CMS Latest Data Release Offers Labs a Tool to Spot Potential Compliance Red Flags
While implementation of the Protecting Access to Medicare Act of 2014 (PAMA) will have labs collecting private payer payment data to report to the Centers for Medicare & Medicaid Services (CMS), CMS has for the third year in a row reported its own data on what Medicare pays physicians, labs and other providers. Earlier this […]
While implementation of the Protecting Access to Medicare Act of 2014 (PAMA) will have labs collecting private payer payment data to report to the Centers for Medicare & Medicaid Services (CMS), CMS has for the third year in a row reported its own data on what Medicare pays physicians, labs and other providers. Earlier this year, CMS released an updated public data set regarding Medicare payments known as the Medicare Provider Utilization and Payment Data: Physician and Other Supplier Public Use File (Physician and Other Supplier PUF).
This CMS data release is intended to further the goal of transparency by making public the Medicare charges from and payments to physicians, labs and other providers. According to CMS’ press release regarding the data set: “The release of timely, privacy-protected data is especially important as the Medicare increasingly pays providers based on the quality, rather than the quantity, of care they give patients. These initiatives contribute to a wide set of CMS activities focused on achieving better care, smarter spending, and healthier people throughout the health care system.”
What’s new this year
CMS said it has updated the data set and its supplemental summary tables including the “Medicare Physician and Other Supplier Aggregate Table” (i.e., one record per NPI) and the “Medicare State/National HCPCS Aggregate Tables” to include standardized payment data so users can compare Medicare payment amounts across geographic areas. “Standardization removes geographic differences in payment rates for individual services, such as those that account for local wages or input prices and makes Medicare payments across geographic areas comparable.”
Additionally, now researchers don’t have to wait for annual extracts under Limited Data Sets (LDS) but can instead request updates to LDS claims files on a quarterly basis.
Details about the data CMS reports the data set has more than 986,000 health care providers who “collectively received $91 billion in Medicare payments.” CMS’ website explains: “The Physician and Other Supplier PUF contains information on utilization, payment (allowed amount and Medicare payment), and submitted charges organized by National Provider Identifier (NPI), Healthcare Common Procedure Coding System (HCPCS) code, and place of service. This PUF is based on information from CMS administrative claims data for Medicare beneficiaries enrolled in the fee-for-service program. The data in the Physician and Other Supplier PUF covers calendar years 2012 through 2014 and contains 100% final-action physician/supplier Part B non-institutional line items for the Medicare fee-for-service population.”
Categories of information (columns in the database spreadsheet) include National Provider Identifier, provider name, provider credentials, entity type/gender of provider, provider address, provider type (i.e. clinical laboratory), place of service, HCPCS code and description, number of services provided (the metrics counting the number of services varies by service), number of Medicare beneficiaries receiving the service, number of beneficiaries per day of service (this category removes double counting of beneficiaries receiving multiple services), average Medicare allowable amount, average submitted charge, average Medicare payment amount, average Medicare standardized amount.
Medicare defines the following terms used in the data set as follows:
- Average Medicare Allowed Amount is the amount Medicare pays plus the deductible, coinsurance and any third party responsibility.
- Average Medicare Payment is the amount Medicare pays after that coinsurance and deductible are subtracted.
- Average Medicare Standardized Amount is the average amount Medicare paid after beneficiary deductible and coinsurance amounts are deducted and after the standardization of Medicare payment (i.e., removal of geographic differences in payment as discussed above).
Information and links to the data sets for the past three data releases (released in 2016, 2015, and 2014 addressing data from 2014, 2013, and 2012 respectively) are available on CMS’ website.
Data offers tool for ferreting out potential fraud
Earlier this year, CMS touted its successes using data via its Fraud Prevention System to spot trends and potential fraud hotspots. While the agency didn’t cite this public use file as a data set it uses, that emphasis on data highlights the potential compliance uses for the Medicare data publicly available in this data set.
In fact, CMS’ questions and answers about the Physician and Other Supplier PUF encourage reporting any suspected fraud found in the data. In answer to the question “What do I do if I think I’ve identified fraud in the Physician and Other Supplier PUF?” CMS answers: “CMS is committed to the prevention and detection of fraud and abuse in the Medicare program and partners with numerous entities in this endeavor, including federal and state law enforcement agencies, the HHS Office of Inspector General, and the U.S. Department of Justice, among others. If you suspect a potential case of Medicare fraud or abuse, please visit http://stopmedicarefraud.gov for information on how to report it.” Further evidence that HHS fully encourages users to ferret out fraud using this public data set is found in a U.S. Department of Health and Human Services letter to the American Medical Asssociation, which emphasizes the ability of such data to reveal fraud as evidence that the public interest in this information outweighs individual physician/provider privacy interests.
