OBJECTIVES: To evaluate provider-level variability across the full perioperative workflow using a computer vision-based artificial intelligence (AI) system that automatically detects and timestamps operating room events.
BACKGROUND: The development and introduction of an artificial intelligence (AI)-based clinical decision support system (CDSS) in surgical departments as part of the "Supporting Surgery with Geriatric Co-management and AI" project addresses the challe...
BACKGROUND: Minimally invasive thoracic surgery has improved lung cancer outcomes but requires enhanced postoperative care. Traditionally, the episodic care model has limited timely and multidimensional monitoring of patients. Recent technological ad...
Medication errors in the operating room (OR) are common and have considerable potential for harm. While not yet widely used in the OR, clinical decision support (CDS) has been shown to prevent medication errors outside of the OR and has the potential...
Perioperative goal-directed haemodynamic therapy (GDHT) includes a variety of protocolised approaches to the assessment and management of the circulatory system and blood flow for patients undergoing surgery. Here we present updated consensus stateme...
Artificial Intelligence (AI) is widely used in various fields, including the health system and related sciences, as one of the significant advances in technology. Many efforts are being made to improve the prevention, diagnosis, prediction, treatment...
Journal of cardiothoracic and vascular anesthesia
Apr 2, 2025
This article is the ninth of an annual series reviewing the research highlights of the year pertaining to the subspecialty of perioperative echocardiography for the Journal of Cardiothoracic and Vascular Anesthesia. The authors thank the editor-in-ch...
PURPOSE OF REVIEW: This review explores advances in risk stratification tools and their applicability in identifying and managing high-risk emergency general surgery (EGS) patients.
BACKGROUND: Artificial intelligence holds the potential to transform perioperative medicine by leveraging complex datasets to predict risks and optimise patient management in response to rising surgical volumes and patient complexity.
Prediction of outcomes in perioperative medicine is key to decision-making and various prediction models have been created to help quantify and communicate those risks to both patients and clinicians. Increasingly, machine learning (ML) is being favo...
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