Achieving Better Health Outcomes with Accurate SDOH Data

Clinical data has long driven decision-making for healthcare providers and insurers, but it tells only part of the story. Healthcare leaders who utilize external data regarding social determinants of health (SDOH) better understand the economic and environmental factors driving patient risk, can better predict health outcomes, and offer more individualized care.

However, getting that data and finding a way to understand and utilize it has been a struggle. Most publicly available datasets have been too generalized to support productive analysis for a particular healthcare environment. A network of research, government, and commercial entities is now making more refined SDOH datasets available, and the potential for more effective interventions is growing because of it.

Why Social Determinants of Health Data Insights Matter

Comprehensive data and analytical tools that utilize SDOH data can better predict health outcomes, giving healthcare providers the opportunity to proactively address risk factors, improve outcomes for patients, and lower costs by utilizing holistic preventive care and treatment plans.

The Problem

When healthcare leaders can draw on actionable insight derived from robust analytics, outcomes improve and treatment becomes more cost-effective. However, few organizations have the pieces in place to achieve the analysis that makes the difference for patients.

SDOH datasets offer tremendous value, but many healthcare provider networks and payers are not yet able to fully utilize this resource. Most organizations that have integrated SDOH analysis rely on publicly available datasets that do not provide adequate granularity for rich analysis.

Many of the most illuminating datasets for organizations within the healthcare space can be identified and obtained only through experience and relationships within the SDOH ecosystem. Even if you know where to look, drawing value from these datasets depends on developing the advanced data science capabilities to reliably predict health issues—and work toward improved outcomes.

The Solution

Drawing from SDOH datasets that correlate to the health conditions of primary concern, healthcare organizations can more accurately predict patient risk and significantly improve outcomes with efficient interventions. Our predictive machine learning models drive improved patient outcomes through tools like these:

  • Real-time dashboards frontline clinicians can use during patient intake or examination. Insight into societal factors driving health risks places clinicians in a better position to provide more holistic treatment and care plans.
  • CRM (customer relationship management) systems and marketing systems enabling healthcare payers to identify their most at-risk members with tailored outreach campaigns. Preventive healthcare efforts lead to less cost for the healthcare payer in the long run.
  • EMR charts available to healthcare practitioners during maternal and infant health screenings, as well as prenatal and postnatal visits, to better inform practitioners of the factors that could place the infant at risk.
  • Case management systems enabling care coordinators in accountable care organizations (ACOs) to use data-driven insights to prioritize their case load and leverage more targeted and effective interventions.

Comprehensive analytical tools that utilize social determinants of health datasets bring undeniable impact—to patients and to the health systems and payers who support them.