The Health Equity Index (HEI) replaces the Reward Factor for Star Year 2027. Will the complex initiative last under Donald Treump’s anti-DEI campaign?
Note: Published in collabortion with Lilac Software. If you are in need of Medicare Advantage (MA) Stars technology and expertise, visit https://lilacsoftware.com .
The Health Equity Index (HEI) is officially here. Why? Well, it replaces the Reward Factor in the Medicare Advantage (MA) Star bonus prorgam for Star Year (SY) 2027, but we are smack in the middle of the measurement years that help MA plans to qualify for the new reward. Why is that? Because as opposed to measure performance, the HEI is a blend of two years of data and lags the usual measurement period a bit more. In the case of SY 2027, the HEI measurement period is a blend of measurement years (MY) 2024 and 2025.
The Centers for Medicare and Medicaid Services (CMS) have spent a great deal of time clarifying for plans many aspects of the HEI over the past two years. The agency had an in-depth webinar on HEI in November 2024. Most importantly, the agency published technical specifications for Star Year 2024 (MY 2021 and 2022 data) and Star Year 2025 (MY 2022 and 2023 data). While a great deal has been resolved, there are still some unknowns.
What do we know now?
- The initial HEI Social Risk Factor (SRF) criteria are limited to Low Income Subsidy/Dual Eligible status and Disability status.
- Plans can obtain a reward based on their HEI performance compared to the other MA Plans.
- There is only the potential to add points to your overall score. There are two potential reward levels depending on the proportion of SRF enrollees in a contract as compared with national statistics. Contracts with SRF enrollment that meet or exceed one-half of the median SRF penetration of all plans can achieve as much as a 0.2 reward. Contracts that meet or exceed the median SRF penetration of all plans can achieve as much as a 0.4 reward.
- The HEI is different from the Categorical Adjustment Index (CAI). The CAI was introduced to take the bias out of some measures related to the varying penetration of SRF populations in contracts and how that impacts performance. The CAI remains and compensates plans with a high threshold of social determinants of health (SDOH)-impacted members with an upward overall Star adjustment. Those with less SDOH-member penetration may get a negative adjustment. The HEI actually rewards plans if they perform well on quality measures with SRF-impacted enrollees.
- The reward is factored on the SRF population performance in member-focused measures – largely limited to clinical, drug, and survey measures. Special Needs Plan (SNP)-only measures are not included.
- Measure-level scores are used for contracts that have data for both years or at least the most recent year of the 2 years, but measure-level scores are not used for contracts that have data for only the first of the 2 years.
- If an enrollee has one of the specified SRFs for only one of the two included measurement years, the performance data for just that year are used.
- The blending of the two measurement years is influenced both by the difference in SRF achievement between the years and the change in the eligible population.
- CMS will apply case mix adjustments to the CAHPS survey measure HEI scoring but will remove indicators that relate to the SRF categories.
- A contract must have at least 500 members to qualify.
- A measure must have a reliability of at least 0.7 when the measure is calculated for the combined subset of enrollees with the specified SRFs across 2 years of data. As well, measure-specific denominator criterion must be met for the subset of enrollees with the specified SRFs across 2 years of data.
- CMS excludes measures when less than 25% of contracts meet the reliability and enrollment thresholds.
- A contract generally will need to be scored in at least half of the measures included in the HEI to be eligible.
- The HEI reward will be added to the unrounded overall and Part C and D summary ratings after the addition of the Categorical Adjustment Index (CAI).
- In the SY 2025 HEI Technical Notes, year-2 predictors, case-mix adjustment coefficients, national means for each of the measures, and explicit calculations and formulas were released. The SY 2025 document (released in the HPMS plan portal in December 2024) is your bible. Plans were waiting for these complex details. It has far more detail than the SY 2024 document. This should allow contracts to closely approximate their HEI measures results and rewards based on the plan-specific and national performance data.
How is the HEI calculated?
