Estimating anesthesia time using the medicare claim: a validation study

Anesthesiology. 2011 Aug;115(2):322-33. doi: 10.1097/ALN.0b013e31821d6c81.


Introduction: Procedure length is a fundamental variable associated with quality of care, though seldom studied on a large scale. The authors sought to estimate procedure length through information obtained in the anesthesia claim submitted to Medicare to validate this method for future studies.

Methods: The Obesity and Surgical Outcomes Study enlisted 47 hospitals located across New York, Texas, and Illinois to study patients undergoing hip, knee, colon, and thoracotomy procedures. A total of 15,914 charts were abstracted to determine body mass index and initial patient physiology. Included in this abstraction were induction, cut, close, and recovery room times. This chart information was merged to Medicare claims that included anesthesia Part B billing information. Correlations between chart times and claim times were analyzed, models developed, and median absolute differences in minutes calculated.

Results: Of the 15,914 eligible patients, there were 14,369 for whom both chart and claim times were available for analysis. For these 14,369, the Spearman correlation between chart and claim time was 0.94 (95% CI 0.94, 0.95), and the median absolute difference between chart and claim time was only 5 min (95% CI: 5.0, 5.5). The anesthesia claim can also be used to estimate surgical procedure length, with only a modest increase in error.

Conclusion: The anesthesia bill found in Medicare claims provides an excellent source of information for studying surgery time on a vast scale throughout the United States. However, errors in both chart abstraction and anesthesia claims can occur. Care must be taken in the handling of outliers in these data.

Publication types

  • Multicenter Study
  • Research Support, N.I.H., Extramural
  • Validation Study

MeSH terms

  • Aged
  • Anesthesia*
  • Female
  • Humans
  • Insurance Claim Review
  • Male
  • Medicare*
  • Regression Analysis
  • Time Factors
  • United States