CMS Data Feeds Dataset
About the Dataset
The CMS Data Feeds Dataset is a comprehensive collection of all current and future CMS data feeds available on cms.data.gov. This dataset transforms each feed into a view, aligning all feed file attributes and casting them to appropriate datatypes. The views are automatically updated when new feed files arrive, and new views are included as CMS publishes new data feeds.
As of March 2026, the dataset encompasses 252 feeds and 4,407 feed files.
See the full list of feeds in the CMS Data Feeds Catalog.
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Dataset Features
167 billion records and growing
252 feeds with automatic addition of new feeds
9,583 feed attributes
19 years of historical data
1.2 TB of data, continuously expanding
Daily updates to ensure data freshness
Automatic quality checks and active monitoring
Designed for seamless ingestion
Data Quality and Maintenance
Dataplex Consulting & Data Products prioritizes data quality through:
Automated data quality checks in all pipelines
Daily monitoring of ingestion and ETL jobs
Timely updates whenever CMS publishes new data
Rigorous processes to ensure data integrity and reliability
Business Applications
Users can leverage this dataset for various purposes, including:
Enriching or augmenting existing datasets
Analyzing published feed metrics over time
Performing segmentation analysis
Training machine learning models
Conducting geospatial analysis
Tracking healthcare trends and performance metrics
Example Use Cases
Analyze telehealth adoption trends across different states
Evaluate the financial performance of home health agencies
Track Medicare spending patterns by drug or geography
Assess hospital performance metrics and patient satisfaction scores
Monitor healthcare-associated infections across facilities
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Data Structure
The dataset consists of three main components:
FEEDS: Contains metadata about each CMS data feed
FEEDS_FILES: Stores information about individual files within each feed
Feed-specific views: Separate views for each feed (e.g., PatientReported_Outcomes, Medicare_Spending)
Entity Relationship Diagram
Sample Queries
Get CMS Feeds to Table Mapping
Find all Feed Views Related to Medicare
Query All 2021 File Records for Medicare Inpatient Hospitals
Get CMS Feeds to Table Mapping
Find all Feed Views Related to Medicare
Query All 2021 File Records for Medicare Inpatient Hospitals
📊 Recent Feed File Updates
Recent CMS feed file updates (last 30 days)
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🆕 Recently Added Feeds
Nursing Homes Including Rehab Services - Data Collection Intervals (Added: March 2026)
What's in this data: This dataset provides information on the quality measure data collection periods for nursing homes, including rehabilitation services. It contains data about various quality measures, their descriptions, and the time frames during which the data was collected, which is crucial for analyzing performance and improving care standards in nursing facilities.
How you can use it:
Analyze trends in quality measures over specified time periods to assess improvement or decline in nursing home performance.
Identify nursing homes with specific quality measure deficiencies to target quality improvement initiatives.
Evaluate the impact of policy changes on quality measures by comparing data collection periods before and after implementation.
Example Queries:
Retrieve the latest quality measures and their corresponding data collection periods for analysis.
Count the number of quality measures by their data period for trend analysis.
Nursing Homes Including Rehab Services - State Inspection Cut Points (Added: March 2026)
What's in this data: This dataset contains health inspection star rating cut points for nursing homes by state, providing thresholds for 1-5 star ratings. It is essential for understanding the quality of nursing home care and for comparing facilities across different regions.
How you can use it:
Comparing inspection cut points across different states to identify regions with higher or lower standards.
Analyzing trends in nursing home ratings over time to assess improvements or declines in care quality.
Utilizing cut points to inform policy decisions and funding allocations for nursing home services.
Example Queries:
Retrieve the inspection cut points for all states to compare star ratings.
Skilled Nursing Facility Quality Reporting Program - Swing Beds (Added: March 2026)
What's in this data: This dataset provides quality measures for swing bed providers participating in the SNF Quality Reporting Program. It includes various metrics such as performance scores and provider details, which are essential for evaluating healthcare quality in skilled nursing facilities.
How you can use it:
Analyze performance trends of skilled nursing facilities over time.
Compare quality measures between different providers and regions.
Evaluate the impact of specific measures on patient outcomes.
Example Queries:
This query shows the number of providers by state for the latest available data.
This query retrieves the latest scores for a specific provider by its CMS certification number.
📋 View the complete CMS Data Feeds Catalog
Get Started
CMS Medicare/Medicaid Data Access
Includes
All 232 feeds, daily updates, full documentation
Support
Email support included
Cancellation
Cancel anytime, no long-term commitment
Support and Contact
For questions or assistance with the CMS Data Feeds Dataset, please contact:
Email: [email protected]
The Dataplex Consulting & Data Products team monitors ingestion and ETL jobs daily to ensure quality and timely delivery.
About Dataplex
Dataplex Consulting & Data Products delivers turnkey, analytics-ready data products that make complex public and commercial data easy to use across modern data platforms. Our data pipelines include automated quality checks and active monitoring to ensure timely, reliable, and well-structured data that is ready for downstream analytics, machine learning, and operational use.
In addition to data products, Dataplex provides data engineering and analytics consulting services to organizations of all sizes. We bring deep, hands-on experience supporting both early-stage companies and large enterprises, helping teams build scalable data platforms, improve data reliability, and become more data-driven.
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