The OIG has released a 48-page guide to "improper payment reduction" for Medicare Advantage plans. The OIG summarizes the project this way:
This toolkit offers Medicare Advantage (MA) organizations information that will enable them to replicate Office of Inspector General's (OIG's) techniques to identify and evaluate high-risk diagnosis codes to ensure proper payments and provide better care for enrollees. This toolkit is meant to be a practical, hands-on device that will help MA organizations improve the accuracy of their submitted diagnoses that are at high risk for being miscoded.
Find the project home page here:
https://oig.hhs.gov/oas/reports/region7/72301213.asp
Find the 48 page PDF here:
https://oig.hhs.gov/oas/reports/region7/72301213.pdf
- The 2023 OIG Toolkit aims to decrease improper payments in Medicare Advantage by identifying and addressing high-risk diagnosis codes.
- It offers practical techniques for Medicare Advantage organizations to ensure accurate payments and improve enrollee care.
- The toolkit is based on OIG's audits, which found significant miscoding of diagnosis codes, impacting around 70% of submissions. It includes programming codes and methodologies used in audits, enabling organizations to replicate OIG's techniques for identifying miscoded diagnoses and enhancing compliance and accuracy in Medicare billing.
The tool kit doesn't have sections that are lab (or genetics-genomics) specific. However, the OIG does state this:
"Although MA organizations make their own payment arrangements with providers for theseservices, CMS requires MA organizations to submit copies of all claims to CMS. These claims include services that can be used for risk adjustment purposes (physician, outpatient, and inpatient) as well as claims whose services are not used for risk adjustment purposes (such as home health services, skilled nursing facility services, durable medical equipment, laboratory services, and x-rays, among other services). We have developed computerized programs to access and analyze these claims data. " (p.5)