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 date ranges for quality measure data collection periods related to nursing home and rehabilitation services. It includes various quality measures that assess the performance and outcomes of skilled nursing facilities, helping stakeholders identify areas for improvement and monitor trends over time.
How you can use it:
Analyze trends in quality measures over time to assess the performance of nursing homes.
Evaluate the effectiveness of rehabilitation services by comparing measures across different facilities.
Identify facilities that consistently meet or exceed quality benchmarks for targeted improvement initiatives.
Example Queries:
Retrieve the latest quality measures along with their respective data collection periods.
Count the number of quality measures for each data period to identify trends in data collection.
Nursing Homes Including Rehab Services - State Inspection Cut Points (Added: March 2026)
What's in this data: This dataset provides health inspection star rating cut points by state, outlining the thresholds for 1-5 star ratings. It is significant for assessing the quality of nursing home care and understanding state-level variations in health inspection performance.
How you can use it:
Analyzing state-level quality of nursing homes based on inspection ratings.
Identifying states with the highest and lowest thresholds for each star rating.
Tracking changes in inspection cut points over time to monitor trends in nursing home quality.
Skilled Nursing Facility Quality Reporting Program - Swing Beds (Added: March 2026)
What's in this data: This dataset contains quality measures for swing bed providers participating in the SNF Quality Reporting Program. It includes detailed information about provider performance, including their scores on various quality measures, which can help identify areas for improvement and ensure quality care.
How you can use it:
Analyze quality scores of swing bed providers to identify high and low performing facilities.
Compare the performance of swing bed providers across different regions to assess geographic disparities in care quality.
Monitor trends in quality measures over time to evaluate the impact of quality improvement initiatives.
Example Queries:
This query retrieves the total number of providers by state, helping to understand the distribution of swing bed facilities across different states.
📋 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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