Early Prediction of Risk Adjustment Factor (RAF) for Medicare Advantage (MA) and Evaluation of its Benefits

Abstract

Among the various risk adjustment models for Medicare and Medicaid programs, the CMS-HCC model is a prospective model for the Medicare Advantage (MA) plans which ensures a plan is paid per the expected risk of the population that it is responsible for. The risk score computed by the CMS-HCC model is called Risk Adjustment Factor (RAF). RAF scores are prospective in nature - data from the previous year of service is used to predict the expected risk and hence the prospective payment in the current year [1]. In this paper we discuss how early prediction of RAF can help in realizing two revenue opportunities, as detailed in the following. The first revenue opportunity is the Accelerated Revenue Opportunity. As an example, consider the service year 2016 and the payment year 2017. Based on the RAF timeline, if some of the services rendered in the second half of 2016 were instead rendered in the first half of 2016, they could have counted towards the subsequent payments starting from Jan 2017 rather than a late (lump-sum) payment in August 2017. The second revenue opportunity, named incremental additional revenue opportunity, relates to payments that never get realized even with some delay. Our contribution in this work is two-fold: (1) We present a method to identify a candidate list of individuals for early inspection in the beginning of any given year. (2) We propose methods to evaluate a given candidate list in terms of its ability to realize the two revenue opportunities mentioned above. The proposed evaluation methods can be used more generally to evaluate other predicted candidate lists. The core of our solution is the identification of a candidate list of individuals to be inspected early in the beginning of any given year. Our proposed method uses RAF scores from the previous two years, and the monetary value of claims under certain condition categories in the past year, and applies machine learning to predict the top 20% of high RAF scores in the current year. The resulting list is considered the candidate list for early RAF inspection in the beginning of the current year. Evaluation of Revenue Opportunities: We demonstrate our methodologies for evaluating revenue opportunities by considering a scenario in which the 2014-2015 data from a large healthcare organization was used to predict the 2016 RAF scores early in the beginning of 2016, and study opportunities that could have been realized using such early prediction. To evaluate the accelerated revenue opportunity, we looked at the lump-sum adjustments made in Aug 2017 for the individuals in the candidate list, and summed over the lump adjustments when they are positive. These late payments could have been received earlier in time if the diagnosis codes identified in the second half of 2016 were instead identified in the first half. Our method for evaluating the incremental additional revenue opportunity is based on the assumption that many conditions in Medicare populations are chronic and the 2017 RAF scores could have been realized earlier in 2016 using early inspection. Specifically, we compared the 2017 and 2016 RAF scores for the individuals in the identified candidate list and determined cases for whom the 2017 RAF score is sufficiently larger than the 2016 RAF score. We consider the delta in RAF scores sufficiently large if the ratio of 2017 RAF over 2016 RAF for an individual is larger than the ratio of c = average(2017 RAF)/average(2016 RAF) where averages here are taken across the population. The delta between RAF 2017 and c×(RAF 2016) marks the missing revenue opportunity for an individual. In the case study mentioned above, our analysis indicated full RAF potential was not captured on 41% (2,048) of the members. The unrealized 2017 payments for these members (based on the unrealized RAF potentials in 2016) is estimated to be $12.69 million. In addition to the missed revenue opportunity observed in the analysis, a significant amount of revenue, approximately $5 million, could have been accelerated to January in 2017. This approach can be used in conjunction with existing analytics to introduce new sources of revenue. The case studies on RAF analytics describes the work in more detail [2]. REFERENCES [1]CMS Risk Adjustment, https://www.cms.gov/Medicare/Health-Plans/MedicareAdvtgSpecRateStats/Risk-Adjustors.html [2] Predictive Risk Adjustment Factor (RAF), http://www.basehealth.com/raf.html