Translating Social Determinants of Health Into Clinical Action

Posted on September 25, 2017 I Written By

The following is a guest blog post by Anton Berisha, MD, Senior Director, Clinical Analytics and Innovation, Health Care, LexisNexis Risk Solutions.
The medical community recognizes the importance of social determinants of health (SDOH) – social, economic and environmental conditions in which people are born, grow, live, work and age that impact their health – as significant and direct risk factors for a large number of health care outcomes.

The negative outcomes include stress, mental health and behavioral disorders, alcoholism and substance abuse, to name a few. Negative SDOH worsen a slew of major chronic conditions, from hypertension and Coronary Artery Disease to obesity; they also lead to lower patient engagement and medication adherence while increasing low-intensity ER visits and hospital admissions and readmissions.

In fact, a study shows that medical care determines only 20% of overall health outcomes while social, economic and environmental factors determine about 50% of overall health. The National Quality Forum, Centers for Disease Control and Prevention and World Health Organization have all acknowledged the importance of addressing SDOH in health care.

Not all SDOH are “created equal”

When it comes to SDOH, there is a misconception that all data regarding a person’s lifestyle, environment, situation and behaviors relate to their health. Although there is a myriad of basic demographic data, survey data and other Electronic Health Records (EHR) data available to providers today, much of it has a limited potential for identifying additional health costs and risks.

The key to addressing SDOH is to use current, comprehensive and longitudinal data that can be consistently linked to specific patient populations and provided in a standardized format. One example is attributes derived from public records data such as proximity to relatives, education, income, bankruptcy, addresses and criminal convictions.

Moreover, each SDOH attribute has to be clinically validated against actual healthcare outcomes. Clinically validating attributes is critical to successful predictive analytics because some attributes do not correlate strongly to health outcomes.

For example, while knowing how close an individual’s nearest relative or associate lives to the patient does correlate to health outcomes; knowing how many of those relatives or associates have registered automobiles does not. Even when attributes are clinically validated, different attributes correlate to different outcomes with different accuracy strengths.

Translating SDOH into actionable intelligence

After SDOH have been correlated to healthcare outcomes, providers have two implementation options. One is to use relevant individual SDOH attributes per outcome in clinical and analytic models to better assess and predict risk for patients. Another is to use SDOH as part of risk scores estimating specific healthcare risks; for e.g., to estimate an individual’s total health care risk over the next 12 months based on cost; a 30-day readmission risk; or a patient engagement score.

Risk estimation can be done either in combination with other types of legacy healthcare data, such as claims, prescription and EHR data or with SDOH alone, in the absence of medical claims.

Recently, a client of LexisNexis® Health Care did an independent study to evaluate the impact and usefulness of Socioeconomic Health Score (SEHS) in risk assessment for several key chronic conditions, when no other data are available. Findings proved that the top decile of SEHS captures significantly more members with given conditions than the bottom decile. The study concluded that the difference was important and very helpful in estimating risks in a newly acquired population without legacy healthcare data.

Integrating SDOH into clinical workflows and care recommendations

Validated SDOH can be presented in a form of risk drivers or reason codes directing the clinician toward the most important factors influencing a given negative outcome for each patient: income, education, housing or criminal records.

The risk drivers and reason codes can then be integrated into workflows within the clinician’s IT systems, such as the EHR or care and case management, in the form of an easy-to-understand presentation. It could be a data alert that is customizable to patients, treatments and conditions, helping the provider make score-based decisions with greater accuracy and confidence. At this point, the SDOH information becomes actionable because it has the following characteristics:

  • It is based on hard facts on every individual.
  • It is based on correlation and statistical significance testing of large pools of patients with similar behavior.
  • It provides clear and understandable reason codes driving the negative outcomes.
  • It can be tied to intervention strategies (outlined below) that have demonstrated positive results.

Clinicians empowered with actionable SDOH information can modify their interventions and follow-up strategies accordingly. Based on resources at hand, patients living in negative SDOH could be either properly managed by clinicians themselves or other medical staff, social workers and newly created roles such as health coaches. Sub-populations at risk could benefit from access to community resources to get help with housing (permanent supportive housing for homeless), transportation, education, childcare and employment assistance.

Moreover, SDOH are particularly effective in helping providers develop a population health management strategy fueled by prioritized tactics for preventive care. Tactics can range from promotion of healthy food to free screening services. For patients with chronic diseases (who can typically be managed appropriately when they adhere to therapy and healthy lifestyle choices), SDOH-informed interventions can help keep them under control and potentially reduce severity. For patients recently released from the hospital, aftercare counseling could prevent complications and readmissions.

To sum it up

Socioeconomic data is a vital force for healthcare risk prediction as it provides a view into the otherwise hidden risks that cannot be identified through traditional data sources. When SDOH are clinically validated and correlated to healthcare outcomes, they help providers better understand an individual’s risk level and address it through appropriate intervention strategies.