The Evolution of Artificial Intelligence and the Intersection of Patient Safety: Continued Quality Improvement Program Grounded in Implementation Science-Presentation Slides

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The document explores the evolution of Artificial Intelligence (AI) in the context of patient safety, focusing on anesthesiology practice. Various professionals with financial relationships to industry players disclose their connections, emphasizing transparency. The American Association of Nurse Anesthesiology offers nursing continuing professional development programs. Key themes include understanding AI application in anesthesiology, its intersection with patient safety, and implementing AI for quality improvement using implementation science. The Anesthesia Patient Safety Foundation (APSF) contributes significantly to perioperative safety priorities.<br /><br />AI's role in healthcare involves analyzing complex data, improving decision-making, and enhancing patient outcomes. Different AI types like machine learning, deep learning, and natural language processing are highlighted for various healthcare applications. The document stresses the importance of implementation science in bridging research evidence with everyday practice to enhance healthcare quality effectively. Qualitative data, workflow considerations, stakeholder involvement, and adaptable strategies are crucial for successful AI implementation.<br /><br />The APSF's commitment to patient safety through research, partnerships, educational initiatives, and global dialogue is underscored. The mention of future perspectives on AI and goals for remote monitoring reflects ongoing advancements in the field. The document also details quality assessment criteria for AI research, aiming to enhance AI's impact on patient care. Overall, the focus is on leveraging AI's potential to enhance patient safety, improve outcomes, and facilitate a collaborative approach to implementing AI technologies effectively.

Keywords

Artificial Intelligence

patient safety

anesthesiology

implementation science

machine learning

deep learning

natural language processing

Anesthesia Patient Safety Foundation

healthcare outcomes

quality improvement