Effect of a Predictive Model on Planned Surgical Duration Accuracy, Patient Wait Time, and Use of Presurgical Resources: A Randomized Clinical Trial.

Journal: JAMA surgery
Published Date: Apr 1, 2021

Abstract

IMPORTANCE: Accurate surgical scheduling affects patients, clinical staff, and use of physical resources. Although numerous retrospective analyses have suggested a potential for improvement, the real-world outcome of implementing a machine learning model to predict surgical case duration appears not to have been studied.

Authors

  • Christopher T Strömblad
    Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Ryan G Baxter-King
    Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Amirhossein Meisami
    Adobe Inc, San Jose, California.
  • Shok-Jean Yee
    Department of Nursing, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Marcia R Levine
    Department of Nursing, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Aaron Ostrovsky
    Health Informatics, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Daniel Stein
    Health Informatics, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Alexia Iasonos
    Epidemiology-Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Martin R Weiser
    Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Julio Garcia-Aguilar
    Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Nadeem R Abu-Rustum
    Gynecology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Roger S Wilson
    Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York.

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