Receipt of Radiology StudiesNCT02249156
The Effect of Financial Incentives on Utilization of Low-cost Providers
An observational study of Receipt of Radiology Studies and Receipt of Laboratory Tests, run by Harvard Medical School (HMS and HSDM).
Trial 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… Read more about this study
- Who is it for?
- Ages 1 year to 99 years
- Conditions the study is for
- Receipt of Radiology Studies, Receipt of Laboratory Tests
- What is being tested?
- Financial incentive for choosing a lower-cost provider
- Type of treatment
- Lifestyle or therapy programme
- How many people
- 0 participants
- Where
- No sites listed yet
- Study dates
- Aug 2014to Mar 2018
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 1 year to 99 years
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: The study population is composed of employees of both the intervention and control employers. We will use medical health plan claims from 2012 to 2014 for laboratory and imaging services across the study population. Health plan claims data will be provided by Castlight Health. The approximate number of providers of laboratory and radiology services who will provide care to this population will be 475,000.
Who may be able to join
- Employee or dependent of an intervention or control employer
Who cannot join
- Not continuously enrolled in health plan and therefore some claims may be missing
About this study
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.
Read the detailed 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.
- Being tested
- Financial incentive for choosing a lower-cost provider
- Keywords
- Health economics, Financial incentives, Health care spending
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Financial incentive for choosing a lower-cost providerBehavioural
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.
| Study group | Role | What they receive |
|---|---|---|
| Financial incentive group | 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. Receives: Behavioral: Financial incentive for choosing a lower-cost provider | |
| Control population | 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Service volume
Service volume for each provider-employer before and after the introduction of the rewards programs. We will estimate models with several potential measures of volume including the number of services performed, the number of unique patients seen by the provider, and the fraction of all services received by the employees.
Measured: In 12 months after intervention initiated
Secondary outcomes (2)
Total spending
Total spending on laboratory and imaging services
Measured: In 12 months after intervention initiated
Utilization of laboratory and imaging services
Measured: In 12 months after intervention initiated
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Sep 2014 (estimated)
- Study startAug 2014
- Primary completionDec 2017 (estimated)
- Study completionMar 2018 (estimated)
Why it stopped: This was an observational study. The owners of the data refused to give us the data so we could not proceed with the analyses.
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Receipt of Radiology StudiesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 0 participants (actual)
Sponsor and investigators
- Lead sponsor
- Harvard Medical School (HMS and HSDM)
- Sponsor type
- University, hospital or other
- Collaborators
- University of Southern California, University of California, Berkeley
- Responsible party
- Principal investigator, Ateev Mehrotra, Harvard Medical School (HMS and HSDM)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT02249156
- Sponsor's protocol ID
- AG043850-01
- Status last verified
- Jul 2019
- First submitted
- 5 Sep 2014
- Last updated
- 16 Jul 2019
- Results posted
- No
- Data monitoring committee
- No
Study at a glance
From the registry record, last updated by the sponsor on 16 Jul 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Effect of Financial Incentives on Utilization of Low-cost Providers
- ClinicalTrials.gov ID
- NCT02249156
- Acronym / sponsor ID
- AG043850-01
- Keywords
- Health economics, Financial incentives, Health care spending
- Study type
- Observational
- Recruitment status
- WithdrawnReason stopped: This was an observational study. The owners of the data refused to give us the data so we could not proceed with the analyses.
- Enrolment
- 0 participants (actual)
- Lead sponsor
- Harvard Medical School (HMS and HSDM)University, hospital or other
- Collaborators
- University of Southern California, University of California, Berkeley
- Study start
- Aug 2014
- Primary completion
- Dec 2017 (estimated)
- Study completion
- Mar 2018 (estimated)
- First posted
- 25 Sep 2014 (estimated)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed 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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Financial incentive group | – | Behavioral: Financial incentive for choosing a lower-cost providerEmployees 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. |
| Control population | – | –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. |
Who may qualify
- Ages
- 1 year to 99 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- The study population is composed of employees of both the intervention and control employers. We will use medical health plan claims from 2012 to 2014 for laboratory and imaging services across the study population. Health plan claims data will be provided by Castlight Health. The approximate number of providers of laboratory and radiology services who will provide care to this population will be 475,000.
Key inclusion criteria (1 listed)
- Employee or dependent of an intervention or control employer
Key exclusion criteria (1 listed)
- Not continuously enrolled in health plan and therefore some claims may be missing
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Financial incentive for choosing a lower-cost providerEmployees 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. | Behavioural | Financial incentive group |
Primary outcome
Service volume
Time frame: In 12 months after intervention initiated
Service volume for each provider-employer before and after the introduction of the rewards programs. We will estimate models with several potential measures of volume including the number of services performed, the number of unique patients seen by the provider, and the fraction of all services received by the employees.
2 secondary outcomes
Total spending
Time frame: In 12 months after intervention initiated
Utilization of laboratory and imaging services
Time frame: In 12 months after intervention initiated
Results and safety
The study was withdrawn before enrolling participants, so there are no results.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Jul 2019.
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Receipt of Radiology Studies and Receipt of Laboratory Tests, run by Harvard Medical School (HMS and HSDM).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: withdrawn, as of the sponsor's last update on 16 Jul 2019. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study was withdrawn before enrolling participants, so there are no results.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Service volume (time frame: In 12 months after intervention initiated). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 16 Jul 2019; AzTrials last refreshed its copy on 7 Nov 2026.