Big data’s power to bend the benefits cost curve

Big Data’s Power to Bend the Benefits Cost Curve

By | Senior Benefits Consultant | 3.4.2015
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The last several years have firmly ensconced us in the age of big data. Everything we’ve read, heard and seen has been supported by millions of data points, churned through lines of code and algorithm to provide us with the best paths to success. Expert insight into data and trends like Freakonomics, Outliers and a host of other popular works publicize the advantages of looking deeper at issues. 

Society, business, individuals can all learn from “big data.” The recent analysis of the impact that 1.5 pounds of air can have on a football game was a study in our ability to analyze. But to what end? Air is what the issue was about and air is all that came of it.

We have the ability to accumulate enormous amounts of information about a wide variety of subjects. Organizations are swimming in data about their customers, their business and themselves. If you can’t or won’t do something with the data, then let’s stop talking about it. But if you are willing and able, and can access some creativity to leverage that data, it can be very powerful and impactful.

First, we need to turn data into insight. Then, we need to turn insight into action.

We work in the insurance industry; specifically, we work with organizations to enhance their group employee benefits programs and maximize the impact of the structure, messaging and pricing of their offering. Our goal is to save our customers’ money and time. How we get there can look very different from client to client. This is where data needs to be informed. Our insight has to take into account culture, circumstance, current economic conditions and so on. Data in a vacuum is, well, vacuous.

Enough of the big picture theoretical banter. Now let’s talk about where the rubber meets the road. Without data, employee benefit cost-control is unattainable in today’s insurance marketplace. Inflationary pressures will continue to drive costs up, which means employers are running out of levers to pull to achieve long-term control. On top of that, data may be hard to come by depending on the size of the employer.

Step one is to identify every source and type of data available. Step two is aggregating the data in a way that is quantifiable and actionable. In the group employee benefits realm, meaningful metrics often include the following:

  • Drug utilization
  • Preventive care utilization
  • High claimants
  • Cost by condition
  • Employee risk factors
  • Administrative cost drivers
  • Employee benefits education gaps
  • Network compliance
  • Medical loss ratio

Depending on the size of the employer, some of the metrics above may be unattainable. For example, it’s common practice for medical insurance carriers to limit the availability of claims data for groups with fewer than 100 participants on plan. Employers should lean on their broker partner to determine what data is available based on size.

Having said that, the approach remains the same. Aggregate as much data as you can get your hands on and identify the underlying cost drivers of your organization’s healthcare expenditure. With a good sense of cost drivers, a meaningful strategy should be then be deployed to begin bending your year-over-year cost curve.

We typically encourage employers to make sure that every piece of a cost-containment strategy is done so in a way that is methodical and measurable. This will ensure that your return on investment can be tracked and will enhance the future evolution of your cost containment strategy.

Consultants in our industry often throw out tactics as the next silver bullet: wellness, disease management, administrative efficiency, consumerism and employee education and communications. At the end of the day, these tactics can impact an organization in a positive way, but it’s imperative that each are deployed in a way that is laser-focused on underlying cost drivers. No two organization’s benefit strategies should look the same.

Big data is a big waste of time unless you have the right strategy wrapped around your healthcare data.


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