The reporting of incidents has a long association with safety in healthcare and anaesthesia, yet many incident reporting systems substantially under-report critical events. Better understanding the underlying reasons for low levels of critical incide...
BACKGROUND: Opioid misuse in the paediatric population is understudied. This study aimed to develop a machine learning classifier to differentiate between occasional and sustained opioid users among children and adolescents in outpatient settings.
BACKGROUND: Effective training in regional anaesthesia (RA) is paramount to ensuring widespread competence. Technology-based learning has assisted other specialties in achieving more rapid procedural skill acquisition. If applicable to RA, technology...
BACKGROUND: Timely detection of modifiable risk factors for postoperative pulmonary complications (PPCs) could inform ventilation strategies that attenuate lung injury. We sought to develop, validate, and internally test machine learning models that ...
BACKGROUND: Artificial intelligence (AI) for ultrasound scanning in regional anaesthesia is a rapidly developing interdisciplinary field. There is a risk that work could be undertaken in parallel by different elements of the community but with a lack...
BACKGROUND: Postoperative respiratory failure is a serious complication that could benefit from early accurate identification of high-risk patients. We developed and validated a machine learning model to predict postoperative respiratory failure, def...
BACKGROUND: ScanNavAnatomy Peripheral Nerve Block (ScanNav™) is an artificial intelligence (AI)-based device that produces a colour overlay on real-time B-mode ultrasound to highlight key anatomical structures for regional anaesthesia. This study com...