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Throughout my almost 30 years as a leader in total rewards and employee benefits, I evaluated hundreds of vendors and sifted through multitudes of ROI studies, each one different from the other. And despite my actuarial background, it was often difficult for me to decipher which results were real and which were not.
It’s not news that health care costs in the US are one of the most significant yet least predictable costs for many companies. As the person responsible for the design and financing of these plans, I was often charged with “doing more with less” which has become an all-too-familiar slogan in today’s economy. Yet US health care costs continue to rise year after year. As a result, we benefit leaders are faced with the daunting task of searching for the combination of health solutions that will deliver the best care and the greatest ROI.
After evaluating ROI studies over the years, I’ve discovered that it’s quite easy to inflate ROI metrics either by cherry-picking the data, extrapolating insights based on an insufficient data set, or overemphasizing certain metrics for marketing without adequate scientific backing. Being that the methodology used to calculate ROI is quite complex, it adds to the challenge of interpreting the study itself.
So how can you sift through the ROI noise?
Being a pragmatic person, I wanted to offer a checklist on How to Evaluate ROI Claims which is intended to be a guide in evaluating the accuracy of ROI studies and discern misleading results from reliable ones so you can make the best decision for you and your organization.
The checklist identifies five points to focus on:
- Sample size: The study sample size should include a number of participants that is representative and comparable to the total members using the benefit.
- Exclusions: The study group should accurately represent the benefit users. Excluding large groups of benefit users from studies weakens the results, as the remaining participants no longer represent typical users.
- Time frame: The study timeframe should include a full year of claims data; a partial year of claims data is a significant weakness as it fails to account for aspects like seasonality.
- Estimated projections: Results derived from real data (which capture past events) are significantly stronger than those relying heavily on estimated projections (which forecast future events).
- Independent review: Independently conducted analysis by third parties provides the most thorough and unbiased validation, ensuring the most accurate and reliable results.
Verifying ROI claims
Using the methodology in this checklist, Lyra recently announced our own ROI results. An underlying tenet of Lyra’s has always been to ‘follow the science’, which keeps our mental health care approach and our published research studies grounded in data and science. Our clinical and health economics researchers work to ensure that the ROI and savings studies we release use rigorous study design and methodologies to remove as much potential bias as possible.
I know all too well that ascertaining true ROI can be a daunting task so I hope you find this checklist helpful as you make your next mental health benefit decision.
Effectively assess ROI of mental health benefits
Download the checklist
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