Risk Stratification Is An Essential First Step In Reducing Health Inequity

The health equity journey begins with stratification

With the pandemic pushing long-neglected racial and socio-economic health disparities into the public eye, momentum is building to more effectively deploy population health management strategies in pursuit of health equity.

Together and alone, payers and providers are working to expand and enhance healthcare data collection and analysis via stratification, or the collection and collation of granular patient data. The goal is to identify cohorts of at-risk patients and then develop targeted interventions that can enhance prevention, strengthen adherence, improve care coordination and outcomes, and boost quality of life.

What is healthcare data stratification?

Stratification involves slicing patient data by age, race, gender, ethnicity, language, sexual orientation, disability and other variables, including socio-economic conditions, or social determinants of health (SDoH). Factors such as employment, housing, economic stability and education can serve as reliable markers for disease propensity as well as indicators of potentially substandard access, care and outcomes.

Why is healthcare data stratification difficult to implement?

The task of collecting and classifying the information required to illuminate and address health inequity is monumental. Significant technical challenges surround the integration of data from multiple sources and systems. Uncertainty exists about the respective roles of payers and providers. Major questions also involve the governance and security of information collected. 

Yet it is clear the issue has become a top national priority. The National Committee for Quality Assurance (NCQA) and Centers for Medicare and Medicaid Services (CMS) are committed to developing mandatory equity requirements. Additionally, alternative payment models are beginning to incorporate SDoH risk factors to incentivize both payers and providers to support health equity. 

The benefits of healthcare data stratification in promoting health equity

Beyond the moral imperative of addressing care disparities to improve quality and outcomes, these efforts can also help decrease the cost burden of chronic diseases, which collectively account for a staggering 84% of all healthcare costs. Payers that haven’t yet begun to develop a comprehensive stratification strategy consequently may want to look at the steps they can take today to align with both the spirit and letter of emerging equity efforts.  

Pervasive, egregious differences in care access and outcomes

COVID-19 has laid bare pernicious health disparities in the U.S. Multiple studies have shown the pandemic has disproportionately impacted individuals with low socioeconomic status, including Black, Indigenous and Hispanic people, resulting in higher rates of infections, hospitalizations and deaths.   

These inequities reflect longstanding disparities in access, care and outcomes experienced by many minorities when compared to White Americans. To a greater or lesser extent, the disparities affect virtually all minorities and run the gamut from access to outcomes. 

African Americans, for example, have the highest mortality rate for all cancers combined when compared with any other racial and ethnic group. They also suffer from hypertension at a rate of 42% versus 29% or non-Hispanic Whites. The infant mortality rate for Black Americans is almost twice the national average. Hispanics are almost three times as likely to be uninsured as Whites, and Hispanic women are 40% more likely to have cervical cancer than White women. Among Asian Americans, tuberculosis was 35 times more common in 2017 than among Whites. 

Emerging health equity mandates

To begin addressing these gaps through patient data stratification, new NCQA Health Effectiveness Data and Information Set (HEDIS®) measures will require health plans to identify members by race, ethnicity and language diversity across commercial, Medicare and Medicaid lines beginning in measurement year (MY) 2022. NCQA has proposed expanding the requirement to five more measures for MY 2023. 

CMS  has articulated six priorities for reducing health disparities, the first of which is an expansion in the collection, reporting and analysis of standardized data. The agency is in the process of refreshing its overall equity plan for Medicare, and already is implementing rules to incentivize providers to decrease health disparities, most notably in the area of treatment for end-stage renal disease (ESRD). 

Envisioning a patient data stratification ecosystem 

Developing an effective patient stratification strategy hinges on data collection, since the initiative’s success will be directly linked to the breadth and depth of the information assembled. Data ecosystems should therefore be designed to unify a wide range of data sources to generate the most holistic picture possible of patient needs.  Key information sources can include: 

Medical claims codes

With medical claims, particular attention should be paid to ICD-10 Z codes, which hospitals can use to identify social determinants, such as education and literacy, employment, housing, lack of adequate food and water, and occupational exposure to risk factors. Although Z codes have been available since 2016, many providers aren’t using them due to uncertainty about who can document social determinants and under what circumstances. Payers should work with their provider networks to develop consistent policies and even incentives for increasing Z code reporting. 

Patient electronic health records

Payers should also explore arrangements that would provide access to providers’ electronic health records in pursuit of greater detail about specific patients and encounters. Developing arrangements to access information from non-affiliated clinics likewise can help create a more complete picture of a patient’s health and underlying social determinants.

Other sources of patient data for stratification

Payers need to look beyond their network providers to identify and seek out additional data sources. These can include:  

• Patient self-reported data, including outcomes and SDoH information via a standardized screening tools
• Community health assessments
• Screening data from social, public health and community-based services
• Wearable device data

It is a given that ensuring a diverse, robust and up-to-date dataset will require collaborative partnerships between payers and providers, community agencies and other entities committed to mitigating health inequity. Whether the payer, provider or some other organization ultimately leads this effort is secondary to the creation of effective infrastructure for sharing, collecting and analyzing patient data.

Health IT makes data more available

As for the latter task, dramatic digital health advances have been made in recent years in the tools available to analyze data. Machine learning and artificial intelligence applications are being deployed to much more rapidly search both structured and unstructured data in pursuit of highly specific information. Organizations that don’t possess these capabilities internally can access them as software-as-a-service via the Cloud. Alternatively, they can go a step further and retain qualified vendors for help in establishing, implementing and managing their entire population health initiative.  

Prioritizing the elimination of systemic health inequities

The goal of reducing and eventually eliminating health inequity admittedly represents an immense, and some would say, impossible challenge. The problem’s roots are tangled and deep and extend in every direction across our society. 

Achieving health equity therefore will require far more than population health and stratification. Yet by beginning to tackle historic inequity at the point where it most critically appears, within the healthcare system, incremental but life-saving change is possible. As Martin Luther King Jr. so poignantly noted:

“Of all forms of discrimination and inequalities, injustice in health is the most shocking and inhuman.”

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