Information commonly exits in silos: Pharmacy benefit managers, health plans, and absence management, disability and enrollment vendors generally all operate independently and invisibly to other health care entities that are caring for the same population of patients.

For a self-insured employer, accessing information from all sources is an easier undertaking than for a fully insured firm, but a great deal of aggregating is still involved. While an employer's health plan may offer some sort of analytic service, it's usually at an extra cost and sometimes doesn't incorporate all the data available.

"What the employers realized is that they were spreading their business around for good business reasons, but it made it hard to get a good picture of where their money was going," says Tamra Lair, a partner with Mercer's health and benefits business and leader of Mercer's analytic and measurement solutions practice. The question was how to take data built for another reason - like paying claims - and "put Humpty Dumpty back together to make that data easily analyzed for another purpose than was originally intended."

 

Employers have 'woken up'

With health care premiums increasing 131% in the last 10 years, employers no longer can ignore that it's imperative that they get as much data as possible about their employee populations. Also, more employers are self-funding - 60% today, compared to 49% a decade ago, which only increases the intensity to gain more information access.

In the pre-Internet age, data warehouses would sift through the data, find a benchmark and then compare individual employer populations against the 60 million other lives that lived in the warehouse. Thankfully, technology has helped such services become more sophisticated, using predictive modeling and systematic measures, according to Scott Haas, vice president of integrated health care metrics at Wells Fargo.

Just as important, though, there's been a change in employer attitudes about what is really driving costs. "Most employers over the past 25 years have believed that their challenge was to manage health and pharmacy claims and tinker with plan design," says Thomas Parry, president of the Integrated Benefits Institute. "Over the past several years, they've woken up and realized that you can't manage costs by managing claims, which has moved them into discussing wellness and engagement. If I'm the VP of benefits and the CFO is asking me what we're getting by investing in all these benefits, I have to integrate the data with the employee as the central point."

 

Examining analytics

A main question around health care analytics tools is whether they really deliver a return on investment. According to Vital Spring Technologies Chairman and CEO Sreedhar Potarazu, it takes 90 days to implement and accurately analyze the results of his company's health care analytics service, and clients generally see ROI in 12-18 months. That said, it's hard to measure an exact ROI for the service, because he says an analytics tool can't erase a company's health care problems automatically.

"The challenge a lot employers have today is that companies make health care decisions [with] no background in health care," Potarazu says.

For Benefit Informatics, which got its start as a data clearinghouse for hospitals in the 1980s and launched as an analytic platform in 2000, ROI is a "moving target," says marketing and communications director Chris Metcalf, as BI only started working with employers two years ago.

What's hot for BI right now is predictive modeling tools, which look into a company's history and predict risk using a Johns Hopkins system.

"We have always had clinical data because it's what the numbers are based [on], but this brings in the numbers around gaps in care," Metcalf says. BI's system is based in the cloud, which he says makes the service more affordable than having to download software.

Self-funded employers, Metcalf knows, have to make tougher decisions because they carry more risk, which makes larger employers more apt to go that route. From 1999 to 2007, the percentage of self-insured plans has risen, but large employers are consistently the largest portion, according to a Kaiser Family Foundation poll.

In the past 10 years, the tools have also developed to be more user-friendly, "to make it as easy as possible, so people who aren't computer science experts can query the data and know how deep and flexible the data is," Lair says.

"If you have 500 employees, you're more likely to be fully-insured, so the amount of data you have access to is less," she continues. "The larger the population, the more granular you can get with the service."

She points out that when a smaller employer uses these tools, they may not be able to go deep into solutions because they don't have as many people with a particular condition. "The robustness is different. It's a more stable picture if you're larger and you want to look at a particular location and what the drives the costs." If a small employer wants to look at a particular location and condition, it might get 10 people. Not exactly something on which to base a wellness program.

The future of analytics tools likely will remain murky until 2014, when employers will choose whether to continue offering health benefits or terminate plans and send employees to shop for health insurance in state-run exchanges, as allowed under health care reform. Until then, as employers ponder the 29.6% of compensation spent on benefits, they will look for answers. Having the data and tools is a place to start.

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