Literature DB >> 28049559

Workflow Dynamics and the Imaging Value Chain: Quantifying the Effect of Designating a Nonimage-Interpretive Task Workflow.

Matthew H Lee1, Andrew J Schemmel1, B Dustin Pooler1, Taylor Hanley2, Tabassum A Kennedy1, Aaron S Field3, Douglas Wiegmann2, John-Paul J Yu4.   

Abstract

PURPOSE: To assess the impact of separate non-image interpretive task and image-interpretive task workflows in an academic neuroradiology practice.
MATERIALS AND METHODS: A prospective, randomized, observational investigation of a centralized academic neuroradiology reading room was performed. The primary reading room fellow was observed over a one-month period using a time-and-motion methodology, recording frequency and duration of tasks performed. Tasks were categorized into separate image interpretive and non-image interpretive workflows. Post-intervention observation of the primary fellow was repeated following the implementation of a consult assistant responsible for non-image interpretive tasks. Pre- and post-intervention data were compared.
RESULTS: Following separation of image-interpretive and non-image interpretive workflows, time spent on image-interpretive tasks by the primary fellow increased from 53.8% to 73.2% while non-image interpretive tasks decreased from 20.4% to 4.4%. Mean time duration of image interpretation nearly doubled, from 05:44 to 11:01 (p = 0.002). Decreases in specific non-image interpretive tasks, including phone calls/paging (2.86/hr versus 0.80/hr), in-room consultations (1.36/hr versus 0.80/hr), and protocoling (0.99/hr versus 0.10/hr), were observed. The consult assistant experienced 29.4 task switching events per hour. Rates of specific non-image interpretive tasks for the CA were 6.41/hr for phone calls/paging, 3.60/hr for in-room consultations, and 3.83/hr for protocoling.
CONCLUSION: Separating responsibilities into NIT and IIT workflows substantially increased image interpretation time and decreased TSEs for the primary fellow. Consolidation of NITs into a separate workflow may allow for more efficient task completion.
Copyright © 2017 Elsevier Inc. All rights reserved.

Mesh:

Year:  2016        PMID: 28049559     DOI: 10.1067/j.cpradiol.2016.11.010

Source DB:  PubMed          Journal:  Curr Probl Diagn Radiol        ISSN: 0363-0188


  3 in total

Review 1.  Bias in Radiology: The How and Why of Misses and Misinterpretations.

Authors:  Lindsay P Busby; Jesse L Courtier; Christine M Glastonbury
Journal:  Radiographics       Date:  2017-12-01       Impact factor: 5.333

2.  Natural Language Processing for Imaging Protocol Assignment: Machine Learning for Multiclass Classification of Abdominal CT Protocols Using Indication Text Data.

Authors:  Brian Arun Xavier; Po-Hao Chen
Journal:  J Digit Imaging       Date:  2022-06-02       Impact factor: 4.903

3.  Characteristics of Durable Quality Improvement: A 6-Year Case Study.

Authors:  John-Paul J Yu; Anthony D Kuner; Tabassum A Kennedy
Journal:  J Am Coll Radiol       Date:  2018-07-18       Impact factor: 5.532

  3 in total

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