Screening of Pneumonia and Urinary Tract Infection at Triage using TriNet
This addresses the issue of prolonged wait-times and reduced healthcare quality for emergency department patients, though it is incremental as it applies existing methods to a specific medical domain.
The paper tackled the problem of inefficient triage workflows in emergency departments by proposing TriNet, a machine learning model for screening pneumonia and urinary tract infection, achieving positive predictive values of 0.86 and 0.93 respectively.
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.