Medical Error

Medical error research focuses on developing automated systems to detect and correct errors in various healthcare settings, aiming to improve patient safety and clinical efficiency. Current research employs machine learning models, including transformer networks, convolutional neural networks, and graph neural networks, often integrated with techniques like chain-of-thought prompting and retrieval augmented generation, to analyze diverse data sources such as surgical videos, clinical notes, and prescription information. These advancements show promise in improving the accuracy of error detection in areas like medication prescribing, surgical procedures, and clinical documentation, potentially leading to significant improvements in healthcare quality and reducing preventable harm.