Organizations interested in approaching population health first need to understand the data available, the nature of the data, and the data's context.(Image: Shutterstock)

Self-insured health systems are adept at looking at individual patients, diagnosing a health problem and pinpointing a solution. Looking across a population to identify and act on health improvement opportunities for their employees is much more challenging.

The reasons for self-insured employers to master population health management are compelling. First, it's the right thing to do by their employees, helping to keep them healthy and head off any problems that might be on the horizon. Second, these organizations are responsible for their employees' health care costs, and effective management can slow cost escalation. Third, research substantiates that healthier employees are more productive, and that minimizing absenteeism—as well as presenteeism—has a positive impact on the organization's bottom line. And finally, they have a wealth of data at their fingertips about their employees, so they can truly be effective at risk identification and stratification, as well as the feedback loop on which interventions work best.

So, how can self-insured employers move past some of the roadblocks they have faced thus far and start to pick up the pace in the pursuit of successful population health management? The key is often in the data.