Daniel Weber1, David McCarthy2, Jay Pathmanathan3. 1. Veterans Administration Boston Healthcare System (VABHS) Boston, MA, USA; University of Massachusetts, Worcester, MA, USA. Electronic address: daniel.weber@umassmemorial.org. 2. Veterans Administration Boston Healthcare System (VABHS) Boston, MA, USA; Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA. Electronic address: david.mccarthy@va.gov. 3. Veterans Administration Boston Healthcare System (VABHS) Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Massachusetts General Hospital, Boston, MA, USA. Electronic address: jay.pathmanathan@va.gov.
Abstract
PURPOSE: EEG interpretation is a fundamental procedural skill in the practice of neurology, but there is no standardized method for educating residents. One-to-one instruction is commonly employed, but is time intensive for supervising physicians, provides arbitrary exposure to normal and abnormal EEG patterns, and often lacks immediate and detailed feedback on performance. Here, we investigated the effectiveness of a novel automated program to assist in educating neurology residents in EEG interpretation. METHODS: An EEG teaching program was developed to provide neurology residents EEG training less dependent on attending supervision. Residents enter interpretations of full-length pre-selected EEGs and receive immediate feedback based on consensus interpretation of supervising epileptologists. Resident learning was assessed based on performance on matched pre- and post-tests covering common EEG findings including artifacts, normal variants, and abnormalities. RESULTS: Twenty residents were included in this analysis: 12 post-graduate year (PGY) 3 and eight PGY 4 neurology residents. All residents showed improvement, from a mean score of 42.7% (95% CI 36.9-48.5%) on the pre-test to 75.4% (95% CI 70.7-80.2%) on the post-test (p<0.001). No significant difference was noted between the classes. Residents reported taking 16-30h to complete this teaching module spread over a 3-week rotation. CONCLUSION: This pilot study demonstrated the effectiveness of an automated EEG teaching program used by neurology residents in training. This tool could serve as an effective method of supplementing resident education.
PURPOSE: EEG interpretation is a fundamental procedural skill in the practice of neurology, but there is no standardized method for educating residents. One-to-one instruction is commonly employed, but is time intensive for supervising physicians, provides arbitrary exposure to normal and abnormal EEG patterns, and often lacks immediate and detailed feedback on performance. Here, we investigated the effectiveness of a novel automated program to assist in educating neurology residents in EEG interpretation. METHODS: An EEG teaching program was developed to provide neurology residents EEG training less dependent on attending supervision. Residents enter interpretations of full-length pre-selected EEGs and receive immediate feedback based on consensus interpretation of supervising epileptologists. Resident learning was assessed based on performance on matched pre- and post-tests covering common EEG findings including artifacts, normal variants, and abnormalities. RESULTS: Twenty residents were included in this analysis: 12 post-graduate year (PGY) 3 and eight PGY 4 neurology residents. All residents showed improvement, from a mean score of 42.7% (95% CI 36.9-48.5%) on the pre-test to 75.4% (95% CI 70.7-80.2%) on the post-test (p<0.001). No significant difference was noted between the classes. Residents reported taking 16-30h to complete this teaching module spread over a 3-week rotation. CONCLUSION: This pilot study demonstrated the effectiveness of an automated EEG teaching program used by neurology residents in training. This tool could serve as an effective method of supplementing resident education.
Authors: Frank A Rasulo; Philip Hopkins; Francisco A Lobo; Pierre Pandin; Basil Matta; Carla Carozzi; Stefano Romagnoli; Anthony Absalom; Rafael Badenes; Thomas Bleck; Anselmo Caricato; Jan Claassen; André Denault; Cristina Honorato; Saba Motta; Geert Meyfroidt; Finn Michael Radtke; Zaccaria Ricci; Chiara Robba; Fabio S Taccone; Paul Vespa; Ida Nardiello; Massimo Lamperti Journal: Neurocrit Care Date: 2022-07-27 Impact factor: 3.532