Bellini Valentina, Domenichetti Tania, Bignami Elena Giovanna
Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, Parma, 43126, Italy.
J Med Syst. 2025 Mar 21;49(1):37. doi: 10.1007/s10916-025-02168-1.
An optimized scheduling system for surgical procedures is considered fundamental for maximizing hospital resource utilization and improving patient outcomes. The integration of Artificial Intelligence (AI) tools and New Technologies is paramount in this project to enable personalized patient care and optimize perioperative clinical pathways. We read with interest the manuscript by Parks et al., which developed a predictive model of surgical case durations. The model appears to adopt a pragmatic approach by analyzing tangible variables and undergoing validation across various types of surgical procedures, which suggests potential avenues for enhancing efficiency and sustainability in healthcare practices. However, we have some observations, particularly regarding the feasibility and practical implementation of the proposed model. A key limitation of the model is the precise definition of surgical duration, which requires further specification. To effectively translate the model into a practical scheduling approach, it is essential to consider total Operating Room (OR) occupancy time as a critical determinant of surgical planning and resource allocation. This includes not only the actual procedural time but also preoperative preparation, anesthesia induction and recovery, cleaning, and material restocking, all of which significantly impact overall scheduling efficiency. Another critical aspect concerns the quality and reliability of the input data, which is fundamental for ensuring the accuracy and effectiveness of the model. Furthermore, the adoption of new technologies should be regarded not merely as an innovation but as a means to develop high-performance, efficient tools that enhance current clinical practice. In this context, machine learning models should not only serve as analytical instruments but also as actionable tools, enabling the transition from predictive insights to strategic planning and optimized scheduling, ultimately improving decision-making and resource allocation. While making accurate predictions is a good starting point, maintaining an active AI model requires investment in resources, such as an increase in the number of surgical cases compared to the current organizational system. It may be beneficial to consider the creation of a multidisciplinary group that could promote the integration of AI with other emerging technologies.
一个优化的外科手术调度系统被认为是最大限度提高医院资源利用率和改善患者治疗效果的基础。在这个项目中，人工智能（AI）工具和新技术的整合至关重要，以实现个性化的患者护理并优化围手术期临床路径。我们饶有兴趣地阅读了帕克斯等人的手稿，该手稿开发了一个手术病例持续时间的预测模型。该模型似乎采用了一种务实的方法，通过分析切实可行的变量并在各种类型的外科手术中进行验证，这为提高医疗实践中的效率和可持续性提供了潜在途径。然而，我们有一些看法，特别是关于所提出模型的可行性和实际实施。该模型的一个关键限制是手术持续时间的精确定义，这需要进一步明确。为了有效地将该模型转化为一种实际的调度方法，将手术室（OR）的总占用时间视为手术规划和资源分配的关键决定因素至关重要。这不仅包括实际手术时间，还包括术前准备、麻醉诱导和恢复、清洁以及材料补充，所有这些都会显著影响整体调度效率。另一个关键方面涉及输入数据的质量和可靠性，这对于确保模型的准确性和有效性至关重要。此外，采用新技术不应仅仅被视为一种创新，而应被视为开发高性能、高效工具以增强当前临床实践的一种手段。在这种背景下，机器学习模型不仅应作为分析工具，还应作为可操作的工具，实现从预测性洞察到战略规划和优化调度的转变，最终改善决策制定和资源分配。虽然做出准确预测是一个良好的起点，但维护一个活跃的AI模型需要资源投入，例如与当前组织系统相比增加手术病例数量。考虑创建一个多学科小组可能会促进AI与其他新兴技术的整合，这可能是有益的。
