Data-Driven Benefit Design

Data-Driven Benefit Design

October 27, 2020

You’ve heard the buzz about the capabilities of big data and AI. Did you realize that by leveraging big data and AI combined with your company’s own data you can improve health and benefit efficiency through better design?

According to the Global Employee Benefits Watch report, 60% of multinational corporations have adopted benefits analytics, and as a result, they are:

  • Twice as likely to budget and predict global costs
  • Twice as likely to be offering benefits employees want
  • 95% more likely to be offering benefits that impact and drive strategic business objectives.
  • Three times more likely to see a reduction in administration errors
  • Twice as likely to see a reduction in carrier overcharges.

BHS benefits specialists know first hand the power of data analytics to reduce cost and proactively target risk populations through benefit design. It’s not just multinational companies that can benefit from this type of analysis.

“There’s a phenomenal opportunity for employee benefit plan sponsors to maximize the impact and efficiency of their benefit offerings through data analytics. The problem is that with so many sources of information, it’s hard for employers to distill the data into actionable insights. That’s where we can help.”

- Jasmine Piggott, BHS benefit specialist

The BHS benefits team leverages the power of data to create innovative benefit solutions using a three-pronged approach: predictive modeling, population health analytics, and company-specific claims analysis.

The benefits team uses cloud-based predictive modeling tools fueled by machine learning and AI to garner better insights. This leads to better benefit design by matching your company’s employee pool with aggregated “look alike” population pools. Not only does this approach help identify risks to cover; it also identifies opportunities for proactive treatment that ultimately reduces your company’s healthcare costs.

HR departments can be a goldmine for data collection that can be parlayed into actionable insight when combined into the right data structure. For example, the collection of the following data will help shape benefit design:

  • Population data/demographics
  • Absenteeism – rate and distribution of sick days
  • Workers Compensation reports
  • Employee feedback to help identify gaps
  • Wellness program reporting

This data can be collected, organized, and “fed back into” cloud-based data repositories for benchmarking, comparison and baseline population predictions.

Intensive claims analysis extends beyond the highest-cost elements to include secondary reporting such as preventative screening, which can have even more predictive power than past claims.

For example, a secondary report would identify all the participants in a population who should be receiving preventative screenings–mammogram, colonoscopy, etc. That list is then cross-referenced with the actual number of screenings provided. These kinds of reports offer an opportunity to be proactive and use tools like population health, wellness programs and participant communication to reduce future claims and costs.

Intervention programs for chronic diseases such as high cholesterol, diabetes, hypertension, CAD, and arthritis are also designed to reduce costs that arise from deferred care.

Historical claims data that is analyzed using big data tools and algorithms helps generate predictions and cost-efficiency recommendations that are then synthesized with your company’s unique objectives, budget, and risk.

In this way, BHS delivers strategic benefit design that is more efficient, competitive, and that proactively promotes a healthy workforce.