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Study uses NLP Tool for EHRs in Colonoscopy Quality Measurement

June 22, 2012
by Gabriel Perna
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A new study from researchers at the University of Pittsburgh School of Medicine and elsewhere has determined that natural language processing (NLP) programs for EHRs can "read" dictated reports and provide information to allow measurement of colonoscopy quality in an inexpensive, automated and efficient manner. The researchers found that the quality variation observed in the study within a single academic hospital system shows how much there is a need for routine quality measurement.

According to the researchers, gastroenterology specialty societies have advocated that providers routinely assess their performance on colonoscopy quality measures. Costs and time required to manually review colonoscopy and pathology reports have hampered such routine measurement.

"Routine measurement is not taking place, primarily because of the inconvenience and expense. Measuring adenoma detection rates and other quality measures typically requires manual review of colonoscopy and pathology reports. To address the difficulty in measuring physician quality, we developed the first NLP–based computer software application for measuring performance on colonoscopy quality indicators," study lead author Ateev Mehrotra, M.D., University of Pittsburgh, School of Medicine, said in a statement. "Our study highlights the potential for NLP to evaluate performance on colonoscopy quality measures in an inexpensive and automated manner. This type of routine quality measurement can be the foundation for efforts to improve colonoscopy quality."

The researchers, in an attempt to demonstrate the potential applications for and the efficiency of NLP-based colonoscopy quality measurement, used a previously validated NLP program to analyze colonoscopy reports and associated pathology notes. The resulting data were used to generate provider performance on colonoscopy quality measures. Using hospitals from the University of Pittsburgh Medical Center health care system, the sample was of 24,157 colonoscopy reports and associated pathology reports from 2008 to 2009.

Researchers found that performance on some colonoscopy quality measures was poor, while others were at benchmark levels, and there was a wide range of performance. They concluded that the study results highlight the potential of NLP to measure performance on colonoscopy quality measures.

The study appears in the June issue of GIE: Gastrointestinal Endoscopy, the monthly peer-reviewed scientific journal of the American Society for Gastrointestinal Endoscopy (ASGE).



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