CMS Analysis Reveals Implicit Bias in Healthcare, Value-Based Payment

An internal analysis of three CMS Innovation Center models revealed instances of implicit bias in healthcare, which disproportionately impacted people of color and low-income individuals.

CMS Innovation Center chief medical officer Dora Lynn Hughes, MD, MPH, and analyst Melissa Majerol, MPH, disclosed the results of the analysis of implicit bias in a Health Affairs Forefront blog post yesterday. The analysis is part of the Innovation Center’s new strategic plan that focuses on five objectives, one of which is to advance health equity.

Under the Innovation Center’s advancing health equity objective, the Center aims to evaluate models for how they affect health equity and boost the number of beneficiaries from underserved communities that receive care through value-based payment models.

Hughes and Majerol reported that the Kidney Care Choices (KCC) Model, Comprehensive Care for Joint Replacement (CJR)Model, and Million Hearts® Cardiovascular Risk Reduction Model all contained some implicit bias, which led to the exclusion of some beneficiaries from the alternative payment and care delivery models.

The three models represented “a small but varies microcosm of the Innovation Center portfolio,” Hughes and Majerol explained. The models represent voluntary, mandatory, and voluntary-mandatory hybrid models and they differ according to financial methodology, beneficiary attribution, risk stratification, and financial risk levels. The authors noted that all three of the models serve diverse populations, but none were designed to reduce healthcare disparities as they were created and put into effect before the Innovation Center’s strategy refresh.

“The assessment examined three models to identify potential sources of bias and found that use of certain risk-assessment and screening tools, provider tools, and payment design and risk-adjustment algorithms has led to the exclusion of some beneficiaries from these models,” they wrote.

“These findings are troubling not only because of the limited access to the benefits of Innovation Center models but also because diverse model participation is critical for robust evaluation and confidence in generalizing results to all of the populations served through CMS programs.”

In one instance, the analysis showed that the use of the traditional estimated glomerular filtration rate (eGFR) in the KCC Model may have resulted in implicit bias since the Innovation Center used eGFR to attribute beneficiaries to participating providers.

The eGFR has historically been adjusted for a patient’s race, since clinical trials have shown that Black patients have higher levels of creatinine, on average. However, the National Kidney Foundation and the American Society of Nephrology have recommended since September 2021 that providers do not use a race-adjusted eGFR, which has been linked to artificially elevated levels in Black patients, which has resulted in care and transplant delays.

The KCC Model launched in January 2022, therefore beneficiaries who may have met the medical eligibility criteria—had chronic kidney disease or end-stage renal disease—may have had their kidney function assessed using the race-adjusted eGFR, the analysis showed.

“The number of Black beneficiaries who may have been excluded from the model cannot be reliably estimated,” Hughes and Majerol wrote.

Since the recommendation for a non-adjusted eGFR, the Innovation Center has provided guidance to KCC Model providers to avoid using the race-adjusted rate and the Center has explored policies to address the use of the race-adjusted method.

But even how CMS pays providers for participating in Innovation Center models may lead to implicit bias in healthcare. The analysis found that target prices for an episode of care under the bundled payment CJR Model were not adjusted for sociodemographic factors. Yet, Black and low-income patients are more likely to be discharged to skilled nursing or rehabilitation facilities, which is associated with higher costs per episode.

“This presents an opportunity for bias within the model as CJR providers could make fewer offers of joint replacement surgery to Black and low-income individuals in an effort to keep spending below the CJR target price and generate savings under the model,” Hughes and Majerol wrote.

An evaluation of the CJR Model has shown that dual-eligible patients—an indicator of lower socioeconomic status—were less likely to be included in a participant’s patient mix under the model. Patients were also less likely to be Black during one year of the CJR Model.

The CMS Innovation Center has looked into risk-adjusted episode targets for the three-year extension of the CRJ Model, which launched this year.

Finally, the analysis showed that the risk calculator used under the Million Hearts® Model to predict 10-year atherosclerotic cardiovascular disease did not properly determine the risk of the disease in patients who selected “Other” as their race.

The actions taken to address implicit bias in the analyzed Innovation Center models are just initial steps CMS intends to take, the blog post concluded.

“[T]he findings underscore the need for a more systematic evaluation of implicit bias in current and new models. To this end, the Innovation Center has begun to develop a step-by-step guide to screen for and mitigate bias in Innovation Center models,” Hughes and Majerol explained.

“This guide will be piloted for use in new models currently in development, with the intention of having all future models screened for implicit bias with this guide prior to launch. In the long term, this critical effort will support CMS’s broader commitment to providing equitable, high-quality care for beneficiaries in the Medicare, Medicaid, and CHIP programs.”