FAIR-EC: A Global Research Network for Fair, Accountable, Interpretable, and Responsible AI in Emergency Care (Preprint)

Abstract

<sec> <title>BACKGROUND</title> The current landscape of Emergency Care (EC) is marked by high demand leading to issues such as Emergency Department boarding, overcrowding and subsequent delays that impact the quality and safety of patient care. Integrating data science into EC can enhance decision-making with predictive, preventative, personalized, and participatory approaches. However, gaps in adherence to fairness, accountability, interpretability, and responsibility are evident, particularly due to barriers in data-sharing, which often result in a lack of transparency and robust oversight in these applications. </sec> <sec> <title>OBJECTIVE</title> The Fair, Accountable, Interpretable and Responsible (FAIR)-EC collaboration adapts the existing FAIR principles to address emerging challenges as data science integrates with EC. This initiative aims to transform EC by establishing ethical artificial intelligence (AI) standards specifically tailored for this integration. By bridging the gap between EC professionals, data scientists and other stakeholders, the collaboration promotes international cooperation that leverages advanced data science techniques to enhance EC outcomes across different care settings. </sec> <sec> <title>METHODS</title> We propose a federated research design that enables analyses of extensive datasets from various global institutions without compromising patient privacy. This approach transforms epidemiological research with advanced data science techniques, emphasizing the harmonization of data for comprehensive analyses across different healthcare systems. </sec> <sec> <title>RESULTS</title> The FAIR-EC initiative has facilitated the collection and analysis of datasets from diverse geographical regions, enabling the examination of regional variations in EC practices. Initial projects have demonstrated promising outcomes, including the successful development of a federated scoring system and the adaptation of association studies and predictive models across various regions. These efforts highlight the feasibility of leveraging advanced data science techniques to address the complexities of EC while preserving patient privacy. </sec> <sec> <title>CONCLUSIONS</title> FAIR-EC integrates data science ethically and effectively into EC, addressing challenges like fragmented data, real-time handoffs, and public health crises. Its federated design harmonizes diverse data streams while preserving privacy, and its emphasis on ethical AI aligns with the dynamic nature of EC. Despite challenges in data variability and system complexity, FAIR-EC establishes a strong foundation for innovation in global EC. </sec>

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