An unsupervised machine learning approach for defining surge levels in emergency medical services

A surge period occurs when demand significantly exceeds available capacity, creating operational strain in emergency medical services (EMS) and leading to measurable declines in system performance. Although surge levels are a critical metric for EMS operations, no established method exists for their objective definition. This study introduces a genetic algorithm-based unsupervised clustering model designed to define surge levels using EMS operational data. Unlike the National Emergency Department Overcrowding Scale, which depends on subjective assessments, the proposed approach objectively categorizes surge levels and supports regional customization through hyperparameter tuning and feature selection. The model's adaptability allows healthcare leaders to determine the desired number of surge-level categories and tailor the feature set to local operational needs. A case study in Nova Scotia, Canada, demonstrates the model's effectiveness, accurately identifying 88.96 % of busy periods with recall and precision of 96.49 % and 78.57 %, respectively. These results indicate that the approach provides a robust and flexible tool for defining surge levels, enabling data-driven decision-making in EMS system management. • ·Propose a novel clustering algorithm to define surge levels in emergency healthcare systems. • Demonstrate the model's accuracy in identifying system busyness in emergency healthcare. • ·Eliminate reliance on subjective data when defining surge levels in healthcare systems. • ·Enable regional customization of surge level definitions in healthcare analytics. • ·Achieve strong predictive performance using healthcare operational data.