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Reject rate analysis in digital radiography: an Australian emergency imaging department case study

INTRODUCTION: Reject analysis in digital radiography (DR) helps guide the education and training of staff, influences department workflow, reduces patient dose and improves department efficiency. The purpose of this study was to investigate rejected radiographs at a major metropolitan emergency imag...

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Autores principales: Atkinson, Samantha, Neep, Michael, Starkey, Deborah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063252/
https://www.ncbi.nlm.nih.gov/pubmed/31318181
http://dx.doi.org/10.1002/jmrs.343
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author Atkinson, Samantha
Neep, Michael
Starkey, Deborah
author_facet Atkinson, Samantha
Neep, Michael
Starkey, Deborah
author_sort Atkinson, Samantha
collection PubMed
description INTRODUCTION: Reject analysis in digital radiography (DR) helps guide the education and training of staff, influences department workflow, reduces patient dose and improves department efficiency. The purpose of this study was to investigate rejected radiographs at a major metropolitan emergency imaging department to help form a benchmark of reject rates for DR and to assess what radiographs are being rejected and why. METHODS: A retrospective longitudinal study was undertaken as an in‐depth clinical audit. The data were collected using automated reject analysis software from two digital x‐ray systems from June 2015 to April 2017. The overall reject rate, reasons for rejection as well as the reject rates for individual radiographers, examination types and projections were analysed. RESULTS: A total of 90,298 radiographic images were acquired and included in the analysis. The average reject rate was 9%, and the most frequent reasons for image rejection were positioning error (49%) and anatomy cut‐off (21%). The reject rate varied between radiographers as well as for individual examination types and projections. CONCLUSIONS: The variation in radiographer reject rates and the high reject rate for some projections indicate that reject analysis is still necessary as a quality assurance tool for DR. A feedback system between radiologists and radiographers may reduce the high percentage of positioning errors by standardising the technical factors used to assess image quality. Future reject analysis should be conducted regularly incorporating an exposure indicator analysis as well as retrospective assessment of individual rejected images.
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spelling pubmed-70632522020-03-16 Reject rate analysis in digital radiography: an Australian emergency imaging department case study Atkinson, Samantha Neep, Michael Starkey, Deborah J Med Radiat Sci Original Articles INTRODUCTION: Reject analysis in digital radiography (DR) helps guide the education and training of staff, influences department workflow, reduces patient dose and improves department efficiency. The purpose of this study was to investigate rejected radiographs at a major metropolitan emergency imaging department to help form a benchmark of reject rates for DR and to assess what radiographs are being rejected and why. METHODS: A retrospective longitudinal study was undertaken as an in‐depth clinical audit. The data were collected using automated reject analysis software from two digital x‐ray systems from June 2015 to April 2017. The overall reject rate, reasons for rejection as well as the reject rates for individual radiographers, examination types and projections were analysed. RESULTS: A total of 90,298 radiographic images were acquired and included in the analysis. The average reject rate was 9%, and the most frequent reasons for image rejection were positioning error (49%) and anatomy cut‐off (21%). The reject rate varied between radiographers as well as for individual examination types and projections. CONCLUSIONS: The variation in radiographer reject rates and the high reject rate for some projections indicate that reject analysis is still necessary as a quality assurance tool for DR. A feedback system between radiologists and radiographers may reduce the high percentage of positioning errors by standardising the technical factors used to assess image quality. Future reject analysis should be conducted regularly incorporating an exposure indicator analysis as well as retrospective assessment of individual rejected images. John Wiley and Sons Inc. 2019-07-18 2020-03 /pmc/articles/PMC7063252/ /pubmed/31318181 http://dx.doi.org/10.1002/jmrs.343 Text en © 2019 The Authors. Journal of Medical Radiation Sciences published by John Wiley & Sons Australia, Ltd on behalf of Australian Society of Medical Imaging and Radiation Therapy and New Zealand Institute of Medical Radiation Technology This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Atkinson, Samantha
Neep, Michael
Starkey, Deborah
Reject rate analysis in digital radiography: an Australian emergency imaging department case study
title Reject rate analysis in digital radiography: an Australian emergency imaging department case study
title_full Reject rate analysis in digital radiography: an Australian emergency imaging department case study
title_fullStr Reject rate analysis in digital radiography: an Australian emergency imaging department case study
title_full_unstemmed Reject rate analysis in digital radiography: an Australian emergency imaging department case study
title_short Reject rate analysis in digital radiography: an Australian emergency imaging department case study
title_sort reject rate analysis in digital radiography: an australian emergency imaging department case study
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063252/
https://www.ncbi.nlm.nih.gov/pubmed/31318181
http://dx.doi.org/10.1002/jmrs.343
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