The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge.

Journal: Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
PMID: 35227446

Abstract

OBJECTIVES: The machine learning prediction model Pacmed Critical (PC), currently under development, may guide intensivists in their decision-making process on the most appropriate time to discharge a patient from the intensive care unit (ICU). Given the financial pressure on healthcare budgets, this study assessed whether PC has the potential to be cost-effective compared with standard care, without the use of PC, for Dutch patients in the ICU from a societal perspective.

Authors

  • Juliette de Vos
    Pacmed B.V., Amsterdam, The Netherlands; Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, The Netherlands. Electronic address: juliettedvos@gmail.com.
  • Laurenske A Visser
    Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, The Netherlands.
  • Aletta A de Beer
    Pacmed B.V., Amsterdam, The Netherlands.
  • Mattia Fornasa
    Pacmed B.V., Amsterdam, The Netherlands.
  • Patrick J Thoral
    Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
  • Paul W G Elbers
    Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
  • Giovanni Cinà
    Pacmed B.V., Amsterdam, The Netherlands.

Keywords

External Resources

Popular Topics
Recent Journals