Government health agencies are often tasked with the impossible: To keep up with the crushing demands of operating a multi-billion-dollar health program while also attempting to navigate complex policy choices with myriad invested stakeholders. The result, understandably, is that systemic changes happen deliberately. Shifting healthcare payment and delivery systems is turning the Titanic, not steering a sports car.
That’s true of our concept of social determinants of health (SDoH), where government health agencies have known the importance of social, environmental, and behavioral factors on our health, but are only in the early stages of implementing strategies to address those challenges. And it’s also true of health equity, where they have understood that black, indigenous, and other people of color have poorer health outcomes than their white peers, but are only now getting around to the business of collecting the right data necessary for making evidence-based decisions to close these gaps.
There are a lot of reasons for the government's deliberate pace of change. It takes time to invest in new data collection tools; money to drive efforts to change delivery systems; and political will to broaden the boundaries of how we define health and who pays for it. But technology should never be a blocker; it should be an accelerator that moves us from knowledge to action, arming us with insights that enable us to make data-driven decisions at scale.
SDoH: It’s Time to Understand the Causes of the Causes
The National Academy of Medicine highlights the fundamental role of SDoH in health outcomes, stating that medical care only makes up 10–20% of the contributors to health outcomes. The vast majority of the factors that influence health outcomes occur outside the walls of hospitals and clinics. And yet the vast majority of our healthcare infrastructure and our healthcare system has been built to address the 10%-20%.
In short, we’ve created a system that is built on people being sick. Prevention and wellness efforts often only begin in earnest with a diagnosis. And even then, the attendant care plan is replete with medication regimens, check-ups, and care coordination efforts targeted at a diagnosis, not a person. Our attempts to address this challenge through strategies like value-based payments, which are built to reward positive outcomes rather than service volumes, have created unintended consequences.
For instance, a recent analysis of three core CMS Innovation Center models found instances of implicit bias, which disproportionately impacted people of color and low-income individuals. As an example, providers participating in the Comprehensive Care for Joint Replacement Model may have been incentivized to make fewer offers of joint replacement surgery to Black and low-income patients because they are more likely to be discharged to a skilled nursing facility or rehab facility, which are associated with higher hospital readmission rates.
CMS’ analysis highlights an important step toward recognizing and achieving equity, as well as addressing SDoH: It’s necessary, but not enough to collect race, ethnicity, and language (REaL) data. Standardizing the collection of this information is critical to recognizing disparities, identifying vulnerabilities—at a population and individual level—and improving both care quality and outcomes, through the equitable allocation of resources. This data helps to stratify performance measures, highlight where inequities exist, and develop data-informed policies and interventions.
However, this data still doesn’t provide the full picture. A person’s ethnicity does not necessarily convey that they live in a food desert and only have access to processed foods, starches, and sweets. A person’s race does not necessarily tell us that they’ve suffered long bouts of toxic stress that have created an inflammatory response or epigenetic change. Nor does a person’s language necessarily give us an insight into how their housing insecurity made them hesitant to undergo chemotherapy treatment.
To bridge this gap, REaL data must be paired with SDoH assessments to create a true “whole-person view.” The next step is to move from data to action, that is, by leveraging meaningful insights to make informed decisions. That’s why investments are being made in technology to help improve the unification and analysis of population health data, factoring in SDoH data, to create longitudinal records to drive actions toward making health services more accessible and affordable.
Social Determinants: The Building Blocks to Improving Care Delivery and Health Outcomes
In a recent webinar, “Digital Transformation of Health and Human Services” led by Innovaccer, the company’s Senior Director of Government Health, Brandon Greife, highlighted the importance of SDoH in shaping Medicaid programs and care delivery as a whole. He explored how the pandemic has had a profound impact on Medicaid programs and has accentuated the need for addressing the long-standing disparities in health outcomes among marginalized and historically underserved communities.
“[The pandemic] gave us a tremendous push, not just to understand the importance of things like social determinants of health, but to actually get on with the business of doing something about it,” Brandon also added, “I think SDoH has long been a buzzword that we’ve understood to be important. I think only now are we beginning to wrap our heads around what we do with that information. How do we deliver services differently? How do we incentivize outcomes differently?”
Federal and state governments have traditionally had difficulty with implementing SDoH-focused programs because they haven’t had the means to quantify the problem. In general, but especially in government, you can’t improve what you don’t measure. And historically, governments have not had the capacity to measure health holistically because they haven’t had the right data to do it.
“As government agencies increasingly understand and prioritize whole-person care, they’ve begun to fill in this data gap by implementing standardized SDoH assessments, incentivizing the collection of key social and demographic data, and investing in critical technology like closed-loop referral systems. Although the impact won’t be immediate, the data foundation that is being built will be critical to designing, building, and overseeing programs that purchase health, not just health care.”
