Date of this Version

11-2024

Document Type

DNP Project

Abstract

Background: The current challenges and limitations within intraoperative pain management stem from traditional approaches and suboptimal real-time pain monitoring, leading to potential opioid under or overdosing, impacting clinical outcomes and patient satisfaction. This project highlighted artificial intelligence’s (AI’s) potential to provide data-driven personalized solutions, optimizing pain management that enables real-time adjustments, in line with the overarching healthcare goal of delivering safer patient-centered care. Methods: The project conducted a systematic literature review utilizing the Johns Hopkins Research Evidence Appraisal Tool to assess AI-powered models' recent advancements and effectiveness in perioperative pain management. This quality improvement project is a multicenter blind approach that assesses anesthesia providers' knowledge and perception of the utilization of artificial intelligence algorithms in intraoperative management and pain reduction. The Qualtrics survey among Florida International University, alumni anesthesia providers was used to measure changes in perception and attitudes before and following an evidence-based educational module that addressed the shortcomings of current pain management practices, explored the scope of the problem, knowledge gaps, ethical considerations, and the AI impact on patients and anesthesia practice, emphasizing the need for customized approaches. Results: This project was a multicenter study that boasts a comprehensive data collection methodology that enabled a multifaceted analysis of anesthesia providers' experiences and attitudes toward AI-guided approach. The statistical analysis included demographic information, knowledge assessments, and perceptions of AI's impact. The study provided a holistic view of the current clinical setting using Qualtrics platform data analysis tools. The findings indicate limited current knowledge of AI for perioperative pain management, with significant potential for educational improvement. Most participants view AI-assisted techniques positively, anticipating its significant role in future anesthesia practices and decision-making support. The post-educational module data showed increased confidence in AI's potential benefits, with 91.6% expressing some level of optimism. These results emphasize the importance of ongoing education, collaborative approaches, and addressing practical challenges to fully realize AI's benefits in anesthesia practice. Conclusions: This project offered a comprehensive review enabling a better understanding of anesthesia providers' experiences and attitudes toward AI-assisted pain management. It also demonstrated the effectiveness of educational interventions in shaping perceptions, suggesting that targeted initiatives can bridge knowledge gaps and foster positive attitudes toward AI in anesthesia.

Comments

Florida International University, Miami, FL.

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