Advertisement

Surgical Block Scheduling Controlled by a Machine: Reality or Science Fiction?

  • Valentina Bellini
  • Umberto Maestroni
  • Elena BignamiEmail author
Systems-Level Quality Improvement
  • 22 Downloads
Part of the following topical collections:
  1. Systems-Level Quality Improvement

Dear Editor, we recently read with great interest a paper by Zhao et al. published in your journal entitled “A Machine Learning Approach to Predicting Case Duration for Robot-Assisted Surgery” [1]. It is an interesting study concerning the novel application of emerging technology; machine learning has been brilliantly used to predict case duration in robotic surgery.

As also pointed out by the Authors, proper management of the surgical unit assumes a significant role in optimizing resources [1]. Accurate surgical scheduling is crucial as it limits the waste of resources when cases end prematurely, as well as the disorganization generated and the possible need for additional staff when prolongation occurs. For an optimal planning, collaboration between surgeons and anesthesiologists is essential, for example to estimate the duration of anesthetic induction and recovery time. At our center, both teams meet every week to discuss the planning for the following week. However, in our...

Notes

Compliance with ethical standards

Conflict of interest

Author Valentina Bellini declares that she has no conflict of interest.

Author Umberto Maestroni declares that he has no conflict of interest.

Author Elena Bignami declares that she has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. 1.
    Zhao, B., Waterman, R. S., Urman, R. D., and Gabriel, R. A., A machine learning approach to predicting case duration for robot-assisted surgery. J. Med. Syst. 43(2):32, 2019.  https://doi.org/10.1007/s10916-018-1151-y.CrossRefPubMedGoogle Scholar
  2. 2.
    Wu, H. L., Chang, W. K., Hu, K. H., Langford, R. M., Tsou, M. Y., and Chang, K. Y., A quantile regression approach to estimating the distribution of anesthetic procedure time during induction. PLoS One 10(8):e0134838, 2015.  https://doi.org/10.1371/journal.pone.0134838.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Fairley, M., Scheinker, D., and Brandeau, M. L., Improving the efficiency of the operating room environment with an optimization and machine learning model. Health Care Manag. Sci., 2018.  https://doi.org/10.1007/s10729-018-9457-3.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and SurgeryUniversity of ParmaParmaItaly
  2. 2.Department of UrologyUniversity-Hospital of ParmaParmaItaly

Personalised recommendations