Ask an MHM Expert: Ian Liphart, Data Analyst  

MyHealthMath (MHM) has unique reporting capabilities for employers, which reveal specific opportunities for them to enhance their benefit packages. We dive into how MyHealthMath’s reporting can help benefit consultants and employers optimize employee benefits with Data Analyst Ian Liphart: 

What’s the coolest part of your job?  

Working across the various teams at MHM is certainly the coolest part of my day-to-day job. I’m able to hear about the various project’s teams are completing. I then get to produce data and analytics for the various departments, including the Customer Success and Implementation team and Sales and Marketing team. Secondly, I get to complete most client analytics projects from start to finish, which allows me to analyze their data and see how they respond to that analysis.  

How can employers use MyHealthMath reports and analytics to their advantage? 

The reports help employers better understand how employees choose a health plan each year. We illustrate the challenges facing their employees, such as strong plan inertia and high costs so that employers can have a sense of where gaps in their benefit plans might exist. We also show how Decision Doc affects employees’ plan decisions—did they move to a more optimal plan; did they contribute to their HSA, and so on. Additionally, we share insights on common questions troubling employees when they begin using our decision support platform.  

Importantly, we also show which plans are serving different populations best based on the reported medical usage. For example, our insights may show that one plan is always ranked as last. In other words, based on employees reported medical needs, this plan never serves any employees particularly well. Benefit teams can then use this data to shape their benefit design following year. 

What’s the most valuable piece of MyHealthMath reporting?  

Benefit teams can see which plans their employees chose, and we provide analytics on which plans are optimal for any given group. Without our reporting, benefit teams can’t see whether employees are choosing well based on their specific needs.  

How does Decision Doc affect movement and the associated costs for employers and employees? 

Studies show that employees typically roll over their health plan 93 percent of the time. Decision Doc interrupts plan inertia. Based on our data, Decision Doc drives plan movement 3 – 5x. Moreover, on average Decision Doc drives decreased costs for both employees and employers.  

Can you filter results by demographic information?  

We can filter results based on employee demographics, as long as we receive the data from the employer. Filtered results can show whether certain populations have different medical needs; they can also show whether some populations are more or less likely to use Decision Doc. We can also show how well (or not) a certain plan serves a certain demographic population. Showing enrollment or plan optimization by income tier can help benefit teams optimize plan design (more on this below).  

How can your reporting be used to address income inequities? 

Employees’ different financial circumstances can influence their plan choice. For example, nearly 6 million people in the U.S. delayed medical care because they didn’t have transportation. Making it easier for people to get to the doctor can have a big impact on equity—especially given that transportation barriers most affect those with less money and those with chronic conditions. MyHealthMath reporting can illustrate these income-based differences in plan choice, so benefit teams can develop mitigating strategies. We then highlight the impact of those strategies.  

MHM data analytics reporting is more robust than other companies, why is that? 

I would imagine our reporting is more robust since our analysis, and the guidance we give employees, are very detailed. All this granularity comes from the information we request from our clients. More granular data allows us to report at a more detailed level. Meaning, we can collect data at the employee level and follow their decisions from their previous health plan choice through open enrollment then onto their plan decision, connecting all those data points. This analysis allows us to have an overall picture of how open enrollment went for any given consumer.  

In your conversations, what are some of the trends that catch employers’ eyes?  

Employers are always intrigued when they see that one of their offered plans is not optimal for any of their employees who used Decision Doc. It might make them reconsider offering the plan, or their contribution to the plan. Another eye-catching trend is employer savings: we calculate how much the employer saved on taxes and premiums when their employees optimize their selection. Our satisfaction numbers are another statistic that always seems to impress employers.  

What’s the future state for these reports and what are you most excited about improving? 

We are seeing an industry push for immediate on-demand analytics and a format that is not static so decision makers can interact with the information they are provided. One of my goals is to push our analytics to be faster, insight accusations to be quicker, and reporting to be more intuitive.  

Check out our previous “Ask An MHM Expert” post with Implementation Specialist Becca Lovett here.