Provider Population Health SDOH Data

Even with the best of intentions, some initiatives to address social determinants of health (SDOH) fall short. Not getting granular enough or diving deep enough into the data is often to blame.

Access to the right data can help identify hurdles to care access, engagement and outcomes for groups of patients or underserved communities. But in most cases healthcare is delivered to one individual at a time, so these aggregate findings may not help each person with his or her unique needs.

Looking at the zip code where an individual lives provides some insight, but it’s often incomplete. In fact, some people whose SDOH appears unremarkable on the surface can still face serious hurdles to managing their health. Considering their health risk scores could provide a more complete picture.

Let’s take the fictional case of the patient Mary*. Mary is 58 years old. She lives with her husband. Her son and daughter-in-law live nearby and support her by driving her to medical appointments and to the pharmacy. Mary goes to see her primary care physician, Dr. Torres* after a couple of years of not having appointments. When Dr. Torres does a blood draw, the lab result shows high A1C levels. Her diabetes is not under control.

Dr. Torres changes Mary’s medication and implores her to take her diabetes seriously. “I’ve been taking it seriously, but it’s hard. It’s hard to manage my health,” Mary responds.

Social Determinants of Health: Individual Insights to the Rescue

The clinic where Mary goes has a new population health program. A care coordinator reviews her chart, looks at her SDOH risk scores and sees that financial instability is a high driver of her medication adherence risk. Mary has a job and insurance, but she has recently missed some shifts, which has caused her income to take a hit. She and her husband had to move several times over the past couple of years to keep ahead of rent increases.

Why is Mary having so many challenges with her medication?

Relying on aggregate data, such as insurance status, Mary may not immediately appear as someone with challenges paying for prescriptions. However, when armed with more nuanced, individual-level data, the care coordinator noted Mary’s high co-pay for her medications that could be a contributing factor. That information, combined with Mary’s shifting economic stability and recent moves, has made it very difficult for her to consistently fill and adhere to her prescription regimen. Furthermore, using the individual-level insight provided by data from LexisNexis® Risk Solutions allows Mary’s care coordinator to proactively help identify specific intervention that could help address Mary’s SDOH barriers.

The care coordinator provides Mary a co-pay benefit card that reduces the copay to no more than $5. Mary’s able to consistently fill her prescriptions, and she and no longer skips doses to stretch her medication between paychecks.

Mary is not alone. According to an October 2022 survey from KFF, 26% of Americans polled said they have difficulty paying for prescriptions. People most likely to face this challenge include those taking four or more medications, people living in a home with chronic illness and individuals with annual household incomes below $40,000.
At the same time, income is a notoriously taboo subject. Healthcare providers may feel uncomfortable asking for detailed assessments about a patient’s household income and other spending priorities. And patients often feel uncomfortable and may tell partial truths if asked. Utilizing additional data sources help highlight the impact of economic stability on an individual’s social drivers of health – without adding burden to the patient or provider within the clinical visit.

Data Helps Put the Puzzle Pieces Together

There’s really no single source of truth for SDOH data. One of the advantages for clients working with LexisNexis Risk Solutions is that we take different sources of data and records with intersecting data points which are linked together at an individual level.

Ultimately, you need a combination of data points around the following:

• How do community factors, such as environment and social group, impact health?
• What’s happening in their household?
• And, most importantly, what are their individual risk factors?

Our social determinants of health datasets take advantage of all three of these different levels of the socioecological model.

Key to Minimizing Provider Burden When Addressing SDOH

Providers should not have to spend time asking a battery of questions to assess risk, nor should they have to ask questions that might make the patient less trusting of them in the future. Imagine a patient’s reaction to a provider asking, “How much money did you make this month,” vs. “Did you know that this medication has a rebate associated with it?” This makes the intervention much more efficient, empathetic, equitable and sustainable. Collecting social determinants of health data is difficult and there are reliable data partners who can provide that data. Z-codes are one example of self-collection that has been slow to take off.  

Providers need to be confident that they have information with high data accuracy, relevancy and recency. The LexisNexis® Risk Solutions social determinants of health data goes through a complex linking process to validate patient identity and ensure that it is linked to the right person. We apply a sophisticated and proprietary referential linking technology to ensure that the individual that’s “over there” is the same one “over here” and can resolve data attributes to a single individual. By aggregating data from thousands of different sources, we really look at the specificity of the linkage as well as the sources of the data itself.

From a quality perspective, not only is data linked to the right person, cleaned, normalized and aggregated, but also maintained in a data warehouse with robust security and privacy standards.

We’ve Been Here Before – and Innovated!

Ultimately, there’s no one silver bullet to advancing health equity. I liken it very much to where we were 10 to 15 years ago around proactively identifying clinical gaps in care through population health programs. Historically, we assumed everyone would follow a clinical protocol – and it was only by examining the profiles and analytics of patients who had gaps in care that we were able to personalize care management and engagement programs that proactively engage individuals in closing gaps in care.

Those programs are highly sophisticated in their use of clinical data, claims history, risk adjustment models and even marketing and communication preference data to meet patients where they are in completing annual wellness exams or seasonal flu shots.

We’re at a similar turning point in the opportunity to utilize data beyond clinical encounters to better understand and engage individuals in supportive social care that improves healthcare access and outcomes. We must continue investing in population health programs, community health workers and community-based resources to achieve health equity and close gaps in health disparities. Combining the right sets of data can accelerate the mission to prioritize patients who may have unknown risks and empower care coordinators with a baseline direction to start the conversation.