title: Causal Analysis of Emergency Department Delays.
abstract: BACKGROUND
  Improvement teams make causal inferences, but the methods they use are based on statistical associations. This article shows how data and statistical models can be used to help improvement teams make causal inferences and find the root causes of problems.
  
  
  METHODS
  This article uses attribution data, competing risk survival analysis, and Bayesian network probabilities to analyze excessive emergency department (ED) stays within one hospital. We use data recorded by ED clinicians that attributed the cause of excessive ED stays to 23 causes for the 70 049 ED visits between March 2011 and April 2014. We use competing risk survival analysis to identify contribution of each cause to the delay. We use Bayesian network models to analyze interaction among different causes of excessive stays and find the root causes of this problem.
  
  
  RESULTS
  This article shows the utility of causal analysis to help improvement teams focus on the root causes of problems. For the example analyzed in the article, most causes for patients' excessive ED stays were related to the hospital operations outside the ED. Therefore, improvement projects inside the ED such as expanding ED, increasing staff at the ED, or improving operations are less likely to have a positive impact on reducing excessive ED stays. On the contrary, interventions that improve hospital occupancy (better discharge, expansion of beds, etc) or improve laboratory response times are more likely to result in positive outcomes.
authors.affiliation: Washington DC Veterans Affairs Medical Center (Drs Kheirbek, Beygi, Alemi, Smith, Fletcher, and Seton and Mr Hawkins); and Department of Applied Business Analytics, School of Management, University of San Francisco, California (Dr Zargoush).
authors.forename: Raya E
authors.initials: RE
authors.lastname: Kheirbek
authors.forename: Shervin
authors.initials: S
authors.lastname: Beygi
authors.forename: Manaf
authors.initials: M
authors.lastname: Zargoush
authors.forename: Farrokh
authors.initials: F
authors.lastname: Alemi
authors.forename: Alyshia W
authors.initials: AW
authors.lastname: Smith
authors.forename: Ross D
authors.initials: RD
authors.lastname: Fletcher
authors.forename: Philip N
authors.initials: PN
authors.lastname: Seton
authors.forename: Brian A
authors.initials: BA
authors.lastname: Hawkins
mesh_terms: D001499:Bayes Theorem; D004636:Emergency Service, Hospital / Q000458:organization & administration*; D006801:Humans; D007902:Length of Stay; D058996:Quality Improvement; D060891:Root Cause Analysis; D016019:Survival Analysis; D061665:Time-to-Treatment