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Erschienen in: Journal of Digital Imaging 1/2018

24.08.2017

Minimizing Barriers in Learning for On-Call Radiology Residents—End-to-End Web-Based Resident Feedback System

verfasst von: Hailey H Choi, Jennifer Clark, Ann K Jay, Ross W Filice

Erschienen in: Journal of Imaging Informatics in Medicine | Ausgabe 1/2018

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Abstract

Feedback is an essential part of medical training, where trainees are provided with information regarding their performance and further directions for improvement. In diagnostic radiology, feedback entails a detailed review of the differences between the residents’ preliminary interpretation and the attendings’ final interpretation of imaging studies. While the on-call experience of independently interpreting complex cases is important to resident education, the more traditional synchronous “read-out” or joint review is impossible due to multiple constraints. Without an efficient method to compare reports, grade discrepancies, convey salient teaching points, and view images, valuable lessons in image interpretation and report construction are lost. We developed a streamlined web-based system, including report comparison and image viewing, to minimize barriers in asynchronous communication between attending radiologists and on-call residents. Our system provides real-time, end-to-end delivery of case-specific and user-specific feedback in a streamlined, easy-to-view format. We assessed quality improvement subjectively through surveys and objectively through participation metrics. Our web-based feedback system improved user satisfaction for both attending and resident radiologists, and increased attending participation, particularly with regards to cases where substantive discrepancies were identified.
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Metadaten
Titel
Minimizing Barriers in Learning for On-Call Radiology Residents—End-to-End Web-Based Resident Feedback System
verfasst von
Hailey H Choi
Jennifer Clark
Ann K Jay
Ross W Filice
Publikationsdatum
24.08.2017
Verlag
Springer International Publishing
Erschienen in
Journal of Imaging Informatics in Medicine / Ausgabe 1/2018
Print ISSN: 2948-2925
Elektronische ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-017-0015-1

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