Abstract
BackgroundPredictive 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.MethodsThe 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.ResultsThe 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.ConclusionsMultivariate 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.
Highlights
Predictive tools are already being implemented to assist in Emergency Department bed management by forecasting the expected total volume of patients
Many factors contribute to the incidence of winter disease outbreaks, so most predictive tools will inevitably fall short at some point
The univariate control chart used in this paper was an exponentially weighted moving average (EWMA) control chart of Flu presentations, referred to subsequently as the univariate control chart, which monitors total flu counts departures from their expected value, where the expected values and variances are calculated exactly as for the Surveillance Tree
Summary
Setting The data available from the EDs in this study were in the form of de-identified unit records for each ED presentation from 2006 to 2009 to any of 18 hospitals across Queensland. In order to determine τ such that the pruning of nodes is conditionally independent of the properties of the nodes themselves (node mean μ and node depth ν), we use bootstrapped, in-control samples from the model for training Using these samples, we run simulations of the EWMA Surveillance Tree partitioning. Given here are the results of the quantile regression used to choose a threshold for each variable such that each variable is likely to signal; signalling is independent of node expected value μ and depth in the tree; and the overall false alarm rate is approximately 3 times per year. The final measure examined the diagnostic properties of the Surveillance tree methodlogy
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