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

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

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Video Summary

The session at the Anesthesia Patient Safety Foundation on Day 4 of the ASA 2024 Annual Congress focused on the role of artificial intelligence (AI) in improving patient safety and quality. Several experts, including CRNAs Patty Riley and Desiree Chappelle, and Dr. Steven Greenberg, discussed AI's applications and challenges in anesthesia.<br /><br />The panel emphasized the importance of AI in clinical decision-making, highlighting ongoing advancements like post-induction hypotension prediction and deep learning applications ranging from cardiac imaging to difficult airway identification through facial recognition. Despite recognizing AI’s potential, the panel also emphasized its limitations, such as bias and reliability of algorithms, and the need for transparent, equitable data.<br /><br />The conversation also stressed the necessity of implementation science—using evidence-based approaches to integrate new technologies effectively. This involves considering human behavior, organizational constraints, and the practical workflow needs of healthcare professionals.<br /><br />Key points addressed included the essential role of stakeholder involvement, from clinicians to patients and health regulators, in ensuring successful implementation. The APSF's commitment to safety and equity in AI-driven tools was reiterated, encouraging active participation from the CRNA community in shaping the future of anesthesia technology.<br /><br />Overall, the session underscored cautious optimism towards integrating AI in anesthesia, emphasizing thorough validation, practical implementation, and ongoing collaboration across the healthcare sector.

Keywords

AI in anesthesia

patient safety

clinical decision-making

post-induction hypotension

deep learning

implementation science

stakeholder involvement

CRNA community

healthcare technology