Why Looking Beyond Community-Level Data Is Key When Addressing SDoH

   

Community Data

With reams of new research being published solidifying the relationship between social determinants of health (SDoH) and public health, it's clear that more needs to be done to screen patients for potential social risk. But that's easier said than done. The U.S. healthcare system is under enormous strain and will continue to be until the COVID-19 pandemic recedes. Payers, providers, and community-based organizations (CBOs) alike are facing increased workloads, and each health plan has its own distinct strategy in place for identifying which social risks to screen for and how to incorporate the results of screenings into the clinical workflow.

That's why, given a lack of time for screening individuals for social risks, a number of organizations have been exploring the use of publicly available community-level data to identify those patients who are most vulnerable to SDoH. In these instances, payers and providers use the community-level findings to determine the social and economic risks that could affect a patient's health based on whether they live in a "cold spot"—those census tracts that have been shown to have more poverty, poorer education, and high levels of social deprivation.

But can community-level data accurately identify social risks at the patient level? 

Only about 48 percent of the time, says a new report from OCHIN published in the Journal of the American Medical Association Network. In their cross-sectional study published last fall, OCHIN researchers looked at two years of patient-level data from social risk screenings included in the electronic health records of 36,578 patients across a national network of community health centers.

Of those patients, 29.7 percent screened positive for one or more social risks—but of those 10,858 patients, 42 percent lived in neighborhoods that were not defined as disadvantaged cold spots.

So while there is significant overlap present between community and patient data, basing healthcare decisions for individual patients based on community-level data means that more than half of patients who would benefit from SDoH interventions could slip through the cracks.

What does this mean for healthcare organizations looking to strengthen the role of social needs assessments in healthcare delivery? Let's unpack three key findings from this important report.

Incorrect assumptions about social risks are prevalent

Sure, the idea seems simple: Use community-level data to measure the socioeconomic status and health of a population as an alternative way to identify patients with social risks. But therein lies a problem known as an ecologic fallacy—where incorrect assumptions are made about an individual based on aggregate-level information about a group of which they are a part.

The OCHIN study found that ecologic fallacy was indeed at work: Despite some overlap between cold spot status and patient-level social risks, the application of community data could cause healthcare providers to miss 40 percent of patients at social risk who lived in more affluent areas, and 57.1 percent of those who lived in cold spots.

So what can be done here? How can payers and providers leverage the value of community data without allowing more than half of patients to potentially fall through the cracks? One answer may be more effective screenings for social needs. 

Payers and providers should explore some of the healthcare technology solutions available that remove a lot of the friction healthcare organizations are currently experiencing—from the Accountable Health Communities (AHC) Health-Related Social Needs (HRSN) Screening Tool and the Protocol for Responding to and Assessing Patients' Assets, Risks, and Experiences (PRAPARE) methodology to robust digital portals that allow for highly customized screening criteria. Not only are these strong paths forward for mixing patient- and community-level data, such methods for social needs assessments can be easily completed by either the patient or anyone assisting them in their search for social services.

Despite the risk of inaccuracy at the patient level, community data is valuable for other purposes

Although community-level data can't speak to patients' needs alone, it can still provide the healthcare system with valuable insights—not to mention substantial ROI—if used in the right ways. Identifying the SDoH prevalent in a community, especially when combined with other social data, can provide critical contextual data that informs how care is delivered. Such data can also inspire community discussion and policy interventions, giving advocates and policymakers key data to use to improve social conditions and help develop value-based payment structures or create new opportunities for risk adjustment.

The U.S. healthcare system could also approach integration for community-level data using successful strategies implemented by other countries. The United Kingdom and New Zealand have used community-level data not only to measure socioeconomic variation across communities and assess community needs, but also to inform research, adjust clinical funding, allocate community resources, and determine policy impact.

There are also a number of localities across the U.S. finding successful paths for integrating community data. Hennepin Health—a safety-net accountable care organization (ACO) serving Hennepin County in Minnesota—leveraged community data to redesign their healthcare workforce and improve coordination among the physical, behavioral, social, and economic dimensions of care for an expanded community of the county's Medicaid beneficiaries.

By focusing on SDoH and preventative care rather than solely on the treatment of acute illness, the new program had a positive impact in shifting care from hospitals to outpatient settings:

  • In the first 12 months after launch, ER visits decreased 9.1 percent while outpatient visits increased 3.3 percent.
  • An increasing percentage of patients received optimal care for diabetes, vascular, and asthma.
  • Hennepin Health realized considerable cost savings, money they were able to reinvest in future improvements.

These kinds of predictive analytics can be a powerful tool for giving payers and providers the insights they need to address the social needs of members and patients. That's one of the reasons Healthify partnered with the healthcare data science company Algorex Health Technologies last summer. Joining forces allows us to enhance the solutions we provide to payers and providers—not only to drive better analysis of SDoH in their communities but also to demonstrate the optimal level of investment for their SDoH initiatives.

SDoH Analysis

The U.S. healthcare system needs better strategies for exploring patient-level risk

Both payers and providers face an ever-increasing workload, which means less time is available for care managers to offer additional patient screenings and for health plans to analyze that data. So integrating robust screenings for social risk into clinical environments is logistically impossible for many providers.

To close the gaps created by using only community-level data for identifying social risks, there is the potential for other less intensive strategies to understand patient-level social risks. These include:

  • using a smaller set of factors to determine social risks at the patient level
  • adopting a multi-question screening instrument that the patient could use without physician input
  • implementing a strategy for single-question screening

Although there are concerns about the validity and reliability of the second and third options, moving further along that path of discovery could generate essential data that fills the gap left by community-level data alone.

Helping organizations to better address SDoH

There's no doubting the relationship between SDoH and healthcare outcomes. And while it's a positive sign that the healthcare system is looking for new ways to assess patients' social risk, there is clearly no shortcut or straight-and-narrow path available for developing patient-level data.

That's where Healthify comes into play.

Our mission has always been clear: Help build a world where no one's health is hindered by their need. We're fundamentally rethinking how healthcare organizations and communities work together to help people thrive—from helping communities build partnerships that better identify social needs to leveraging new technologies that make care coordination and data mining around social services and population needs at the community level more accessible.

Listen to Healthify's webinar Leveraging Predictive Analytics and Community-Level Data to Inform SDoH Interventions to get real-life examples of how health plans are using data to identify and target members for specific SDoH interventions.

Watch the Webinar

Topics: healthcare delivery care coordination SDoH data

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