After-hour trauma-radiograph interpretation in the emergency centre of a District Hospital

Liu, Yi-Ying Melissa; O'Hagan, Suzanne; Holdt, Frederik Carl; Lahri, Saʼad; Pitcher, R

doi:10.1016/j.afjem.2022.04.001

Cited by 5 publications

(2 citation statements)

References 36 publications

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“…Evidence suggests that emergency nurse practitioners were at least as good as senior house officer (SHO)-level doctors at interpreting MIU skeletal radiographs [ 18 , 19 ]. There is evidence to suggest that junior doctors' accuracy in the interpretation of trauma radiographs is lower when compared to registrar or consultant counterparts [ 20 , 21 ]. Tachakra et al (2002) [ 22 ] were able to demonstrate that emergency medicine consultants were at least as good as consultant radiologists at interpreting minor injury unit skeletal radiographs by contrast.…”

Section: Discussionmentioning

confidence: 99%

Assessing Referrals to a Trauma and Orthopaedic Department: Evaluation of a Traffic Light System for Virtual Fracture Clinic in the Emergency Department and Urgent Care

Menyah,

Garcia,

McCormack

et al. 2023

Cureus

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Background The Trauma and Orthopaedic (T&O) on-call service receives referrals from the emergency department (ED), general practice (GP) and urgent treatment centres (UTCs) and requests for inpatient reviews. The virtual fracture clinic (VFC) pathway allows ED and UTC clinicians to assess, discharge and refer when necessary. For VFC, the on-call orthopaedic consultant reviews the cases the next working day and makes an appropriate plan. This pathway consists of a traffic light system, in which practitioners can either safely discharge with written advice (green), refer to the VFC (yellow) or refer to the on-call team (red). Method The aim of this study was to assess how the VFC pathway was being utilised. All referrals to the T&O on-call team over three weeks were evaluated retrospectively. The following referrals were excluded: fractured femur, head injury, trauma calls and back pain pathway. The following data were collected: patient details, diagnosis, referral source, reason for referral, plan, double booking with VFC and appropriateness. Results A total of 191 referrals were analysed. Most referrals are from the ED (51%) and UTC (23%). Of the referrals, 39% were deemed to be inappropriate. Of the inappropriate referrals, 35% should have been referred directly to the VFC rather than the on-call team. A significant minority (7%) of inappropriate referrals were referred to the on-call team and VFC. Conclusion Education and collaboration are required with the ED and UTC to ensure the proper use of the VFC pathway. Immediate radiograph reporting may also be beneficial.

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Section: Discussionmentioning

confidence: 99%

Assessing Referrals to a Trauma and Orthopaedic Department: Evaluation of a Traffic Light System for Virtual Fracture Clinic in the Emergency Department and Urgent Care

Menyah,

Garcia,

McCormack

et al. 2023

Cureus

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“…This increase in fractures likely corresponds to a similar increase in the number of radiologic examinations performed, which is the main modality used in fracture diagnostics. Only approximately 10% of radiographs [4] permit the detection of a fracture, which means that in order to detect the 178 million fractures, theoretically, radiographs from 1.78 billion examinations would need to be reviewed annually [5]. Reviewing and reporting this high volume of diagnostic images around the clock would create a situation with a high risk for diagnostic error [6], which ranges from 4% to around 30% depending on the study sample [7].…”

Section: Introductionmentioning

confidence: 99%

Fusion of Electronic Health Records and Radiographic Images for a Multimodal Deep Learning Prediction Model of Atypical Femur Fractures

