JMIR Publications

ABSTRACT

Background:

Prehospital telemedicine triage systems combined with machine learning (ML) methods have the potential to improve triage accuracy and safely redirect low-acuity patients from attending the emergency department (ED). However, research in prehospital settings is limited, but needed; ED overcrowding and adverse patient outcomes are increasingly common.

Objective:

In this scoping review, we sought to characterize the existing methods for ML-enhanced telemedicine emergency triage.

Methods:

A scoping review was conducted, querying multiple databases (MEDLINE, PubMed, Scopus, and IEEE Xplore) through February 24, 2023 to identify potential ML-enhanced methods and, for those eligible, relevant study characteristics were extracted, including prehospital triage setting, types of predictors, ground truth labelling method, ML models used, and performance metrics. We conducted a narrative synthesis of findings to establish a knowledge base in this domain and identify potential gaps to be addressed in forthcoming ML-enhanced methods.

Results:

165 unique records were screened for eligibility and 15 were included in the review. Most studies applied ML methods during emergency medical dispatch (n=7, 47%) or used chatbot applications (n=5, 33%). Patient demographics and health status variables were the most common predictors, with a notable absence of social variables. Frequently used ML models included support vector machines and tree-based methods. ML-enhanced models typically outperformed conventional triage algorithms and we found a wide range of methods used to establish ground truth labels.

Conclusions:

This scoping review observed heterogeneity in dataset size, predictors, clinical setting (triage process), and reported performance metrics. Standard structured predictors, including age, sex, and comorbidities, across articles suggest the importance of these inputs, however, there was a notable absence of other potentially useful data, including medications, social variables, and health system exposure. Ground truth labelling practices should be reported in a standard fashion as the true model performance hinges on these labels. This review calls for future work to form a standardized framework, thereby supporting consistent reporting and performance comparisons across ML-enhanced prehospital triage systems.