Faculty of Information Technology, Monash University, Clayton, Melbourne, VIC 3800, Australia.
Eastern Health Clinical School Monash University, Box Hill, Melbourne, VIC 3128, Australia.
Sensors (Basel). 2022 Jun 30;22(13):4968. doi: 10.3390/s22134968.
Length of Stay (LOS) is an important performance metric in Australian Emergency Departments (EDs). Recent evidence suggests that an LOS in excess of 4 h may be associated with increased mortality, but despite this, the average LOS continues to remain greater than 4 h in many EDs. Previous studies have found that Data Mining (DM) can be used to help hospitals to manage this metric and there is continued research into identifying factors that cause delays in ED LOS. Despite this, there is still a lack of specific research into how DM could use these factors to manage ED LOS. This study adds to the emerging literature and offers evidence that it is possible to predict delays in ED LOS to offer Clinical Decision Support (CDS) by using DM. Sixteen potentially relevant factors that impact ED LOS were identified through a literature survey and subsequently used as predictors to create six Data Mining Models (DMMs). An extract based on the Victorian Emergency Minimum Dataset (VEMD) was used to obtain relevant patient details and the DMMs were implemented using the Weka Software. The DMMs implemented in this study were successful in identifying the factors that were most likely to cause ED LOS > 4 h and also identify their correlation. These DMMs can be used by hospitals, not only to identify risk factors in their EDs that could lead to ED LOS > 4 h, but also to monitor these factors over time.
在澳大利亚急诊部（ED），住院时间（LOS）是一个重要的绩效指标。最近的证据表明，超过 4 小时的 LOS 可能与死亡率增加有关，但尽管如此，许多 ED 的平均 LOS 仍持续超过 4 小时。先前的研究发现，数据挖掘（DM）可用于帮助医院管理这一指标，并且仍在继续研究确定导致 ED LOS 延迟的因素。尽管如此，对于 DM 如何利用这些因素来管理 ED LOS，仍然缺乏具体的研究。本研究增加了新兴文献，并提供了证据，表明通过使用 DM 预测 ED LOS 延迟是可行的，可以提供临床决策支持（CDS）。通过文献调查确定了 16 个可能影响 ED LOS 的相关因素，并将其用作创建六个数据挖掘模型（DMM）的预测因子。基于维多利亚州急诊最低数据集（VEMD）的一个提取用于获取相关患者详细信息，并使用 Weka 软件实现了 DMM。本研究中实施的 DMM 成功地识别了最有可能导致 ED LOS>4 小时的因素，并确定了它们之间的相关性。这些 DMM 可被医院使用，不仅可以识别可能导致 ED LOS>4 小时的 ED 中的危险因素，还可以随时间监测这些因素。
