Despite billions of dollars in incentives to support the adoption of electronic medical records, evidence that these systems improve the efficiency or quality of care has been scarce. But a new study shows that natural-language processing—a branch of computer science that employs linguistics to analyze regular speech—may greatly increase the utility of these records in improving care.
Researchers used this approach to sift through physicians’ notes, the richest and most complicated aspect of electronic medical records, for postsurgical complications such as pneumonia and sepsis. The method proved considerably more accurate than other automated systems. They say similar approaches could be used for a variety of applications, including predicting which patients are at risk, and developing automated tools that help doctors choose treatments.
“You can finally see how clinical data can be used to measure patient safety more systematically, and that we will really be able to use these things to manage care,” says Ashish Jha, a physician at Harvard Medical School who wrote an editorial accompanying the paper. The paper and editorial were published this week in Journal of the American Medical Association.
One of the most anticipated benefits of electronic medical records is computerized tracking of patients and institutions—to detect whether a particular patient is at risk for a specific complication, for example, or a specific department or hospital is performing more poorly than others.
Automated tracking is already in use in prescribing; for example, to detect when two medicines interact. Because prescription information is a highly structured part of the medical record, it has been fairly easy to analyze with software. However, harnessing the vast information available in less structured parts of the medical record, such as clinicians’ notes—which contains free-form entries about the patient’s history and status, including postsurgical complications—is much harder.
“If we can’t access that information, we will have a hard time monitoring records to improve care,” says Jha. “This paper is so powerful because it shows you can do this.”
Harvey Murff, a physician at Vanderbilt University, and collaborators tackled the problem using natural-language processing algorithms that incorporate certain rules of speech and language into analysis. For example, a keyword search could retrieve all instances of the word “pneumonia,” but natural-language processing could also take into account modifiers, such as “no signs of” pneumonia, that would yield a more accurate count.