In August 2021, the Centers for Medicare & Medicaid Services (CMS) authorized the implementation of an algorithm that tries to predict the racial and ethnic identities of patients. While this may allow the CMS to identify racial and ethnic groups that suffer from lacking health equity, its use can be controversial, particularly when the accuracy of the system fails.
The Medicare Bayesian Improved Surname Geocoding (MBISG) model was developed by CMS with the help of a contractor to estimate the probability of Medicare Advantage Plan beneficiaries belonging to each of six racial/ethnic groups. The MBISG model calculates these estimates by using Medicare administrative data, first and surname matching, and geocoded residential addresses linked to the 2010 U.S. Census.
Though the model results in high concordance levels between its estimates and self-reported race and ethnicity by certain individuals, the CMS recognizes the model’s decreased accuracy for those who self-identify as American Indian, Alaskan Native, or multiracial. CMS also recognizes the potential for bias being introduced into the model due to factors such as this decreased level of accuracy, which can be problematic since the end goal of the MBISG model is to evaluate the quality of care being received by each racial/ethnic group.
The MBISG model arose as an effort to mitigate the lack of racial and ethnic data for Medicare beneficiaries, which the CMS uses to track health equity gaps among different groups. The CMS recognizes that self-reported race and ethnicity data is preferred, but not always available or accurate due to limited race/ethnicity options in the past as well as reluctance to disclose this information among certain people. The MBISG estimations are meant to account for situations in which individuals might have identified with a racial or ethnic group that was not available as an option for them when self-reporting, which was often the case prior to 1980 when the only options available were “White,” “Black,” and “Other.”
Bloomberg Law published an article covering the MBISG model on August 5, 2021. The Bloomberg article describes CMS race/ethnicity estimation algorithms including the MBISG, points out their flaws, and encourages the use of caution when employing these algorithms due to bias concerns. The focus is mainly on why this technology is flawed and its usage concerning. However, the article also emphasizes the purpose of these algorithms and how the CMS intends to use them as part of the process of understanding health inequities among different racial and ethnic groups.
A key point in the article regards how medical groups, such as the Association of American Medical Colleges perceive proposed estimation algorithms such as the MBISG model. They are generally concerned with the accuracy of the data provided by such algorithms and push for the CMS to invest in improved patient data collection methods. However, CMS states that despite past efforts to improve data collection and quality, Medicare administrative data continues to present varying accuracy when it comes to identifying patients belonging to different racial and ethnic groups.
The Bloomberg article suggests that, in theory, the implementation of the MBISG model and similar algorithms can have a positive impact because they provide a more accurate representation of the demographic makeup of Medicare beneficiaries. This allows CMS to detect which groups might require additional care if the care they are currently receiving is inadequate. However, it is important that both Medicare and CMS are transparent with patients and other stakeholders regarding these algorithms due to their use of personal information and their potential to impact decision-making in healthcare.
Journalists can continue researching these estimation algorithms in healthcare — and their potentially controversial implications — by exploring the transparency aspect of the situation, discussing how CMS and Medicare are planning on disclosing information regarding these algorithms to stakeholders including doctors and patients. It would also be interesting to look into how Medicare plans on improving their current patient data collection methods since this is an important process and, if ever sufficiently improved, data collection could remove the need for these types of estimation algorithms in healthcare.