Screening of Pneumonia and Urinary Tract Infection at Triage using
  TriNet
Stephen Z. Lu
Due to the steady rise in population demographics and longevity, emergency
department visits are increasing across North America. As more patients visit
the emergency department, traditional clinical workflows become overloaded and
inefficient, leading to prolonged wait-times and reduced healthcare quality.
One of such workflows is the triage medical directive, impeded by limited human
workload, inaccurate diagnoses and invasive over-testing. To address this
issue, we propose TriNet: a machine learning model for medical directives that
automates first-line screening at triage for conditions requiring downstream
testing for diagnosis confirmation. To verify screening potential, TriNet was
trained on hospital triage data and achieved high positive predictive values in
detecting pneumonia (0.86) and urinary tract infection (0.93). These models
outperform current clinical benchmarks, indicating that machine-learning
medical directives can offer cost-free, non-invasive screening with high
specificity for common conditions, reducing the risk of over-testing while
increasing emergency department efficiency.