Overview

This trial is active, not recruiting.

Conditions receipt of radiology studies, receipt of laboratory tests
Treatment financial incentive for choosing a lower-cost provider
Sponsor Harvard Medical School
Collaborator University of Southern California
Start date August 2014
End date December 2015
Trial identifier NCT02249156, AG043850-01

Summary

Several employers in the US have introduced a program where their employees receive a financial incentive to receive lower cost care. Under this "Rewards" program, patients are free to choose providers but if they visit a pre-determined low-cost laboratory or radiology facility (called a "rewards provider"), they receive a financial incentive. The financial incentive is typically in the form of a Health Savings Account (HSA) contribution. The dollar amount varies by employer. This study will use medical claims data to examine if this program leads to an increase in the volume of services performed by low-cost providers and decreased health care spending.

United States No locations recruiting
Other Countries No locations recruiting

Study Design

Observational model cohort
Time perspective retrospective
Arm
Employees of employers who have introduced the Rewards program. Currently there are two employers who have introduced the Rewards program, but there might be others that introduce it in the coming months. All intervention (and control) employers are customers of Castlight and use their price transparency product.
financial incentive for choosing a lower-cost provider
Employees of the intervention group receive money (either as a payment to their health savings account or directly as a check) if they obtain a radiology test or laboratory test from what a low-cost or rewards provider. The amount of money per test varies by the employer and type of test. A provider is identified as low-cost or rewards if their costs are in the lowest 10-20% among all providers in the community. Again there is a range because the relative cutoff has varied across the employers that have implemented this program.
Large employers who have not introduced the Rewards program, but work with Castlight and use their transparency product. It will be ideal if the control population looks similar to the intervention population in key characteristics - age, level of illness, industry of the employer (for example, manufacturing), pre-intervention spending, and geographic distribution. If possible, across a pool of potential control employers, we will identify control employers that look the most similar across these characteristics in the pre-intervention period. Another possible strategy we might use is to weight the individuals in the control population in our analyses by how similar they appear to those in the intervention population.

Primary Outcomes

Measure
Service volume
time frame: In 12 months after intervention initiated

Secondary Outcomes

Measure
Total spending
time frame: In 12 months after intervention initiated
Utilization of laboratory and imaging services
time frame: In 12 months after intervention initiated

Eligibility Criteria

Male or female participants at least 1 year old.

Inclusion Criteria: - Employee or dependent of an intervention or control employer Exclusion criteria: - Not continuously enrolled in health plan and therefore some claims may be missing

Additional Information

Official title The Effect of Financial Incentives on Utilization of Low-cost Providers
Description 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. Regression discontinuity regressions Our second study design is a regression discontinuity design. Providers are designated as rewards providers based on their relative cost within a geographic market. Providers are ranked based on an index of prices and providers below a pre-specified ranking or threshold on this index are designated as rewards providers. The regression discontinuity model will compare changes in volumes between providers that are just above this threshold with providers that are just below this threshold. Instrumental Variables Analysis Our third study design uses an instrumental variable analysis. The dollar amount of the financial incentive varies between the employers who have introduced the program. In this analysis we will exploit that difference. We will assess whether the effects of rewards on service volume vary by the size and nature of the rewards. Independent variables As independent variables, we will use employer, month, year, geography, year X geography, and provider fixed effects. If there are changes in the employee population before and after the introduction of the Rewards program, we will control for those differences.
Trial information was received from ClinicalTrials.gov and was last updated in March 2015.
Information provided to ClinicalTrials.gov by Harvard Medical School.