Medicare and Exchange Risk Adjustment: Data Quality Matters

Plans/Issuers participating in the Exchange may think they have dodged a bullet because HHS has stated payments will not be adjusted during the first two years of the program as a result of RADV audits. However, other remedies such as prosecution under the False Claims Act may still be applied to non-compliant issuers (health plans).

With CMS processing the results of the first Medicare RADV audit subject to extrapolation and with the inaugural audits for the Exchanges kicking off in just a few months, plans need to have a blueprint of how they are going to minimize their audit exposure through data analytics. Because of the different demographics of the Exchange population vs. the Medicare population, health plans in the Exchanges have a learning curve to overcome to address some of the more common coding issues associated with diagnoses for this younger population. The HHS-HCC model is more complex than Medicare and has 15 different payment models based on 5 metal levels and 3 different age bands: the adult model (ages 21+), the child model (ages 2-20) and the infant model (ages 0-1). Pregnancy, newborn and congenital coding rules need special focus in order to receive the appropriate reimbursement. Plans need to be proactive in their approach to data integrity in order to remain competitive and minimize government take-backs.

Whether you rely on multiple vendors, an internal team, or a combination of the two, GHG can help you streamline the execution of your risk adjustment approach, and build a roadmap to ensure you’re keeping stride with CMS and HHS expectations in both compliance and health care outcomes. Our services include:

• Risk Adjustment Strategies – Retrospective, Prospective and Concurrent Outreach strategies, evaluation of staffing structure and levels
• Quality Assurance Programs – Proactive programs to improve data accuracy
• Data Analytics – Identifying data gaps and appropriate gap closures
• End to End Process Review – Testing for dropped data and recommendations for best practices in data processing
• Provider and Coder Education/Coding – including ICD 10
• Risk Mitigation – Identifying unsubstantiated diagnosis codes
• Data Validation – Mock Audits
• Vendor Audits – Coding accuracy, data completeness
• Requests for Proposals (RFP) – Developing RFPs and/or the evaluation of RFP vendor responses

This blog was originally posted on blog.gormanhealthgroup.com.
John Gorman: Under John’s leadership, Gorman Health Group has become the leading professional services and solutions firm for government-sponsored health care, providing thought leadership and expert strategic, operational, and technology-based solutions. Read more.