CMS Home Medical Equipment Product Databank

About the Dataset

The CMS Home Medical Equipment Product Databank is a comprehensive dataset providing detailed reimbursement information for Durable Medical Equipment, Prosthetics, Orthotics, and Supplies (DMEPOS) products and services across the United States. Covering the period from 2014 to 2022, this dataset offers critical insights into product performance, market trends, and supplier behaviors in the home medical equipment industry.

Key aspects of the dataset include:

• Reimbursement data for DMEPOS products and services

• Information on referring providers, suppliers, and Medicare beneficiaries

• Detailed breakdown of claims and reimbursement amounts

• Annual updates within 24 hours of new CMS data releases

• Coverage of approximately 1,300 records per year in the full subscription

Dataset Features

• Comprehensive Coverage: 9 years of data from 2014 to 2022, offering a robust foundation for analyzing long-term trends in home medical equipment reimbursement.

• Detailed Product Information: Includes HCPCS codes and descriptions for DMEPOS products and services rendered by suppliers.

• Extensive Metrics: Provides data on the number of referring providers, suppliers, Medicare beneficiaries, total claims, and reimbursement amounts for each product and service.

• Regular Updates: Dataset is updated annually within 24 hours of each new CMS data release, ensuring access to the most current information.

• Flexible Access Options: Offers both a trial version with 200 rows per year and a full subscription with approximately 1,300 records per year.

Data Quality and Maintenance

• Data Source: Derived from official CMS data releases, ensuring reliability and accuracy.

• Update Frequency: Annual updates, with the most recent data available as of September 2024.

• Quality Assurance: Rigorous data cleaning and validation processes to maintain high data quality standards.

• Version Control: Each update is versioned, allowing for tracking of changes over time.

Business Applications

• Product Performance Analysis

• Market Trend Identification

• Competitive Benchmarking

• Healthcare Finance Insights

• Reimbursement Strategy Optimization

• Supplier Behavior Analysis

Example Use Cases

1. Healthcare Providers: Track product performance and optimize reimbursement strategies based on claims and payment data.

2. Market Analysts: Leverage historical data to analyze trends in Medicare reimbursement and provider behavior over time.

3. Payers & Insurers: Assess reimbursement patterns and predict future trends for cost-saving opportunities and strategic decision-making.

4. Medical Equipment Manufacturers: Identify high-performing products and market gaps to inform product development and marketing strategies.

5. Healthcare Consultants: Provide data-driven recommendations to clients on optimizing their DMEPOS product offerings and reimbursement strategies.

Data Structure

The dataset includes the following key columns:

Column Name	Data Type	Description
row_number	NUMBER	Unique ID for each row in the report.
year	NUMBER	The year the data was reported.
product	VARCHAR	Betos Classification System description with the corresponding HCPCS code in parentheses.
hcpcs_code	VARCHAR	(Searchable) HCPCS code for the specific product or service furnished by the DMEPOS supplier.
hcpcs_description	VARCHAR	Detailed description of the HCPCS code and the related product or service.
total_referring_providers	NUMBER	The number of providers who referred the DMEPOS products or services.
total_providers	NUMBER	The number of suppliers rendering DMEPOS products or services.
total_medicare_beneficiaries	NUMBER	The number of unique Medicare beneficiaries linked to the DMEPOS claims submitted by the suppliers.
total_provider_claims	NUMBER	Total number of claims submitted by suppliers for the products/services ordered by referring providers.
total_services	NUMBER	Number of DMEPOS products or services provided, with varying metrics depending on the service.
medicare_allowed_amount	NUMBER	Total Medicare allowed amount for all services rendered.
submitted_charges_amount	NUMBER	Total submitted charges for all services provided.
medicare_payments_amount	NUMBER	The total amount paid by Medicare for the services, calculated as the average supplier Medicare payment amount multiplied by the total number of services.
created_at	TIMESTAMP	The timestamp when the record was created by Dataplex.
updated_at	TIMESTAMP	The timestamp when the record was last updated by Dataplex.

Sample Queries

1. Top 10 DMEPOS products by Medicare payment amount in 2022:

SELECT "HCPCS Code", "HCPCS Description", "Medicare Payments Amount"
FROM cms_home_medical_equipment
WHERE "Year" = 2022
ORDER BY "Medicare Payments Amount" DESC
LIMIT 10;

1. Year-over-year growth in total services for a specific HCPCS code:

WITH yearly_services AS (
  SELECT "Year", "HCPCS Code", "Total Services",
         LAG("Total Services") OVER (PARTITION BY "HCPCS Code" ORDER BY "Year") AS prev_year_services
  FROM cms_home_medical_equipment
  WHERE "HCPCS Code" = 'E0601'
)
SELECT "Year", "HCPCS Code", "Total Services",
       ("Total Services" - prev_year_services) / prev_year_services * 100 AS growth_percentage
FROM yearly_services
WHERE prev_year_services IS NOT NULL
ORDER BY "Year";

1. Average Medicare allowed amount per service by product category in 2022:

SELECT SUBSTRING("Product" FROM 1 FOR POSITION('(' IN "Product") - 1) AS product_category,
       AVG("Medicare Allowed Amount" / "Total Services") AS avg_allowed_per_service
FROM cms_home_medical_equipment
WHERE "Year" = 2022
GROUP BY product_category
ORDER BY avg_allowed_per_service DESC;

Support and Contact

For questions, support, or custom data requests related to the CMS Home Medical Equipment Product Databank, please contact:

• Email: [e-mail]

About Dataplex

Dataplex Consulting & Data Products is a comprehensive data consulting firm that empowers clients with reliable cloud data and analytics solutions. Our team of certified experts in data engineering and analytics offers tailored services to support data-driven enterprises.