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Posts Tagged ‘Peter Greene’

PLEASE SIT DOWN. MACHINE WILL SEE YOU SHORTLY, NUMBER 445343-01.

In ARTIFICIAL INTELLIGENCE, WARNING on July 31, 2011 at 10:29 pm
An electronic medical record example

FIRST RULE OF ROBOTICS, DO NO HARM TO HUMANS, THE SECOND RULE. . .

 

IF YOU READ THIS carefully, one finds that Johns Hopkins says this: “More technology, less doctor = less technology more doctor. War is peace and peace is war.  You figure if there is just a tiny little bit of a falsehood contained in the piece below?

Consult with your doctors early and often. That’s not just good advice for patients; it’s what Stephanie Reel, the top IT officer at Johns Hopkins Medicine, says healthcare technology leaders must do to master intelligent medicine.

Speaking at this week’s InformationWeek Healthcare IT Leadership Forum in New York, Reel, head of IT at Johns Hopkins Medicine since 1994 and Chief Information Officer at the University since 1999, said the institution’s success in technology innovation is directly attributable to its habit of involving clinicians in IT projects. That point was backed up by Dr. Peter Greene, Johns Hopkins’ Chief Medical Information Office, who joined a panel discussion I led exploring “What’s Next In Intelligent Medicine.”

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There have been plenty of innovations at Johns Hopkins Medicine, a $5 billion-a-year organization that includes a renowned medical school, five hospitals, a network of physician offices, and massive research operations. The institution was among the pioneers of electronic health records (EHRs) through a clinical information system deployed in the early 1990s. The effort succeeded, Reel says, because it was initially supported by half a dozen clinicians who worked with IT to develop the system.

This interdisciplinary group has since grown to include about 75 people, and it still meets every month to “listen to the people on the front lines who are trying to make a difference,” Reel said.

Johns Hopkins’ clinical information system has evolved to embrace the latest EHR technologies, and it has also become the foundation for what Johns Hopkins calls “smart order sets.” These order sets have built-in checks, balances, and analytics to ensure that appropriate procedures, tests, and protocols are followed as appropriate for each patient.

Among the hundreds of smart order sets now in use at Johns Hopkins, one guides decision on appropriate regimens for diabetics. Hundreds of variables and possible recommendations are preprogrammed into the order set, but the right regimen is determined though the combination of known patient history, up-to-the-moment clinical measures, and feedback provided by doctors on a series of questions conditionally asked by the system based on known patient data and the clinician’s answers to key questions.

Smart order sets are developed by specialists and extensively studied by peer-review groups before they are embedded into patient care workflows. “The challenge is that you have to do a lot of custom work that isn’t included in off-the-shelf EHR products, so you can’t take on everything,” said Greene.

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.

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