28 Feb SDOH, Doncha Know: Innovation lessons from Minnesota’s Medicaid ACO program
Addressing the social determinants of health has emerged as a key trend in health innovation. Health insurers, hospitals, and governments are rolling up their sleeves to test and refine capabilities to identify and meet the social needs of their communities, from the population level down to the individual.
To this end the biggest frontier for innovation is Medicaid, for three reasons.
- Medicaid is the biggest health insurer in the country, covering over 74 million lives — that’s one in five Americans.
- Medicaid serves as the official program for the underserved. Addressing social determinants is especially important among people with low income, disabilities, or other living conditions that aren’t purely clinical in nature.
- As a publicly run program, Medicaid is the perfect sandbox to test new approaches to multi-sector partnerships, payment, data management, and other key levers to develop healthier, more responsive communities over time.
Unfortunately, all of that doesn’t make Medicaid an easy space to work in. To wit, check out the topics listed under one of the challenge areas in a recent industry survey, whose respondents were half-comprised of health plan executives.
So, how are the state-level agencies that administer Medicaid programs faring in their work with health plans, care providers, and community organizations to address SDOH? A recent webinar by CHCS delved into some early takeaways from Medicaid ACO programs, with deep dives into both Minneosta and Rhode Island’s approaches. In addition to the above bullets, the business mandates of Medicaid ACOs to manage the total cost and outcomes for their vulnerable populations represents the rubber of SDOH strategy hitting the road of the real-world.
We’ll focus the rest of this recap on Minnesota’s story, which is at a particularly important juncture.They’ve reached the critical stage of “knowing what they don’t know” in terms of how partnerships, data, payment, and reporting need to be structured.
Minnesota’s IHP program: An early template for SDOH
The North Star State is entering the fifth year of their Medicaid ACO, called the Integrated Health Partnership program (IHP). The state’s Department of Human Services (DHS) has successfully grown the original 11-site, 100k patient population into a 24-site program covering 460k people. Over this time, they’ve also achieved some notable outcomes: $213m in cost savings, 7% and 14% reductions to emergency department visits and hospital stays, respectively.
As their program manager Mat Spaan explained, this earned them some agency swagger; they were able to garner the cachet and backing needed explore making new tweaks and expansions to the program. In 2018 they’ve launched the “2.0” phase of the program, which expands clinical focus to address SDOH. 13 of the 24 IHP sites are participating — voluntarily — to try to achieve more cost savings by doing more for their membership beyond clinical care.
After reviewing the basic financial risk and payment setup of IHP 2.0 contracts (see slide 21), the session went into several of the new program enhancements that address SDOH.
Minnesota requires in writing that 2.0 participants have formal partnerships in place with community-based organizations (CBO), like food banks, housing placement services, and other social service providers. Moreover the IHP must structure these partnerships sustainably, such that a health system isn’t sending more patients out to CBOs without first ensuring they have adequate resources to scale their operations. A little bit of common sense never hurts.
Metrics for Health Equity
These are measures of SDOH-related performance that earn IHP sites their population-level payments from the state. This represents one of the trickiest, unknown areas for any organization implementing a SDOH program, because there aren’t commonly accepted or standardized metrics to gauge efforts, let alone outcomes, of connecting people with resources. Minnesota’s DHS was quick to offer two caveats up front.
First, in the first year of this effort, these metrics are being developed as they go. For now, they are “tied to idiosyncratic interventions that the IHP are doing, based on certain target populations, or based on specific issues that their communities might be experiencing.”
Second, the health equity metrics are generally process oriented at this stage, rather than outcomes focused. This means that instead of requiring health systems to follow patients through the referral to a social program (which would require data collection capabilities, well-defined metrics, and other standardization in the trenches of the community that frankly just don’t exist yet), they require basic checklist measurement of actions taken, similar to how HEDIS measures gauge cancer screenings or flu shots.
A few quick examples: To address their local opioid crisis, health equity measures might look at the number of ‘closed loop’ patient referrals, or the number of patients assigned a specific substance abuse care plan. For food insecurity, IHP’s partner with food banks in their communities through a food prescription model; these measures might look at the number of diabetics or patients with documented food insecurity who get support in the form of food boxes.
Social Risk Adjustment
Another critical issue related to the measurement of SDOH involves how to gauge how needy a specific population is. If 100 people are mostly healthy, and another 100 have documented chronic disease, it makes sense that the latter group should cost more to care for — this is generally called a risk adjustment. Similarly, patients with unmet needs require more funding to support — the tricky part is, those needs are often unmet because they’re un-diagnosed, unacknowledged, and misunderstood by both patient and caregiver. While US health systems have become quite good at using clinical data to customize risk, the same is not true for data of the socioeconomic, behavioral, or non-clinical variety.
Minnesota started with in-house research to identify the factors with the most impact on disease prevalence, poor outcomes, or cost. This was done through the medical director’s office, funded by the occasional grants or random pocket of funding through the legislature. Their approach combined existing research and risk scoring models used nationally (e.g. Johns Hopkins ACG model) with data they obtained from the community, other state agencies, and other means. Finally, they crunched these data into an equation, working with their actuaries to see where social factors are prevalent next to other medical factors, diseases, diagnoses, or demographics.
There are many areas that DHS just doesn’t have good data on. With food insecurity for example, they could obtain basic geographic patterns, but not any individual level data, which maked specific risk calculation challenging and led them to leave specific risk adjustment for this domain out of the final calculations at this time.
Challenges abound — How can the private sector help?
While the efforts in Minnesota are in their early days and weeks, some major pain points have already become clear. Social determinants are tough to quantify and assess, in part because of a lack of metrics, and in part because this is a new frontier for the health systems doing the heavy lifting to transform their care delivery models based on new types of data, community partners, and other moving parts. The challenges on the measurement front get amplified on the management front, where reporting, accountability, and reimbursement remain largely open questions as they relate to SDOH.
More background about Minnesota’s approach is available in this topic paperfrom the Robert Wood Johnson Foundation, including details on how they identified vulnerable populations and quantified them using the data they had available. As explained therein, the risk adjustment issue represents a major policy speedbump that could make or murder the ability of innovative SDOH programs like Minnesota’s to break even financially:
“Without a corresponding adjustment in their payment models, states may financially penalize Managed Care Organizations (MCOs) and ACOs for caring for people with significant social challenges, or for creating innovative programs to meet the needs of these individuals. By taking steps to adjust payments for SDOH, states can effectively encourage providers to innovate and develop better services — including services that go beyond health services — for people who often experience poor outcomes.”
The good news is that the data science behind risk adjustment, predictive analytics, and multi-sector data mashing might be even more advanced outside of the clinical realm. From online advertising to polling prediction, we entered the big data era long ago from a technology perspective. Healthcare systems around the country have started turning to novel data management tools to understand more about their commercial populations, with an eye on back-end efficiency for supply chain and operations, and front-end customization for the era of consumer-driven care.
The worlds of clinical care delivery, public health, and health IT continue to converge. Over the coming years, we should expect some forward-thinking Medicaid ACOs and other organizations implementing SDOH programs to leverage these new approaches to address social needs of their communities.