Docs go to tech for needed advice

June 2, 2014 in Medical Technology

This story was produced in collaboration with 

Long Island dermatologist Kavita Mariwalla knows well how to treat acne, burns and rashes. But when a patient came in with a potentially disfiguring case of bullous pemphigoid—a rare skin condition that causes large, watery blisters—she was stumped.

The medication doctors usually prescribe for the autoimmune disorder wasn’t available. So she logged in to Modernizing Medicine, a Web-based repository of medical information and insights, for help.  

Within seconds, she had the name of another drug that had worked in comparable cases.

“It gives you access to data, and data is king,” she said of Modernizing Medicine. “It’s been very helpful especially in clinically challenging situations.”  

The system, one of a growing number of similar tools around the country, lets her tap into the collective knowledge of 4,000 providers and 13 million patients, as well as data on treatments other doctors provide patients with similar profiles. Then it spits out recommendations.  

Tech titans like Google, Facebook, Microsoft and Apple already have made huge investments in artificial intelligence to deliver tailored search results and build virtual personal assistants. That approach is starting to trickle down into healthcare too, thanks in part to the push under the health reform law to leverage new technologies to improve outcomes and reduce costs, and to the availability of cheaper and more powerful computers. 

Computers can’t replace doctors at the bedside, but they are capable of crunching vast amounts of data and identifying patterns humans can’t. Artificial intelligence can be a tool to take full advantage of electronic medical records, transforming them from mere e-filing cabinets into full-fledged doctors’ aides that can deliver clinically relevant, high-quality data in real time. 

“Electronic health records [are] like large quarries where there’s lots of gold, and we’re just beginning to mine them,” said Dr. Eric Horvitz,  who is the managing director of Microsoft Research and specializes in applying artificial intelligence in health care settings.

[See also: Healthcare data goes from big to great.]

Increasingly, physician practices and hospitals around the country are using supercomputers and homegrown systems to identify patients who might be at risk for kidney failure, cardiac disease or postoperative infections and to prevent hospital readmissions, another key focus of health reform. 

And they’re starting to combine patients’ individual health data—including genetic information—with the wealth of material available in public databases, textbooks and journals to help come up with more personalized treatments.  

For now, the recommendations from Modernizing Medicine are largely based on what is most popular among fellow professionalssay, how often doctors on the platform prescribe a given drug or order a particular lab test. But next month, the system will display data on patient outcomes that the company has collected from its subscribers over the past year. Doctors will also be able to double-check the information against the latest clinical research by querying Watson, IBM’s artificially intelligent supercomputer. 

 “What happens in the real world should be informed by what’s happening in the medical journals,” said Daniel Cane, CEO of Florida-based Modernizing Medicine. “That information needs to get to the provider at the point of care.” 

[See also: How hospitals can make big data pay big.]

‘Quick and Seamless’ 

Using homegrown systems, doctors  at Vanderbilt University Medical Center in Nashville and St. Jude’s Medical Center in Memphis  are getting pop-up notifications—not unlike those on an iPhone—within individual patients’ electronic medical records. 

The alerts tell them, for instance, when a drug might not work for a patient with certain genetic traits. It shows up in bright yellow at the top of a doctor’s computer screen – hard to miss.

“With a single click, the doctor can prescribe another medication. It’s a very quick and seamless process,” said Vanderbilt’s Dr. Joshua Denny, one of the researchers who developed  the system there.

Denny and others used e-medical records on 16,000 patients to help computers predict which patients were likely to need certain medications in the future. 

Take the anti-blood clot medication Plavix. Some people can’t break it down. The Vanderbilt system warns doctors to give patients likely to need the medication a genetic test to see whether they can. If not, it gives physicians suggestions on alternative drugs.

Doctors heed the computer’s advice about two-thirds of the time, figuring in for example, the risks associated with the alternative medication.

“The algorithm is pretty good,” says Denny, referring to its ability to predict who’s going to need a certain drug. “It was smarter than my intuition.”

So far, computers have gotten really good at parsing so-called structured data—information that can easily fit in buckets, or categories. In health care, this data is often stored as billing codes or lab test values.

But this data doesn’t capture patients’ full-range of symptoms or even their treatments.  Images, radiology reports and the notes doctors write about each patient can be more useful. That’s unstructured data, and computers are less savvy at handling it because it requires making inferences and a certain understanding of context and intent. 

That’s the stuff humans are really good at doing — and it’s what scientists are trying to teach machines to do better.

“Computers are notoriously bad at understanding English,” said Peter Szolovits, the director of MIT’s Clinical Decision Making Group. “It’s a slow haul, but I’m still optimistic.” 

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