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.

翻译：由于人口结构及寿命的持续增长，北美地区急诊就诊量逐年攀升。随着就诊患者数量的增加，传统临床工作流程变得超负荷且效率低下，导致候诊时间延长、医疗质量下降。其中，分诊医疗指令受制于有限的人力负荷、不准确的诊断及侵入性过度检查。针对这一问题，我们提出TriNet：一种用于医疗指令的机器学习模型，能够在分诊阶段对需要后续检测确诊的病症实现自动化一线筛查。为验证筛查潜力，我们利用医院分诊数据训练TriNet，其在肺炎检测（0.86）和尿路感染检测（0.93）中均达到较高阳性预测值。该模型优于当前临床基准指标，表明机器学习医疗指令可提供低成本、非侵入性且对常见病症具有高特异性的筛查方案，从而在提升急诊效率的同时降低过度检查风险。