Data analytics has been a hot topic in Benefits Magazine lately.
Whether it’s used to figure out how to better communicate with plan participants or how to contain health care costs, data analytics can be a powerful tool for employee benefit plans.
The Employee Benefits Glossary defines data analytics as the process of inspecting, cleaning, transforming, interpreting and modeling data to discover trends, patterns and other information to make benefit plan decision and changes.
Here are some of the key takeaways from Benefits Magazine articles on ways that plan sponsors can leverage data analytics:
1. To communicate more effectively with plan participants.
Example: Looking at data such as the bounce rate of emails sent to participants, the number of unique website visits or responses to a mailed piece can provide lessons for improving communication. Using data such as age of participants can help plan sponsors create more personalized communication. Participants nearing retirement would receive different messages about the retirement plan compared with participants who are just starting out in the workforce.
2. To uncover clues about fraud, waste, abuse and errors.
Example: Health care claims can be analyzed for factors such as the length of stay for a procedure. A participant who had an unusually short length of stay for a complex surgery should raise suspicion. It may be a clue that the health plan was billed for the wrong procedure. Analytics also can look at anomalies; for example, an ambulance bill without an accompanying hospital bill may be a clue that fraud is occurring.
3. To measure the impact of health management programs.
Example: Plan sponsors look at the outcomes (e.g., health care costs and return-to-work times) of program participants compared with those of nonparticipants, which vendors do a better job of managing chronic disease and high-risk members, and whether there are subsets of employees (e.g., by region, job category or demographic group) for whom the program is working better than others. A health plan sponsor that discovers better outcomes among participants might consider adding new, targeted programs to increase the chance of engaging nonparticipants.
4. To validate wellness program vendor claims.
Example: Armed with demographic data, plan sponsors can look at factors such as which worker populations the vendor is targeting, what assumptions the vendor built into its “expected benefits” statement and how the vendor plans to track return on investment (ROI). A wellness solution tailored to working moms likely would have little benefit for a plan or employer with a greater percentage of male employees, for instance. If a vendor promises an unrealistic engagement rate when predicting results, the expectation should be adjusted.
Learn More About How Plan Sponsors Can Use Data Analytics
For a more detailed look at data analytics, members can check out these Benefits Magazine articles:
- “Validate Wellness Vendors With a Data-Driven Approach” by Jason Elliott, August 2019
- “Benefit Communications: Getting Started With Data Analytics” by C. Anton Ames, February 2019
- “Six Ways to Reduce Employee Health Costs Through Data Analytics” by Marilyn Schlein Kramer and Bryan Curran, December 2018.
- “Uncovering Hidden Health Plan Costs With Data Analytics” by Laurent Laor, January 2018.
And register now to attend the upcoming Data Analytics for Group Health Plans Virtual Conference on Tuesday, November 12, 2019 from 10:30 a.m. – 2:30 p.m. ET. This virtual conference will share specific strategies for how to use health care plan data to spur effective change without passing additional costs to participants.
Kathy Bergstrom, CEBS
Senior Editor, Publications, at the International Foundation
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