Johns Hopkins Execs: How To Make Medicine SmarterJohns Hopkins Execs: How To Make Medicine Smarter

Johns Hopkins has prioritized based on risk, developing smart order sets for high-morbidity scenarios such as diabetic management and anticoagulation management.

For example, the institution has been widely recognized for its work on preventing venus thromboembolism (VTE), a dangerous blood-clotting condition that has been decreased by embedding intelligent risk-factor algorithms into admissions, post-operative, and patient-transfer order sets.

The approach has raised VTE assessment rates significantly, and VTE incidents have dropped significantly among at-risk patients, which is a huge achievement when lives are at stake.

One big risk to all this work is alert fatigue -- the common problem whereby so-called intelligent systems and devices fire off so many alerts they are simply ignored. To minimize this, Johns Hopkins has built role-based and history-driven rules into many of its smart order sets.

Cardiologists, for example, would be assumed to be aware of the dangers of ordering both Cumadin and Ameoderone for the same patient whereas an intern would be shown an alert. But if an intern had already cleared such an alert for a particular patient on a particular day, the system recognizes that history and won't alert that intern again that day regarding that patient.

At the cutting edge of intelligent medicine is personalized care. Smart order sets are part of Johns Hopkins' strategy on that front, and the institution also has at least half a dozen departments working on other forms of personalized care.

The Department of Oncology, for instance, is exploring the use of genomics -- study of the DNA of individual patients and of cancer cells. Even today, the presence of specific genetic biomarkers can trigger patient-specific recommendations about using, or not using, certain drugs, tests and protocols.

In the future, the DNA of both the patient and his or her cancer will be "readily available and integrated into every decision we're making about your care," Greene said, though he acknowledged that might be years from now.

Given that there are 3 billion base pairs in the human genome, the most advanced work will involve big-data computing. Oncology researchers at Johns Hopkins are collaborating with the university's Department of Astronomy, which has a data center with rack upon rack of graphical processing units (GPUs, not CPUs) that are routinely applied to large-scale computational astronomy calculations.

Reel and Greene encouraged their peers to push the use of predictive analytics and use of data on the clinical side of their operations. Electronic health records and intelligent medicine aren't where they should be, Reel said, in part because the financial incentives have favored administrative uses of the technology -- reducing cost rather than improving diagnostics and clinical care.

"We talk often about productivity gains in medicine because of the introduction of technology, and systems tend to reward quantity rather than quality," she said.

The next step in intelligent IT support for medicine, she said, would be to work with clinicians to minimize time-wasting usability, interoperability, and security hurdles so they can spend less time interacting with technology while still getting ever-smarter decision support. That, she said, will give doctors more time with their patients.