Big data makes a difference at Penn Medicine

The team of clinicians and medical informatics experts led by Mike Draugelis, chief data scientist at Penn Medicine in Philadelphia, is busy these days. Using insights from a massively parallel computer cluster that stores a huge volume of data, the team is building prototypes of new care pathways, testing them out with patients and feeding the results back into algorithms so that the computer can learn from its mistakes.

This big data approach to improving the quality of care has already produced one significant success: The Penn team has improved the ability of clinicians to predict which patients are at risk of developing sepsis, a highly dangerous condition, and it can now identify these patients 24 hours earlier than it could before the algorithm was introduced.

Draugelis and his colleagues work in the hospital of the University of Pennsylvania. On the academic research side, the university's medical school has launched an Institute of Biomedical Informatics (IBI) to do basic research using big data techniques. Announced in 2013, IBI is now coalescing a few months after naming Jason Moore, Ph.D., who founded a similar institute at Dartmouth, as its director. IBI will focus its efforts on precision medicine, a hot field that is starting to take off as genomic sequencing costs drop.

The effort to link genomic differences with "phenotypes" – the variations in patients’ characteristics and diseases – has been underway for five years, says C. William Hanson III, M.D., chief medical information officer and vice president of Penn Medicine and a member of IBI. But he sees this kind of research quickly accelerating.

Steven Steinhubl, M.D., director of digital medicine at the Scripps Translational Science Institute in La Jolla, Calif., agrees. "We're still on the rising part of the curve of what we're going to learn from big data," he says. "It's rapidly growing, but it will accelerate even more as large medical centers like UPenn take advantage of the data they're already collecting and add genomics on top of that."

Changing clinical pathways

Draugelis' team at Penn Medicine is using algorithms to tweak the guidelines that doctors and nurses follow in diagnosing and treating particular conditions. When a protocol changes, he explains, the clinical team must develop a new care pathway that specifies each step in the workflow of clinicians. It is very intensive work, and so is coding the changes that must be made in the algorithm to adjust to the feedback from the frontline of patient care.

"We're working in two week sprints, where the clinicians adjust their pathways, and we adjust the algorithms to their needs," Draugelis notes.
The team builds a prototype of a new pathway for a particular condition about once every six months. Currently, it is focusing on finding a better way to predict which patients have congestive heart failure and which are likely to be readmitted after discharge from the hospital. In addition, the team is working on acute conditions such as maternal deterioration after delivery and severe sepsis.