As companies seek to make health plans more impactful and efficient, analytics can reveal hidden pockets of opportunity.-Joseph Warren
Employee wellness plans and health benefit plans have been growing more customizable over time. Yet even the most robust suite of cafeteria-style offerings can fail to appropriately and efficiently deliver care solutions and support. To bridge this gap, and continue the ongoing trend toward more individualized and cost effective care, organizations will want to combine health data analytics with social health insights to design truly tailored and impactful care solutions.
Social health insights, formally Social Determinants of Health (SDoHs), can be linked to as much as 50 percent of a person’s overall health and health outcomes, according to the Institute for Clinical Systems Improvements. This makes them a much stronger lever for potential changes in outcomes and efficacy than health behaviors (30% impact) or even access to professional medical care (20% impact). In the paragraphs ahead, some of the ways SDoH data can be used to better serve specific populations, to give insights on access and use of health systems, and to manage compliance to Care Guidelines will be discussed.
While companies have typically limited their window of focus to employees, expanding the focus to employees’ communities can provide multiple opportunities to tailor health care and wellness programming in more nuanced and effective ways. Medicare and Medicaid groups, along with many hospital networks, are already making SDoH a part of their planning and referral systems. Yet when private employers join in or add onto these efforts, it opens up entirely new possibilities.
For example, it’s well known that certain parts of urban and near-urban America are essentially “food deserts,” with few healthy food options available. Workers from these areas may thus be challenged to maintain a healthful diet, even if they’d like to do so. This can lead to obesity, diabetes, high cholesterol levels, and many other chronic health conditions that link back to the convenient availability of fresh, healthy food options in one’s local neighborhood. So, if an employer runs analytics on its workforce and notices many nutritionally linked health problems and cross-references the workforce against known food desert areas, it can uncover an opportunity zone that might otherwise be missed.
What might a data-driven solution look like? In Michigan, Lansing’s Eastside Community, a known food desert, saw its Allen Neighborhood Center campaign among area employers to sponsor the renovation of a community center so that it could accommodate a standing farmer’s co-op. Employers who invested in the opening of this store could then coordinate their wellness programing to incentivize buying organic foods from the new co-op, and some even sponsored weekly food boxes of in-season produce to be delivered to their offices for team members. In this way, the overall health profile of the neighborhood went up, which is expected to lead to lower healthcare costs for employers and fewer absences due to chronic health issues among family members.
How SDoH data illustrates who accesses and uses health systems
Along with recognizing that employees based in Lansing’s Eastside Community lacked access to fresh, healthy food shopping, SDoH analytics revealed that many members of the community were heavy users of the local emergency room. However, data analysis showed that many of the visits were for health issues that did not drive other parts of the city to the ER. What was the difference, and how could SDoH data lead employers in the community toward a more cost-effective and supportive solution?
One issue specific to the Eastside was the presence of a large downtown hospital but few urgent care type clinics. Those that were open were often accessible only by car, whereas the hospital could be reach easily via bus. Few were open outside of traditional work hours, while the hospital ER was guaranteed 24/7. Since many Eastside residents worked manufacturing shifts and were heavy users of public transport, SDoH combined with utilization data indicated that opening an urgent care center with second-shift friendly hours on a bus line might shift residents away from expensive ER visits and toward more cost-efficient care solutions.
Where SDoH data intersects with compliance to Care Guidelines
Building on these insights, it is possible to see where SDoH data intersects with compliance to Care Guidelines. In a community that’s a food desert, compliance with dietary recommendations is likely to be low. In communities with high ER usage and limited clinic access, keeping up with routine testing, screening, and vaccination schedules is likely to be low.
It is, once again, a time where health analytics and SDoH can shape company spending. Investing in on-site nurses or time from a mobile clinic could make a dramatic difference in the number of employees who get follow up testing or maintain care schedules. This in turn helps employers mitigate the effects of flare-ups of chronic conditions and the costs of severe cases of diseases that could be caught and controlled with timely screening.
Concluding thoughts
Firms that want their health plans and health spending to do more need to expand their focus beyond the narrow window of their employee pool. Workers’ communities and the SDoH associated with those communities is one of the largest levers organizations can pull to change overall health and specific individual outcomes. By paying attention to health data layered with SDoH information, firms can see how to better serve specific populations, which parts of the health care system their employees are accessing (and why), plus the likelihood that employee groups will comply with care guidelines for their conditions based on their home environments. It is definitely one step further than most organizations have gone in the past, but truly an area where the results can have both immediate impacts and long-term positive benefits for employers and employees alike.