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CMS Finalizes ACA Risk Adjustment, Error Rate Calculation Changes
The new ACA risk adjustment program will average the current and previous benefit year’s error rates, instead of relying solely on the previous year’s error rate, to adjust risk scores.
CMS has issued a final rule to regulate the Affordable Care Act risk adjustment data validation program.
The risk adjustment data validation program, run by HHS, certifies the integrity of data that payers submit to HHS for risk adjustment payment transfer.
“The final rule announced today builds upon the agency’s ongoing efforts to update parameters for the HHS-operated risk adjustment program, which is critical to maintaining a strong and stable insurance market and encouraging broader issuer participation resulting in more choice for consumers,” the press release from November 24 stated.
Risk adjustment payment transfer is intended, in part, to disincentivize payers from only accepting healthy enrollees by offering payment for high-cost patients.
Risk adjustment is calculated separately for different markets, so states calculate their individual non-catastrophic, catastrophic, and small group or merged market risk pools distinct from one another.
The finalized rule changes the HHS risk adjustment data validation program’s error rate calculation and HHS-RADV results application.
Changes to the HHS risk adjustment data validation error rate calculation
The error rate relies upon how often a payer fails to validate the audited enrollees’ conditions, also known as the payer’s “failure rate.”
CMS establishes hierarchical condition category (HCC) groups and determines failure rates for each HCC group separately.
Some year-to-year variation in the payer failure rate is expected. However, if a payer’s condition group exhibits an unusual failure rate for its HCC category—one that exceeds the national benchmark for failure rates— HHS makes will place the payer’s outlier group into a low, medium, or high failure rate group.
HHS has largely abided by the risk adjustment program as outlined in section 1343 of the Affordable Care Act since benefit year 2017, but CMS found that some changes to the error rate calculation were necessary.
First, starting with the 2019 benefit year, CMS will compensate for the challenge of categorizing conditions within an HCC by changing how it groups medical conditions in the same HCC.
Second, CMS will seek to eliminate the “payment cliff” by decreasing the severity of the risk score adjustments for payers that landed on the border of the threshold for risk adjustment.
Finally, CMS acknowledged that negative failure rates are not always indicative of accurate data but, rather, that they may mean a payer failed to identify conditions that should have been included in the calculation. To account for this, CMS is changing the error rate calculation for payers with negative failure rates.
“These changes are intended to strengthen program integrity by reducing possible incentives for issuers to underreport diagnoses during initial risk adjustment data submission,” CMS stated.
“These changes will also promote fairness by ensuring that issuers are not penalized in HHS-RADV when a difference in diagnosis for an enrollee has no effect on risk, as well as by ensuring that issuers that receive adjustments are receiving adjustments in proportion to the errors identified through HHS-RADV.”
Changes to applying HHS risk adjustment data validation results
Up until now, risk adjustment had been prospective. CMS took the results from the previous benefit year and used them to adjust risk scores for the new benefit year.
“When we finalized the prospective HHS-RADV results application policy in the 2014 Payment Notice, we did not anticipate the extent of the changes that could occur in the risk profile of enrollees or market participation in the individual and small group markets from benefit year to benefit year,” CMS acknowledged in the final rule.
In order to adjust for the shifts from year to year, the finalized rule will leverage an average error rate, calculating the error rate by averaging the current and previous benefit years’ error rates. This only applies to non-exiting issuers.
“HHS would calculate an average value for the 2021 and 2020 benefit years’ HHS-RADV error rates and apply this average error rate to 2021 plan liability risk scores and risk adjustment transfers,” the rule explained.
“This approach would result in one final HHS-RADV adjustment to 2021 benefit year plan liability risk scores and risk adjustment transfers, reflecting the average value for the 2021 and 2020 benefit years’ HHS-RADV error rates. The adjustments to transfers would be collected and paid in accordance with the 2021 benefit year HHS-RADV timeline.”
For new entrants to a state exchange in benefit year 2020, CMS would average its 2020 risk adjustment data validation results and the 2019 and 2020 risk adjustment data validation results for its competitors in the same risk pool. Exiting payers will only be evaluated for positive error rate outliers.
“CMS is committed to continuing to monitor and refine the HHS-RADV methodology and program requirements,” the press release concluded. “CMS designed the final rule to help improve the predictability of HHS-RADV results, while mitigating the burden to issuers.”
CMS has touted the successes of the risk adjustment program in the Affordable Care Act marketplaces. The program was effective at spreading financial risk among payers, a 2019 report found.
In the final rule’s text, CMS noted that the impetus behind the HHS rule’s approach is “fundamentally different” from the approach in Medicare Advantage risk adjustment data validation.
Medicare Advantage risk adjustment payments have declined between 2009 and 2017, but the lower payments have caused health plans to tighten their belts in order to maintain beneficiaries’ access to care and affordable coverage.
Medicare Advantage risk adjustment data validation has seen its fair share of contention between CMS and payers in the past year.
America’s Health Insurance Plans (AHIP) opposed proposed rule-making that would use extrapolation for auditing to determine Medicare Advantage risk adjustment, similar to methods used in Medicare.