Schilcher,

Nilsson,

Andlid

et al. 2023

Preprint

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Atypical femur fractures (AFF) represent a very rare type of fracture that can be difficult to discriminate radiologically from normal femur fractures (NFF). AFFs are associated with drugs that are administered to prevent osteoporosis-related fragility fractures, which are highly prevalent in the elderly population. Given that these fractures are rare and the radiologic changes are subtle currently only 7% of AFFs are correctly identified, which hinders adequate treatment for most patients with AFF. Deep learning models could be trained to classify automatically a fracture as AFF or NFF, thereby assisting radiologists in detecting these rare fractures. Historically, for this classification task, only imaging data have been used, using convolutional neural networks (CNN) or vision transformers applied to radiographs. However, to mimic situations in which all available data are used to arrive at a diagnosis, we adopted an approach of deep learning that is based on the integration of image data and tabular data (from electronic health records) for 159 patients with AFF and 914 patients with NFF. We hypothesized that the combinatorial data, compiled from all the radiology departments of 72 hospitals in Sweden and the Swedish National Patient Register, would improve classification accuracy, as compared to using only one modality. At the patient level, the area under the ROC curve (AUC) increased from 0.966 to 0.987 when using the integrated set of imaging data and seven pre-selected variables, as compared to only using imaging data. More importantly, the sensitivity increased from 0.796 to 0.903. We found a greater impact of data fusion when only a randomly selected subset of available images was used to make the image and tabular data more balanced for each patient. The AUC then increased from 0.949 to 0.984, and the sensitivity increased from 0.727 to 0.849. These AUC improvements are not large, mainly because of the already excellent performance of the CNN (AUC of 0.966) when only images are used. However, the improvement is clinically highly relevant considering the importance of accuracy in medical diagnostics. We expect an even greater effect when imaging data from a clinical workflow, comprising a more diverse set of diagnostic images, are used.

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Preliminary image evaluation performance of radiographers in one New Zealand District: a 6‐month prospective study

Lewis,

Mdletshe,

Doubleday

et al. 2024

J of Medical Radiation Sci

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IntroductionPreliminary image evaluation (PIE) is a system where radiographers alert emergency department referrers to the presence or absence of abnormalities on acute extremity X‐ray examinations. PIE and similar systems have been utilised in the United Kingdom (UK) and Australia due to a shortage of radiologists to provide a timely report. As New Zealand (NZ) faces a similar shortage, PIE should be considered to address the negative impact this has on patients. The aim of this study was to determine the effect of regular feedback and education on radiographers' performance when detecting and describing acute abnormalities on extremity X‐ray examinations in ED.MethodsA prospective longitudinal study design was utilised for this study. The sensitivity, specificity, accuracy, and accuracy of PIEs performed by seven radiographers at a public provincial district in NZ were assessed over a 6‐month period, with the participants provided monthly results along with regular e‐mailed feedback on common errors.ResultsThe mean for sensitivity, specificity, and accuracy calculated with a 95% confidence interval over the 6‐month period were 92.8% (89.9, 95.8), 94.9 (93.1, 96.8), and 94.2 (91.9, 96.5), respectively. When the month‐to‐month results were analysed, the results demonstrated an improvement in participants' sensitivity, specificity, and accuracy over the 6‐month period.ConclusionThe results of this study demonstrated that radiographers who participated in the study can perform PIE to a high standard that is comparable with the findings from international studies and demonstrated an improvement over 6 months. Therefore, PIE may be useful in NZ to aid ED clinicians in their clinical decisions when a radiology report is unavailable.

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After-hour trauma-radiograph interpretation in the emergency centre of a District Hospital

Cited by 5 publications

References 36 publications

Assessing Referrals to a Trauma and Orthopaedic Department: Evaluation of a Traffic Light System for Virtual Fracture Clinic in the Emergency Department and Urgent Care

Assessing Referrals to a Trauma and Orthopaedic Department: Evaluation of a Traffic Light System for Virtual Fracture Clinic in the Emergency Department and Urgent Care

Fusion of Electronic Health Records and Radiographic Images for a Multimodal Deep Learning Prediction Model of Atypical Femur Fractures

Preliminary image evaluation performance of radiographers in one New Zealand District: a 6‐month prospective study

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