AI-Driven Solutions for Operating Theatre Efficiency Enhancement - Jellouli, Wiam El; Ouhammou, Yousra; Gaabouiri, Mohammed El; Alioui, Mohamed; Nadir, Houda; Bensghir, Mustapha; Elalaa, Khalil Abou (Medical Research and Its Applications Vol. 1, 2024)
Operating theatre efficiency remains a crucial concern within health care systems, directly influencing the timeliness and effectiveness of surgical care. It is important for a variety of reasons, including patient satisfaction, cost savings, medical team morale, improved infection control, and reduced turnaround time. However, persistent challenges like surgical delays, suboptimal scheduling, and inefficient resource allocation persist. Artificial Intelligence (AI) has emerged as a promising avenue to address these challenges and optimize operating theatre efficiency. This article provides an in-depth exploration of the implications of AI in improving surgical punctuality, scheduling precision, and resource allocation. Key components of AI-driven strategies encompass machine learning models, intelligent management systems, and optimization algorithms. Recent research demonstrates that machine learning models exhibit remarkable accuracy in predicting surgical case durations, leading to improved and streamlined surgical scheduling and punctuality. Simultaneously, intelligent management systems play a pivotal role in facilitating, patient flow management, and optimizing resource distribution. The application of optimization algorithms, including genetic algorithms, is instrumental in resolving intricate scheduling dilemmas and curtailing waiting times. Optimization algorithms improve operating theatre efficiency by minimizing downtime, reducing patient waiting times, and maximizing resource utilization through careful scheduling. The integration of AI into efforts to enhance operating theatre efficiency promises numerous benefits, including improved patient care standards, reduced costs, and heightened operational efficiency. However, challenges pertaining to data quality, interpretability, and organizational adaptability, need to be addressed rigorously. Ethical and legal considerations of patient privacy, data security, and algorithm biases must be scrupulously managed while using AI in healthcare. To harness AI's full potential, future advancements should focus on real-time data analytics, predictive modeling, and autonomous decision-making. These inferences from this article underscore AI's transformative impact on optimizing operating theatre efficiency and emphasise the need for well-defined ethical guidelines and comprehensive regulations to ensure responsible implementation.