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

BMC Health Services Research

Research

Riikka Lahtela¹  Janne Liisantti²  Sanna Lahtinen²  Merja Ahonen²  Juuso Heikkinen³  Minna Mäkiniemi⁴  Miia Jansson⁵  Pasi Ohtonen⁶  Sirpa Jämsä⁷  Timo Alalääkkölä⁸ 

[1] Department of Anesthesiology, Oulu University Hospital, Oulu, Finland;Department of Anesthesiology, Oulu University Hospital, Oulu, Finland;MRC Oulu, Research Group of Anesthesiology, Oulu, Finland;Division of Orthopedic and Trauma Surgery, Department of Surgery, Medical Research Center, Oulu University Hospital, Oulu, Finland;Oulu University Hospital, Oulu, Finland;Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland;Research Unit of Surgery, Anesthesia and Intensive Care, Oulu University Hospital, University of Oulu, Oulu, Finland;Sense Organ Diseases Centre, Oulu University Hospital, Oulu, Finland;Testing and Innovations, Oulu University Hospital, Oulu, Finland;

关键词: AI;    Human-in-loop;    human-AI interaction;    Machine learning;    Care pathway;   

DOI  :  10.1186/s12913-022-08780-y

 received in 2022-05-05, accepted in 2022-11-02,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundArtificial 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.MethodsA prospective content validity assessment was conducted in five university hospitals in three different countries using an electronic survey. The content of the survey was formed from clinical requirements, which were formulated into generic statements of required AI functionalities. The relevancy of each statement was evaluated using a content validity index. In addition, weighted ranking points were calculated to prioritize the most relevant functionalities of an AI-enhanced care pathway planning and scheduling system.ResultsA total of 50 responses were received from clinical professionals from three European countries. An item-level content validity index ranged from 0.42 to 0.96. 45% of the generic statements were considered good. The highest ranked functionalities for an AI-enhanced care pathway planning and scheduling system were related to risk assessment, patient profiling, and resources. The highest ranked functionalities for the user interface were related to the explainability of machine learning models.ConclusionThis study provided a comprehensive list of functionalities that can be used to design future AI-enhanced solutions and evaluate the designed solutions against requirements. The relevance of statements concerning the AI functionalities were considered somewhat relevant, which might be due to the low level or organizational readiness for AI in healthcare.

【 授权许可】

CC BY   
© The Author(s) 2022