Kirby Jessica J, Knowles Heidi C, Asad Saba, d'Etienne James P, Huggins Charles, Hoot Nathan, Schrader Chet, Moore Julie, Bryant Judson, Wang Hao
Department of Emergency Medicine, JPS Health Network, Fort Worth, TX 76104, USA.
Department of Internal Medicine, JPS Health Network, Fort Worth, TX 76104, USA.
Medicine (Baltimore). 2024 Dec 27;103(52):e40763. doi: 10.1097/MD.0000000000040763.
Left without being seen (LWBS) is a quality care metric associated with patient-centered outcomes. The risks affecting LWBS are complex and interventions targeting certain risks have diverse effects. We aimed to use different artificial intelligence and machine learning (AI/ML) algorithms to identify the risks affecting LWBS, implement triple interventions specifically targeted at such risks, and compare daily LWBS rate changes before and after the intervention. This is a retrospective observational study. Single urban Emergency Department (ED) daily throughput data from March 1, 2019, to February 28, 2023, were used for AI/ML model prediction. Model performance including accuracy, recall, precision, F1 score, and area under the receiver operating characteristics (AUC) were reported. The top risks affecting the LWBS were identified using the important function of the AI/ML feature. Triple interventions were implemented. The average daily LWBS rate was compared before (March 1, 2019, to February 28, 2023) and after (June 1, 2023, to May 31, 2024). A total of 1919 daily throughput metrics were analyzed, including 1461 daily metrics before the intervention, 92 daily metrics during the wash period, and 366 daily metrics after the intervention. Using data before the intervention, the Extreme Gradient Boosting (XGBoost) and Random Forest AI/ML algorithms predicted LWBS with a similar favorable performance. The 3 common factors influencing the increased daily LWBS rate were triage-to-bed (wait time), boarding time, and door-to-triage in the ED. Rapid triage, direct bedding, and boarding reduction (triple intervention) were implemented on March 1, 2023. We found 4.82% of daily LWBS before the triple intervention compared to 1.93% of daily LWBS after the triple intervention (P < .001). AI/ML approaches can identify common factors that are highly related to LWBS with favorable performance. Triple interventions targeting these factors can reduce the daily LWBS rate by approximately 60%, indicating the efficiency of the ED operational management.
未就诊离开(LWBS)是一项与以患者为中心的结果相关的医疗质量指标。影响LWBS的风险很复杂,针对某些风险的干预措施具有不同的效果。我们旨在使用不同的人工智能和机器学习(AI/ML)算法来识别影响LWBS的风险,实施专门针对此类风险的三重干预措施,并比较干预前后的每日LWBS率变化。这是一项回顾性观察研究。使用2019年3月1日至2023年2月28日单个城市急诊科(ED)的每日就诊量数据进行AI/ML模型预测。报告了模型性能,包括准确率、召回率、精确率、F1分数和受试者操作特征曲线下面积(AUC)。使用AI/ML特征的重要功能识别影响LWBS的主要风险。实施了三重干预措施。比较了干预前(2019年3月1日至2023年2月28日)和干预后(2023年6月1日至2024年5月31日)的每日LWBS率。共分析了1919个每日就诊量指标,包括干预前的1461个每日指标、洗脱期的92个每日指标和干预后的366个每日指标。使用干预前的数据,极端梯度提升(XGBoost)和随机森林AI/ML算法预测LWBS的性能相似且良好。影响每日LWBS率增加的3个常见因素是分诊到床位(等待时间)、住院时间和急诊科的门到分诊时间。2023年3月1日实施了快速分诊、直接安置床位和减少住院时间(三重干预)。我们发现三重干预前每日LWBS率为4.82%,而三重干预后为1.93%(P <.001)。AI/ML方法可以识别与LWBS高度相关的常见因素,性能良好。针对这些因素的三重干预措施可使每日LWBS率降低约60%,表明急诊科运营管理的效率。