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Comment & Response
July 14, 2021

Simply Adjusting for Schedulers’ Bias in Estimated Case Durations Can Accomplish the Same Objectives of Improving Predictions as Use of Machine Learning

Author Affiliations
  • 1The University of Iowa, Iowa City
  • 2University of Miami Health System, Miami, Florida
JAMA Surg. 2021;156(11):1074-1075. doi:10.1001/jamasurg.2021.3126

To the Editor Strömblad et al prospectively evaluated a machine learning method to predict operating room times of 863 cases using a hospital’s current process; 21% of the performed cases were excluded.1 Among the 327 cases in the machine learning arm (intervention), the mean absolute error was 50 minutes and the mean error was −4 minutes.1 Among the 356 cases using the current process (control) arm, the mean absolute error was 59 minutes and the mean error was −34 minutes, the latter being a remarkably large amount of bias in case duration prediction.1 The authors describe the control method as “…the electronic health record [EHR] standard method for deriving case duration estimates supplemented with the surgeon or scheduler’s estimate.”1

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