The Biden administration is encountering a perennial problem in its push to better embed health equity in payment models run by the CMS: a lack of reliable data.
Variable race and ethnicity data in Medicare and Medicaid claims is presenting a challenge for determining whether CMS Innovation Center models are reaching, enrolling and helping underserved beneficiaries, according to a new white paper.
For the paper, regulators analyzed 17 existing or recently completed models to determine their impact on health equity from 2018 through 2022, using model evaluation reports and claims and administrative data.
The CMS found that models explicitly designed to address health equity were reaching a higher proportion of racial and ethnic minorities.
However, many of the models faced complications to addressing the needs of underserved populations, including small population sizes or missing data. Poor data quality “presents a challenge for understanding whether models reach and enroll underserved individuals,” the white paper says.
Some models not specifically designed to address health equity also show potential to do so, but “we cannot draw definitive conclusions from our evaluations,” regulators said in the white paper.
Race and ethnicity data is incomplete in both Medicare and Medicaid datasets, both because racial and ethnic categories are limited and because the data is missing entirely for a “substantial proportion” of individuals, according to the CMS.
Other underserved populations such as sexual orientation, gender identity and religious minorities are “generally not identifiable at all,” regulators said.
Shoddy data collection and sharing is a frequent speedbump for efforts to improve the quality and delivery of healthcare in the U.S. During the pandemic, for example, COVID-19 data was disjointed and duplicative, hamstringing the nation’s public health response.
Data gaps and inconsistencies have also plagued policymakers’ and companies’ pursuit of health equity. For example, unstandardized and incomplete vaccination data by race and ethnicity has made it difficult to determine inequities in the uptake of COVID-19 vaccines.
Closing health equity gaps has been a key priority for the Biden administration. Regulators are currently reviewing models ran by the CMMI, which tests novel payment methods for healthcare delivery and services in a bid to push the industry toward value.
The CMMI has launched more than 50 models since its inception in 2010.
However, few of the center’s models have resulted in cost savings or better quality of care, causing a bipartisan group of legislators to advocate for more oversight of the agency. Only four models to date have met the criteria for expansion and gone on to permanently become part of Medicare.
The CMMI is currently undergoing a strategic revamp to focus more on equity.
Regulators have paused a number of models during the review, which found evidence of implicit bias in three payment models.
The CMMI plans to consider health equity at the start of model design moving forward, and is also exploring additional strategies to encourage participation among providers serving a more diverse population or people with higher levels of social need, according to the latest white paper.
Regulators said they’re also working to improve data quality in claims and administrative datasets to aid health equity evaluations. Providers often have more accurate and specific data, so CMMI said it will require additional data from participants to identify and address health inequities.
The CMS also plans to require contractors hired to evaluate CMS models take health equity into account.
“We believe this multi-pronged approach will help us to measure the success our models achieve in advancing health equity,” an agency spokesperson told Healthcare Dive.
