Machine learning algorithms, including Neural Networks and Gradient Boosting, demonstrated robust performance for estimating surgical times for Trauma and Orthopaedics procedures.
Do machine learning-based predictive algorithms improve surgical time estimation compared to traditional subjective estimates or historical averages for Trauma and Orthopaedics procedures?
Machine learning algorithms, particularly Neural Networks and various regression models, show robust performance in predicting surgical durations for orthopaedic procedures, potentially improving scheduling efficiency.
Traditional elective patients' surgical scheduling relies on plan-makers' subjective estimates or historical averages, leading to inefficiencies such as surgery cancellations or underutilisation of resources. Machine learning (M/L)-based predictive algorithms offer a promising solution with data-driven models to forecast surgical times, however, their application in NHS hospital settings remains limited. This study explores the implementation of multiple M/L algorithms for surgical time estimation for Trauma and Orthopaedics related procedures in an NHS Trust hospital. Results indicate that Neural Networks, along with ElasticNet regression, Gradient Boosting, and Bayesian Ridge regression models, demonstrate robust performance. Additionally, expansion to procedure specific models, built separately for each procedure shows promising results. This study contributes insights into the integration of M/L algorithms into healthcare digital resources, paving the way for enhanced surgical planning strategies. Future research will focus on integrating the predictive models into a comprehensive AI driven Digital Twin framework for simulation and optimisation-driven automated decision-making.
Sapkota et al. (Fri,) conducted a other in Trauma and Orthopaedics related procedures. Machine learning (M/L)-based predictive algorithms vs. Subjective estimates or historical averages was evaluated on Surgical time estimation. Machine learning algorithms, including Neural Networks and Gradient Boosting, demonstrated robust performance for estimating surgical times for Trauma and Orthopaedics procedures.