Artificial intelligence-enhanced care pathway planning and scheduling system: content validity assessment of required functionalities.

Journal: BMC health services research
PMID: 36510176

Abstract

BACKGROUND: Artificial intelligence (AI) and machine learning are transforming the optimization of clinical and patient workflows in healthcare. There is a need for research to specify clinical requirements for AI-enhanced care pathway planning and scheduling systems to improve human-AI interaction in machine learning applications. The aim of this study was to assess content validity and prioritize the most relevant functionalities of an AI-enhanced care pathway planning and scheduling system.

Authors

  • Miia Jansson
    Research Group of Medical Imaging, Physics and Technology, University of Oulu, Oulu University Hospital, Oulu, Finland. Electronic address: miia.jansson@oulu.fi.
  • Pasi Ohtonen
    Research Unit of Surgery, Anesthesia and Intensive Care, Oulu University Hospital, University of Oulu, Oulu, Finland.
  • Timo Alalääkkölä
    Testing and Innovations, Oulu University Hospital, Oulu, Finland.
  • Juuso Heikkinen
    Division of Orthopedic and Trauma Surgery, Department of Surgery, Medical Research Center, Oulu University Hospital, Oulu, Finland.
  • Minna Mäkiniemi
    Oulu University Hospital, Oulu, Finland.
  • Sanna Lahtinen
    Department of Anesthesiology, Oulu University Hospital, Oulu, Finland.
  • Riikka Lahtela
    Department of Anesthesiology, Oulu University Hospital, Oulu, Finland.
  • Merja Ahonen
    Department of Anesthesiology, Oulu University Hospital, Oulu, Finland.
  • Sirpa Jämsä
    Sense Organ Diseases Centre, Oulu University Hospital, Oulu, Finland.
  • Janne Liisantti
    Department of Anesthesiology, Oulu University Hospital, Oulu, Finland.

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