title: Development of an algorithm to identify preoperative medical consultations using administrative data.
abstract: BACKGROUND
  Preoperative consultation by internal medicine specialists may help improve the care of patients undergoing major surgery. Population-based administrative data are an efficient approach for studying these consultations at a population-level. However, administrative data in many jurisdictions lack specific codes to identify preoperative medical consultations, as opposed to consultations for nonoperative indications.
  
  
  OBJECTIVE
  To develop an accurate claims-based algorithm for identifying preoperative medical consultations before major elective noncardiac surgery.
  
  
  METHODS
  We conducted a multicenter cross-sectional study in Ontario, Canada. Preoperative medical consultations identified by medical record abstraction were compared with those identified by linked administrative data (physician service claims, hospital discharge abstracts).
  
  
  METHODS
  We randomly selected 606 individuals, aged older than 40 years, who underwent elective intermediate-to-high-risk noncardiac surgery at 8 randomly selected hospitals between April 1, 2002 and March 31, 2004.
  
  
  RESULTS
  Medical record abstraction identified preoperative medical consultations in 317 patients (52%). The optimal claims-based algorithm for identifying these consultations was a physician service claim for a consultation by a cardiologist, general internist, endocrinologist, geriatrician, or nephrologist within 4 months before the index surgical procedure. This algorithm had a sensitivity of 90% (95% confidence interval [CI]: 86-93), specificity of 92% (95% CI: 88-95), positive predictive value of 93% (95% CI: 89-95), and negative predictive value of 90% (95% CI: 86-93).
  
  
  CONCLUSIONS
  A simple claims-based algorithm can accurately identify preoperative medical consultations before major elective noncardiac surgery. This algorithm may help enhance population-based evaluations of preoperative care, provided that the requisite linked administrative healthcare data are present.
authors.affiliation: Institute for Clinical Evaluative Sciences, Toronto, ON, Canada. d.wijeysundera@utoronto.ca
authors.forename: Duminda N
authors.initials: DN
authors.lastname: Wijeysundera
authors.forename: Peter C
authors.initials: PC
authors.lastname: Austin
authors.forename: Janet E
authors.initials: JE
authors.lastname: Hux
authors.forename: W Scott
authors.initials: WS
authors.lastname: Beattie
authors.forename: D Norman
authors.initials: DN
authors.lastname: Buckley
authors.forename: Andreas
authors.initials: A
authors.lastname: Laupacis
mesh_terms: D000328:Adult; D000465:Algorithms; D003430:Cross-Sectional Studies; D017558:Elective Surgical Procedures; D005260:Female; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data*; D008297:Male; D057234:Preoperative Period; D012017:Referral and Consultation / Q000458:organization & administration*; D015203:Reproducibility of Results