Department of Emergency Medicine, Chang Gung Memorial Hospital and Chang Gung University, College of Medicine, Taoyuan City, 333, Taiwan.
Department of Emergency Medicine, Chang Gung Memorial Hospital, Keelung and Chang Gung University, College of Medicine, No. 5 Fushing St., Gueishan Shiang, Taoyuan City, 333, Taiwan.
BMC Emerg Med. 2024 Jan 30;24(1):20. doi: 10.1186/s12873-024-00939-6.
Unscheduled return visits (URVs) to emergency departments (EDs) are used to assess the quality of care in EDs. Machine learning (ML) models can incorporate a wide range of complex predictors to identify high-risk patients and reduce errors to save time and cost. However, the accuracy and practicality of such models are questionable. This review compares the predictive power of multiple ML models and examines the effects of multiple research factors on these models' performance in predicting URVs to EDs.
We conducted the present scoping review by searching eight databases for data from 2010 to 2023. The criteria focused on eligible articles that used ML to predict ED return visits. The primary outcome was the predictive performances of the ML models, and results were analyzed on the basis of intervals of return visits, patient population, and research scale.
A total of 582 articles were identified through the database search, with 14 articles selected for detailed analysis. Logistic regression was the most widely used method; however, eXtreme Gradient Boosting generally exhibited superior performance. Variations in visit interval, target group, and research scale did not significantly affect the predictive power of the models.
This is the first study to summarize the use of ML for predicting URVs in ED patients. The development of practical ML prediction models for ED URVs is feasible, but improving the accuracy of predicting ED URVs to beyond 0.75 remains a challenge. Including multiple data sources and dimensions is key for enabling ML models to achieve high accuracy; however, such inclusion could be challenging within a limited timeframe. The application of ML models for predicting ED URVs may improve patient safety and reduce medical costs by decreasing the frequency of URVs. Further research is necessary to explore the real-world efficacy of ML models.
非计划性急诊科复诊（URV）被用于评估急诊科的医疗质量。机器学习（ML）模型可以整合广泛的复杂预测因子，以识别高危患者并减少错误，从而节省时间和成本。然而，此类模型的准确性和实用性仍存在争议。本综述比较了多个 ML 模型的预测能力，并探讨了多个研究因素对这些模型预测急诊科 URV 能力的影响。
我们通过对 2010 年至 2023 年的八个数据库进行搜索，开展了本次范围界定综述。纳入标准为使用 ML 预测 ED 复诊的合格文章。主要结果为 ML 模型的预测性能，分析结果基于复诊间隔、患者人群和研究规模。
通过数据库搜索共确定了 582 篇文章，其中 14 篇文章被选入进行详细分析。逻辑回归是使用最广泛的方法；然而，极端梯度提升的性能通常更优。复诊间隔、目标人群和研究规模的变化并未显著影响模型的预测能力。
这是第一项总结使用 ML 预测急诊科 URV 的研究。开发实用的 ML 预测模型用于预测急诊科 URV 是可行的，但要将 ED URV 的预测准确性提高到 0.75 以上仍具有挑战性。纳入多个数据源和维度是使 ML 模型实现高精度的关键，但在有限的时间内进行这样的纳入可能具有挑战性。应用 ML 模型预测急诊科 URV 可通过减少 URV 的发生来提高患者安全性和降低医疗成本。需要进一步研究以探索 ML 模型的实际效果。
