Would you buy a product that promised that 60 percent of the time it works every time? Maybe for caricature news anchors like Ron Burgundy, there is no question that a method (exotic cologne) with this type of track record (for attracting women) would be a good investment. But what if that rate was found to be true for a surgery that cost tens of thousands of dollars and might save your life—with a small risk of serious complications? Or a relatively cheap allergy medication that was prone to causing headaches?

Such calculations—sometimes more eloquently phrased—are a mainstay of health care, especially as providers, patients and insurers face soaring costs and shrinking budgets.

But most of the current assessments of costs, risks and likely benefits are averages from a broad population. So any given individual—or subset of individuals—could have vastly different outcomes. And for those who have looked deeply at the data, those instances of great success or devastating side effects are not always random—a pattern that indicates that averages are not only superficial, but also can be hazardous.

"The choice that maximizes the population's health," write the authors of a new essay on the subject, "is not always the same as the best choice for a specific individual." And that is where our current health care calculations often fall short, says John Ioannidis, of the Stanford School of Medicine's Prevention Research Center and co-author of anew paper, published online July 12 in PLoS Medicine.

The medical field is awash in contingencies—every patient his or her own universe of variables. So when a new treatment is under evaluation, researchers often assess it in terms of how many high-quality days it adds to patients' lives on average—and the price is averaged out across that extra time. So an arthritis drug might run on average $4,849 per every extra year of "best quality life" (i.e. quality-adjusted life year, or QALY) that it delivers for an average patient. These reckonings allow for easy comparisons of a variety of different drugs and procedures. But of course this flat dollar value fails to acknowledge that some groups of people might see far greater benefits from the medication, whereas other groups might suffer worse outcomes than if they took a comparatively higher-priced medication—or perhaps than if they took no medications at all. Knowing those odds for specific patients or types of patients at least could save time, anguish and money wasted on ineffective interventions.

Some researchers have been calling for better individualized data for years. Richard Kravitz, a professor and co-vice chair of research at the University of California, Davis Division of General Medicine, co-authored a paper outlining the hazards of averages, several years ago. "It should seem obvious that treatment effects are not necessarily the same for everyone," he and his co-authors wrote in the 2004 article. "What may not be so obvious is that misapplying averages can cause harm, by either giving patients treatments that do not help or denying patients treatments that would help them."

A complex computation might not be able to dial up your own personal chances of having success with—or complications from—any given treatment any time too soon. But Ioannidis and his colleagues hope that with better information and analysis, providers and insurers can help to optimize the application of the interventions that are already out there.

Deficient in data
In this awkward era of trying to cut medical costs while improving care, researchers are often faced with a paucity of data on which to base their calculations.

Right now, the cost-effectiveness of a handful of interventions has been parsed for different subpopulations of patients. But Kravitz says that "it's really hard to get reliable information" for subpopulations. In fact, it's even hard to get reliable information on the general population. Big clinical trials are expensive, time-consuming and complicated. So setting them up to examine different subgroups of people—with enough in each group to make findings statistically significant—is a challenge. But Kravitz considers it a crucial step to the world of more personalized and more efficient treatment.

Ioannidis agrees. "I see this as a mainstream research effort," he says.

These sorts of tailored prescriptions would be a boon to those who do not fit in the comfortable center of the bell-curve distribution for any given intervention. But, says Kravitz, it could encounter resistance within the existing healthcare machinery. "It will drive the payers crazy," he says. For insurance companies and government programs that are accustomed to streamlined averages, this new model "breaks apart the single rule that applies to everybody." In the end, however, it could save money by helping to select the intervention that will have the most benefit delivered at the lowest cost for a certain profile, say, a middle-aged diabetic Asian-American man who is at high risk for a stroke—not just the general population.

Electronic health records, with their promise of tracking hundreds millions of people's medical data, could be a boon for those looking to create near-individual-level predictions for an intervention's benefit. "Bringing to bear the large databases for these problems will be part of the solution," Kravitz says.

But, cautions Ioannidis, these storehouses of data points will not necessarily be an adequate substitute for rigorously run trials. "We need to carefully validate the accuracy of the information they provide," he says.

The move for more individualized cost-effectiveness assessment has already started to influence the way trials are designed, Kravitz notes. Researchers are putting more emphasis on stratifying various groups in their study populations. But those larger and more complex trials hinge on funders that also must be convinced that more personalized effectiveness is going to be, if not required in the future, at least broadly desired.

Market solutions?
The larger financial burden consumers are likely to carry for their own care might end up eventually helping to bring about more demand for individual effectiveness estimates. With only knowledge of the average success rate for a surgical procedure (and full insurance coverage), a patient is likely to pick the one that delivers the best average outcome or has the lowest average risk regardless of cost—if the patient has the luxury of choosing at all (often payers and providers will make the choice based on average cost per quality-adjusted life year for the general population).

As patients are paying more out-of-pocket for care, interventions might be up for tougher scrutiny. Drug companies and doctors that have been able to market their goods and services directly to consumers could face competition to demonstrate that their offerings really will bring the best—and most economical—outcomes for a particular profile of patient.

But Kravitz suggests that there is only so much the average patient is actually going to assess when faced with different costs and so-called quality-adjusted life years. "There's going to need to be a lot of work on how to communicate the implications of these numbers to ordinary people," Kravitz says. "They're kind of difficult for economists and doctors to grasp," he adds. And even if perfect information were possible, people are not always coolly logical when it comes to their own or a loved one's healthcare decisions.

In an ideal market, more detailed predictions about how well an advertised drug or surgery might be expected to work for an individual consumer could theoretically drive at least slightly better, cheaper treatments. "At the moment consumers are at the mercy of corporate structures" without a sense of their place on the patient outcome curve, Ioannidis says. So making people more aware of "individualized cost-effectiveness will hopefully make their choices more rational—or at least better informed." And it will be up to providers, payers and policy makers to use the new information to make larger care decisions more rational, too.