Because one size does not fit all: Data-driven incentive design

Blog – August 10, 2016
Ksenia Arie

By Ksenia Arie, Product Manager, RedBrick Health

When it comes to incentive plan design, we all want the same thing: help individuals be healthier, happier and more productive. At RedBrick Health, we see program participation increase when the design is easy to follow for individuals using choice architecture and providing enough options that are relevant to their personal interests and goals. If individuals feel that they are being forced into one modality with no choice, they feel put off by the program and do not participate.

We have also found that in a choice model, various intervention modalities are similarly effective at producing clinically meaningful change. For example, our data show that consumers’ health metrics improve regardless of whether they use digital coaching, activity tracking or phone coaching.

Outcome-based rewards have been around for a long time; however, their popularity has risen significantly over the past several years as employers have begun shifting more financial accountability to consumers. After all, it seems intuitive: directly reward the health improvement you want to see in your population. But there may be an adverse effect: our research found that participation-based rewards worked equally well in producing biometric outcomes and we found evidence that outcome-based reward designs may depress participation, especially among those at elevated risk.

To avoid costly design mistakes, it is important to take a step back and make sure that your reward design is aligned with real evidence about what works, and that it doesn’t inadvertently create the wrong experience for your participants. Our What the Best Do Better approach provides emerging evidence-based advice on what combination of configurable features can help lead to your designed outcome.

Through data research and analysis, we increasingly know what works and what doesn’t when it comes to health and well-being program and reward design. We welcome learning from you too. What observations do you have on the relationship between reward design and program results?