Tag Archives: RAPS

How will the pandemic impact Medicare Advantage revenue and risk scores in 2021?

While there is a great deal of focus on resource availability and handling a potential influx of severe inpatient cases resulting from COVID-19 infections, the majority of the United States saw a dramatic reduction in healthcare services around March and April 2020 and measurable reductions continue with great variation across the nation. 

As with many prospective risk adjustment models, Medicare Advantage (MA) and Part D (PD) risk scores are based on medical claims, more specifically diagnoses from face-to-face visits from the year prior to the year in which the risk score drives revenue. For 2021 MA payments, 2020 diagnoses are the basis of the final risk scores. To the extent that beneficiaries delay or avoid care, there may be fewer face-to-face encounters with providers where diagnoses can be recorded and applied toward 2021 risk scores.  

While the Centers for Medicare and Medicaid Services has announced additional flexibilities in including telehealth-based diagnoses in risk score calculations, a significant reduction in overall services is likely to result in a material reduction in both MA and PD risk scores. In this article, Milliman’s Rob PipichKarin Cross, and Deana Bell discuss the results of an analysis they performed to support 2021 MA and PD bids. They present nine scenarios intended to illustrate a range of potential outcomes on 2021 MA and PD risk scores. 

Survey analyzes differences between Medicare Advantage RAPS and EDS risk scores

As the Centers for Medicare and Medicaid Services (CMS) continues to phase out the Risk Adjustment Payment System (RAPS) as a source for risk adjustment diagnoses, it is important that Medicare Advantage organizations (MAOs) understand the expected impact of the Encounter Data Processing System (EDS) as the single source of diagnoses for calculating risk scores and the impact this transition may have on revenue.

Milliman professionals have periodically conducted surveys that identified the average difference between RAPS-based and EDS-based risk scores. The most recent survey results show that EDS scores have now caught up to or exceeded RAPS scores in many cases, whereas surveys from prior years showed that EDS were generally lower than RAPS scores.

Milliman actuaries David Koenig, Emily Vandermause, and Rebecca Gergen discuss the results in their paper entitled “Have we reached parity between Medicare Advantage RAPS and EDS risk scores?

Medicare Advantage’s transition from RAPS to EDS risk scores

In 2017, there were many changes to Medicare Advantage (MA) risk adjustment as the transition continued from Risk Adjustment Processing System (RAPS) data to Encounter Data System (EDS) data. MA organizations will also experience complexity and challenges in payment year (PY) 2019.

This article by Milliman’s Deana Bell, David Koenig, and Charlie Mills compares EDS and RAPS risk scores and details some of the program highlights from the past 12 months:

• A 25% EDS weight for PY 2017
• EDS file layout updates
• PY 2016 EDS deadline extension and change to payment timing
• PY 2017 RAPS and EDS deadline extensions
• Including inpatient RAPS diagnoses in EDS risk scores for PY 2019




Transition from RAPS to EDS data decreases Medicare Advantage risk scores

Milliman consultants Deana Bell, David Koenig, and Charlie Mills performed a study of how the transition from Risk Adjustment Processing System (RAPS) data to Encounter Data System (EDS) data is affecting payment year (PY) 2016 risk scores and revenue for Medicare Advantage organizations (MAOs). Fifteen MAOs participated in the study, reflecting a cross section of small- and medium-sized organizations and representing over 900,000 members in 154 plans. The consultants offer perspective in their article “Impact of the transition from RAPS to EDS on Medicare Advantage risk scores.”

Overall, the study found that the median percentage difference between PY 2016 risk scores based on RAPS and the EDS-based risk scores is 4.0%. The percentage difference is larger for special needs plans (SNPs) and smaller for general enrollment plans as shown in Figure 1. The prior year’s diagnoses make up a larger component of SNP members’ risk scores, compared to general enrollment plans, so the risk score impact for SNP plans is larger.

[The authors] have not attempted to quantify what portion of the difference between RAPS and EDS is due to incompleteness of the EDS submissions, issues with CMS’s return files (revised MAO-004 files), changes to filtering logic, and the effect of claims coding errors.

As an illustration, the potential Part C PY 2016 revenue using the median difference of -4% between RAPS and EDS results in a reduction of approximately $40 per member per year, assuming approximately $800 in Part C risk-adjusted revenue and a 1.0 RAPS-only risk score. To the extent that this -4% gap persists in future years, the revenue impact will grow because the EDS-based risk score will make up an increasing portion of the final risk score (e.g., with the 25% EDS weight in PY 2017, the per member reduction would be about $100 per year).

This article is the second in a series of articles on the transition to EDS. For more information about the EDS and RAPS data used in MA risk scores, read “Medicare Advantage and the Encounter Data Processing System: Be prepared.”




Encounter data submission consideration for Medicare Advantage organizations

The Centers for Medicare and Medicaid Services (CMS) is shifting to the Encounter Data System (EDS) as the sole basis for risk scores of Medicare Advantage organizations (MAOs). EDS data will have a substantial influence on risk scores and revenue as early as payment year (PY) 2016.

In this article, Milliman consultants offer perspective on several analyses MAOs should consider to prepare data submissions for CMS. The authors also discuss some challenges MAOs may face when verifying that the data submitted through the EDS are complete and accurate and that all appropriate diagnosis codes are being accepted for risk adjustment by CMS.

Here’s an excerpt:

To provide an effective review of an MAO’s diagnosis code submissions, the following analytics can be undertaken:

• Calculation of risk scores from each diagnosis source: RAPS, EDS, and source claims/ chart review data
• Plan-level and member-level comparisons of risk scores based on each diagnosis source
• Analysis of submission gaps
• Analysis of coding gaps

To perform the EDS submission review, a possible first step is to create a “plan report card,” which summarizes the risk scores under accepted RAPS and EDS submissions and the risk scores based on all diagnosis sources (claims and chart review data) after applying the MAO’s specific RAPS filtering logic and the EDS filtering logic released by CMS.

Figure 1 provides an example of a potential Plan Report Card for PY 2016 EDS submission review. In this example, there is a 4.1% gap between the EDS risk scores and the risk scores after the MAO applied the EDS filtering logic to the source claims data. Also, based on the CMS return data, the EDS risk scores are four points lower than the RAPS scores. This indicates that the EDS submissions may be incomplete and that there are diagnoses in the source claims data that the CMS filtering logic has rejected.

encounter-data-processing-system_figure-1

If submissions to CMS contain all necessary data elements to successfully pass the filtering logic, the risk scores based on RAPS and EDS return data should match the risk scores calculated from the source claims and chart review data. In addition, if the RAPS and EDS filtering logic are the same, the RAPS and EDS risk scores should also be the same. However, there can be gaps between what is submitted and accepted by CMS and the claims and chart review data because of:

• Incomplete data submissions (e.g., claims being inadvertently filtered out or dropped, missing chart review data)
• Inaccurate data submissions (e.g., the wrong medical codes, such as incorrect bill type, being used in the submissions)
• CMS system errors (e.g., failure to match diagnosis data with the correct member)
• Other potential process errors

Furthermore, comparison of the RAPS and EDS risk scores will indicate whether the MAO’s revenue is being adversely affected by the move from RAPS to EDS. A focused look at the MAO’s own coding practices as they compare to Medicare FFS coding standards and EDS filter criteria can identify the coding gaps that may drive lower risk scores under EDS.