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首页> 外文期刊>BMC Medical Informatics and Decision Making >Detecting and diagnosing hotspots for the enhanced management of hospital emergency departments in Queensland, Australia
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Detecting and diagnosing hotspots for the enhanced management of hospital emergency departments in Queensland, Australia

机译：检测和诊断热点，以增强澳大利亚昆士兰州医院急诊科的管理

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Background Predictive tools are already being implemented to assist in Emergency Department bed management by forecasting the expected total volume of patients. Yet these tools are unable to detect and diagnose when estimates fall short. Early detection of hotspots, that is subpopulations of patients presenting in unusually high numbers, would help authorities to manage limited health resources and communicate effectively about emerging risks. We evaluate an anomaly detection tool that signals when, and in what way Emergency Departments in 18 hospitals across the state of Queensland, Australia, are significantly exceeding their forecasted patient volumes. Methods The tool in question is an adaptation of the Surveillance Tree methodology initially proposed in Sparks and Okugami (IntStatl 1:2–24, 2010). for the monitoring of vehicle crashes. The methodology was trained on presentations to 18 Emergency Departments across Queensland over the period 2006 to 2008. Artificial increases were added to simulated, in-control counts for these data to evaluate the tool’s sensitivity, timeliness and diagnostic capability. The results were compared with those from a univariate control chart. The tool was then applied to data from 2009, the year of the H1N1 (or ‘Swine Flu’) pandemic. Results The Surveillance Tree method was found to be at least as effective as a univariate, exponentially weighted moving average (EWMA) control chart when increases occurred in a subgroup of the monitored population. The method has advantages over the univariate control chart in that it allows for the monitoring of multiple disease groups while still allowing control of the overall false alarm rate. It is also able to detect changes in the makeup of the Emergency Department presentations, even when the total count remains unchanged. Furthermore, the Surveillance Tree method provides diagnostic information useful for service improvements or disease management. Conclusions Multivariate surveillance provides a useful tool in the management of hospital Emergency Departments by not only efficiently detecting unusually high numbers of presentations, but by providing information about which groups of patients are causing the increase.
机译：背景技术预测工具已经在实施中，以通过预测患者的预期总人数来协助急诊科的病床管理。然而，这些工具无法在估计不足时进行检测和诊断。尽早发现热点，即出现异常高数量的患者亚群，将有助于当局管理有限的卫生资源并有效地沟通新出现的风险。我们评估了一种异常检测工具，该工具可以告知澳大利亚昆士兰州18家医院的急诊科何时，以何种方式严重超出其预计的患者数量。方法有问题的工具是对最初在Sparks和Okugami（IntStatl 1：2–24，2010）中提出的Surveillance Tree方法的改编。用于监视车辆碰撞。在2006年至2008年期间，对方法论进行了培训，并向昆士兰州的18个急诊科进行了介绍。对这些数据的模拟控制下的计数进行了人工增加，以评估工具的敏感性，及时性和诊断能力。将结果与单变量控制图的结果进行比较。然后将该工具应用于2009年（H1N1流感（或“猪流感”）流行年份）的数据。结果发现，在被监视人群的一个子组中发生增加时，监视树方法至少与单变量，指数加权移动平均值（EWMA）控制图一样有效。该方法与单变量控制图相比具有优势，因为它可以监视多个疾病组，同时仍然可以控制总体虚警率。即使总数保持不变，它也能够检测急诊科演示的组成变化。此外，“监视树”方法提供了有助于改善服务或疾病管理的诊断信息。结论多变量监测不仅可以有效地检测出异常大量的报告，而且还可以提供有关导致病因增加的患者群体的信息，从而为医院急诊科的管理提供了有用的工具。

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