While there is nothing new under the sun it does seem there have been some progress in utilising Natural Language Processing (NLP) for clinical and research purposes.
Posted: August 24, 2011 - 11:45 am ET
Are computer programs that read text-based medical records ready for prime-time use in quality improvement? Maybe so, according to research published in the latest issue of the Journal of the American Medical Association.
Quality-improvement researchers concluded that computerized natural language processing of free-text portions of patient medical records was more effective in identifying quality lapses in post-operative surgical patients than a computerized review of discrete data elements in those records. Natural language processing, or NLP, is the use of computers to read and process information expressed in human language.
The researchers looked at the randomly selected records of 2,974 hospitalized surgical patients at six U.S. Veterans Affairs Department medical centers from 1999 to 2006 that were reviewed through the Veterans Affairs Surgical Quality Improvement Program.
A report on their findings, "Automated Identification of Postoperative Complications Within an Electronic Medical Record Using Natural Language Processing," appears in the Aug. 24/31 issue of JAMA.
In conducting the study, researchers obtained from the VA's VistA electronic health-record system narrative clinical notes, such as discharge summaries, progress notes, operative notes, microbiology reports, imaging reports and outpatient visit notes.
The quality-improvement program records had been assessed for 20 "patient safety indicators" developed by the Agency for Healthcare Research and Quality that rely on structured administrative data, such as ICD-9 codes, from hospital discharge records to identify possible adverse events.
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Wednesday, August 24, 2011
Researchers use natural language processing to flag postsurgical complications in physicians' notes.
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."
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.
The bottom line here is that we now have essentially proven technology which in the right circumstances can make a real difference to what we know about what is going on in the health system. Another tool seems to be becoming very much more useful.