The data has indeed been used in past years to scout out potential red flags in reimbursement patterns. Mainstream media have used the data to question Medicare revenues and billing practices of specific entities involved in lab testing. G2 Compliance Advisor (GCA) noted in prior years the Wall Street Journal’s articles citing the Medicare data to question billing for drug testing (See “News-At-A-Glance: Another WSJ Story Implicating Laboratories,” G2 Compliance Advisor, 11/14, p. 12) and a Washington Post article targeting a pain clinic whose physicians included top Medicare recipients of reimbursement for a specific service (See “CMS Data Dump Reveals Pain for Alabama Clinic,” G2 Compliance Advisor, 5/14, p. 3). Most notably, the Wall Street Journal cited the CMS 2014 Data release in raising questions about Medicare reimbursement to Health Diagnostic Laboratory (HDL), questioning the lab’s status as one of the top recipients of Medicare payments for specific tests and services. For example, the article highlighted HDL for receiving 93 percent of all Medicare funds paid for all labs for a specific procedure in 2012 and 64 percent of all Medicare payments for its top nine tests. (See “Wall Street Journal Article Illustrates Increased Compliance Risks for Labs,” G2 Compliance Advisor, 9/14, p.1) HDL ultimately was the subject of government investigation and settled allegations of kickbacks and medically unnecessary testing for $47 million.
While the value of the data has been questioned—industry stakeholders have argued that the data provides an incomplete picture and doesn’t address quality of or costs to provide services—the government and the media aren’t the only ones that can find this data useful. Labs looking to gauge their Medicare reimbursement against others in the industry can find value in the data and the interactive features provided to manage and view it. A lab can review its own reimbursement reported in the data set to look for red flags indicating potential compliance issues. For example, if the data shows a lab receives significantly higher Medicare revenue or is performing significantly higher volume for a particular test than other labs, that could be a red flag. Or if review of the data for the past three years shows a significant change concerning any particular test, that too could signal a potential compliance issue warranting a closer look.
How to Use Data CMS provides the data in two publicly accessible formats: 1) an interactive online data set that users can navigate using filters; and 2) a downloadable “tab delimited file format” that “requires importing into database or statistical software.”
In the online data set, CMS has provided search functions, filters and tools to help users make sense of the vast amount of information available in the data set. For example, users can search the data by entering a provider number, specific provider name or by searching for entity names containing a specific word. Data can also be organized using filters to search a specific type of provider in a specific state for a specific HCPCS code.
Visual graphics tools improve understanding by offering a variety of ways to visually display search results. For example, once filters are applied to gather a specific set of information, that data can be visually displayed for comparison with bar graphs, line graphs, pie charts, bubbles and tree maps.
While not perfect or complete, the Medicare data can be one more compliance tool to help lab compliance professionals spot anomalies and trends in a lab’s Medicare payments. And at least for Medicare services, a laboratory can compare its charges and volume against others in the industry.
Takeaway: CMS continues to promote transparency by releasing, for the third year in a row, a public accessible data set revealing Medicare charges and payments to labs and other providers.
Transparency Continues to Trump Privacy
The trend is transparency. CMS has overruled stakeholder’s concerns about the impact of these data releases on privacy, responding that no beneficiary identifying information is provided. Further, to prevent indirect exposure of Medicare beneficiaries, CMS indicates any aggregated records from 10 or fewer beneficiaries are excluded from the Physician and Other Supplier PUF. HHS specifically addressed privacy concerns in a 2014 letter to the American Medical Association (AMA) explaining “the Department weighed the privacy interests of physicians and the public’s interest in shedding light on Government activities and operations and has determined that the public’s interest outweighs the privacy interests.”
The Department concluded that the data to be released would assist the public’s understanding of Medicare fraud, waste, and abuse, as well as reveal payments to physicians for services furnished to Medicare beneficiaries, which are governed by statutory requirements that CMS must follow.” HHS cited the Wall Street Journal’s ability to use Medicare payment data to identify fraud as evidence of the public interest in the information. Therefore, it concluded “release of physician-identifiable payment information will serve a significant public interest by increasing transparency of Medicare payments to physicians, which are governed by statutory requirements, and shed light on Medicare fraud, waste, and abuse.”
Additionally, because the data set informs the public about Medicare payments, types of services paid for under Medicare, and Medicare payments to specific physicians, the agency argued it fostered “a more informed debate about the appropriate Medicare payment for particular services.” Finally, CMS said this data was in keeping with a shift under the Affordable Care Act toward greater transparency, coordination of care, and sharing of information to increase efficiency, quality, and value of care while lowering costs.
HHS cited the various programs measuring quality of care and providing tools to help the public compare providers, as well as some state laws that require providers publicly reveal charges and payment information as evidence of the “changing nature” of what is publicly shared about physician services and payment. All this transparency means, HHS concluded, that “the physicians’ privacy interest in payment data is not the same as it was over 30 years ago or even 5 years ago.”
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