Machine learning for surgical time prediction.

Journal: Computer methods and programs in biomedicine
Published Date:

Abstract

BACKGROUND AND OBJECTIVE: Operating Rooms (ORs) are among the most expensive services in hospitals. A challenge to optimize the OR efficiency is to improve the surgery scheduling task, which requires the estimation of surgical time duration. Surgeons or programming units (based on people's experience) typically do the duration estimation using an experience-based strategy, which may include some bias, such as overestimating the surgery time, increasing ORs' operational cost.

Authors

  • Oscar Martinez
    Robotic Surgery, OhioHealth Dublin Methodist Hospital, Dublin, Ohio, USA.
  • Carol Martinez
    SpaceR group, University of Luxembourg, Luxembourg.
  • Carlos A Parra
    Pontificia Universidad Javeriana, School of Engineering, Bogotá, Colombia.
  • Saul Rugeles
    Hospital Universitario San Ignacio, Department of Surgery, Bogotá, Colombia.
  • Daniel R Suarez
    Pontificia Universidad Javeriana, School of Engineering, Bogotá, Colombia. Electronic address: d-suarez@javeriana.edu.co.