Pharmaceutical advancements have played a major role in improving the United States’ health and life expectancy. But ...
According to the American Heart Association, Black Americans experience a 45% higher rate of mortality from stroke and a 30% higher rate of mortality from cardiovascular disease compared to non-Hispanic White Americans. During the COVID-19 pandemic, Black/African American, Hispanic/Latino, American Indian, and Alaska Native populations experienced higher rates of hospitalizations and death compared to the White population. When looking at the impact of income on health, low-income Americans have a higher rate of heart disease, diabetes, and stroke. These are just a few of many examples of health inequity.
In addition to the human health toll, health inequity adds an estimated $320 billion per year in health spending in the United States. According to the firm Deloitte, if left unaddressed, health inequity could cost the US $1 trillion by 2040.
To address this health equity gap , the Centers for Medicare & Medicaid Services recently proposed the creation of a Health Equity Index.
In this article, we review:
- What is the CMS Health Equity Index?
- Goals of the CMS Health Equity Index
- Reasons for Developing the Health Equity Index
- Populations Covered by the CMS Health Equity Index
- Benefits and Implications of the CMS Health Equity Index
- Health Equity Index Challenges
- The Role of Medication Optimization in the CMS Health Equity Index
What is the CMS Health Equity Index?
The health equity index (HEI) is a CMS-created score to incentivize Part C and Part D health plans to improve care for the most vulnerable patients. The index will consolidate a subset of Star Ratings measures, such as the measures included in CAHPS (Consumer Assessment of Health Providers and Systems), into one score.
For each measure considered in each eligible patient population, health plans will receive a score between -1, 0, and 1, based on their performance. The health equity index will then be calculated using the weighted sum of points for all measures — using the Star Ratings measure weights — divided by the weighted sum of the number of measures used to calculate the score.
The final score will vary from -1 to +1. Only health plans who can be assessed in at least half of the eligible measures will receive a health equity index.
Health plans performing well on the health equity index will receive a reward, which will replace the current reward factor (for consistently high performance). The index will take into effect in 2027.
Goals of the CMS Health Equity Index
The objective of the CMS Health equity index is to promote equitable care by incentivizing health plans to focus more resources on effective interventions and perform better for their most vulnerable patients.
According to the Agency for Healthcare Research and Quality (AHRQ), equitable care is care that doesn’t vary in quality regardless of an individual’s personal characteristics (e.g. gender, ethnicity, geographic location, and socioeconomic status).
Through the health equity index, the CMS will encourage health plans to collect health equity data in a more systematic way, which is one of the priorities of the CMS framework for health equity.
Reasons for Developing the Health Equity Index
Addressing Historical Disparities
Health inequity has long been present in the United States largely due to structural racism that shapes the US healthcare system into a system disadvantaging racial and ethnic minority populations. For instance, in the 1950s, states were allowed to build racially separate and unequal health facilities. Federal health programs to care for the poorest patients were generally underfunded. Racial minority workers were usually kept in low-wage jobs which prevented them from paying for healthcare even when it was accessible to them.
Medicare and Medicaid were established during the civil rights era and constituted an important, although not perfect, first step to start addressing historical health inequity. Both programs provided health insurance to people who otherwise could not afford it and thus increased access to healthcare.
For decades, the programs have changed and tried to adapt in an attempt to better fulfill their mission to provide care to all populations. Yet, inequities exist.
For example, racial and ethnic minority patients are less likely to receive age-appropriate cancer screening compared with White patients. Minority groups are more likely to live in areas with shortages of healthcare providers. And Black patients living in nursing homes have a higher risk of poorer quality care than their White counterparts.
By incentivizing Medicare health plans to provide excellent care for historically discriminated populations, the health equity index can become an additional tool to decrease historical inequities.
Recognizing Social Determinants of Health
Social determinants of health (SDoH) are social factors that can affect healthcare access and outcomes, such as the environment a person was born in and live in, their ages, their sexual orientation, or their employment status.
According to the Centers for Disease and Infection Control (CDC), SDoHs also include economic and social policies, racism, climate change, and political systems.
Studies have shown that SDoHs can be associated with almost half of the deaths among working-class adults in the US, while medical care is responsible for 10 to 15 percent of preventable deaths in the US.
The health equity index will enable health plans to have a clearer picture of how SDoHs impact their members’ health outcomes. With that insight, they will then be able to focus their resources on interventions that can mitigate that impact.
