Deep multitask ensemble classification of emergency medical call incidents combining multimodal data improves emergency medical dispatch

Objective: To develop a predictive model to aid non-clinical dispatchers to classify emergency medical call incidents by their life-threatening level (yes/no), admissible response delay (undelayable, minutes, hours, days) and emergency system jurisdiction (emergency system/primary care) in real time. Materials: A total of 1 244 624 independent retrospective incidents from the Valencian emergency medical dispatch service in Spain from 2009 to 2012, comprising clinical features, demographics, circumstantial factors and free text dispatcher observations. Methods: A deep multitask ensemble model integrating four subnetworks, composed in turn by multi-layer perceptron modules, bidirectional long short-term memory units and a bidirectional encoding representations from transformers module. Results: The model showed a F1 score of 0.771 in life-threatening classification, 0.592 in response delay and 0.801 in jurisdiction, obtaining a performance increase of 13.2%, 16.4% and 4.5%, respectively, with regard to the current in-house triage protocol of the Valencian emergency medical dispatch service. Discussion: The model captures information present in emergency medical calls not considered by the existing in-house triage protocol, but relevant to carry out incident classification. Besides, the results suggest that most of this information is present in the free text dispatcher observations. Conclusion: To our knowledge, this study presents the development of the first deep learning model undertaking emergency medical call incidents classification. Its adoption in medical dispatch centers would potentially improve emergency dispatch processes, resulting in a positive impact in patient wellbeing and health services sustainability.