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Estimating Procedure Times for Surgeries by Determining Location Parameters for the Lognormal Model

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Abstract

We present an empirical study of methods for estimating the location parameter of the lognormal distribution. Our results identify the best order statistic to use, and indicate that using the best order statistic instead of the median may lead to less frequent incorrect rejection of the lognormal model, more accurate critical value estimates, and higher goodness-of-fit. Using simulation data, we constructed and compared two models for identifying the best order statistic, one based on conventional nonlinear regression and the other using a data mining/machine learning technique. Better surgical procedure time estimates may lead to improved surgical operations.

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Authors and Affiliations

  1. A.J. Palumbo School of Business Administration, Duquesne University, Pittsburgh, PA, USA

    William E. Spangler

  2. Department of Anesthesiology, Queen's University, Kingston General Hospital, Kingston, Ontario, Canada

    David P. Strum

  3. Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA, USA

    Luis G. Vargas & Jerrold H. May

Authors
  1. William E. Spangler
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  2. David P. Strum
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  3. Luis G. Vargas
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  4. Jerrold H. May
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Corresponding author

Correspondence to Jerrold H. May.

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Spangler, W.E., Strum, D.P., Vargas, L.G. et al. Estimating Procedure Times for Surgeries by Determining Location Parameters for the Lognormal Model. Health Care Management Science 7, 97–104 (2004). https://doi.org/10.1023/B:HCMS.0000020649.78458.98

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  • DOI: https://doi.org/10.1023/B:HCMS.0000020649.78458.98

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