Populations Covered by the CMS Health Equity Index
The CMS will establish the health equity index for:
- Members with disabilities.
- Members who qualify for low-income subsidies.
- Members who qualify for dual Medicare and Medicaid eligibility.
In the future, the CMS may consider including other at-risk populations such as members with a specific area deprivation index, a measure of a neighborhood socioeconomic landscape that factors in income, education, employment, and housing quality.
Benefits and Implications of the CMS Health Equity Index
The health equity index will enable the CMS to create standards for data collection and Medicare health plans to collect health equity data systematically.
A systematic collection of such data is crucial to identify where disparities exist. Even more importantly it can help focus the resources and interventions necessary to address disparities, measure progress, and hold health plans accountable to providing excellent care for all populations.
Having the health equity index will also empower patients to choose plans that provide excellent care, since the score could eventually be included in the Medicare plan finder.
Health Equity Index Challenges
Establishing the health equity index will help us understand and address many healthcare disparities but several issues may pose a challenge to the creation and use of this new measure.
Incomplete data reporting. One of the issues is that the CMS programs don’t collect all health equity data, due to limited authority and enrollees sometimes opting out of voluntary reported data.
The index will first focus on specific at-risk populations. But there are still many other groups of people who experience health inequities yet won’t be included in the initial health equity index assessments because of the lack of data. For example, the CMS doesn’t collect SDoH data such as income.
Lack of health equity data standard. Another challenge is that there’s currently no health equity data standard.
For Medicare members, the CMS receive their sociodemographic data from the Social Security Administration, while the states send information for people enrolled in Medicaid, the Children’s Health Insurance Program (CHIP), and health insurance marketplace programs.
Each program has its own variation of data standards and the lack of a common health equity data environment may be a challenge to the access, integration, and use of the information.
Lack of health equity data for subgroups of the population. There is limited health data on patient subgroups which prevents the understanding of health disparities that may exist between different subgroups and their needs. For instance, race-related data is often presented for non-hispanic white, Hispanic, African-American, Asian, and Pacific Islander individuals. Yet, one study that looked at cardiovascular disease and stroke burden amongst different Asian subpopulations, found significant differences between each subgroup.
Implicit bias in health data collection. While developing the health equity index, the CMS may miss some important data due to possible implicit bias in health data collection and analysis. For example, because there is a limited understanding of how sexual orientation and gender identity (SOGI) can shape an individual’s health, it’s been historically challenging for the CMS to develop standards around SOGI data that would help to understand and address the needs of individuals based on their SOGI.
The Role of Medication Optimization in the CMS Health Equity Index
The Link Between Medication Use and Health Equity
Medication use can be the source of health inequity and lead to an increased risk of mortality, morbidity burden, patient safety issues, and poor quality of life.
This inequity in medication-related outcomes often comes from patient-related factors such as education, language, and cultural beliefs about illness and medications.
Race and ethnicity can also contribute to medication-related health inequity by limiting access to medications.
For instance, Black patients tend to experience financial barriers to accessing prescription medications more often compared to their white counterparts (70% of the time vs 55%). And a study published in the Journal of Racial and Ethnic Health Disparities found that in Los Angeles County, Black and Hispanic patients are more likely to live in pharmacy deserts than White patients.
In a study published in the Journal of the American Clinical Pharmacy College, researchers found that among American Indian and Alaska Natives, individuals with Medicaid coverage and longer travel to their pharmacy were less likely to utilize clinical pharmacy services. But individuals who did use pharmacies were less likely to experience high blood pressure.
How Medication Optimization Improves Health Outcomes for Underserved Populations
Pharmacy is one of the most used healthcare benefits, and access to medications is essential to manage chronic conditions. Proper medication optimization can help reduce health disparities and positively affect health equity. Studies have shown that providing patient-centered medication optimization and counseling can have positive impacts on underserved communities and populations.
For instance, an article looking at the impact of a medication optimization on heart failure care in African American men found that it led to a smaller proportion of patients who received the intervention being readmitted to the hospital within 30-days, versus patients who didn’t receive the intervention (11.5% vs. 42.9%; p = .03). The intervention consisted of medication reconciliation, medication cost/formulary review, discharge medication counseling, self-monitoring resources, and a post-discharge phone followup.
