ArticleActa radiologica open2018
Process improvement for reducing side discrepancies in radiology reports.
Article in Acta radiologica open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Reporting efficiency in diagnostic imaging: Can plug-and-play general-purpose large language models outperform conventional speech recognition?European radiology · 2026Article
- A Neural Network Approach to Identify Left-Right Orientation of Anatomical Brain MRI.Brain and behavior · 2025Article
- Evaluation of guided reporting: quality and reading time of automatically generated radiology report in breast magnetic resonance imaging using a dedicated software solution.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- Beware (Surprisingly Common) Left-Right Flips in Your MRI Data: An Efficient and Robust Method to Check MRI Dataset Consistency Using AFNI.Frontiers in neuroinformatics · 2020Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLaterality errors in radiology reports can lead to serious errors in management. PURPOSE: To reduce errors related to side discrepancies in radiology reports from thoracic imaging by 50% over a six-month period with education and voice recognition software tools. MATERIAL AND
methodsAll radiology reports at the Thoracic Imaging Division from the fourth quarter of 2016 were reviewed manually for presence of side discrepancies (baseline data). Side discrepancies were defined as a lack of consistency in side labeling of any abnormality in the "Findings" to "Impression" sections of the reports. Process map and Ishikawa fishbone diagram (Microsoft Visio) were created. All thoracic radiologists were educated on side-related errors in radiology reports for plan-design-study-act cycle 1 (PDSA #1). Two weeks later, voice recognition software was configured to capitalize sides (RIGHT and LEFT) in the reports during dictated (PDSA# 2). Radiology reports were analyzed to determine side-discrepancy errors following each PDSA cycle (post-interventional data). Statistical run charts were created using QI Macros statistical software.
resultsBaseline data revealed 33 side-discrepancy errors in 47,876 reports with an average of 2.5 errors per week (range = 1-8 errors). Following PDSA #1, there were seven errors pertaining to side discrepancies over a two-week period. Errors declined following implementation of PDSA #2 to meet the target of 0.85 side-discrepancy error per week over seven weeks.
conclusionAutomated processes (such as capitalization of sides) help reduce left/right errors substantially without affecting reporting turnaround time.
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