As a former benefits leader, I've spent plenty of hours looking at population data to better understand trends and areas of opportunity. Looking at our top drivers of spend was typically a good place to start. After I identified an area of focus, I would brainstorm and hypothesize how to solve the problem, implement a program or service (sometimes home grown and sometimes through a third party), and ultimately evaluate its efficacy. In other words, did it have the impact I thought it would?

Understanding the outcomes — and ultimately the ROI or VOI of a program or service — isn't always straightforward. You can't just "test" a health plan benefit by offering it to some plan members and not others. In order to measure the cost avoidance (or perceived cost avoidance) of a program or service, you often have to compare outcomes of those who participated in the benefit to those who are their "match" (demographics, conditions, etc.) within the population but who did not participate in the program. For physical health programs, you may have the ability to look at changes in clinical risk factors, respective claims spend, and changes in prescription use in order to assess the ROI of your program. For example, when measuring the effectiveness of a diabetes treatment or program's impact on A1C, you can look at A1C measurements before and after a benefits implementation to see if the clinical values are moving into a controlled state.

Another example comes from the fertility space, where there are several metrics that can be tracked to prove the efficacy of a program: pregnancy rate, miscarriage rate, singleton and multiple birth rates, and live birth rate. Ideally, you want this data pulled directly from your experiences, not from extrapolated averages based on nationally reported data.