“Whole Health” Data Drives More Successful Member Interventions


By Shyam Karunakaran

An ounce of prevention is better than cure.

Most payers agree with this sage adage, but it’s much easier said than done. After all, payers usually have a limited view of members’ physical and mental health conditions until they’ve been diagnosed, when it’s too late for an ounce of prevention.

Yet when payers identify membership cohorts using factors other than diagnosis and procedure codes, they often uncover data patterns that reveal ways to enable preventive care or, at least, early intervention. Such “whole health” assessments are essential to value-based models of care and health equity because they allow us to create member support structures that improve long-term outcomes at lower cost.

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, use, and clinical data. With this well-rounded perspective, payers can identify and resolve issues before they escalate.

Capture comprehensive health data
Analytics that understand members holistically allow payers to form more accurate member cohorts, which, in turn, can lead to better member management programs. However, to arrive at these analyzes requires data that incorporates aggregate health characteristics.

Data on mental health and behavioral health are particularly important for obtaining a holistic view of health. Research shows that some of the least compliant cohorts – and therefore most at risk of adverse events, readmissions, etc. – are those with physical comorbidities plus mental health or behavioral health problems. Yet obtaining information about mental and behavioral health can be difficult.

While some organizations consider using social media to obtain information about mental health, behavioral health, and SDOH, this approach is running into legal landmines. Extracting comprehensive health data from third-party sources (eg, LexisNexis) offers a less risky alternative, but is also usually quite expensive.

In contrast, self-reported health risk assessments (HRAs) can be a treasure trove of more cost-conscious overall health information. Now that many HRAs are digitally captured, they can enable faster detection of member health needs. This is especially true when HRA data is combined with other datasets.

Local communities and municipalities can suggest other sources of comprehensive health data to consider. For example, the city of Dearborn, Michigan recently received funding to collect community health information. As civic health efforts like this mature, data-sharing partnerships will become an integral part of population health initiatives and health equity efforts.


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