Researchers analyzed nearly 3,000 medical records from patients who had surgery at six medical centers that are part of the Veterans Health Administration for signs of pneumonia, sepsis, deep vein thrombosis, pulmonary embolism, and myocardial infarction. Tracking adverse incidents after surgery can help hospitals and health-care systems monitor how well an institution is following safety guidelines. But current methods can require extensive manpower—manually going through records to identify complications—or lack accuracy. “We wanted to try to replicate what a human would do, but in a way that would be scalable to a larger health-care setting and more cost-effective,” says Murff.
While developing the algorithms involved some trial and error, the end result was highly sensitive—they could identify between 80 percent and 90 percent of the complications previously noted in a manual review by trained nurses. The natural-language processing approach was more sensitive than another automated method that used billing codes to identify postsurgical complications. For example, Murff’s approach detected 82 percent of acute renal failure cases, compared with 38 percent for the billing-codes approach.
However, the new approach was less specific in many cases, detecting more false positives. “I think with more iterations, we could improve that,” says Murff. His team is now working on using the data in clinicians’ notes to predict patients’ risks of complications or other safety hazards.
One of the benefits of natural-language processing is its flexibility. Jha says the approach could be used for a number of applications. Most notable, he says, are clinical decision support tools, “where you give physicians ideas for how to treat patients better. Giving physicians suggestions that take information in clinical notes into account would be very powerful.”
Nuance, a leading maker of voice-recognition software, is already developing commercial systems that use natural-language processing to analyze medical information. The company is collaborating with the IBM team that developed Watson, the robot made famous by beating human contestants on the television game show Jeopardy, to apply the robot’s natural-language processing tools to medicine.