Building a Machine Learning-based Ambulance Dispatch Triage Model for Emergency Medical Services

Background:In charge of dispatching the ambulances,Emergency Medical Services(EMS)call center specialists often have difficulty deciding the acuity of a case given the information they can gather within a limited time.Although there are protocols to guide their decision-making,observed performance can still lack sensitivity and specificity.Machine learning models have been known to capture complex relationships that are subtle,and well-trained data models can yield accurate predictions in a split of a second.Methods:In this study,we proposed a proof-of-concept approach to construct a machine learning model to better predict the acuity of emergency cases.We used more than 360,000 structured emergency call center records of cases received by the national emergency call center in Singapore from 2018 to 2020.Features were created using call records,and multiple machine learning models were trained.Results:A Random Forest model achieved the best performance,reducing the over-triage rate by an absolute margin of 15%compared to the call center specialists while maintaining a similar level of under-triage rate.Conclusions:The model has the potential to be deployed as a decision support tool for dispatchers alongside current protocols to optimize ambulance dispatch triage and the utilization of emergency ambulance resources.

Implementation science:A critical but undervalued part of the healthcare innovation ecosystem

Healthcare systems face many competing demands and insufficient resources.Service innovations to improve efficiency are important to address this challenge.Innovations can range from new pharmaceuticals,alternate models of care,novel devices,and the use other technologies.Suboptimal implementation can mean lost benefits.This review article aims to highlight the role of implementation science,summarize how settings have leveraged this methodology to promote translation of innovation into practice,and describe our own experience of embedding implementation science into an academic medical center in Singapore.Implementation science offers a range of methods to promote systematic uptake of research findings about innovations and is gaining recognition worldwide as an important discipline for health services researchers.Health systems around the world have tried to promote implementation research in their settings by establishing(1)dedicated centers/programs,(2)offering funding,and(3)building knowledge and capacity among staff.Implementation science is a critical piece in the translational pathway of“evidence to innovation”.The three efforts we describe should be strengthened to integrate implementation science into the innovation ecosystem around the world.