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01-03-2019 | Systems-Level Quality Improvement

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

Authors: Valentina Bellini, Umberto Maestroni, Elena Bignami

Published in: Journal of Medical Systems | Issue 3/2019

Excerpt

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. …

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.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.

Title: Surgical Block Scheduling Controlled by a Machine: Reality or Science Fiction?
Authors: Valentina Bellini
Umberto Maestroni
Elena Bignami
Publication date: 01-03-2019
Publisher: Springer US
Published in: Journal of Medical Systems / Issue 3/2019
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-019-1178-8

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