Queueing inspired feature engineering to improve and simplify patient flow simulation metamodels

We explore the interplay between domain-informed feature engineering, model performance, and model interpretability. This is a hybrid modelling and simulation study that merges the application of discrete-event simulation with alternative metamodelling techniques for modelling patient flow in health care. We consider two cases: a tandem queueing system of obstetric hospital units and a transient analysis of an outpatient clinic in which a finite number of scheduled patients arrive for care. We use several metamodels including various types of linear models, random forests, and neural networks. We evaluate the performance improvement of metamodel estimation when empowered with supplementary queueing theory knowledge. We consider three knowledge levels: no knowledge (no queueing-inspired features), basic (simple queueing features), and advanced (sophisticated queueing approximations). Our results show that queueing-related inputs improve the accuracy for the metamodels, independent of the model type. Moreover, queueing-related inputs improve model explainability and can lead to more parsimonious models. This has positive practical implications for implementing these types of models in actual health-care analytic projects.