Analytics are key to wellness success. Here’s why

What do benefits managers have in common with Walmart? Both have the power to leverage data to create a sustainable competitive advantage.

Like other leading retailers, Walmart mines vast quantities of data and applies predictive analytics to fuel solutions that improve store checkout processes, maximize inventory turnover and optimize product placement. Data analytics also helps the company identify shoppers’ preferences and personalize their shopping experiences. New parents, as identified by prior purchases, might receive digital coupons for infant products, for instance.

Walmart’s data intelligence gives the international retailer the ability to act upon insights quickly. One Halloween, for example, a novelty cookie generated high sales across the United States, but no sales at all in two U.S. stores. The company’s data analytics swiftly ascertained that the cookies were never put on the shelves at those stores. The problem was resolved immediately through high-visibility product placement.

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Employee benefits managers have similar opportunities to maximize their companies’ investments. The effective use of disparate data can help employers optimize their health, wellness and other benefits programs, and pinpoint the true value of their total rewards.

A data-driven approach to benefits analytics

Three out of five U.S. employers use health screenings and risk assessments to help employees detect conditions earlier, when treatment might be more effective and costs lower. However, the majority of employers do not measure the impact of these programs.

Those that do assess a program’s impact typically compare the dollars spent on it with the medical claims saved. Forward-thinking benefits managers, however, are examining the total value of investment (VOI) instead. This innovative approach analyzes not only the effect of a wellness initiative on medical costs, but also its influence on productivity, absenteeism, disability costs and other factors.

By aggregating and analyzing different types of data — such as claims and non-claims data — benefits managers can determine crucial correlations between preventive screenings, health outcomes and healthcare costs. Thus, they can develop more targeted benefits packages that reduce costs while improving overall employee health and productivity.

Case Study: Implementation of predictive analytics in preventative screenings

One recent initiative undertaken by a state employee health plan demonstrates the power of data analytics to reveal the VOI of preventive cancer screenings.

The state provides medical benefits to around 205,000 employees and dependents. The agency that administers the benefits program wanted to know whether preventive cancer screenings improved health outcomes, and whether the program was cost effective. Analyzing screening and claims data showed that 6% to 8% of those who underwent screenings for breast, colorectal or cervical cancer received a diagnosis of cancer or a related condition. The follow-up and all-important question was: did those members experience different outcomes than members whose cancers were not detected through screenings?

The results indicated a high VOI for members’ preventive cancer screenings:

· The majority of new cases of breast, colorectal and cervical cancer were detected through preventive screenings.
· Among members who received preventive screenings, 5% to 11% underwent treatments because of screening results — and not just for cancer. Treatments included removal of benign tumors or polyps.
· Those diagnosed with breast, colorectal or cervical cancer through the screenings experienced less invasive treatments and had fewer complications than those diagnosed through other means.
· New cases of breast and cervical cancer diagnosed through the preventive screenings had lower costs, on average, than cases detected through other means.

Positive action through data

This cancer screening example illustrates how data analysis can empower benefits managers to improve employees’ health outcomes while reducing costs. Analytics can help employers invest in more effective care management resources, as well as design benefits packages that provide positive VOI in wellness, screening and preventive care.

With the cost of health benefits continuing to rise, it’s critical to leverage data to determine the total value of wellness investments. Just as retailers use data analytics to improve the retail experience and increase profits, benefits managers should use data analytics to guide the design and evaluation of benefits and other rewards.

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