OBJECTIVES: Critical incident reporting systems (CIRS) collect narrative reports on medical errors, but emotional signals within these reports, potential indicators of perceived risk and systemic weakness, are rarely examined. This cross-sectional st...
BACKGROUND: Disappointing medical care (DMC) encompasses cases of medical failures, malpractice, or errors. Literature suggests that individuals' perceptions of harm resulting from medical procedures influence their intention to seek legal recourse a...
OBJECTIVE: To evaluate the transferability of BERT (Bidirectional Encoder Representations from Transformers) to patient safety, we use it to classify incident reports characterised by limited data and encompassing multiple imbalanced classes.
Nurses are at increased risk of making professional errors due to a combination of interrelated factors. We investigated the effects of sleepiness, fatigue, psychological hardiness, and demographic factors on the frequency of medical errors among act...
BACKGROUND: Wrong-site surgery (WSS) is a critical but preventable medical error, often resulting in severe patient harm and substantial financial costs. While protocols exist to reduce wrong-site surgery, underreporting and inconsistent documentatio...
Generative artificial intelligence (AI) technologies have the potential to revolutionise healthcare delivery but require classification and monitoring of patient safety risks. To address this need, we developed and evaluated a preliminary classificat...
The last few years have seen a boom in the popularity of artificial intelligence (AI) around the world, and the health care sector has not been immune from what has been perceived by some as a revolutionary technology. Although AI has been around for...
Clinical reasoning has been characterized as being an essential aspect of being a physician. Despite this, clinical reasoning has a variety of definitions and medical error, which is often attributed to clinical reasoning, has been reported to be a l...
Intensive Care Units (ICUs) have undergone enhancements in patient safety, and artificial intelligence (AI) emerges as a disruptive technology offering novel opportunities. While the published evidence is limited and presents methodological issues, c...
Artificial intelligence (AI) applications, in the form of machine learning and deep learning, are being incorporated into practice in various aspects of medicine, including radiation oncology. Ample evidence from recent publications explores its util...
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