ABSTRACT
INTRODUCTION: New Zealand has seen an increase in the X-ray examinations in the emergency departments (ED), and the radiology report is generally unavailable immediately. This requires practitioners managing the patient to take the responsibility of detecting any abnormalities in the images and using such information for the management of the patient. There is, therefore, a need for consideration of the contribution that radiographers could make in the accurate management of the patients in ED in New Zealand. The aim of this study was to assess if an intensive preliminary image evaluation (PIE) training course improved radiographer accuracy, sensitivity, and specificity on extremity X-ray examinations in a regional ED in New Zealand. METHOD: A pre-post-intervention design was employed for this study. Seven radiographers working at a regional base hospital in New Zealand undertook image evaluation tests to evaluate their ability to detect and describe abnormalities prior to and following a 2-day intensive PIE training course. The training concentrated on acute extremity abnormalities. Tests were then scored to determine sensitivity, specificity, and accuracy. RESULTS: Following an intensive PIE training course, the post-intervention test mean demonstrated an improved sensitivity by an average of 3.99% (89.01-93.0), specificity improved by an average of 6.13% (79.77-85.90%), and accuracy improved by an average of 3.33% (77.55-80.87%). CONCLUSION: This study demonstrated that an intensive training course in PIE improved the participants' sensitivity, specificity, and accuracy when evaluating acute extremity X-ray examinations in ED at the study site, however further research is required to see if these results also represent clinical ability. IMPLICATION FOR PRACTICE: The NZ healthcare system could benefit by the introduction of a radiographers' PIE system. It is therefore recommended that when introducing PIE into an ED in New Zealand, radiographers should undertake additional training to improve image evaluation sensitivity, specificity, and accuracy prior to participation.
Subject(s)
Radiology , Humans , Radiology/education , Pilot Projects , New Zealand , Clinical Competence , Emergency Service, HospitalABSTRACT
INTRODUCTION: The current technological developments in medical imaging are centred largely on the increasing integration of artificial intelligence (AI) into all equipment modalities. This survey assessed the perspectives of African radiographers on the integration of AI in medical imaging in order to offer unique recommendations to support the training of the radiography workforce. METHODS: An exploratory cross-sectional online survey of radiographers working within Africa was conducted from March to August 2020. The survey obtained data about their demographics and perspectives on AI implementation and usage. Data obtained were analysed using both descriptive and inferential statistics. RESULTS: A total of 1020 valid responses were obtained. Majority of the respondents (n = 883,86.6%) were working in general X-ray departments. Of the respondents, 84.9% (n = 866) indicated that AI technology would improve radiography practice and quality assurance for efficient diagnosis and improved clinical care. Fear of job losses following the implementation of AI was a key concern of most radiographers (n = 625,61.3%). CONCLUSION: Generally, radiographers were delighted about the integration of AI into medical imaging, however; there were concerns about job security and lack of knowledge. There is an urgent need for stakeholders in medical imaging infrastructure development and practices in Africa to start empowering radiographers through training programmes, funding, motivational support, and create clear roadmaps to guide the adoption and integration of AI in medical imaging in Africa. IMPLICATION FOR PRACTICE: The current study offers unique suggestions and recommendations to support the training of the African radiography workforce and others in similar resource-limited settings to provide quality care using AI-integrated imaging modalities.
Subject(s)
Artificial Intelligence , Radiology Department, Hospital , Allied Health Personnel , Cross-Sectional Studies , Humans , RadiographySubject(s)
Allied Health Personnel , Radiographic Image Enhancement , Child , Humans , Radiation Dosage , South AfricaABSTRACT
INTRODUCTION: Digital radiography (DR) poses the risk of unnoticed increases in patient dose, potentially resulting in the overexposure of paediatric patients. In some jurisdictions, there is a shortfall in the knowledge and application of safe paediatric radiation dose protocols. In such instances, radiographers can lack an understanding of the relationship between exposure index (EI) and the visual appearances of image noise, thereby resulting in under- or overexposure to the paediatric patient. METHODS: A quantitative, descriptive research study was conducted at six purposively selected diagnostic radiology departments in Gauteng, South Africa. The study was based on a 29-item questionnaire seeking to quantify the radiographers' knowledge of paediatric radiation dose protocols and whether their knowledge translated into safe radiation dose practice. RESULTS: A 94% (61/65) response rate was achieved. Results show a low number of respondents (35, 57%) expressed a positive opinion of their knowledge of paediatric radiation dose protocols and EI; however, 41 (67%) participants were unaware of the function of EI. CONCLUSION: The study revealed that, in Gauteng, there is an increased demand for further training in paediatric radiation dose protocols and a need for radiographers to improve awareness of exposure factors. IMPLICATIONS FOR PRACTICE: Improving radiographers' lack of knowledge of paediatric radiation dose protocols in Gauteng is important. It is essential that radiographers receive continuous training and education highlighting the importance of justification and optimisation of radiological examinations.