3 research outputs found

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

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    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. Methods: A 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. Results: A 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. Conclusion: This 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

    The Reference Site Collaborative Network of the European Innovation Partnership on Active and Healthy Ageing.

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    International audienc

    The Reference Site Collaborative Network of the European Innovation Partnership on Active and Healthy Ageing.

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    International audienceSeventy four Reference Sites of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) have been recognised by the European Commission in 2016 for their commitment to excellence in investing and scaling up innovative solutions for active and healthy ageing. The Reference Site Collaborative Network (RSCN) brings together the EIP on AHA Reference Sites awarded by the European Commission, and Candidate Reference Sites into a single forum. The overarching goals are to promote cooperation, share and transfer good practice and solutions in the development and scaling up of health and care strategies, policies and service delivery models, while at the same time supporting the action groups in their work. The RSCN aspires to be recognized by the EU Commission as the principal forum and authority representing all EIP on AHA Reference Sites. The RSCN will contribute to achieve the goals of the EIP on AHA by improving health and care outcomes for citizens across Europe, and the development of sustainable economic growth and the creation of jobs
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