Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA.
Department of Anesthesia, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA.
Otolaryngol Head Neck Surg. 2023 Feb;168(2):241-247. doi: 10.1177/01945998221076480.
Optimizing operating room (OR) efficiency depends on accurate case duration estimates. Machine learning (ML) methods have been used to predict OR case durations in other subspecialties. We hypothesize that ML methods improve projected case lengths over existing non-ML techniques for otolaryngology-head and neck surgery cases.
Deidentified patient information from otolaryngology surgical cases at 1 academic institution were reviewed from 2016 to 2020. Variables collected included patient, surgeon, procedure, and facility data known preoperatively so as to capture all realistic contributors. Available case data were divided into a training and testing data set. Several ML algorithms were evaluated based on best performance of predicted case duration when compared to actual case duration. Performance of all models was compared by the average root mean squared error and mean absolute error (MAE).
In total, 50,888 otolaryngology surgical cases were evaluated with an average case duration of 98.3 ± 86.9 minutes. Most cases were general otolaryngology (n = 16,620). Case features closely associated with OR duration included procedure performed, surgeon, subspecialty of case, and postoperative destination of the patient. The best-performing ML models were CatBoost and XGBoost, which reduced operative time MAE by 9.6 minutes and 8.5 minutes compared to current methods, respectively.
The incorporation of other easily identifiable features beyond procedure performed and surgeon meaningfully improved our operative duration prediction accuracy. CatBoost provided the best-performing ML model.
ML algorithms to predict OR case time duration in otolaryngology can improve case duration accuracy and result in financial benefit.
优化手术室（OR）效率取决于准确的手术持续时间估计。机器学习（ML）方法已被用于预测其他亚专科的 OR 手术持续时间。我们假设 ML 方法可以提高耳鼻喉头颈外科手术病例的预计手术时长，优于现有的非 ML 技术。
回顾了 2016 年至 2020 年期间一家学术机构耳鼻喉科手术病例的匿名患者信息。收集的变量包括术前已知的患者、外科医生、手术和手术设施数据，以捕捉所有现实的贡献因素。可用病例数据分为训练和测试数据集。根据与实际手术持续时间相比预测手术持续时间的最佳性能，评估了几种 ML 算法。通过平均均方根误差和平均绝对误差（MAE）比较所有模型的性能。
总共评估了 50888 例耳鼻喉科手术病例，平均手术持续时间为 98.3±86.9 分钟。大多数病例为普通耳鼻喉科（n=16620）。与 OR 持续时间密切相关的病例特征包括手术进行的程序、外科医生、病例的亚专科以及患者术后的去向。表现最佳的 ML 模型是 CatBoost 和 XGBoost，与当前方法相比，分别将手术时间 MAE 降低了 9.6 分钟和 8.5 分钟。
除了手术进行的程序和外科医生之外，纳入其他易于识别的特征显著提高了我们的手术持续时间预测准确性。CatBoost 提供了性能最佳的 ML 模型。
用于预测耳鼻喉科 OR 病例时间持续时间的 ML 算法可以提高病例时间持续时间的准确性，并带来经济效益。
