Machine learning models to predict surgical case duration compared to current industry standards: scoping review.

Journal: BJS open
Published Date:

Abstract

BACKGROUND: Surgical waiting lists have risen dramatically across the UK as a result of the COVID-19 pandemic. The effective use of operating theatres by optimal scheduling could help mitigate this, but this requires accurate case duration predictions. Current standards for predicting the duration of surgery are inaccurate. Artificial intelligence (AI) offers the potential for greater accuracy in predicting surgical case duration. This study aimed to investigate whether there is evidence to support that AI is more accurate than current industry standards at predicting surgical case duration, with a secondary aim of analysing whether the implementation of the models used produced efficiency savings.

Authors

  • Christopher Spence
    Academic Surgical Unit, South West London Elective Orthopaedic Centre, Epsom, Surrey, UK.
  • Owais A Shah
    Academic Surgical Unit, South West London Elective Orthopaedic Centre, Epsom, Surrey, UK.
  • Anna Cebula
    Academic Surgical Unit, South West London Elective Orthopaedic Centre, Epsom, Surrey, UK.
  • Keith Tucker
    South West London Elective Orthopaedic Centre, Epsom, UK.
  • David Sochart
    Academic Surgical Unit, South West London Elective Orthopaedic Centre, Epsom, Surrey, UK.
  • Deiary Kader
    Academic Surgical Unit, South West London Elective Orthopaedic Centre, Epsom, Surrey, UK.
  • Vipin Asopa
    South West London Elective Orthopaedic Centre, Epsom, UK.