title: A Novel Method to Identify the Start and End of the Winter Surge in Demand for Pediatric Intensive Care in Real Time.
abstract: OBJECTIVE
  Implementation of winter surge management in intensive care is hampered by the annual variability in the start and duration of the winter surge. We aimed to develop a real-time monitoring system that could identify the start promptly and accurately predict the end of the winter surge in a pediatric intensive care setting.
  
  
  METHODS
  We adapted a method from the stock market called "Bollinger bands" to compare current levels of demand for pediatric intensive care services to thresholds based on medium-term average demand. Algorithms to identify the start and end of the surge were developed using Bollinger bands and pragmatic considerations. The method was applied to a specific pediatric intensive care service: the North Thames Children's Acute Transport Service using eight winters of data (2005-2012) to tune the algorithms and one winter to test the final method (2013/2014).
  
  
  METHODS
  A regional specialized pediatric retrieval service based in London, United Kingdom.
  
  
  RESULTS
  The optimal Bollinger band thresholds were 1.2 and 1 SDs above and below a 41-day moving average of demand, respectively. A simple linear model was found to predict the end of the surge and overall surge demand volume as soon as the start had been identified. Applying the method to the validation winter of 2013/2014 showed excellent performance, with the surge identified from November 18, 2013, to January 4, 2014.
  
  
  CONCLUSIONS
  We have developed and tested a novel method to identify the start and predict the end of the winter surge in emergency demand for pediatric intensive care.
authors.affiliation: 1UCL Clinical Operational Research Unit, University College London, London, United Kingdom. 2Children's Acute Transport Service, Great Ormond Street Hospital NHS Foundation Trust, London, United Kingdom. 3Respiratory, Anaesthesia, and Critical Care Group, Institute of Child Health, University College London, London, United Kingdom.
authors.forename: Christina
authors.initials: C
authors.lastname: Pagel
authors.forename: Padmanabhan
authors.initials: P
authors.lastname: Ramnarayan
authors.forename: Samiran
authors.initials: S
authors.lastname: Ray
authors.forename: Mark J
authors.initials: MJ
authors.lastname: Peters
mesh_terms: D000465:Algorithms; D005544:Forecasting / Q000379:methods*; D006301:Health Services Needs and Demand / Q000706:statistics & numerical data*; D006801:Humans; D015278:Intensive Care Units, Pediatric / Q000458:organization & administration / Q000706:statistics & numerical data*; D016014:Linear Models; D008131:London; D012621:Seasons; D055872:Surge Capacity; D014187:Transportation of Patients / Q000706:statistics & numerical data