The Effect of Financial Incentives on Utilization of Low-cost Providers

This will be an observational study using Differences in Differences and Regression Discontinuity designs.

In designing our analytic methods for this observation study, we had to consider two key potential sources of bias. First, rewards providers might differ from non-rewards providers in observed and unobserved ways such as such as quality or convenience. This might confound the true effect of the rewards program on service volume. That is, an analysis of the rewards program that simply compares service volume of rewards and non-rewards providers after launch of the rewards program will capture both the effects of the rewards program as well as the effects of other differences between rewards and non-rewards providers. Second, service volume of rewards program might change due to other factors coincident with the launch of the rewards program. Our proposed statistical methods attempt to address both these sources of confounding. We will examine the data in a series of way to test the robustness of our findings.

Difference-in-differences (DD) linear regression We will use a difference-in-differences regression to examine within-employer changes in provider utilization following the implementation of the rewards program.

In the first set of regressions we will use data from employers who have implemented the rewards program. These regressions will compare changes in provider volume following the launch of the rewards program for rewards providers (first difference) to change in service volume for non-rewards providers during the same time period. We hypothesize that rewards providers will experience a greater increase in volume than non-rewards providers. This analysis uses non-rewards providers from the same employer as a control group and assumes that reward providers would have experienced the same change in volume as non-reward providers in the absence of the rewards program.

In another set of regressions we will use data from both employers who have launched the rewards program and employers who have not launched the rewards program. This analysis will be only feasible for providers who have a unique id across employers and who see patients from both rewards and non-rewards employers. These regressions will compare changes in provider volume for rewards providers coming from rewards employers (first difference) to changes in service volume for the same reward providers coming from non-reward employers during the same time period. We hypothesize that rewards providers will experience a greater increase in volume coming from rewards employers than non-rewards employers. This analysis uses non-reward employers as the control group and assumes that reward providers would have experienced similar increase in service volume from rewards and non-rewards employers in the absence of the rewards program. This analysis helps to address the potential bias that rewards providers had an increase in volume because of other factors such as quality or convenience.