生成式人工智能赋能麻醉护理学医护合作临床实训教学案例编制OA

Objective To explore the application value of generative artificial intelligence(GAI)in developing clinical training cases for anesthesia nursing collaboration,and to develop high-fidelity simulated training cases and verify the feasibility.Methods An in-terdisciplinary team was established to develop training cases based on a clinical real-world case repository,with initial cases generat-ed by GAI and subsequently refined through professional review.A total of 24 junior anesthesiology nursing staff were recruited to con-duct a 4-hour pre-experiment using the case of"Emergency Anesthesia for Massive Intraoperative Hemorrhage in General Anesthe-sia".Case feasibility was evaluated using case design efficiency,case quality scoring,training satisfaction,and the Core Competency Scale for Anesthesiology Nursing Specialist.Results The efficiency of case generation was significantly improved with GAI.The core elements of the cases were scientifically and appropriately designed,with training value and content quality receiving recognition,lead-ing to a marked enhancement in the core competencies of anesthesia nursing staff(t=2.841,P=0.016).Conclusion It is feasible to develop practical training cases with GAI,which enhances case production efficiency and scenario diversity,strengthening the cultiva-tion of healthcare collaboration capabilities,and providing an innovative approach for the reform of interdisciplinary medical training education.