Controversy is brewing in the healthcare insurance community since the much anticipated Medicare Advantage (MA) analysis due to be released at the AcademyHealth annual research meeting was cancelled. Today, MA plans enroll more Medicare-eligible beneficiaries than Fee-for-Service (FFS). The desire to understand the needs and services these beneficiaries have and are receiving is great.
The Centers for Medicare & Medicaid Services (CMS) has been collecting an abundance of data from health plans that offer MA plans since 2012. This “encounter data,” as it’s often referred to, is an extremely lengthy and complex 837 claim that provides a wealth of information. CMS is conducting analysis on this information to start to understand the MA population in the same way it understands Medicare FFS, additionally providing the same type of statistical metrics. Everyone who is a part of healthcare was looking forward to seeing what these results would say. It would provide a new benchmarking standard for MA plans against which to measure. It was the next step in advancement, so to speak.
CMS “erred on the side of caution” by not releasing the analysis because there were too many questions remaining about the results. Although this action upset the healthcare community, it was the right move to make on CMS’ part. There is nothing worse than releasing analytical information for all to use and then having to discredit the analysis and retract the results identified. Although CMS is experienced in working with large amounts of data, this type of analysis goes beyond just a technical capability to manage the data. It’s about understanding the contents of the information received. The format of an 837 is the same, but the manner in which a health plan receives, processes, and stores it differs from plan to plan. Analyzing inconsistent information can cause drastic changes in the overall results.
The Analysis of Medicare Encounter Data that was due to be released at the AcademyHealth was for 2014 dates of service. In 2014, health plans were not paid using encounter data, and many health plans struggled tremendously with even submitting the information required to CMS. The same encounter data being used for analytical purposes is slowly moving towards being used to pay health plans, but they are not quite there. CMS has been slowly migrating health plans away from Risk Adjustment Processing System (RAPS) submissions to Encounter Data Processing System (EDPS) submissions to compensate health plans based on the risk of their members. This transition has been a much slower and painful process than CMS anticipated. There is a blended weight taken between the RAPS submission and the EDPS submission to come up with an overall weighted risk score for which the health plans are paid. You will see in the graph below the usage of encounter data for health plan payments didn’t start until 2016 and was anticipated to increase each year until 2020 when EDPS would be 100%, however, for the 2018 payment year, the weight for EDPS was reduced from 2017 to 15%. So the encounter data information is not technically used to pay health plans just yet. Its results carry a very low weight when blended in with the RAPS information. Just because a small percentage of encounter data is used in calculating payments for 2016 and beyond doesn’t mean the analytical results for 2014 are creditable enough to be released for usage across the industry.
|RAPS to EDPS Transition for Risk Score Calculation|
Many questions still remain about the statistical information for the MA population. The need in the industry to have this type of analytical information is very high and much needed for continued growth and quality of care for all Medicare beneficiaries, but if it lacks confidence from CMS about the underlying creditability, then the information is useless to us anyway. To further assist in the advancement to obtain country-wide analytics, health plans should be focusing on the creditability of the content they are submitting to CMS. Once CMS validates the integrity of the results, there will be many more advancements that can come next.
To learn more how Gorman Health Group can help you achieve data integrity with your risk adjustment data submissions contact me directly at firstname.lastname@example.org.
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