AI Revolutionizes Perioperative Nursing Practices
Chicago, Thursday, 30 October 2025.
Janice Kelly highlights AI’s transformative role in enhancing perioperative care, focusing on improved patient outcomes and trust in technology integration.
AI’s Role in Enhancing Patient Outcomes
Artificial Intelligence is playing a pivotal role in transforming perioperative care by improving patient outcomes and enhancing workflow efficiencies. According to Janice Kelly, President of AORN Syntegrity, AI applications in the operating room range from preoperative planning to postoperative recovery, significantly impacting how nurses manage perioperative care. The integration of AI tools assists in monitoring patient vitals, predicting potential complications, and optimizing surgical schedules, leading to a more streamlined and safer surgical environment [1].
Building Trust in AI Integration
One of the major challenges identified in the adoption of AI in nursing is building trust among healthcare professionals. Many nurses express hesitation in relying on AI, primarily due to concerns about accuracy and reliability. Janice Kelly emphasizes the importance of overcoming these barriers by fostering a culture of transparency and education. By integrating AI tools that are user-friendly and demonstrating clear benefits, healthcare organizations can enhance trust and encourage widespread adoption [1].
Real-World Applications of AI in Perioperative Workflows
AI’s impact is evident in several real-world applications within perioperative settings. For instance, AI algorithms are utilized to assist with anesthetic dosing, thereby reducing the risk of human error. Additionally, AI-driven predictive analytics help identify patients at risk of complications, allowing for timely interventions. These applications not only enhance patient safety but also optimize hospital resources, making the healthcare delivery process more efficient [1].
Ethical and Practical Considerations
While the benefits of AI in perioperative nursing are clear, ethical and practical considerations must be addressed. Issues such as data privacy, algorithmic bias, and the need for ongoing training are crucial for the successful integration of AI technologies. Ensuring that AI systems are designed with these considerations in mind will be essential for maintaining the integrity and effectiveness of healthcare delivery [1].