By Shyam Karunakaran

An ounce of prevention is worth a pound of cure.

Most payers agree with this wise adage, but it’s much easier said than done. After all, payers typically have limited views into members’ physical and mental health conditions until after they’ve been diagnosed—when it’s too late for the ounce of prevention.
Yet when payers identify member cohorts using factors other than just diagnosis and procedure codes, they often uncover data patterns that reveal ways to enable preventive care—or, at least, early interventions. Such “whole health” assessments are essential to value-based care models and health equity because they let us create member support structures that improve long-term outcomes at lower costs.

The question is, how are they accomplished?

The key is to analyze mental health, behavioral health, and social determinants of health (SDOH) data in conjunction with traditional claims, utilization, and clinical data. With this well-rounded outlook, payers can ascertain and address issues before they escalate.

Capture whole health data
Analytics that understand members holistically permit payers to form more precise member cohorts—which, in turn, can lead to better member management programs. Arriving at those analytics, however, requires data that incorporates whole health characteristics.

Mental health and behavioral health data are particularly important for achieving a whole health view. Research shows that some of the least compliant cohorts—and therefore, most at risk for adverse events, readmissions, etc.—are those with physical comorbidities plus mental health or behavioral health conditions. Yet obtaining mental and behavioral health information can be difficult.

While some organizations are exploring using social media to get mental health, behavioral health, and SDOH insights, this approach is fraught with legal landmines. Mining whole health data from third-party sources (e.g., LexisNexis) offers a less risky alternative—but it’s also generally quite expensive.

In contrast, self-reported Health Risk Assessments (HRAs) can be a more cost-conscious treasure trove of whole health information. Now that many HRAs are captured digitally, they can enable faster detection of members’ health needs. That’s especially true when HRA data is combined with other data sets.

Local communities and municipalities may offer other sources of whole health data to consider. For example, the town of Dearborn, Mich., was recently granted funds to collect community health information. As civic health efforts like this mature, data-sharing partnerships will become integral to population health initiatives and health equity efforts.

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