Literature DB >> 26210525

The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload.

Robert J McDonald1, Kara M Schwartz2, Laurence J Eckel2, Felix E Diehn2, Christopher H Hunt2, Brian J Bartholmai2, Bradley J Erickson2, David F Kallmes3.   

Abstract

RATIONALE AND
OBJECTIVES: To examine the effect of changes in utilization and advances in cross-sectional imaging on radiologists' workload.
MATERIALS AND METHODS: All computed tomography (CT) and magnetic resonance imaging (MRI) examinations performed at a single institution between 1999 and 2010 were identified and associated with the total number of images for each examination. Annual trends in institutional numbers of interpreted examinations and images were translated to changes in daily workload for the individual radiologist by normalizing to the number of dedicated daily CT and MRI work assignments, assuming a 255-day/8-hour work day schedule. Temporal changes in institutional and individual workload were assessed by Sen's slope analysis (Q = median slope) and Mann-Kendall test (Z = Z statistic).
RESULTS: From 1999 to 2010, a total of 1,517,149 cross-sectional imaging studies (CT = 994,471; MRI = 522,678) comprising 539,210,581 images (CT = 339,830,947; MRI = 199,379,634) were evaluated at our institution. Total annual cross-sectional studies steadily increased from 84,409 in 1999 to 147,336 in 2010, representing a twofold increase in workload (Q = 6465/year, Z = 4.2, P < .0001). Concomitantly, the number of annual departmental cross-sectional images interpreted increased from 9,294,140 in 1990 to 94,271,551 in 2010, representing a 10-fold increase (Q = 8707876/year, Z = 4.5, P < .0001). Adjusting for staffing changes, the number of images requiring interpretation per minute of every workday per staff radiologist increased from 2.9 in 1999 to 16.1 in 2010 (Q = 1.7/year, Z = 4.3, P < .0001).
CONCLUSIONS: Imaging volumes have grown at a disproportionate rate to imaging utilization increases at our institution. The average radiologist interpreting CT or MRI examinations must now interpret one image every 3-4 seconds in an 8-hour workday to meet workload demands.
Copyright © 2015. Published by Elsevier Inc.

Keywords:  Cross-sectional imaging; fatigue; imaging volumes; utilization; workload

Mesh:

Year:  2015        PMID: 26210525     DOI: 10.1016/j.acra.2015.05.007

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


  58 in total

1.  Deep Learning-Based Detection of Intracranial Aneurysms in 3D TOF-MRA.

Authors:  T Sichtermann; A Faron; R Sijben; N Teichert; J Freiherr; M Wiesmann
Journal:  AJNR Am J Neuroradiol       Date:  2018-12-20       Impact factor: 3.825

2.  Radiology resident MR and CT image analysis skill assessment using an interactive volumetric simulation tool - the RadioLOG project.

Authors:  Pedro Augusto Gondim Teixeira; Romain Cendre; Gabriela Hossu; Christophe Leplat; Jacques Felblinger; Alain Blum; Marc Braun
Journal:  Eur Radiol       Date:  2016-05-10       Impact factor: 5.315

Review 3.  Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives.

Authors:  Z Shi; B Hu; U J Schoepf; R H Savage; D M Dargis; C W Pan; X L Li; Q Q Ni; G M Lu; L J Zhang
Journal:  AJNR Am J Neuroradiol       Date:  2020-03-12       Impact factor: 3.825

4.  Baseline Survey of the Neuroradiology Work Environment in the United States with Reported Trends in Clinical Work, Nonclinical Work, Perceptions of Trainees, and Burnout Metrics.

Authors:  J Y Chen; F J Lexa
Journal:  AJNR Am J Neuroradiol       Date:  2017-05-18       Impact factor: 3.825

Review 5.  European trends in radiology: investigating factors affecting the number of examinations and the effective dose.

Authors:  Hamidreza Masjedi; Mohammad Hosein Zare; Neda Keshavarz Siahpoush; Seid Kazem Razavi-Ratki; Fatemeh Alavi; Masoud Shabani
Journal:  Radiol Med       Date:  2019-12-16       Impact factor: 3.469

6.  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

Review 7.  Artificial Intelligence in Musculoskeletal Imaging: Current Status and Future Directions.

Authors:  Soterios Gyftopoulos; Dana Lin; Florian Knoll; Ankur M Doshi; Tatiane Cantarelli Rodrigues; Michael P Recht
Journal:  AJR Am J Roentgenol       Date:  2019-06-05       Impact factor: 3.959

8.  Effects of Interpersonal Skills Training on MRI Operations in a Saturated Market: A Randomized Trial.

Authors:  Amna A Ajam; Xuan V Nguyen; Ronda A Kelly; Joseph A Ladapo; Elvira V Lang
Journal:  J Am Coll Radiol       Date:  2017-04-28       Impact factor: 5.532

9.  An Open-Source, Vender Agnostic Hardware and Software Pipeline for Integration of Artificial Intelligence in Radiology Workflow.

Authors:  Jae Ho Sohn; Yeshwant Reddy Chillakuru; Stanley Lee; Amie Y Lee; Tatiana Kelil; Christopher Paul Hess; Youngho Seo; Thienkhai Vu; Bonnie N Joe
Journal:  J Digit Imaging       Date:  2020-08       Impact factor: 4.056

Review 10.  Artificial intelligence in radiology.

Authors:  Ahmed Hosny; Chintan Parmar; John Quackenbush; Lawrence H Schwartz; Hugo J W L Aerts
Journal:  Nat Rev Cancer       Date:  2018-08       Impact factor: 60.716

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.