The optimization of patient flow within hospital emergency rooms (ERs) continues to be a significant challenge in healthcare operations. The protracted waiting times directly impact the patient satisfaction along with efficient resource allocation. This study presents a robust machine learning framework for the accurate prediction of ER waiting times. We identified and engineered key predictive features including patient acuity, staffing ratios, and temporal factors such as time of day and seasonality by leveraging a dataset of 5,000 patient encounters.

Our methodology encompassed a comparative analysis of various machine learning algorithms including linear models, tree-based ensembles, and support vector machines to ascertain the optimal predictive architecture. Model performance was assessed via a rigorous 5-fold cross-validation process, employing Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The findings reveal that a hyperparameter-tuned Stacked Ensemble Model yielded superior performance by achieving coefficient of determination (R²) of 0.9473. This outcome indicates a strong correlation between the model's predictions and the observed wait times, thus validating its ability to encapsulate the intricate and nonlinear dynamics inherent in ER operations.

The developed model offers a validated data-driven tool for hospital administrators facilitating proactive resource management and ameliorated patient communication. This study highlights the capacity of advanced analytics to address significant operational inefficiencies within healthcare, presenting a trajectory towards an enhanced patient experience and more efficacious allocation of clinical resources.