How has the COVID-19 pandemic affect the rejection rates and repeat reasoning of digital radiography?

Jastaniyyah, Duaa F.; Jawhari, Abdulkarim A.; Mugharbel, Abdullah T.; Khafaji, Mawya A.; Albahiti, Sarah H.

doi:10.15537/smj.2021.42.9.20210254

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…Firstly, the data were collected during a period that included lockdown restrictions due to the COVID‐19 pandemic, which may have impacted the normal workflow of the radiology departments. It is possible that the lockdown periods due to the COVID‐19 pandemic may have influenced the duration of examinations and the technique used by the MITs, which could have impacted the rate of positioning errors 24,25 …”

Section: Discussionmentioning

confidence: 99%

“…It is possible that the lockdown periods due to the COVID‐19 pandemic may have influenced the duration of examinations and the technique used by the MITs, which could have impacted the rate of positioning errors. 24 , 25 …”

Section: Discussionmentioning

confidence: 99%

“…It is possible that the lockdown periods due to the COVID-19 pandemic may have influenced the duration of examinations and the technique used by the MITs, which could have impacted the rate of positioning errors. 24,25 Additionally, this study was conducted between radiology departments that had different patient populations; RAD 1 provided radiology services to inpatient and trauma patients while RAD 2 provided outpatient examinations. A repeat of this image RA at the individual radiology departments following interventions would allow for the comparison of image RRs and selection of image rejection reasons that reflect the specific department variations.…”

Section: Limitations Of Studymentioning

confidence: 99%

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Digital radiography reject analysis: A comparison between two radiology departments in New Zealand

Bantas

Sweeney

Mdletshe

2023

J of Medical Radiation Sci

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Introduction Image reject analysis (RA) in direct digital radiography (DDR) is an important quality indicator tool. Analysis of rejected images is a component of quality assurance (QA) programmes, with the overall aim of reducing patient radiation dose. This study aimed to compare differences in image rejection rates (RR) and the reasons for rejection between two radiology departments. Methods A retrospective quantitative descriptive study of images performed across the two radiology departments (RAD 1 and RAD 2) acquired with DDR systems between the beginning of February and the end of May 2021 was undertaken. Collected data included the medical imaging technologist (MIT) selection of image rejection reasons for different anatomic regions and compared between the two radiology departments. Results A total of 47,046 images and 29,279 images were acquired at RAD 1 and RAD 2, respectively, with an overall image rejection rate of 7.86% at RAD 1 and 5.91% at RAD 2. The primary reason for image rejections was positioning errors, 79.4% and 77.3% recorded at RAD 1 and RAD 2, respectively. Significant differences were demonstrated between the two radiology departments for image rejection rates and selected reasons for rejection for most anatomical body groups. Conclusion The implementation of image RA remains a key part of QA in radiology departments utilising DDR systems. This study recommends interventions based on image RRs for examinations taking into consideration the department‐specific variations and imaging protocols used.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Limitations Of Studymentioning

confidence: 99%

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Digital radiography reject analysis: A comparison between two radiology departments in New Zealand

Bantas

Sweeney

Mdletshe

2023

J of Medical Radiation Sci

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Multi‐centre digital radiography reject analysis for different clinical room use types: The establishment of local reject reference levels for public hospital departments

Serra,

Neep,

Ryan

2024

J of Medical Radiation Sci

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IntroductionReject analysis in digital radiography helps guide the training of staff to reduce patient radiation dose and improve department efficiency. The purpose of this study was to perform a multi‐centre, vendor agnostic reject analysis across different room usage types, and to provide benchmarks for comparison.MethodsRetrospective reject and exposure log data were collected via USB from fixed general X‐ray systems across multiple Australian sites, for collation and analysis. The overall reject rate, local reject reference level, absolute and relative reject rates for body part categories, reject rates by room usage types and the reject rate for each reason of rejection were calculated.ResultsData were collected from 44 X‐ray systems, across 11 hospitals. A total of 2,031,713 acquired images and 172,495 rejected images were included. The median reject rate was 9.1%. The local reject reference level (LRRL), set as the 75th percentile of all reject rates, was 10.6%. Median reject rates by room type were emergency (7.4%), inpatients + outpatients (9.6%), outpatients (9.2%), and hybrid (10.1%). The highest absolute reject rates by body part were chest (2.1%) and knee (1.4%). The highest relative rates by body part were knee (18.1%) and pelvis (17.2%). The most frequent reasons for image rejection were patient positioning (76%) and patient motion (7.5%).ConclusionsThe results compare well with previously published data. The range of reject rates highlights the need to analyse typical reject rates in different ways. With analysis feedback to participating sites and the implementation of standardised reject reasons, future analysis should monitor whether reject rates reduce.

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Assessment of Rejected Radiographs during planar imaging procedures

Otayni¹,

Aftan²,

Nammazi³

et al. 2023

Journal of Radiation Research and Applied Sciences

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How has the COVID-19 pandemic affect the rejection rates and repeat reasoning of digital radiography?

Abstract: Objectives: To compare the digital radiology rejection rates and reasoning pre-COVID-19 (PC) and during COVID-19 (DC) from September 2019 to August 2020.Methods: This record-base retrospective study where data were extracted from the radiography equipment

Cited by 3 publications

References 8 publications

Digital radiography reject analysis: A comparison between two radiology departments in New Zealand

Digital radiography reject analysis: A comparison between two radiology departments in New Zealand

Multi‐centre digital radiography reject analysis for different clinical room use types: The establishment of local reject reference levels for public hospital departments

Assessment of Rejected Radiographs during planar imaging procedures

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