Joining Brandon on the webinar, Mary-Sara Jones, principal business development manager at Amazon Web Services (AWS), discussed how San Diego County did an analysis that identified three behaviors—smoking, exercise, and diet—that drove four chronic illnesses responsible for 50% of preventable deaths. That data allows governments to focus resources where they can have the most impact and also enable them to make smarter decisions.
“What is the Medicaid goal your organization is trying to achieve? That should be the starting point and the North Star, if you will, for managing that data and the governance across its life,” Mary-Sara said.
“Creating robust methods for data creation must be paired with a strong vision of making the most out of that data.”
Equity and whole-person care are now becoming those North Stars. Setting those priorities upfront enables us to design data collection systems, value-based payment structures, and policies to achieve those goals.
SDoH Data: A Catalyst for Change
Building on the importance of SDoH, Paul Grundy MD, chief transformation officer at Innovaccer and a panelist in this webinar, cited an example of one of Innovaccer’s clients that intelligently harnessed the company’s Data Activation Platform—the #1 healthcare data and analytics platform according to KLAS—to gather SDoH data and understand health trends.
The health system was seeing a significant uptick in the hospitalization of Type 2 diabetics whose blood glucose level had fallen to a dangerous level. That type of information is interesting, but Innovaccer worked with the customer to drive toward something actionable.
“When we began to really dive into the data around that particular group of people who were being hospitalized, what we found was a population in a few zip codes and a time period that corresponded with the end of their pay period,” Dr. Grundy explained. “We had identified a population that was running out of food, and if you’ve run out of food, you’re going to be hypoglycemic.”
Fortunately, hypoglycemia has a relatively simple and inexpensive treatment—eating some food to increase blood glucose in mild cases or glucagon injections for severe episodes—but in the emergency setting, there is no such thing as simple or inexpensive. That highlights the importance of incentivizing a health system to move beyond treating individuals’ symptoms and introduce data-driven decisions to promote holistic wellness. In this case, once the health system identified the problem, they connected patients to FindHelp—the nation’s largest community resource search and referral network—to ensure appropriate referrals to food pantries and food banks.
The relatively simple act of referring individuals to a source of healthy food saved the health system thousands of dollars, but more importantly, it created a more positive experience for individuals who may have been stuck in a cycle of hunger and hospitalization.
These types of data-driven decisions are becoming prevalent across the healthcare landscape. They’re also becoming critical as we are increasingly recognizing the need to fundamentally change the financial incentives underlying our healthcare systems. As an example, states like North Carolina have undergone rigorous processes to develop definitions and fee schedules for services that can be used to address SDoH. Similarly, California received a waiver from CMS, allowing Medicaid plans to offer non-clinical “in lieu of” services that include things like housing support and medically tailored meals.
In parallel, CMS has recognized the challenge that profound inequities, rooted in intersecting SDoH, pose in truly achieving “value-based care.” They are actively working hard to avoid the pitfalls of pay-for-equity approaches that attempt to risk-adjust away disparities and are earnestly seeking feedback on how to remove potential bias and account for social risk factors in their value-based programs. In so doing, they are taking sure-footed steps toward leveraging data and designing rewards for equity-focused providers and plans that are providing high-quality care to underserved communities.
These critical innovations are beginning to incrementally shift financial incentives toward whole-person care while also recognizing that full-time healthcare providers cannot also be full-time social workers.
The Road Ahead: Accelerating Digital Transformation by Addressing SDoH
With the increasing interest of Medicaid agencies in service delivery models focused on whole-person care and health equity, there is a need to put data at the center of this transformation. Although Medicaid agencies have traditionally been data-rich enterprises, they haven’t always had the data readiness, digital maturity, and insights needed.
Claims and encounters, as examples, can tell us a lot, but they were fundamentally created to make sure a provider could be paid, not designed to capture information to improve population health. This makes data integration—and informed decision-making—across the healthcare ecosystem critical.
In the webinar, Brandon recommends government health agencies leverage advances in interoperability standards and progress in EHR penetration to make better use of clinical data, and Mary-Sarah highlights the untapped value of the data collected when referring individuals to community-based organizations. Both sources enable state health agencies to move from being sickness-centered and reactive to being whole-person-centered and proactive.
“I think we’ve been stuck in a rut of thinking about interventions based on retrospective analysis of already lagged claims data. We’re dealing with what happened a year ago and hoping that we can effectively influence policy that we want to implement a year from now,” Brandon argued.
“And I think things like clinical data really allow us to move up those decision-making timeframes and enable us to put the outputs of those analyses back into provider workflows, so we can see results much more quickly.”
No one data source can achieve health equity or whole-person care. A person–their hopes, their goals, their families, their trauma–is more than just the data we have about them. But for governments to make meaningful progress in creating a system that promotes wellness rather than treats sickness, they must begin to create a robust and integrated data foundation. That will take investment in technologies to unify and activate this data from disparate sources, enable interoperability, drive crucial insights, and incentivize equity and care delivery to realize health equity and whole-person care