J Emerg Nurs. 2021 Mar;47(2):265-278.e7. doi: 10.1016/j.jen.2020.11.001. Epub 2020 Dec 24.
Triage is critical to mitigating the effect of increased volume by determining patient acuity, need for resources, and establishing acuity-based patient prioritization. The purpose of this retrospective study was to determine whether historical EHR data can be used with clinical natural language processing and machine learning algorithms (KATE) to produce accurate ESI predictive models.
The KATE triage model was developed using 166,175 patient encounters from two participating hospitals. The model was tested against a random sample of encounters that were correctly assigned an acuity by study clinicians using the Emergency Severity Index (ESI) standard as a guide.
At the study sites, KATE predicted accurate ESI acuity assignments 75.7% of the time compared with nurses (59.8%) and the average of individual study clinicians (75.3%). KATE's accuracy was 26.9% higher than the average nurse accuracy (P <.001). On the boundary between ESI 2 and ESI 3 acuity assignments, which relates to the risk of decompensation, KATE's accuracy was 93.2% higher, with 80% accuracy compared with triage nurses 41.4% accuracy (P <.001).
KATE provides a triage acuity assignment more accurate than the triage nurses in this study sample. KATE operates independently of contextual factors, unaffected by the external pressures that can cause under triage and may mitigate biases that can negatively affect triage accuracy. Future research should focus on the impact of KATE providing feedback to triage nurses in real time, on mortality and morbidity, ED throughput, resource optimization, and nursing outcomes.
分诊对于通过确定患者的 acuity、资源需求以及建立基于 acuity 的患者优先级来减轻就诊量增加的影响至关重要。本回顾性研究的目的是确定历史电子健康记录 (EHR) 数据是否可与临床自然语言处理和机器学习算法 (KATE) 一起使用，以生成准确的 ESI 预测模型。
KATE 分诊模型是使用来自两个参与医院的 166,175 例患者就诊数据开发的。该模型针对使用紧急严重指数 (ESI) 标准正确分配 acuity 的研究临床医生随机选择的就诊数据进行了测试。
在研究地点，KATE 预测准确的 ESI acuity 分配的准确率为 75.7%，而护士为 59.8%，个体研究临床医生的平均准确率为 75.3%。KATE 的准确率比护士的平均准确率高 26.9%（P<.001）。在 ESI 2 和 ESI 3 acuity 分配之间的边界处，即与代偿风险相关的边界，KATE 的准确率高 93.2%，而分诊护士的准确率为 80%，准确率为 41.4%（P<.001）。
KATE 提供的分诊 acuity 分配比本研究样本中的分诊护士更准确。KATE 独立于上下文因素运行，不受可能导致分诊不足的外部压力影响，并且可能减轻可能对分诊准确性产生负面影响的偏见。未来的研究应重点关注 KATE 实时向分诊护士提供反馈对死亡率和发病率、ED 吞吐量、资源优化和护理结果的影响。
