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February 19, 2018
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Karen Handmaker
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Economics Outcomes
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Reimbursement Follows Respect: Leading States Pay to Address Social Determinants of Health

For too long, the “Social Determinants of Health” (SDH) have been the big elephant in the exam room. While no one denies that a diabetic living alone in a food desert is less likely to reduce his A1c than someone with a support system and easy access to healthy food, healthcare providers are held to the same quality goals for both patients.

Compounding the problem, in our entrenched fee-for-service world, only medical services like physician services and lab tests are billable; salaries for community health workers and prescriptions for food are not. Even new “alternative payment models” like Accountable Care Organizations and Patient-Centered Medical Homes don’t explicitly fund “quality improvement initiatives” designed to address SDH in individuals or populations.

A few states, such as Massachusetts and Minnesota, are beginning to incorporate SDH factors into their payment models. Money talks—paying up front for risk indicators such as housing instability and neighborhood stress gives real cred to these key SDH.

Awareness of the effects of SDH continues to build beyond front-line providers and population health experts throughout U.S. healthcare sector. Even as far back as 1854 John Snow used community assessment factors to identify the source of the cholera epidemic. While there are many reasons today’s healthcare system lags in addressing this elephant, the data provide an inescapable connection: Now we know that one’s zip code (“ZNA”) has a greater impact on health than one’s genetic code (DNA). [1]

At these early stages of the seismic shift to value-based care, its focus on improved outcomes at lower cost demand that we include factors beyond what we’ve used. The rising prominence of SDH parallels the move toward value-based care because solving for SDH is integral to improving health outcomes.

Since over 35% [2]of the U.S. population (more than 113m people) receives care through publicly funded programs, the government and society have a significant stake in improving the exogenous factors that impact health, such as air quality, public safety, education, food access and affordable housing.

It’s no surprise that Medicaid managed care is at the forefront of activity around SDH interventions. States face ballooning Medicaid budgets and they’re signing more risk-based contracts with health systems and managed care organizations. With the financial incentives flipped from volume to value, these contracts encourage investment in SDH innovations.

The shift to value creates an “aha” moment for SDH. As we say at 4sightHealth, value follows payment. Payment based on volume that’s limited to billable medical services for individuals creates high spending on medical services for individuals who can pay. Payment based on results, such as improved health for populations, initiates programs that address the causes of poor health outcomes. You get what you pay for.

Medicaid programs in Massachusetts and Minnesota are jumping into the transformation by including SDH in their payment models. [3] And big-data analytics are fundamental to their approaches.

Let’s look at Massachusetts as a bellwether. With the leverage of an 1115 waiver and the guidance of Dr. Arlene Ash and her team at the University of Massachusetts, the state Medicaid program now incorporates “unstable housing” and “neighborhood stress score” factors into the risk-adjusted capitated premium for Medicaid ACO enrollees. This is a first among Medicaid programs.

Dr. Ash says, “the risk-based payment model for MCOs and ACOs that we built for MassHealth now adjusts not only for age, sex and medical diagnoses, but also for social risks (including disability, housing problems and neighborhood indicators of economic stress). We’re proud of that refinement.” [4]

Consider details from the new MassHealth risk-adjustment model.

  • Unstable housing means 3 or more addresses in a year
  • “Neighborhood stress score” takes into account:
    • % families with incomes < 100% Federal Poverty Line (FPL)
    • %<200% FPL
    • % adults unemployed
    • % households receiving public assistance
    • % households with no cars
    • % single parent households
    • % adults 25+ with no high school degree

Two important components make the MassHealth changes more remarkable to observers.

  1. None of the factors in the stress score or the unstable housing data come from traditional sources of data for risk-adjustment or capitation rate setting in healthcare, such as medical claims and EMRs.
  2. MassHealth will rely on a combination of new data sources and big-data analytics capabilities to do proactive risk-adjustment, risk stratification and care management interventions. Further, without big-data analytics power, MassHealth can’t measure the results of this payment model accurately.

In 2015, the Minnesota legislature directed its Medicaid program to develop a model that would increase payments for enrollees living with factors that create health disparities. “Health care disparities are essentially SDOHs ‘in action,’ meaning disparities in health outcomes are often evidence of underlying social and economic risk factors.” [5]

Like Massachusetts, Minnesota is also using sophisticated big data analytics to determine which SDH factors should be included in its Medicaid payment models. To do this, the researchers are including a wide range of non-medical data such as homelessness, immigration status, primary language, number of children in the household, and involvement of child protective services to assess their impact on health outcomes. In 2018 the Minnesota Medicaid program will work on ways to incorporate the results into future payment models and interventions.

Despite knowing for decades that SDH affect population and individual health outcomes, addressing these factors has always been outside of how providers on the front lines of healthcare get paid. With the payment model turned upside down to value, our American innovation engine has now revved up to develop payment models that take SDH into account up front, giving providers some room to invest in here-to-fore grant-funded or unfunded programs to address health disparities. The elephant in the exam room is now coming into full view and making its weight felt.

Massachusetts and Minnesota are leading the way in paying providers to succeed with the highest risk, most vulnerable populations. At 4sightHealth, where we know payment drives value and outcomes, we’ll be rooting for their success. 

 

SOURCES

  1. https://www.salon.com/2016/11/12/how-zip-codes-have-an-impact-on-health/
  2. https://www.kff.org/other/state-indicator/total-population/?dataView=0&currentTimeframe=0&sortModel=%7B%22colId%22:%22Location%22,%22sort%22:%22asc%22%7D
  3. https://www.healthmanagement.com/wp-content/uploads/SHVS_SocialDeterminants_HMA_July2017.pdf
  4. http://www.themedicalcareblog.com/risk-adjustment-interview-with-arlene-ash/
  5. https://www.healthmanagement.com/wp-content/uploads/SHVS_SocialDeterminants_HMA_July2017.pdf

About the Author

Karen Handmaker

Karen is an engaging population health management expert with a passion for new models and technologies to improve health and healthcare with the “consumer at the center.”
She is widely recognized for cultivating and maintaining strong, long term multi-level client relationships, strategic planning, thought leadership and industry knowledge. Karen is a recognized speaker, writer and trainer on population health management and primary care transformation. She earned admission into the IBM Industry Academy, is a NCQA PCMH Certified Content Expert, and a longtime member of the Population Health Management Journal Editorial Board.
Karen’s current areas of interest include integrating health and social care, enhancing personalized health and wellness through analytics and machine learning applications, championing and enabling market-driven products and services that produce measurable value across stakeholders.
Karen lived in Hong Kong for six years where she co-founded Fiscal Health, a first-of-a kind local healthcare consulting firm offering a range of services in managed care and health economics Karen received a BA in American Studies at Trinity College in Hartford, CT (Phi Beta Kappa) and her Master in Public Policy from Harvard University.

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