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INTRODUCTION: The environmental impact of radiology and radiotherapy activities is influenced by the energy consumption of equipment, the life cycle of consumables, waste generation, and CO2 emissions caused by staff travel. This study aims to investigate radiographers' perception and knowledge of environmental sustainability issues. METHODS: An online survey was created and distributed to European radiographers and therapeutic radiographers. The survey questions (n = 43) include demographic data; questions on their perceptions and actions regarding environmental sustainability in healthcare, energy consumption, emissions from staff travel, waste generation from radiological procedures; the role of radiographers in addressing sustainability issues within their departments. RESULTS: A total of 253 responses were collected from 27 European countries. About their perception on sustainability issues, most participants considered environmental sustainability in healthcare as very important. According to 63.6% (n = 161) of respondents, the energy consumption of radiological equipment is the major source of environmental footprints from radiology activities. Additionally, 44.7% (n = 113) believe that conducting diagnostic examinations remotely could reduce environmental footprints from staff commuting About their actions at workplace, over 70% (n = 192) reported turning off devices after use. Attention to waste recycling is high, but limited to paper, plastic and glass. Contrast agents recycling procedures are implemented by 13% (n = 33). The absence or unawareness of environmental sustainability procedures in the workplace was reported by 66% (n = 167). Radiographers could play an active role in environmental sustainability programs for 243 (96.1%) participants. CONCLUSION: This study provides a comprehensive overview of European radiographers' knowledge and perceptions concerning environmental sustainability issues. While radiographers recognize the importance of a green radiology department, significant gaps remain in their understanding of eco-friendly initiatives in radiology units' activities. IMPLICATION FOR PRACTICE: Enhancing radiographers' skills with sustainability expertise could promote a greener culture within radiology departments.
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INTRODUCTION: The healthcare sector invests significantly in communication skills training, but not always with satisfactory results. Recently, generative Large Language Models, have shown promising results in medical education. This study aims to use ChatGPT to simulate radiographer-patient conversations about the critical moment of claustrophobia management during MRI, exploring how Artificial Intelligence can improve radiographers' communication skills. METHODS: This study exploits specifically designed prompts on ChatGPT-3.5 and ChatGPT-4 to generate simulated conversations between virtual claustrophobic patients and six radiographers with varying levels of work experience focusing on their differences in model size and language generation capabilities. Success rates and responses were analysed. The methods of radiographers in convincing virtual patients to undergo MRI despite claustrophobia were also evaluated. RESULTS: A total of 60 simulations were conducted, achieving a success rate of 96.7% (58/60). ChatGPT-3.5 exhibited errors in 40% (12/30) of the simulations, while ChatGPT-4 showed no errors. In terms of radiographers' communication during the simulations, out of 164 responses, 70.2% (115/164) were categorized as "Supportive Instructions," followed by "Music Therapy" at 18.3% (30/164). Experts mainly used "Supportive Instructions" (82.2%, 51/62) and "Breathing Techniques" (9.7%, 6/62). Intermediate participants favoured "Music Therapy" (26%, 13/50), while Beginner participants frequently utilized "Mild Sedation" (15.4%, 8/52). CONCLUSION: The simulation of clinical scenarios via ChatGPT proves valuable in assessing and testing radiographers' communication skills, especially in managing claustrophobic patients during MRI. This pilot study highlights the potential of ChatGPT in preclinical training, recognizing different training needs at different levels of professional experience. IMPLICATIONS FOR PRACTICE: This study is relevant in radiography practice, where AI is increasingly widespread, as it explores a new way to improve the training of radiographers.
