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Development of an algorithm to identify preoperative medical consultations using administrative data


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.

Research Design — 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).

Subjects — 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.



Wijeysundera DN, Austin PC, Hux JE, Beattie WS, Buckley DN, Laupacis A. Med Care. 2009; 47(12):1258-64.

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