The evolution of value-based care has impacted the healthcare industry as a whole. One repercussion no one was really expecting is the spotlight on inequities in the system. In this third installment of our value-based care series we will examine how value-based care is used to identify and address inequities in healthcare through standardization and accountability.
In the United States there is a higher maternal mortality rate than in other comparable countries. If we dig into the data even more, we see that African American mothers are twice as likely as Caucasian mothers to become part of this statistic. Other data reveals that the life expectancy decreased for every segment in 2020 with the gap between non-Hispanic whites and African Americans widening as well.
We even see inequalities in the cost of healthcare. Those living in rural areas pay more in both total dollars and in out-of-pocket dollars. Due to the lack of provider availability, they have longer wait times and receive less chronic and mental health care.
Furthermore, the technical divide, both through age and income, has become even more prevalent for healthcare as telehealth and real-time monitoring becomes the norm. At the beginning of the vaccination roll out, scheduling a COVID-19 vaccination became dependent on being tech-savvy with a good internet connection. These are issues that are front and center in the United States, as well as salient around the world.
Solving Inequalities in Healthcare with Outcome-Focused Treatments
Bias in any form, whether implicit or explicit, can be addressed when looking at how the outcomes of treatments performed. This data point is calculated when the denominator of any healthcare measure includes everyone that qualifies, regardless of age, race, or location and is divided into a numerator that expresses positive outcomes. If a subset of that denominator doesn’t get the same quality of care as the rest, they will not be counted in the numerator of the equation, thereby revealing lower quality performance than what is possible. If these measurements are standardized across a program, it can quickly highlight various issues.
For example, assume a hospital system in a lower-socioeconomic status rural area is observing a higher incidence of diabetes related to ER visits. This can become a red flag that requires further investigation. By first looking at the issue and then diving into the root causes, solutions might be able to come into play. We might find that patients are not as aware of dietary and exercise programs that are available, or we might find that patients aren’t taking their medicines as prescribed due to costs. Knowing the root cause of the problem would enable the hospital to identify cost-effective, long-term solutions such as connecting patients with a seminar about managing their diabetes or finding grants to help low-income patients afford their medications.
This approach both identifies where there is a problem so that the problem can be investigated and solutions to the problems can be proposed to improve outcomes for those more likely to receive inadequate care. Low-income families and the elderly are least likely to have access to a computer or have the technical know-how to sign up for a Covid-19 vaccine. We are seeing how communities are mobilizing by getting churches and librarians involved to help people sign up. Only when a challenge or problem is identified can we begin to look for innovative solutions.
The Need to Integrate Social Determinants of Communities that are Served
The ability to look at the data and recognize which groups are not being adequately served enables healthcare providers to go a step further and actively tie the equity issues to quality metrics. In fact, the National Quality Forum has developed disparity-sensitive measures to “promote equal treatment of all patients who enter the healthcare system and reduce healthcare disparities in health outcomes, the healthcare system must be deliberate about addressing these factors and mitigating their impact.”
We’ve seen Medicaid programs that are tying together such measures to their programs in which part of the care is social-needs screening. Others, such as Michigan Medicaid, are implementing pay-for-performance programs based on implementing “evidence-based models to reduce racial disparities in low birthweight”.
However, such measures may be complicated by the fact that social determinants are not always collected. And, if done poorly, a focus on value-based care can make things worse by encouraging providers to cherry-pick patients by punishing those providers working with patients that might have more social needs. However, this can be addressed by either incorporating social determinants data points into risk-adjustment algorithms accounting for the population mix. Providers can then anticipate the extra needs that might come about based on social determinants into payment structures. This method can allow those who might have been underserved and in turn need more care to have access to the treatment they need and start to address the issues at play.
Either way, accountability, the backbone of value-based care, can be used as a way to address inequities in our health system. These issues cannot be necessarily solved with one-size-fits-all medicine, but it can demand that the outcomes be equal. By focusing on how to make the outcomes equal, we will focus on the needs of underserved communities. This alone will do a massive lift in contributing to equity in healthcare.
In the next article we will look at innovation and how value-based care encourages innovation to improve healthcare in a myriad of ways.