As noted, a subset of overall measures is included in the HEI calculation. Industry-wide performance is separated into a bottom third, middle third, and top third of performance. The score awarded for each measure is -1 (bottom), 0 (middle) and +1 (top). Lilac uses the mathematical term “tertiles” (similar to the more-often used quartiles) for the three groups created by the two breakpoints (bottom/middle and middle/top). The scores are then weighted by each measure’s weight (for the current Star Year) and added up to arrive at an achievement factor (percentage of the total possible). This is then multiplied against either 0.2 (those at or above half the median SRF penetration but less than the median) or 0.4 (those at or above the SRF median penetration). This is the final HEI reward.
Lilac Software is asking CMS these questions
While a great deal was clarified by CMS this year and in the SY 2025 HEI Technical notes, there are a number of things that are still ambiguous or not clarified. Lilac Software has submitted the following to CMS for possible clarification and answers:
- For Plan All Cause Readmissions, how are the observed and expected pass rates adjusted across the years (what is the formula and the year adjustment coefficient)?
- What is the formula to calculate coefficients b1 (year adjustment)?
- As to reliability:
- What is the between-contract variance of the mean for each measure among SRF enrollees?
- Does “reliability when the measure is calculated for the combined subset of enrollees with the specified SRFs across two years of data” (Step 3) mean it is calculated on the sum of enrollees across two years. That is, we would count twice an enrollee if he enrollee is in both years for HEI, because the enrollee’s contribution to the numerator could change. As an example, an enrollee could be non-compliant for BCS the first year and be compliant the second year, which would change the score and in turn the error variance)?
- How are the error variances calculated for each measure across 2 years (what is the formula)?
What should plans do?
There is a great deal plans can do to push their HEI performance as we go through MY 2025.
- Review reports made available via HPMS – In December 2023, CMS provided contracts with calculations of the HEI reward using data from the 2023 and 2024 Star Ratings to assist them in implementation. These reports are available in HPMS. In December 2024, CMS provided the same for 2024 and 2025 Star ratings. This will be continued each December until official HEI data are published with the announcement of the HEI in SY 2027 in October 2026. The December 2023 report represented the blends for MY 2021 and 2022 and the December 2024 report represented the blends for MY 2022 and 2023. As noted above, the SY 2025 HEI Technical Notes have a great deal of expanded information for calculations. Collectively these reports will give an indication of whether you have contracts likely to be eligible for the HEI ratings boost. With the exception of the open questions we have with CMS, plans should be able to reasonably estimate their HEI performance on measures and for the summary and overall HEI rewards.
- Track members that qualify for Low Income Subsidy/Dual Eligible and Disability – The lowest hanging fruit to start proactively managing your plan’s HEI performance is to get foundational tagging and tracking in place. You need to ensure you know which members meet one of the criteria to be eligible for HEI inclusion and have the ability to cut data and generate reports based on members’ HEI eligibility status.
- Calculate which contracts are HEI eligible – Once you have all your members properly tagged, you can do analysis to determine which contracts are eligible for rating boosts from HEI. This is crucial for accurate forecasting of Star ratings and informing the best allocation of resources to drive improvement. For many contracts, you will be able to determine their HEI eligibility with “back of envelope” analyses. But in other cases, it will take more sophisticated, precise analyses to determine a contract’s HEI reward.
- Develop intervention strategies targeting Low Income Subsidy/Dual Eligible and Disability members – To actually improve your HEI score you need to perform better with HEI eligible members on relevant measures. This requires creating dedicated initiatives on how to best close gaps and otherwise drive performance in populations that are often challenging to influence.
- Forecast tertiles (Advanced) – The strongest HEI management program requires an informed view of each measure’s tertiles (the thresholds between bottom/middle and middle/top) for HEI performance benchmarking. Forecasting tertiles will enable you to get a sense of how the measure scores across your rated contracts stand relative to the forecasted tertiles. This requires more sophisticated data science and deep HEI expertise but is a worthwhile use of resources for plans where HEI has a chance to be meaningful.
