Use SDOH + Analytics to power better outcomes for underserved population

Whether you’re a payer, provider or patient, on the front lines of care, sitting in front of a computer or receiving treatment, you’ve been affected by the pandemic. Of all the groups who participate in healthcare in one way or another, perhaps no single group has suffered more over the last year during the pandemic than the underserved— those people without ready access to needed healthcare services.

Social Determinants of Health (SDOH)—the social, physical, financial, religious and educational environments in which we live—have been shown to have an outsized impact on the health of many populations, but especially the underserved. If you have difficulty finding safe, affordable housing, food, transportation and healthcare services, it stands to reason you’d have less time to focus on health-related activities like exercise, maintaining a low-fat diet, adhering to prescribed medication, or other daily healthy habits. The more-than-year-long pandemic has served to exacerbate the inequities.

While the economy has improved in recent months, we have a long way to go to reach pre-pandemic levels. As recently as February, KFF reported many people have experienced a loss of work, lower income, difficulty paying for household expenses, a lack of food and more. As factors contributing to SDOH, these daily challenges have a compounding effect not only on the healthcare consumer’s everyday life but also on receiving healthcare services.

“There is extensive research that concludes that addressing social determinants of health is important for improving health outcomes and reducing health disparities. Before the pandemic, a variety of initiatives existed to address social determinants of health both in health and non-health sectors. The COVID-19 pandemic exacerbated already existing health disparities for a broad range of populations, but specifically for people of color,” according to KFF.

In Los Angeles, for example, hospitalizations for avoidable admissions fell among white patients during the first six months of the pandemic. In contrast, Black patients continued to use hospitals for treatment that could have been performed in a less intense medical environment, indicating these patients have less access to outpatient care.

“Racial disparities in potentially avoidable hospitalizations increased during the COVID-19 pandemic at a large urban health system,” according to research published in the American Journal of Preventive Medicine. “Given that pre-pandemic rates of potentially avoidable hospitalizations were already higher among racial and ethnic minorities, especially African Americans, this finding should cause alarm and lead to further exploration of the complex factors contributing to these disparities. These findings suggest that without careful consideration of these factors, well-intended ‘one-size-fits-all’ efforts to reduce potentially avoidable hospitalizations could further widen disparities.”

As the government rolls out vaccines nationwide, healthcare organizations must prepare for and move toward solving the coming healthcare services influx as well as ongoing care disparities. As more people are vaccinated, they again will feel comfortable seeing a healthcare provider in person. “Three in ten (30%) adults reported delaying medical care in the last four weeks due to the pandemic and 39% reported symptoms of depression or anxiety,” according to the KFF report.

How can healthcare providers prepare?

First, plan for a potential increase in patients with chronic conditions whose disease was made worse from having the coronavirus and being negatively affected by SDOH. “The association of social inequalities and COVID-19 morbidity is further compounded in the context of underlying chronic respiratory conditions, such as asthma, where there is a possible additive, or even multiplicative, effect on COVID-19 morbidity. Several adverse social determinants that impact the risk of COVID-19 morbidity also increase asthma morbidity, including poverty, smoke exposure, and race or ethnicity,” according to The Lancet.

The second move is exploring and understanding the healthcare organization’s data. Each day healthcare creates vast amounts of data as part of the healthcare process. That data is often a mishmash of patient, operational and financial information trapped in different applications with no easy or convenient way to extract and consume it. This extraction difficulty manifests as an active hindrance to effectively helping underserved populations. Accessing and making sense of the information is crucial to developing workable initiatives to support social determinants.

Many healthcare organizations face the problem of incorporating SDOH data into larger healthcare strategies to improve health outcomes and health and wellness. The National Academies of Sciences, Engineering, and Medicine found, however, “Interoperability and data sharing between health care and social care are hampered by the lack of infrastructure, data standards, and modern technology architecture shared between and among organizations.” Nevertheless, data is and will continue to be a significant part of making social determinants successful on a national level.

Recognize, then act

For the healthcare industry to find its way to new and innovative SDOH programs and identify those who may benefit, the populations must be identified. While some organizations use referrals following face-to-face meetings with prospective program members, analytics can be utilized to identify many potential enrollees quickly and efficiently at scale, as well.

“Analytical capabilities in healthcare can be used to identify patterns of care and discover associations from massive healthcare records, thus providing a broader view for evidence-based clinical practice,” according to an article published in Technological Forecasting & Social Change. “Healthcare analytical systems provide solutions that fill a growing need and allow healthcare organizations to parallel process large data volumes….”

No matter how the data is collected and processed, healthcare organizations need to ask several questions before diving into creating or purchasing solutions with pre-populated data analytics models:

• Data is available; how do we use it?
• What data is actionable?
• How can we apply the data to the membership or population?

Once the reasons are quantified by the healthcare organization, it’s important to really delve into a few specific areas within the data. Today, it’s important to understand the factors contributing to SDOH challenges. Data analytics can help you do that when using the Centers for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI). “The CDC SVI ranks each tract (subdivided counties) on 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes.”

Healthcare organizations should consider ingesting SVI-related information into existing models that already include basic claims data collected by the healthcare organization, such as:

• Demographics
• Provider used
• Facility used
• Service type

Getting a handle on these pieces of data can help your organization better understand members and population subsets, while making it easier to develop and implement concrete outreach and treatment plans and programs for every population, including the underserved. A good example of successful SVI use comes from Tucson, Arizona.

The Arizona Department of Health Services used SVI data to target communities that otherwise would have a difficult time receiving the COVID-19 vaccine. SVI information helped the state focus one specific neighborhood with advertising, social media and town halls— with the goal to get more people vaccinated. As a result, Arizona is one of two states with greater vaccination rates among populations with high social vulnerability than those with low vulnerability.

As COVID-19 vaccines are made readily available throughout the US to large numbers of people regardless of age or co-morbidities, it will be incumbent on health plans to understand the ramifications of untapped data that may lead to less-than-optimal health outcomes for the underserved and worsening financial and business results for the organization.