This document discusses the use of digital health and machine learning in emergency department triage. It reviews 5 research articles on this topic that use machine learning algorithms like random forest and deep learning to more accurately predict patient outcomes like critical care need, hospitalization, and mortality compared to traditional triage methods. The studies demonstrate that machine learning can reduce under-triage of critically ill patients and over-triage of less ill patients. The author concludes that machine learning and deep learning offer opportunities to improve triage accuracy and potentially be incorporated directly into triage systems.

1. Emergency Department Triage
and Digital Health
Dr Hugh Singleton
Emergency Physician

2. Search Strategy
 UniMelb online library
 Ovid MedLine
 Search keywords
 Health Informatics
 Emergency Medicine
 Triage
 Machine Learning
 Results
 Between 20 and 90 results shortlisted for various search combinations
 10 articles shortlisted
 5 articles chosen

3. Decision Rationale
 Reviewed articles
 Chose top 5 based on following criteria:
 Relevance
 Potential impact
 Realistic
 Achieved statistical and real-world significance

4. Machine Learning-Based Electronic Triage More Accurately
Differentiates Patients With Respect to Clinical Outcomes
Compared With the Emergency Severity Index
 Levin, S., Toerper, M., Hamrock, E., Hinson, J., Barnes, S., Gardner, H… (2018). Machine Learning-
Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes
Compared With the Emergency Severity Index. Annals of Emergency Medicine. 71(5). Retrieved
from https://www.annemergmed.com/article/S0196-0644(17)31442-7/fulltext
 Highlights
 Outcomes were Critical Care, Emergency Procedure and Hospitalisation
 Secondary ”clinical” outcomes – elevated troponin or lactate
 Looked at E-Triage vs traditional triage (ESI)
 E-Triage - Used Machine Learning (Random Forest) to predict outcomes
 Patients up-triaged from a Triage Level Three were 5x more likely to need critical care

5. Validation of deep-learning based triage and
acuity score using a large national dataset
 Kwon, J-m., Lee, Y., Lee, Y., Lee, S., Park, H., Park, J. (2018). PLoS ONE. 13(10): e0205836. Retrieved
from https://doi.org/10.1371/journal.pone.0205836
 Highlights
 Outcomes – In-Hospital Mortality, Critical Care and Hospitalisation
 Creation of a DTAS (Deep Learning Triage and Acuity Scale)
 Compared Deep-Learning with KTAS, MEWS and more traditional Logistic Regression and Random Forest Machine
Learning algorithms
 DTAS showed a much higher AUROC and AUPRC with statistical significance compared with other
modalities assessed for mortality, Critical Care and Hospitalisation

6. Machine Learning Based Prediction of Clinical Outcomes
for Children During Emergency Department Triage
 Goto, T., Camargo, C., Faridi, M., Freishtat, R., Hasegawa, K. (2019) JAMA Network Open. 2(1).
Retrieved from https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2720586
 Highlights
 Outcomes – Critical Care and Hospitalisation
 Derived 4 Machine Learning based algorithms
 Lasso Regression, Random Forest, Gradient-boosted Decision Tree and Deep Neural Network
 Demonstrated that Machine Learning had a better discriminatory ability; it was less likely to under-triage
critically ill children and over-triage children who are less ill

7. Validity of the Manchester Triage System in emergency patients
receiving life-saving intervention or acute medical treatment – a
prospective observational study in the emergency department
 Graff, I., Latzel, B., Glien, Procula., Fimmers, R., Dolscheid-Pommerich, R. (2018). Journal of
Evaluation in Clinical Practice. 25(398-403). Retrieved from
https://onlinelibrary.wiley.com/doi/abs/10.1111/jep.13030
 Highlights
 Outcomes – Life Saving Intervention (LSI) and Acute Emergency Treatment (AET)
 Prospective observational study
 Looked at the ability of the Manchester Triage System (MTS) to identify patients requiring LSI to Triage
Category 1 and AED to either category 1 or 2
 Confirmed the sensitivity of MTS whilst balancing the need of avoiding over-triage of false positive
patients

8. A Machine Learning Approach to Predicting Need for
Hospitalization for Pediatric Asthma Exacerbation at the
time of Emergency Department Triage
 Patel, S., Chamberlain, D., Chamberlain J. (2018) A Machine Learning Approach to Predicting
Need for Hospitalization for Pediatric Asthma Exacerbation at the time of Emergency Department
Triage. Academic Emergency Medicine Journal. Retrieved from
https://www.ncbi.nlm.nih.gov/pubmed/30382605
 Highlights
 Outcome: Hospitalisation
 Defined and tested four machine learning algorithms
 Decision Tree, LASSO Regression, Random Forest, Gradient Boosting machine
 AUROC of 0.85 for hospitalisation

9. Triage future predictions
 My “best of” Triage and Informatics in the ED gives some insight into the future direction of this
topic
 The importance of triage, its accuracy and ability to predict outcomes is significant
 Machine learning offers possibilities into improving the accuracy of triage, either as a support to
traditional triage or as a major part of the triage process itself
 Deep learning is another, even more advanced iteration of Machine Learning where the
algorithm is able to learn what it is required to know and what importance to place on it

10. Thank you