title: The sensitivity of adverse event cost estimates to diagnostic coding error.
abstract: OBJECTIVE
  To examine the impact of diagnostic coding error on estimates of hospital costs attributable to adverse events.
  
  
  METHODS
  Original and reabstracted medical records of 9,670 complex medical and surgical admissions at 11 hospital corporations in Ontario from 2002 to 2004. Patient specific costs, not including physician payments, were retrieved from the Ontario Case Costing Initiative database.
  
  
  METHODS
  Adverse events were identified among the original and reabstracted records using ICD10-CA (Canadian adaptation of ICD10) codes flagged as postadmission complications. Propensity score matching and multivariate regression analysis were used to estimate the cost of the adverse events and to determine the sensitivity of cost estimates to diagnostic coding error.
  
  
  RESULTS
  Estimates of the cost of the adverse events ranged from $16,008 (metabolic derangement) to $30,176 (upper gastrointestinal bleeding). Coding errors caused the total cost attributable to the adverse events to be underestimated by 16 percent. The impact of coding error on adverse event cost estimates was highly variable at the organizational level.
  
  
  CONCLUSIONS
  Estimates of adverse event costs are highly sensitive to coding error. Adverse event costs may be significantly underestimated if the likelihood of error is ignored.
authors.affiliation: Department of Health Policy, Management and Evaluation, Faculty of Medicine, University of Toronto, 95 Bertmount Ave, Toronto, ON, M4M 2X8, Canada. gavin@preyrasolutions.com
authors.forename: Gavin
authors.initials: G
authors.lastname: Wardle
authors.forename: Walter P
authors.initials: WP
authors.lastname: Wodchis
authors.forename: Audrey
authors.initials: A
authors.lastname: Laporte
authors.forename: Geoffrey M
authors.initials: GM
authors.lastname: Anderson
authors.forename: G
authors.initials: G
authors.lastname: Ross Baker
mesh_terms: D000368:Aged; D016022:Case-Control Studies; D059019:Clinical Coding; D005260:Female; D017721:Hospital Costs / Q000706:statistics & numerical data*; D006753:Hospital Records / Q000191:economics / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D016555:Matched-Pair Analysis; D019300:Medical Errors / Q000191:economics*; D008875:Middle Aged; D015999:Multivariate Analysis; D009864:Ontario; D057216:Propensity Score; D015203:Reproducibility of Results
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