Rena Yadlapati1, Rajesh N Keswani2, Jody D Ciolino3, David P Grande2, Zoe I Listernick2, Dustin A Carlson2, Donald O Castell4, Kerry B Dunbar5, Andrew J Gawron6, C Prakash Gyawali7, Philip O Katz8, David Katzka9, Brian E Lacy10, Stuart J Spechler5, Roger Tatum11, Marcelo F Vela12, John E Pandolfino2. 1. Division of Gastroenterology and Hepatology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois. Electronic address: rena.yadlapati@northwestern.edu. 2. Division of Gastroenterology and Hepatology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois. 3. Department of Preventive Medicine-Division of Biostatistics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois. 4. Division of Gastroenterology & Hepatology, Medical University of South Carolina, Charleston, South Carolina. 5. University of Texas Southwestern Medical Center and the Dallas VA Medical Center, Department of Internal Medicine, Division of Gastroenterology and Hepatology, Dallas, Texas. 6. Division of Gastroenterology, University of Utah, Salt Lake City, Utah. 7. Division of Gastroenterology, Washington University School of Medicine, St. Louis, Missouri. 8. Division of Gastroenterology, Albert Einstein Medical Center, Thomas Jefferson University, Philadelphia, Pennsylvania. 9. Division of Gastroenterology, Mayo Clinic, Rochester, Minnesota. 10. Division of Gastroenterology, Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire. 11. Department of Surgery, University of Washington, Seattle, Washington. 12. Division of Gastroenterology, Mayo Clinic Arizona, Scottsdale, Arizona.
Abstract
BACKGROUND & AIMS: Quality esophageal high-resolution manometry (HRM) studies require competent interpretation of data. However, there is little understanding of learning curves, training requirements, or measures of competency for HRM. We aimed to develop and use a competency assessment system to examine learning curves for interpretation of HRM data. METHODS: We conducted a prospective multicenter study of 20 gastroenterology trainees with no experience in HRM, from 8 centers, over an 8-month period (May through December 2015). We designed a web-based HRM training and competency assessment system. After reviewing the training module, participants interpreted 50 HRM studies and received answer keys at the fifth and then at every second interpretation. A cumulative sum procedure produced individual learning curves with preset acceptable failure rates of 10%; we classified competency status as competency not achieved, competency achieved, or competency likely achieved. RESULTS: Five (25%) participants achieved competence, 4 (20%) likely achieved competence, and 11 (55%) failed to achieve competence. A minimum case volume to achieve competency was not identified. There was no significant agreement between diagnostic accuracy and accuracy for individual HRM skills. CONCLUSIONS: We developed a competency assessment system for HRM interpretation; using this system, we found significant variation in learning curves for HRM diagnosis and individual skills. Our system effectively distinguished trainee competency levels for HRM interpretation and contrary to current recommendations, found that competency for HRM is not case-volume specific.
BACKGROUND & AIMS: Quality esophageal high-resolution manometry (HRM) studies require competent interpretation of data. However, there is little understanding of learning curves, training requirements, or measures of competency for HRM. We aimed to develop and use a competency assessment system to examine learning curves for interpretation of HRM data. METHODS: We conducted a prospective multicenter study of 20 gastroenterology trainees with no experience in HRM, from 8 centers, over an 8-month period (May through December 2015). We designed a web-based HRM training and competency assessment system. After reviewing the training module, participants interpreted 50 HRM studies and received answer keys at the fifth and then at every second interpretation. A cumulative sum procedure produced individual learning curves with preset acceptable failure rates of 10%; we classified competency status as competency not achieved, competency achieved, or competency likely achieved. RESULTS: Five (25%) participants achieved competence, 4 (20%) likely achieved competence, and 11 (55%) failed to achieve competence. A minimum case volume to achieve competency was not identified. There was no significant agreement between diagnostic accuracy and accuracy for individual HRM skills. CONCLUSIONS: We developed a competency assessment system for HRM interpretation; using this system, we found significant variation in learning curves for HRM diagnosis and individual skills. Our system effectively distinguished trainee competency levels for HRM interpretation and contrary to current recommendations, found that competency for HRM is not case-volume specific.
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