At UNCHC, the software is digging into written mammography reports and finding abnormal results, then presenting them for followup examinations, says Carlton Moore, associate professor of medicine at UNCHC.
"We looked at a random sample of mammography reports taken from our electronic medical records done over the past five years," Moore says. Two physicians reviewed the reports; then IBM's software went through the same reports, and the team compared the two findings.
The software found 98 percent of the abnormalities that the physicians found. "It was actually very effective" and could be tweaked to be 100 percent effective, Moore says. UNCHC's results have been written up and submitted to a research journal for possible publication.
At present, UNCHC has a home-grown electronic medical record, but is in the process of switching over to Epic by next May, and is looking to integrate IBM's NLP software with Epic after that.
Moore says physicians' crazy day-to-day workflow makes a place for NLP to flag abnormalities for followup that otherwise would be overlooked.
"There's a lot of information coming physicians' way and they have to process it," Moore says. "They have a lot of interruptions. They're writing a note and might get interrupted, because a patient just called or a nurse wants you to see a patient right away, so it's very easy for things to kind of fall through the cracks."