title: The use of statistical process control for monitoring institutional performance in trauma care.
abstract: BACKGROUND
  In recent years, performance monitoring has gained increasing attention as a tool for evaluating the delivery of health care services and is a topic of increasing importance in trauma systems. The main objective of this article is to illustrate a proactive method for assessing the performance of trauma centers in England and Wales, while taking into account common causes of variation. The aim is to present a methodology that is easily interpretable and avoids the spurious ranking of hospitals which can often lead to the misinterpretation on what is perceived to be the best and worst performing hospital, as measured by a prespecified performance indicator.
  
  
  METHODS
  The Ws statistic was introduced over 10 years ago to quantify the performance of trauma care systems through definitive outcome based evaluation (DEF) methods. Little advancement on this methodology has been made since its introduction. In this article, we highlight some of the limitations and problems associated with these DEF methods and introduce the funnel plot, a form of control chart to monitor hospital performance.
  
  
  RESULTS
  The number of patients included in a Ws statistical analysis can seriously change the ranking of a hospital. These complex issues with ranking means that league tables (or standings charts), which form part of the DEF method are an unsatisfactory method to represent performance indicators. The funnel plot methodology is an alternative graphical method for monitoring hospital performance, which has no emphasis on ranking. We demonstrate the method using mortality data and length of stay as the performance indicators.
  
  
  CONCLUSIONS
  The funnel plot is a flexible, attractively simple method for comparing hospital performance and avoids spurious ranking of hospitals in league tables. The method can be applied to any number of performance indicators and can help formulate hypotheses about individual hospital characteristics likely to improve performance.
authors.affiliation: Centre for Medical Statistics and Health Evaluation, Shelley's Cottage, University of Liverpool, Liverpool, United Kingdom. j.j.kirkham@liv.ac.uk
authors.forename: Jamie John
authors.initials: JJ
authors.lastname: Kirkham
authors.forename: Omar
authors.initials: O
authors.lastname: Bouamra
mesh_terms: D015982:Bias; D003196:Computer Graphics; D003627:Data Interpretation, Statistical; D004739:England; D006276:Health Facility Size / Q000706:statistics & numerical data; D017052:Hospital Mortality; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D008485:Medical Audit / Q000706:statistics & numerical data*; D010043:Outcome and Process Assessment, Health Care / Q000592:standards / Q000706:statistics & numerical data*; D011785:Quality Assurance, Health Care / Q000592:standards / Q000706:statistics & numerical data*; D019984:Quality Indicators, Health Care / Q000706:statistics & numerical data; D015996:Survival Rate; D014193:Trauma Centers / Q000706:statistics & numerical data*; D014852:Wales; D014949:Wounds, Nonpenetrating / Q000401:mortality / Q000628:therapy*