A new study shows that natural language processing programs can "read" dictated reports and provide information to allow measurement of colonoscopy quality in an inexpensive, automated and efficient manner. The quality variation observed in the study within a single academic hospital system reinforces the need for routine quality measurement. 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).
Gastroenterology specialty societies have advocated that providers routinely assess their performance
on colonoscopy quality measures. Such routine measurement has been hampered by the costs and time required to manually review colonoscopy and pathology reports. Natural language processing (NLP) is a field of computer science in which programs are trained to extract relevant information from text reports in an automated fashion.
"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," said study lead author Ateev Mehrotra, MD, MPH, University of Pittsburgh, School of Medicine. "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."
Colonoscopy is a cost-effective and common method of screening for colorectal cancer. However, colonoscopy may be imperfect in screening because, among other reasons, physicians miss adenomas, the precursors to colorectal cancer. There is great variation among physicians in the proportion of colonoscopies in which an adenoma is found as well as variations in other aspects of colonoscopy quality. This has led gastroenterology specialty societies to call for physicians to regularly monitor their performance on colonoscopy quality measures so that care can be improved.
The researchers' objective was to demonstrate the potential applications for and the efficiency of NLP-based colonoscopy quality measurement. In a cross-sectional study design, they 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. Nine hospitals in the University of Pittsburgh Medical Center health care system participated in the study. The study sample consisted of 24,157 colonoscopy reports and associated pathology reports from 2008 to 2009. Main outcome measurements were provider performance on seven quality measures: American Society of Anesthesiologsts (ASA) classification indicated; informed consent documented; quality of bowel preparation described; cecal landmarks noted; adenoma detection; withdrawal time documented; and biopsy taken for chronic diarrhea.