In another study looking at hundreds of Black individuals with diabetes, researchers found that individuals who received a pharmacist-led intervention experienced a reduction in Hba1C (− 0.4%, p-value = .01).
In a study that measured the impact of bilingual pharmacy services on diabetes outcomes in a predominantly Hispanic population, authors found that the median HbA1C decreased from 10.55% (pre-intervention) to 9.1% (post-intervention) (p < 0.0001).
How can Arine Help Health Plans Achieve their Health Equity Objectives?
Identifying health disparities
Arine’s medication intelligence platform utilizes clinical data such as pharmacy claims and hospitalization history to create a 360 view of the patient. But it also integrates broader information including demographic, social, and behavioral data to understand the member population. Arine actively looks for disparities by analyzing the specific vulnerable subpopulations, including low-income and underrepresented member populations. In fact, Arine was the recipient of an AWS grant that supports organizations that are developing solutions to reduce health inequities. Arine is using the grant to evaluate how SDoH impacts medication-taking behavior to help maximize the benefit from medication therapy and improve patient outcomes.
Arine also looks for both underutilization of care and tests the performance of their predictive models for racial bias. To ensure that interventions are distributed equitably, the Arine team uses rigorous bias/fairness methodologies within its machine learning-driven patient prioritization engine.
Personalized Care that Integrates SDoH
Additionally, the platform creates personalized questions that help the clinical team go even deeper and uncover the reasons for medication-related problems. As a result, health plans using the platform can provide their members with customized care that truly takes into account all aspects of a patient’s life, and deliver this in a scalable manner. For instance, if a member lives in a low-income area and is nonadherent, the platform will prompt the clinician to ask about cost barriers and recommend lower-cost options.
This is exactly what SCAN Health Plan, a Medicare Advantage health plan client of Arine, did. Through implementing a multi-pronged approach, and understanding the unique needs of the blank and Latinx member population, Arine was able to implement powerful and timely interventions that in eighteen months eventually decreased the health disparities observed – reducing the racial and ethnic gap seen in medication adherence by 35%.
Arine’s platform uses predictive analytics to identify patients at greater risk of experiencing medication-related problems and most likely to be impacted by small changes to their medication therapies. The medication intelligence platform then prompts care team members with the most effective interventions before the patient experiences the medication-related problem. The platform can also be used to match care team members to patients based on language capabilities or cultural backgrounds as appropriate.
Finally, through a machine learning process, the platform learns which interventions have the greatest impact, and uses this to hone the selection of patients and the type of interventions recommended for a specific population. Doing so helps health plans improve their members’ health outcomes faster.
One of Arine’s clients was able to improve their scores in a low-income subsidy plan across several quality measures including statin adherence and A1C control measures, in seven short months. These quality improvements were achieved by addressing not just clinical needs, but also Social Determinants of Health, including language and transportation needs.
Reducing the Cost of Care
By addressing health disparities, Arine has been able to help health plans significantly reduce the cost of care for at-risk patients.
One Medicaid plan that partnered with Arine to reach its complex members was able to reduce hospital readmissions by 47% for its engaged member population, 40% of which were in rural communities. The same plan experienced a 15% reduction in the total cost of care.
Addressing At-Risk Populations
Other Medicaid clients of Arine have also experienced a significant decrease in prescription and medical spending after working with Arine to better support their members with behavioral health conditions. For example, one client was looking for a scalable and provider-centric solution that could impact member behavioral health outcomes and reduce the total cost of care. They engaged Arine to input social determinants of health data, medical, pharmacy, and behavioral health claims, and socioeconomic data for a 360-degree view of each member’s needs. They then used this information to identify recommendations for prescribers that highlighted the evidence-based need for medication change, including access issues, adherence issues, cost issues, or other drug-related problems, to allow prescribers to make informed decisions for member care. Through this program, the client realized a 45-55% reduction in behavioral health polypharmacy and significant annual savings per engaged member per year in behavioral health spend.
Health inequities take a huge toll on US lives and the resources dedicated to their care. By implementing the Health equity index, the CMS is leading the way in making health equity a priority.
The new index will incentivize health plans to start collecting and analyzing their health equity data more systematically and to bring the necessary resources to address identified health disparities.
With its patient-centered medication intelligence platform that integrates SDoH data sets, Arine can support health plans in providing excellent care to all their members.