medicare program enrollment and health care utilization and expenditure data from the Centers for Medicare & Medicaid Services (CMS). Linkage of the NCHS survey participants with the CMS Medicare data provides the opportunity to study changes in health status, health care utilization and costs, and prescription drug use among Medicare enrollees. Medicare is the federal health insurance program for people who are 65 or older, certain younger people with disabilities, and people with End-Stage Renal Disease.

The purpose of the project is to detect unreported Supplemental Security Income (SSI) recipient admissions to Title XIX institutions. A file containing SSN's of SSI recipients (all eligible individuals and members of eligible couples in current pay) will be matched against the Health Care Financing Administration's (HCFA) Minimum Data Set (MDS) database which contains admission, discharge, re-entry and assessment information about persons in Title XIX facilities for all 50 States and Washington, D.C. This database is updated monthly. The match will produce an output file containing MDS data pertinent to SSI eligibility on matched records. This data will be compared back to the SSR data to generate alerts to the Field Offices for their actions.

The CMS Program Statistics - Medicare Skilled Nursing Facility tables provide use and payment data for skilled nursing facilities. For additional information on enrollment, providers, and Medicare use and payment, visit the CMS Program Statistics page. Below is the list of tables: MDCR SNF 1. Medicare Skilled Nursing Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Entitlement, Yearly Trend MDCR SNF 2. Medicare Skilled Nursing Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Demographic Characteristics and Medicare-Medicaid Enrollment Status MDCR SNF 3. Medicare Skilled Nursing Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Area of Residence MDCR SNF 4. Medicare Skilled Nursing Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Entitlement and Covered Days of Care MDCR SNF 5. Medicare Skilled Nursing Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Facility and Bedsize MDCR SNF 6. Medicare Skilled Nursing Facilities: Distribution of Medicare Covered Skilled Nursing Facility Days, by State of Provider and Major Resource Utilization Groups (RUG)-III (versions 2013-2018 only)

The CMS Program Statistics - Medicare Providers summary tables provide data on institutional (i.e., hospitals, skilled nursing facilities, home health agencies, hospices, etc.) and non-institutional (i.e., physicians, nonphysicians, specialists, and suppliers) providers. For additional information on enrollment, providers, and Medicare use and payment, visit the CMS Program Statistics page. Below is the list of tables: MDCR PROVIDERS 1. Medicare Providers: Number of Medicare Certified Institutional Providers, Yearly Trend MDCR PROVIDERS 2. Medicare Providers: Number of Medicare Certified Inpatient Hospital and Skilled Nursing Facility Beds and Beds Per 1,000 Enrollees, Yearly Trend MDCR PROVIDERS 3. Medicare Providers: Number of Medicare Certified Facilities, by Type of Control, Yearly Trend MDCR PROVIDERS 4. Medicare Providers: Number of Skilled Nursing Facilities and Medicare Certified Hospitals, and Number of Beds, by State, Territories, Possessions and Other Areas MDCR PROVIDERS 5. Medicare Providers: Number of Medicare Certified Providers, by Type of Provider, by State, Territories, Possessions, and Other Areas MDCR PROVIDERS 6. Medicare Providers: Number of Medicare Non-Institutional Providers by Specialty, Yearly Trend MDCR PROVIDERS 7. Medicare Providers: Number of Medicare Non-Institutional Providers, by State, Territories, Possessions, and Other Areas, Yearly Trend

Content

This file is a point in time snapshot of enrollment level data for providers actively enrolled in Medicare.

Context

This is a dataset hosted by the Centers for Medicare & Medicaid Services (CMS). The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore CMS's Data using Kaggle and all of the data sources available through the CMS organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Annie Spratt on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This dataset is distributed under NA

The CMS Program Statistics - Medicare Part A & Part B - All Types of Service tables provide use and payment data by type of coverage and type of service. For additional information on enrollment, providers, and Medicare use and payment, visit the CMS Program Statistics page. Below is the list of tables: MDCR SUMMARY AB 1. Medicare Part A and Part B Summary: Utilization, Program Payments, and Cost Sharing for All Original Medicare Beneficiaries, by Type of Coverage and Type of Service, Yearly Trend MDCR SUMMARY AB 2. Medicare Part A and Part B Summary: Utilization, Program Payments, and Cost Sharing for Aged Original Medicare Beneficiaries, by Type of Coverage and Type of Service, Yearly Trend MDCR SUMMARY AB 3. Medicare Part A and Part B Summary: Utilization, Program Payments, and Cost Sharing for Disabled Original Medicare Beneficiaries by Type of Coverage and Type of Service, Yearly Trend MDCR SUMMARY AB 4. Medicare Part A and Part B Summary: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Coverage, Demographic Characteristics, and Medicare-Medicaid Enrollment Status MDCR SUMMARY AB 5. Medicare Part A and Part B Summary: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Coverage and by Area of Residence MDCR SUMMARY AB 6. Medicare Part A and Part B Summary: Utilization and Program Payments for Original Medicare Beneficiaries, by Type of Entitlement, Amount of Program Payments, Type of Coverage, and Type of Service

This data package contains claims-based data about beneficiaries of Medicare program services including Inpatient, Outpatient, related to Chronic Conditions, Skilled Nursing Facility, Home Health Agency, Hospice, Carrier, Durable Medical Equipment (DME) and data related to Prescription Drug Events. It is necessary to mention that the values are estimated and counted, by using a random sample of fee-for-service Medicare claims.

The Centers for Medicare & Medicaid Services (CMS) Special Terms and Conditions (STC) Datasets.

NCHS has linked data from various surveys with 1999-2013 Medicare program enrollment and health care utilization and expenditure data from the Centers for Medicare & Medicaid Services (CMS). Linkage of the NCHS survey participants with the CMS Medicare data provides the opportunity to study changes in health status, health care utilization and costs, and prescription drug use among Medicare enrollees. Medicare is the federal health insurance program for people who are 65 or older, certain younger people with disabilities, and people with End-Stage Renal Disease.

Abstract

This is a Medicare dataset released by the Center for Medicare and Medicaid Services (CMS) and accessed via BigQuery. All data is from 2014.

Documentation

For more information regarding the CMS data, click here.

From BigQuery:

This public dataset was created by the Centers for Medicare & Medicaid Services. The data summarizes the utilization and payments for procedures, services, and prescription drugs provided to Medicare beneficiaries by specific inpatient and outpatient hospitals, physicians, and other suppliers. The dataset includes the following data - common inpatient and outpatient services, all physician and other supplier procedures and services, and all Part D prescriptions.

Providers determine what they will charge for items, services, and procedures provided to patients and these charges are the amount that providers bill for an item, service, or procedure.

The Innovation Center Model Summary Information dataset contains various data points related to CMS Innovation Center models, demonstrations, programs, and initiatives. This can includes name, start and end date, statutory or regulatory authority, keywords, stage of implementation, participants, beneficiaries and physicians impacted, and categories related to health care quality, cost, payment, and delivery.