Mahoney had to convince skeptics that the likely long-term savings from more faithful medication usage justified the expense. Pitney Bowes would be footing a much heftier portion of employees’ drug bills at a time when consultants were forecasting a 22 percent to 24 percent increase in drug costs. And it would also forfeit the rebates that pharmaceutical companies offer for giving their drugs preferred status. Pitney Bowes was potentially adding close to a million dollars to its pharmacy bill.

After phasing in the price cuts over two years, all that Mahoney and Hom could do was wait to see if the model was right. As they waited, they kept a close eye on Pitney Bowes’ pharmacy costs. "We didn’t see the huge spike in pharmacy costs that would have been the indicator of failure," says Mahoney.

Prognosis: Cautious Optimism

Their patience was rewarded this year with good news: The median cost of care for employees with asthma decreased 15 percent in 2002, while costs for diabetes patients fell 12 percent. Mahoney reports heaving a huge sigh of relief when he realized that reality bore out the Medical Scientists model’s prediction. He attributes the lower costs to people refilling their prescriptions more faithfully, and taking "control" drugs that prevent problems (and costly hospitalizations) instead of more expensive "rescue" drugs. Mahoney says that the rate for patients obtaining control drugs is up approximately 20 percent. In the case of asthma and diabetes drugs, Mahoney reports that Pitney Bowes’ prescription costs are actually down about 10 percent after 18 months because patients need fewer additional medications to treat emergencies or side effects."In your wildest dreams, [lowering drug prices] is not first thing you’d do," says Mahoney. "But this turned out to be appropriate."

One reason that Pitney Bowes achieved such promising results is that Mahoney and Hom didn’t second-guess the data from Medical Scientists and resisted the temptation to lower prices willy nilly. Statin drugs, for instance, are very effective at lowering cholesterol, but their prices were not reduced because the model found no link between patients failing to take these drugs and high costs.

As it turns out, lowering the cost of hypertension drugs did not increase compliance or lower treatment costs. "What’s true for asthma and diabetes may not be true for hypertension," says Mahoney. Predictive modeling "is going to be the key to helping us understand all that."

Gartner’s Burghard says that predictive modeling has been used for years by insurance companies to set rates. But calculating the probability that someone will live X number of years to determine his life insurance premium is easier than figuring out what causes health-care costs to increase and how best to control them. Merely investing in predictive modeling won’t yield a return-just a projection that, say, costs will increase by a certain percentage. "The return comes when you figure out why and what you’re going to do about it-and then only if what you do about it makes a difference," Burghard explains.