Put whole health data into action
Once multifaceted data is captured, using it to move the needle on outcomes requires breaking free of entrenched ways of thinking. This can be accomplished by applying analytics tools designed to answer questions by finding relevant patterns within the data.

For example, payers commonly analyze claims and utilization data but seldom tap into precise clinical data—such as medication dosages—that could help them better understand where their members stand on the disease continuum. Leveraging clinical details plus social, geographic, financial, and other information opens new opportunities to perceive members at an entirely different level, allowing for enhanced benefits structures and outcomes.
The more whole health data is integrated into the analytics picture, the more effectively payers can tailor their member outreach. Pairing census and SDOH data with machine learning (ML) algorithms, for instance, can help determine the likelihood of member engagement.

Some analytics solutions use whole health data to create an “engagement score” for various member cohorts. Even for members with the same clinical condition, the engagement score can drive different strategies to ensure the highest likelihood of success. The more non-clinical data is layered into those algorithms, the more precise the scores become.

Let’s say, for example, that a payer identifies 1,400 members as having a particular HEDIS care gap. Of those members, 300 have a high engagement score and thus are more likely to close the gap if approached by their payer. Armed with that insight, the payer can prioritize reaching those members to improve outcomes quickly. In the meantime, care managers can devise alternative engagement strategies for the other members based on the data. For instance, suppose it turns out that pharmacy access is a barrier to care in a specific geographic region. In that case, the payer might consider launching a pharmacy-by-mail program for those members.

Create impactful interventions
Value-based and accountable care reimbursement models have intensified healthcare’s focus on early interventions and preventive care. While many data and analytics platforms offer limited views into members’ needs, truly comprehensive member awareness is only possible when the analytics leverage whole health data, including mental health, behavioral health, and SDOH.

If an ounce of prevention really is worth a pound of cure, then payers must be able to spot the barriers, opportunities, and patterns that point the way to successful wellness campaigns and outreach. Analytics built on whole health data give payers a complete member picture that enables more impactful interactions.

About the author: Shyam Karunakaran is the senior vice president & market head for Health Plans at CitiusTech.
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