使用机器学习和临床自然语言处理改进急诊科ESI视力分配
Improving Emergency Department ESI Acuity Assignment Using Machine Learning and Clinical Natural Language Processing
摘要：
有效的分诊对于通过准确确定患者的紧急程度、对资源的需求以及建立有效的基于紧急程度的患者优先级来减轻容量增加的影响至关重要。这项回顾性研究的目的是确定是否可以提取历史EHR数据，并与临床自然语言处理（C-NLP）和最新的ML算法（KATE）合成，以产生高度准确的ESI预测模型。使用来自两家参与医院的166，175例患者就诊，开发了用于分诊过程的ML模型（KATE）。然后，该模型进行了测试，对一个黄金集，来自随机样本的分诊遇到在研究中心和正确的敏锐度分配记录研究临床医生使用紧急情况严重程度指数（ESI）标准作为指导。在两个研究中心，KATE预测准确的ESI敏锐度分配的时间为75.9%，相比之下，护士（59.8%）和平均个人研究临床医生（75.3%）。KATE的准确率比平均护士准确率高26.9%（p值< 0.0001）。在与失代偿风险相关的ESI 2和ESI 3敏锐度分配之间的边界上，KATE高93.2%，准确率为80%，而分诊护士的准确率为41.4%（p值< 0.0001）。KATE提供的分诊敏锐度分配比本研究样本中的分诊护士更准确。KATE独立于环境因素运作，不受可能导致分流的外部压力的影响，并可能减轻可能对分流分配准确性产生负面影响的种族和社会偏见。未来的研究应该集中在KATE提供反馈的影响，分诊护士在实时的，KATE对死亡率和发病率的影响，ED吞吐量，资源优化和护理成果。
英文摘要：
Effective triage is critical to mitigating the effect of increased volume by accurately determining patient acuity, need for resources, and establishing effective acuity-based patient prioritization. The purpose of this retrospective study was to determine whether historical EHR data can be extracted and synthesized with clinical natural language processing (C-NLP) and the latest ML algorithms (KATE) to produce highly accurate ESI predictive models. An ML model (KATE) for the triage process was developed using 166,175 patient encounters from two participating hospitals. The model was then tested against a gold set that was derived from a random sample of triage encounters at the study sites and correct acuity assignments were recorded by study clinicians using the Emergency Severity Index (ESI) standard as a guide. At the two study sites, KATE predicted accurate ESI acuity assignments 75.9% of the time, compared to nurses (59.8%) and average individual study clinicians (75.3%). KATE accuracy was 26.9% higher than the average nurse accuracy (p-value < 0.0001). On the boundary between ESI 2 and ESI 3 acuity assignments, which relates to the risk of decompensation, KATE was 93.2% higher with 80% accuracy, compared to triage nurses with 41.4% accuracy (p-value < 0.0001). KATE provides a triage acuity assignment substantially 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 the racial and social biases that can negatively affect the accuracy of triage assignment. Future research should focus on the impact of KATE providing feedback to triage nurses in real time, KATEs impact on mortality and morbidity, ED throughput, resource optimization, and nursing outcomes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
