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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—Reply

Author Affiliations
  • 1Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
  • 2Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, New York
JAMA Surg. 2021;156(11):1075. doi:10.1001/jamasurg.2021.3129

In Reply We appreciate Dexter and Epstein for their interest in our article. Prior to developing the randomized clinical trial,1 we explored different modeling approaches and data inputs. We selected the random forest model because it provided the best results on a testing set.

We investigated the claim that a simple 2-parameter linear regression model would achieve the same outcome vis-à-vis reduced patient wait time. The proposed approach is applied on the training set used to derive the parameters; ideally, it would have been validated on a testing set that the model has not been trained on. It is not possible to estimate the direct effect on outcome metrics such as patient wait time, surgeon wait time, and use of preoperative surgical resources, which would require an actual implementation. Below are the questions posed by Dexter and Epstein, along with our corresponding answers.

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