Operating Room Time Prediction: An Application of Latent Class Analysis and Machine Learning

Abstract

Objective: The objective of this work is to build a prediction model for Operating Room Time (ORT) to be used in an intelligent scheduling system. This prediction is a complex exercise due to its high variability and multiple influential variables. Materials and methods: We assessed a new strategy using Latent Class Analysis (LCA) and clustering methods to identify subgroups of procedures and surgeries that are combined with prediction models to improve ORT estimates. Three tree-based models are assessed, Classification and Regression Trees (CART), Conditional Random Forest (CFOREST) and Gradient Boosting Machine (GBM), under two scenarios: (i) basic dataset of predictors and (ii) complete dataset with binary procedures. To evaluate the model, we use a test dataset and a training dataset to tune parameters. Results and discussion: The best results are obtained with GBM model using the complete dataset and the grouping variables, with an operational accuracy of 57.3% in the test set. Conclusion: The results indicate the GBM model outperforms other models and it improves with the inclusion of the procedures as binary variables and the addition of the grouping variables obtained with LCA and hierarchical clustering that perform the identification of homogeneous groups of procedures and surgeries.