- Simulate HEI rewards (Advanced) – With all the component pieces to HEI reward forecasting a plan can simulate different scenarios. This is especially valuable for thinking through scenarios for measures that are closest to the tertile thresholds, which is practically useful when making Star strategy and resource allocation decisions. Again, it requires advanced data science capabilities but for many plans the juice will be worth the squeeze.
Check out one of our earlier blogs on HEI for an expanded view on best practices for HEI here: https://lilacsoftware.com/health-equity-best-practices/
What will be the impact of moving from the Reward Factor to the HEI?
CMS put the best spin on it, but the move from the Reward Factor to HEI is a negative for MA plans at least in the short term.
In the November 2024 webinar, CMS says its simulations show most contracts had no change in their highest Star Rating (up or down) — 83% of MA-PD and 80% of PDP contracts had no change. The agency said roughly the same number of contracts qualified for a hypothetical HEI reward as qualified for the historical reward factor in the 2024 Star Ratings: 32% of MA-PD contracts qualified for a hypothetical HEI reward compared to 36% that qualified for the historical reward factor.
But other data – even from CMS — paint a more negative picture. In the 2024 Final Rule, CMS estimated that the reward system change would result in program savings of $670 million in 2028, increasing to $1.05 billion in 2033. Some outside analyses confirm that drop in revenue initially and say that in total just 0.1 of the 0.4 potential HEI Reward across all contracts will be awarded. This compared with 0.3 of 0.4 for the Reward Factor.
An earlier CMS simulation said 1.7 percent (7) of MA prescription-drug contracts gained a half star on the overall rating, while 13.4 percent (54) of contracts lost a half star on the overall rating.
Could the HEI go away?
While MA plans are ready for HEI and its impacts, the Trump administration is busy eliminating all of the previous administration’s Diversity, Equity, and Inclusion (DEI) initiatives. Now, Trump has started to extend this to some policy touching on health equity and social determinants of health.
CMS pulled Biden-era information on health equity/social determinants of health (SDOHs) for the Medicaid and Children’s Health Insurance Program (CHIP) through Section 1115 waivers. CMS says it will only consider state waiver applications on a rolling basis and then determine whether the applications meet federal requirements.
What does this mean?
- The Biden administration encouraged the expansion of Medicaid coverage via 1115 waivers to tackle SDOH barriers and impacts to encourage better health equity.
- The CMS guidance served as a model and telegraphed ways to expedite approval of the waivers.
- The Trump administration has pulled this guidance and encouragement back.
- States can still apply for such expansions of benefits to tackle SDOHs, but approval is perhaps less likely.
The move is unfortunate. Whatever you think of the debate over DEI or the expansiveness of the SDOH benefits in Medicaid under Biden, studies and data show overwhelmingly that disparities in health across race and ethnicity are real and that the presence of SDOHs are a greater predictor of costs and quality outcomes than underlying disease states themselves.
Based on some writings from conservative policy wonks on SDOH, there was some hope that the Trump administration might embrace ongoing work on SDOHs. But other forces, such as cost and conservative views that healthcare should not coddle, may be winning here in the administration.
The big question is what this could mean for the Medicare Advantage Health Equity Index (HEI). Will HEI be next for the chopping block and will Trump stay with the Reward Factor?
Again, the HEI is slated to hit in Star Year 2027 as a replacement for the Reward Factor. But plans are already crunching numbers and seeking to improve health equity because the measurement years for HEI in Star Year 2027 are 2024 and 2025. It likely would have to issue a rule suspending or removing the HEI this year (or very early next) to have it removed for Star Year 2027. This is because bid creation for 2027 benefits begins in early 2026.
As noted, the HEI is initially slated to save on government outlays in Star compared with the Reward Factor, so eliminating the HEI would actually cost Medicare money. This runs counter to the spending reduction agenda the administration has. That could influence the decision. Only time will tell.
#cms #medicareadvantage #stars #hei #quality
— Marc S. Ryan
