Literature DB >> 29929936

It is About "Time": Academic Neuroradiologist Time Distribution for Interpreting Brain MRIs.

Altaib Al Yassin1, Mohammad Salehi Sadaghiani2, Suyash Mohan2, R Nick Bryan2, Ilya Nasrallah2.   

Abstract

RATIONALE AND
OBJECTIVES: Efficiency is central to current radiology practice. Knowledge of report generation timing is essential for workload optimization and departmental staffing decisions. Yet little research evaluates the distribution of activities performed by neuroradiologists in daily work.
MATERIALS AND METHODS: This observational study tracked radiologists interpreting 358 brain magnetic resonance imaging (MRI) in an academic practice over 9 months. We measured the total duration from study opening to report signing and times for five activities performed during this period: image viewing, report transcription, obtaining clinical data, education, and other. Attendings, fellows, and residents reading studies independently and attendings over-reading trainee-previewed studies were observed.
RESULTS: Ten attendings, 12 fellows, and 13 residents spent a mean of 11, 18, and 16 minutes reading brain MRIs independently. Mean duration was significantly different comparing attendings in all assignments to fellows (18.36 ± 1.05 minutes, p = 0.0001) or residents (16.31 ± 1.11 minutes, p = 0.001) but not between fellows/residents. Mean duration among attendings reading independently versus over-reading trainees was not statistically different. Attendings spent the same time on image viewing (4.07-5.33 minutes) with or without trainees. Attending transcription time was shortest when over-reading trainees (2.24 minutes) and longest when reading independently (4.20 minutes), demonstrating benefit of the draft report. Fellows and Residents spent longer on image viewing (7.14 minutes and 8.06 minutes, respectively) and transcription (7.02 minutes and 5.40 minutes, respectively) than attendings reading independently.
CONCLUSION: Neuroradiologist time/activity distributions for reading brain MRI studies were measured, setting the stage to establish a benchmark for future reference and suggesting opportunities for greater efficiency. Furthermore, report production time can be decreased when a draft report is available.
Copyright © 2018. Published by Elsevier Inc.

Keywords:  Brain MR; Report production time; Work flow

Mesh:

Year:  2018        PMID: 29929936     DOI: 10.1016/j.acra.2018.04.014

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  4 in total

Review 1.  Artificial intelligence for precision education in radiology.

Authors:  Michael Tran Duong; Andreas M Rauschecker; Jeffrey D Rudie; Po-Hao Chen; Tessa S Cook; R Nick Bryan; Suyash Mohan
Journal:  Br J Radiol       Date:  2019-07-26       Impact factor: 3.039

2.  Artificial Intelligence System Approaching Neuroradiologist-level Differential Diagnosis Accuracy at Brain MRI.

Authors:  Andreas M Rauschecker; Jeffrey D Rudie; Long Xie; Jiancong Wang; Michael Tran Duong; Emmanuel J Botzolakis; Asha M Kovalovich; John Egan; Tessa C Cook; R Nick Bryan; Ilya M Nasrallah; Suyash Mohan; James C Gee
Journal:  Radiology       Date:  2020-04-07       Impact factor: 11.105

3.  Am I Ready to Be an Independent Neuroradiologist? Objective Trends in Neuroradiology Fellows' Performance during the Fellowship Year.

Authors:  J H Masur; J E Schmitt; D Lalevic; T S Cook; L J Bagley; S Mohan; A P Nayate
Journal:  AJNR Am J Neuroradiol       Date:  2021-03-04       Impact factor: 3.825

4.  Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance.

Authors:  Jeffrey D Rudie; Jeffrey Duda; Michael Tran Duong; Po-Hao Chen; Long Xie; Robert Kurtz; Jeffrey B Ware; Joshua Choi; Raghav R Mattay; Emmanuel J Botzolakis; James C Gee; R Nick Bryan; Tessa S Cook; Suyash Mohan; Ilya M Nasrallah; Andreas M Rauschecker
Journal:  J Digit Imaging       Date:  2021-06-15       Impact factor: 4.903

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.