title: Detecting and diagnosing hotspots for the enhanced management of hospital Emergency Departments in Queensland, Australia.
abstract: 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.
authors: Sarah
authors: S
authors: Bolt
authors: CSIRO Computational Informatics, Locked Bag 17, 1670 North Ryde NSW, Australia. ross.sparks@csiro.au.
authors: Ross
authors: R
authors: Sparks
mesh_terms: D003198:Computer Simulation; D004196:Disease Outbreaks / Q000517:prevention & control*; D004636:Emergency Service, Hospital / Q000458:organization & administration*; D006801:Humans; D015233:Models, Statistical; D062486:Public Health Surveillance / Q000379:methods*; D011793:Queensland; D012680:Sensitivity and Specificity