An end-to-end solution for out-of-hospital emergency medical dispatch triage based on multimodal and continual deep learning.

Abstract

The objective of this study was to build a multimodal, multitask predictive model-named E2eDeepEMC²-to improve out-of-hospital emergency incident severity assessments while coping with shifts in data distributions over time. We drew on 2054694 independent incidents recorded by the Valencian emergency medical dispatch service between 2009 and 2019 (excluding 2013), combining demographic, temporal, clinical and free-text inputs. To handle temporal drift, our model integrates continual learning strategies and comprises three encoder modules (for context, clinical data and text), whose outputs are merged to predict the life-threatening level, admissible response delay and emergency system jurisdiction. Compared with the Valencian Region's existing in-house triage protocol, E2eDeepEMC² achieved absolute F1-score gains of 18.46% for life-threatening level, 25.96% for response delay and 3.63% for jurisdiction. Compared to non-continual learning baselines, it also outperformed them by 3.04%, 9.66% and 0.58%, respectively. Deployment of E2eDeepEMC² is currently underway in the Valencian Region, underscoring its practical impact on real-world emergency dispatch decision-making.