Using Data to Drive Health Outcomes

Given the hour (8 a.m.) and the setting (the Aria Resort & Casino in Las Vegas), Ronald Leopold, M.D., wasn’t so sure that the time was ideal this morning for a talk centered on getting the most out of employee-healthcare data.

Nevertheless, Leopold—the national practice leader of health outcomes at Willis Towers Watson—soldiered on in the opening session of Human Resource Executive’s Health and Benefits Leadership Conference’s second day, delivering a lively presentation that focused on harnessing healthcare-claims data to better control coverage costs.

In “Doing the Math: Data-Driven Health Outcome Strategies for Employers,” Leopold first asked attendees to rate, by a show of hands, their comfort with data analytics on a scale of 1 to 5, with 1 being the lowest and 5 the highest.

While a scattering of audience members indicated the highest level of comfort with data analytics, and a few ranked themselves as “4s,” the clear majority considered themselves to be “3”s—“not bad, but leaving some room to improve,” noted Leopold.

His goal at that moment, he said, was to help attendees better understand available data “to make the best [benefits] decisions on behalf of their employees.”

In 2016, “we are poised for a new era,” said Leopold, “where we see healthcare costs starting to trend slightly upward.”

For employers, being ready for this uptick entails making efforts to lower employee health risks—implementing effective wellness programs and making plan-design changes that encourage employees to become more responsible for their healthcare, for example—and, in turn, lower healthcare costs.

Leopold urged attendees to “demand the story” beyond typical metrics such as average employee hospital stays and number of employees with a given disease or condition, for instance.

This type of descriptive data “doesn’t always give us a lot,” said Leopold.

“Go deeper, and get diagnostic data to find out why” these numbers are what they are, “and do predictive analysis as well.

“Look at data and use algorithms—which you in HR may not have, but carriers will have, and some consultants will have, and data aggregators will have—to determine [your population’s] health risks,” he continued.

“This,” said Leopold, “is practicing predictive analytics. Then we can see what’s likely to happen in the future … and we’ll get better results.”