Natural language processing as an alternative to manual reporting of colonoscopy quality metrics.

Autor: Raju GS; Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA., Lum PJ; Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA., Slack RS; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA., Thirumurthi S; Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA., Lynch PM; Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA., Miller E; Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA., Weston BR; Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA., Davila ML; Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA., Bhutani MS; Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA., Shafi MA; Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA., Bresalier RS; Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA., Dekovich AA; Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA., Lee JH; Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA., Guha S; Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA., Pande M; Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA., Blechacz B; Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA., Rashid A; Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA., Routbort M; Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA., Shuttlesworth G; Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA., Mishra L; Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA., Stroehlein JR; Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA., Ross WA; Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Jazyk: angličtina
Zdroj: Gastrointestinal endoscopy [Gastrointest Endosc] 2015 Sep; Vol. 82 (3), pp. 512-9. Date of Electronic Publication: 2015 Apr 22.
DOI: 10.1016/j.gie.2015.01.049
Abstrakt: Background and Aims: The adenoma detection rate (ADR) is a quality metric tied to interval colon cancer occurrence. However, manual extraction of data to calculate and track the ADR in clinical practice is labor-intensive. To overcome this difficulty, we developed a natural language processing (NLP) method to identify adenomas and sessile serrated adenomas (SSAs) in patients undergoing their first screening colonoscopy. We compared the NLP-generated results with that of manual data extraction to test the accuracy of NLP and report on colonoscopy quality metrics using NLP.
Methods: Identification of screening colonoscopies using NLP was compared with that using the manual method for 12,748 patients who underwent colonoscopies from July 2010 to February 2013. Also, identification of adenomas and SSAs using NLP was compared with that using the manual method with 2259 matched patient records. Colonoscopy ADRs using these methods were generated for each physician.
Results: NLP correctly identified 91.3% of the screening examinations, whereas the manual method identified 87.8% of them. Both the manual method and NLP correctly identified examinations of patients with adenomas and SSAs in the matched records almost perfectly. Both NLP and the manual method produced comparable values for ADRs for each endoscopist and for the group as a whole.
Conclusions: NLP can correctly identify screening colonoscopies, accurately identify adenomas and SSAs in a pathology database, and provide real-time quality metrics for colonoscopy.
(Copyright © 2015 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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