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

Han Wang; Qin Xiang Ng; Shalini Arulanandam; Colin Tan; Marcus E H Ong; Mengling Feng

doi:10.34133/hds.0008

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

Health Data Sci. 2023 Mar 15:3:0008. doi: 10.34133/hds.0008. eCollection 2023.

Authors

Han Wang¹, Qin Xiang Ng², Shalini Arulanandam², Colin Tan², Marcus E H Ong^{3

4

5}, Mengling Feng^{1

6}

Affiliations

¹ Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore.
² Singapore Civil Defence Force, Singapore.
³ Health Services Research Centre, Singapore Health Services, Singapore.
⁴ Health Services and Systems Research, Duke-NUS Medical School, National University of Singapore, Singapore.
⁵ Department of Emergency Medicine, Singapore General Hospital, Singapore.
⁶ Institute of Data Science, National University of Singapore, Singapore.

Abstract

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.