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Unlock Cost Savings With Predictive Benefits Data

When it comes to health care analytics, many industries have only begun to scratch the surface. The sheer volume and depth of such data provides not only a descriptor of the past but also a predictor of the future. And for HR decision-makers, having that kind of crystal ball can make all the difference.

That’s especially true for health care benefits, which consistently rank as one of many employers’ top three business expenses, reports Forbes. Finding the right benefits package with enough services to cover gaps in care without costly extras that employees won’t use can be challenging. But with predictive analytics, it’s easier than you might think.

By using claims reports and trends data of employee health patterns at large, every employer can build the right plan as cost-effectively as possible, trimming out unnecessary bells and whistles to get at the services employees want (and need) the most. Here’s how.

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1. Smarter Benefits Management

If the analytics show inefficient uses of the plan by employees, for example choosing brand names over generics or excessive use of the emergency room, it might flag the need to call in reinforcements. After all, employees might not choose to make those decisions — perhaps they don’t know the alternatives.

Depending on a broker’s services, those “reinforcements” could include trained specialists who can counsel employees toward making better health care choices. Sometimes, brokers can educate employees about low-cost alternatives for care plans or preventive coverage, or even serve as resources if employees have questions about upcoming care needs, like a hip replacement or pregnancy.

By looking at health care analytics — and especially at patterns that could indicate a need for more employee education — employers can help preempt poor decisions, rein in inefficiencies and cut excessive costs.

2. Informed Wellness Programs

Another plus side to predictive benefits data is that it can serve as a major proof point for preventive wellness programs.

If aggregate data shows that employees submit claims for recurrent chronic illnesses that could be made better with healthy lifestyle choices (including diabetes and heart disease), it might be worth the investment to put more resources toward those types of programs.

After all, nearly 7 in 10 employers have already done so, reports the Society for Human Resources Management (SHRM). Depending on employee needs, great examples of those resources include smoking-cessation programs, physical-activity trackers, nutritional guidance and stress-reduction support.

Those programs not only help those with chronic illness, but they can also preventively help those who don’t yet have a chronic illness but whom predictive data says are higher risk for getting one someday. In both scenarios, employees and employers alike benefit from the cost savings and improved outcomes.

3. Spotting Gaps in Care

Sometimes, claims data can tell you not just how people do use their health plan, but also how they don’t.

For example, repeated emergency room visits for the exact same underlying issue, such as a panic attacks, could indicate a dire need for expanded mental health coverage or programs so that employees can get ongoing maintenance support.

Or, if employees routinely file out-of-network benefits for unique types of specialists it might be worth investigating to see if the policy could be adapted to accommodate them.

The Power of Biometric Data

While claims reports are just one of the business analytics tools that can yield helpful insights, some employers have unlocked even deeper cost savings through biometric screenings, reports SHRM.

In exchange for special perks like employee discounts or other incentives, employees undergo employer-paid biometric tests that include readings of blood pressure, cholesterol, triglycerides and body mass index.

Then, armed with the knowledge of that deeper level of data, employers can customize plans and programs for their workforce.

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