A Pill, A Scalpel, A Database - InformationWeek

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Software // Information Management

A Pill, A Scalpel, A Database

Health care is embracing IT to analyze a glut of medical data, find new cures, and provide more-personalized treatment

A few generations ago, most good physicians knew pretty much all there was to know about medicine. Today, no doctor can keep up with the explosion of medical and health information. It's a problem with consequences for how we live and die. Yet, the solutions--while still out of reach--are taking shape as information technology transforms mass-market medicine into the equivalent of a hospital built for one.

This isn't a story of miracle cures and happy endings. But we can paint a picture of three big areas in which health care is showing great promise and some progress in harnessing the spiraling volume of medical information in ways that improve care. The first area is filtering and then delivering information to the bedside, giving doctors data uniquely tailored to, say, a patient with kidney problems and a "genetic marker" that makes that person unlikely to respond to standard treatment.


The second involves putting existing data into forms that make it more useful fuel for all these efforts. So, instead of collecting billing code data that just says "malignant tumor," it will say the size of the tumor, adding a level of depth that doesn't exist today.

The third area is using analytics to mesh data that provide new insights, such as predicting more quickly when a drug trial has failed, so patients can be taken off experimental drugs that aren't making them well sooner.

Doctors need to take a wide range of factors into account in treating patients, says eHealth Initiative's Marchibroda. "Nirvana would be to do this on the fly."

Doctors need to take a wide range of factors into account in treating patients, says eHealth Initiative's Marchibroda. "Nirvana would be to do this on the fly."

Photo by Cat Gwynn
"We're headed to a new frontier," promises Janet Marchibroda, CEO of eHealth Initiative, a nonprofit group trying to improve health-care quality, safety, and efficiency through the adoption of IT. Other industries, including "the postal service, financial services, you name it," make very effective use of IT for data analysis to measure and improve quality--and ultimately that's what it can do for health care, she says.

One key measurement of this effort is how long it takes to get from a "eureka" moment in the lab to helping a patient get well. "It takes 14 to 17 years for new medical breakthroughs to become standard [practices] in health care," says Brett Davis, global solutions executive for IBM Healthcare and Life Sciences. New research and collaboration tools are beginning to make a difference, and the gap between discovering medical breakthroughs and applying and delivering them is shrinking, Davis says.

Harnessing this wealth of health-care information is dubbed "evidence-based medicine," because the findings that result from mining and analyzing the data serve as evidence of important correlations, such as whether more cancer patients with a certain genetic marker responded better to drug A versus drug B. That kind of information can help a doctor figure out the best chemotherapy plan for a patient.

It also should lead to tailored treatments that are based on everything known about a patient and similar patients and the most effective therapies for those people. IT pros in other industries will recognize many of the tools and techniques; it's not unlike how retailers capture data on customer buying patterns to decide which sweater to display in a particular store or how a stock trader combines multiple data flows to make a buy decision. To date, there's better data backing decisions about sweaters and stocks than those about people's health.

Bedside Delivery

One challenge is that there isn't one authoritative source for the latest medical advances. Even if there were, most doctors don't have an effective electronic platform to tap into real-time research and apply that data at the point of care.

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