Paul M Mertes, Claire Morgand, Paul Barach, Geoffrey Jurkolow, Karen E Assmann, Edouard Dufetelle, Vincent Susplugas, Bilal Alauddin, Patrick Georges Yavordios, Jean Tourres, Jean-Marc Dumeix, Xavier Capdevila
BACKGROUND: Reporting and analysis of adverse events (AE) is associated with improved healthcare learning, quality outcomes, and patient safety. Manual text analysis is time-consuming, costly, and prone to human errors. We aimed to demonstrate the feasibility of machine learning and natural language processing (NLP) approaches for early predictions of adverse events and provide input to direct quality improvement and patient safety initiatives. METHODS: We used machine learning to analyze 9,559 continuously reported AE by clinicians and healthcare systems to the French National Health accreditor (HAS) between January 1, 2009, and December 31, 2020, for a total of 135,000 unique de-identified AE reports...
May 6, 2024: Anaesthesia, Critical Care & Pain Medicine