Why SDoH Programs Fall Short — and What It Takes to Make Them Work

When SDoH programs fail, it’s not because the underlying idea is flawed. There’s plenty of evidence that the conditions in which people live, work, learn, play and age can affect their physical and mental health.

But when making funding decisions, health plans have historically had to rely largely on guesswork, making broad assumptions about which populations need the most help. And once programs are in progress, plans find themselves directionless, with no concrete way of determining whether or not any objectives are being achieved.

The underlying problem is the data itself. For decades, the industry has struggled to define social determinants of health (SDoH) in consistent, quantifiable terms. The data that’s been available has tended to be broad and imprecise, often captured at a neighborhood level rather than by person. If there is an individual level available, it’s usually incomplete, drawn from patient surveys or medical claim Z-codes, which makes it time-consuming and costly to obtain and aggregate.

Throwing more money at SDoH programs isn’t going to magically fix them. Instead, plans should be focusing on building out three specific capabilities that, together, can noticeably alter the playing field.

1. Making individual-level data the focus – Claims data has an inherent limitation: It only covers billed care. To get an accurate, comprehensive picture of a member’s health, that data needs to be integrated with two other data categories: clinical data (any information related to patient or medical care, such as laboratory test results, immunization history and prescribed medications) and social risk data, which comes from a vast variety of public and private sources and captures factors like financial strain, food insecurity, housing instability, transportation barriers, digital access and social isolation.nThis individual-level data, rather than broad demographic assumptions, provides the foundation that makes everything else possible.

2. Using stacked risks for precision targeting- Rather than trying to address broad problems with overly broad solutions, plans with a deep trove of individual member data can start to prioritize their efforts based on the largest drivers of care. Knowing that food insecurity affects a large population isn’t nearly as useful as knowing about an individual who has food insecurity, a high-risk pregnancy and a lack of reliable transportation. These stacked risks help answer the question of why members whose clinical data is very similar can have very different cost trajectories. From there, individuals with the highest risk scores can be grouped together in cohorts to receive the care they need in the most efficient possible way.

This targeted strategy is demonstrating proven results on a national level. Earlier this year, the Centers of Medicare and Medicaid Services (CMS) issued a report on the Accountable Health Communities Model, which was initiated in 2017 with the goal of reducing health care expenditures by connecting beneficiaries to community resources related to their core needs. The study found $200 million in net savings among more than 1 million beneficiaries, largely from reduced hospital stays and emergency room visits. The biggest reductions were among members with multiple, complex needs of the kind that individual-level data is designed to uncover.

3. Proving effectiveness through a closed feedback loop – Health plan CFOs looking for an expected ROI over a specific time horizon often skip the crucial step of verification, so they have no way of knowing if a program is working. This, more than any other factor, is why programs tend to lose funding. It’s also the reason why plans don’t undertake broad programs to address multiple intervention strategies, instead emphasizing more focused strategies such as “food is medicine” programs in food deserts.

Even when plans are ready to act, two additional barriers often slow progress: getting the right data to the right intervention programs through interoperable systems and pre-established APIs, and then closing the loop to measure the true impact on members’ health, cost and utilization over time. Without that feedback mechanism, even a well-targeted and effective program has no way of proving its success, hindering its ability to get additional funding or even continue at its current level. 

Confidence over guesswork

The major advantage of this three-step SDoH approach is that it replaces guesswork and allows health plans, regardless of scale, to allocate available resources with a high degree of confidence. Knowing which individuals to approach and why, focusing on targeted interventions, then monitoring the overall impact on those members’ health, cost and utilization trends finally provides the true ROI that payers have been looking for. Medicare Advantage plans can find value as well by integrating SDoH in their care management strategies to identify quality improvement opportunities and drive reimbursement.

The biggest benefit, ultimately, is realized by health plan members, who receive the individualized assistance they need to overcome barriers to care and improve their quality of life.

Photo: Andrew Bret Wallis, Getty Images

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