Closing Healthcare Gaps Among Specific Patient Populations

An abundance of actionable data exists, making it possible for care managers to be as specific as they need to be, whether targeting individual patients or populations to close healthcare gaps.

By Medecision

In the increasingly complicated world of care management, specificity is your friend. You need specificity in data to identify and close gaps in care so you can improve health while controlling costs. You rely on specificity in reporting to demonstrate compliance with a raft of performance, contractual and regulatory requirements.

And to support whole-person care, you strive to understand the specific socioeconomic barriers that stand in the way of better outcomes.

Fortunately, an abundance of actionable data exists, making it possible for care managers to be as specific as they need to be, whether targeting individual members or populations.

Closing Healthcare Gaps Requires a More Complete Picture of Health

“Going forward, next-generation care management will require organizations to do a better job of understanding and predicting factors relevant to care delivery and outcomes,” Medecision’s Tamara Cull, senior vice president of customer success, wrote in a February 2018 article for Managed Healthcare Executive. “Payers must be able to transition from thinking about members in terms of existing disease (e.g., diabetes) and instead thinking of them more holistically and prospectively, as unique individuals.”

A lot has happened in the four years since the publication of that article—reflecting just how important making that transition is and heightening awareness of the role social determinants of health (SDoH) play in achieving positive healthcare outcomes. The more care managers get a thorough picture of an individual or a population, the greater their ability to proactively confront both physical and socioeconomic issues, creating effective interventions and managing risk.

The Data Exists

The data is out there, both clinical and nonclinical, and is ripe for robust analytics. Clinical data can include electronic health records (EHRs), administrative data, claims data, patient/disease registries, health surveys and clinical trials data, according to “Data Resources in the Health Sciences” from the Health Sciences Library at the University of Washington.

Social determinants of health (SDoH) data reflect a wide range of economic, employment, social and living conditions essential to understanding specific patients and populations. Examples include where they live, work and play; availability of fresh fruits and vegetables; access to parks or other venues for walking and exercise; and transportation options. The day-to-day realities captured by SDoH data can shine a bright light on healthcare gaps and barriers, whether they are related to access, technology, language or culture.

Identifying and Closing Gaps

Once a gap has been identified, a care manager can use the data to set priorities and begin to develop an action plan for target populations and individuals. Specific steps will rise to the forefront.

A care manager who is seeking to close gaps in care for diabetes patients, for example, could develop an action plan based on encouraging them to get their A1c levels checked regularly, adhere to their medication regimen and see their eye doctor. (For those who need a ride to the doctor’s office, transportation can also be incorporated into the care equation.)

If seeking to improve care to an underserved and vulnerable population, the first step might be to rebuild trust so that providers can forge personal, one-to-one relationships with individual members. That process might entail outreach to that specific community, meeting those members where they are—and speaking to them in their language, with sensitivity to cultural differences. Again, SDoH data is likely to play a significant role, informing the delivery of “whole person” care and helping to bridge any gaps or divides that are hindering care.

The insights provided by data might well align with existing programs—or suggest a necessity for the creation of new ones. As Cull details in her article for Managed Healthcare Executive:

“These can include bundled payment programs, quality reporting programs, ACO (accountable care organization) programs, and others that target specific disease states or socioeconomic issues. With the ability to apply clinical intelligence rules and predictive modeling to data sets—and thus identify population concerns automatically—care managers can focus on connecting directly with members instead of having to mine data themselves. That saves time and increases care management effectiveness overall.”

In addition, Cull says, such a tailored approach can pave the way for better member engagement and outcomes, while moving beyond disease treatment to prevention and wellness

For more tips on how to ensure your care management program is operating at its best,
download our guide 5 Key Strategies for Successful Care Management.