Data analytics has the potential to help employers not only reduce long-term cost trends but also to enhance employee satisfaction and engagement as well as to improve employee health and productivity.
But how?
It’s not enough to simply have access to data, two leaders from Lockton, Inc. explained during a recent webcast from the International Society of Certified Employee Benefit Specialists. Benefits leaders must use a strategic process to ensure that they are correctly interpreting data and putting it to the best use.
Benefits professionals often talk about how they have a gut feeling or intuition that a program will work, said Brenda Mason, vice president of people solutions for Lockton. Using data analytics, they can meld that intuition with evidence to create a higher likelihood of success for a benefits program.
“[Data] could be coming from your carrier partners, your point solution partners, your broker and consultant partners,” Mason explained. “The idea is to cast the net as wide as you can to find these disparate sources of data and use them in a meaningful way.”
Mason and Alyssa Thomas, GBA, senior vice president of people solutions at Lockton, suggested the following seven best practices for benefits professionals that want to use data analytics.
- Start with the question.
Benefits leaders should know what their business objective is, what decisions they’re trying to inform and what hypothesis they’re testing. One timely example is the question of whether to cover glucagon-like peptide-1 (GLP-1) receptor agonist drugs for weight loss.
- Validate the data.
Once the question has been determined, benefits professionals should make sure they are looking at good data—considering factors such as completeness, consistency and accuracy. “Are we looking at a report from our vendor that we’re just trusting as being true, or are we really taking a look at the accuracy of the data?” Mason asked.
For example, one set of data from a health plan vendor might present gross claims while another looks at net claims. Or a data set might be missing—For instance, one major insurer carrier doesn’t report mental health data.
“Treat data as guilty until proven innocent,” Mason warned. “Assume that it’s not good data until you can provide that it is good data. It’s really important not to rush this step.”
- Integrate quantitative and qualitative data.
Quantitative data is “hard data” such as biometrics, utilization and cost trends. Qualitative data is soft data such as employee feedback from focus groups or employee resource groups (ERGs).
Returning to the GLP-1 example, a soft signal would be a vocal member population demanding coverage of the drugs for weight loss. And it proves the point that plans need to incorporate soft signals into their decisions, Thomas said.
Having that qualitative data will also help benefits staff sell new programs to company and plan leaders and to employees, she added. “We know that as humans we connect better to stories than we do to percentage points. You’ll have a more compelling story if you have some qualitative data to back it up.”
- Use a sequential process to analyze data.
“What we want to do is make sure that we’re not missing anything,” Mason said. Key factors to consider in a consultative analysis are:
- Demographics: For example, do near retirees make up a large portion of workers, or does the population include a lot of young women of child-bearing age?
- Financials: What’s driving the trend?
- Utilization: How is the population accessing and using health care services?
- Risk and illness burden: What conditions are prevalent? Do care gaps exist?
For example, when evaluating a virtual musculoskeletal (MSK) point solution, benefits professionals should consider the makeup of their population—Would they value the ability to meet with a physical therapist online, or would they prefer an in-person appointment?
An employer that has high maternity costs can better understand those costs by considering whether its workforce is younger or older, since factors such as infertility or higher risk pregnancies may be contributing to the higher costs.
- Look at the data through multiple lenses.
“You don’t want to be myopic,” Mason said. For example, she suggested that instead of looking only at the health insurance carrier provider’s report, benefit professionals should consider survey data or another external source, such as peer groups. “It’s good to have several different interpretations of the same type of data in order to get a better picture.”
When looking at total rewards programs, “it makes sense more than ever to take a look at what your peer groups are doing to make sure you are not missing something or you are falling out of a competitive stance as an employer of choice.”
Data also should be segmented by relevant subpopulations, which might include age, condition, plan type, geography and more. Trends should also be viewed over time.
- Don’t overreact to outliers.
Not all variation is meaningful, Mason said. “Outliers shouldn’t drive strategy unless you can determine that there are real systemic issues behind it,” she noted. “Don’t have a knee-jerk reaction. Take a step back and think critically.”
- Put the pieces together.
“Once we’ve laid out all our data, we’ve made sure that it’s credible, we’ve got multiple sources and we’ve done the steps in terms of critical thinking, don’t jump to a summary,” Mason warned.
Benefit professionals should sit back and group the evidence, considering what the evidence points to and how confident they are in the information. Then they should consider what actions should be taken, whether it’s to implement a new program or tweak another.
“Your executive team and your financial team, as well as every single member at your open enrollment meetings, is going to want to know why,” she said. “You need to have that reasoning . . . be able to back that up.”
