Tag Archives: Deana Bell

Webinar: Medicare Advantage risk scores

Payment year 2017 is a key year for Medicare Advantage (MA) plans, as encounter data is weighted 25% and has been shown to result in lower risk scores and revenue. An upcoming Milliman webinar hosted by Charlie Mills and Deana Bell will explore how MA plans have prepared for the transition to encounter data, and highlight best practices for monitoring financial results and encounter data submissions. The webinar entitled “Medicare Advantage risk scores: Best practices in financial monitoring and encounter data submissions” is scheduled for October 19 from 11 a.m. – 12 p.m. PT (2 p.m. – 3 p.m. ET).

For more information or to register, click here.

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.