Chamarthi Balaiah, Polu Omkar Reddy, Anumula Sathish Krishna, Ushmani Azhar, Kasralikar Pratik, Aleem Syed Abdul
Technology and Innovation, Info Services LLC, Livonia, USA.
Technology and Innovation, City National Bank, Los Angeles, USA.
Cureus. 2025 Apr 22;17(4):e82796. doi: 10.7759/cureus.82796. eCollection 2025 Apr.
The operating room (OR) is a high-stakes, resource-intensive environment where inefficiencies in scheduling, workflow, and resource allocation can significantly impact patient outcomes and healthcare costs. Emerging technologies such as natural language processing (NLP) and machine learning (ML) offer data-driven solutions to optimize surgical workflows, particularly when integrated with structured project planning principles. This systematic review evaluated how NLP and ML techniques, grounded in project management methodologies, can enhance OR management by improving surgical scheduling, workflow efficiency, and resource utilization. A systematic search of PubMed, Scopus, Web of Science, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and Association for Computing Machinery (ACM) Digital Library was conducted between January 1, 2020, and March 15, 2025, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Inclusion criteria focused on studies applying NLP or ML to surgical workflow analysis within a project planning framework. Primary outcomes included improvements in surgical duration prediction, post-anesthesia care unit (PACU) length-of-stay estimation, and OR scheduling efficiency. Nineteen studies met the eligibility criteria, encompassing diverse surgical specialties and geographical settings. Most employed retrospective observational designs using ML models such as ensemble learning, neural networks, and regression-based algorithms. Several studies demonstrated that ML models significantly outperformed traditional scheduling and prediction approaches, while NLP, particularly ClinicalBERT, improved accuracy when analyzing unstructured clinical texts. Risk of bias assessment using the Prediction model Risk Of Bias ASsessment Tool (PROBAST) revealed that five studies were of low risk, eight moderate risk, and six high risk, primarily due to limitations in analysis and external validation. Overall, integrating NLP and ML with project planning principles presents a promising approach to optimizing OR workflows, enhancing efficiency, reducing costs, and improving patient outcomes. However, broader clinical adoption will require cross-institutional validation, improved interpretability, and ethical artificial intelligence (AI) governance.
手术室是一个高风险、资源密集型的环境，其中调度、工作流程和资源分配方面的低效率会显著影响患者的治疗结果和医疗成本。自然语言处理（NLP）和机器学习（ML）等新兴技术提供了数据驱动的解决方案，以优化手术工作流程，特别是与结构化项目规划原则相结合时。本系统评价评估了基于项目管理方法的NLP和ML技术如何通过改善手术调度、工作流程效率和资源利用来加强手术室管理。按照系统评价和Meta分析的首选报告项目（PRISMA）2020指南，于2020年1月1日至2025年3月15日对PubMed、Scopus、科学引文索引、电气和电子工程师协会（IEEE）Xplore以及美国计算机协会（ACM）数字图书馆进行了系统检索。纳入标准侧重于在项目规划框架内将NLP或ML应用于手术工作流程分析的研究。主要结果包括手术持续时间预测、麻醉后护理单元（PACU）住院时间估计和手术室调度效率的改善。19项研究符合纳入标准，涵盖了不同的外科专业和地理区域。大多数研究采用回顾性观察设计，使用集成学习、神经网络和基于回归的算法等ML模型。几项研究表明，ML模型显著优于传统的调度和预测方法，而NLP，特别是ClinicalBERT，在分析非结构化临床文本时提高了准确性。使用预测模型偏倚风险评估工具（PROBAST）进行的偏倚风险评估显示，五项研究为低风险，八项为中度风险，六项为高风险，主要是由于分析和外部验证方面的局限性。总体而言，将NLP和ML与项目规划原则相结合是优化手术室工作流程、提高效率、降低成本和改善患者治疗结果的一种有前景的方法。然而，更广泛的临床应用将需要跨机构验证、提高可解释性以及符合伦理的人工智能（AI）治理。
