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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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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. |
format | Online Article Text |
id | pubmed-7063252 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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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