Machine learning for predicting duration of surgery and length of stay: A literature review on joint arthroplasty.

Journal: International journal of medical informatics
PMID: 39293161

Abstract

INTRODUCTION: In recent years, different factors such as population aging have caused escalating demand for hip and knee arthroplasty straining already limited hospitals' resources. To address this challenge, focus is put on medical and operational efficiency improvements. This includes an increased use of machine learning (ML) to predict duration of surgery (DOS) and length of stay (LOS) for total knee and total hip arthroplasty, which can be utilized for optimizing resource allocation to satisfy medical and operational limitations. This paper explores the development and performance of ML models in predicting DOS and LOS.

Authors

  • Mohammad Chavosh Nejad
    Department of Materials and Production, Aalborg University, Fibigerstræde 16, 2-109, Aalborg Ø 9220, Danmark. Electronic address: mohammadcn@mp.aau.dk.
  • Rikke Vestergaard Matthiesen
    Department of Materials and Production, Aalborg University, Fibigerstræde 16, 2-115, Aalborg Ø 9220, Danmark. Electronic address: rikkevm@mp.aau.dk.
  • Iskra Dukovska-Popovska
    Department of Materials and Production, Aalborg University, Fibigerstræde 16, 2-107, Aalborg Ø 9220, Danmark. Electronic address: iskra@mp.aau.dk.
  • Thomas Jakobsen
    Department of Orthopaedics, Aalborg University Hospital, Hobrovej 18-22, Aalborg Universitetshospital, Aalborg Syd 9000, Danmark. Electronic address: thomas.jakobsen@rn.dk.
  • John Johansen
    Department of Materials and Production, Aalborg University, Fibigerstræde 16, 2-114, Aalborg Ø 9220, Danmark. Electronic address: jj@mp.aau.dk.

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