AI Nurses Network: The Importance of Clinical Research Networks in Nursing

Siobhan O'Connor, Crina Grosan, Rebecca J. Oakey, Mengying Zhang, Xiaoyang Li, Emma Stanmore, David Woodcock, Jo Armes, Joanne Cull

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Abstract

Clinical research networks (CRNs) are growing in popularity due to the complex and interdisciplinary nature of health research and the need to generate robust scientific evidence that improves patient outcomes and healthcare delivery (Brown et al. 2016). These networks can include a range of nursing and other researchers, practicing clinicians from different specialties, Patient and Public Involvement and Engagement (PPIE) members, and administrative staff. Such networks can facilitate research collaboration in a topic area, strengthen education and training, and build researcher capacity alongside improving the research culture and environment (Robinson et al. 2015). Several nursing-specific CRNs are active, such as a kidney care research network in the United Kingdom (Anderson 2020), a global network on disability nursing (Fisher et al. 2024), and a national clinical nurse leadership network in the United States (Bender et al. 2019), all of which report positive outcomes as well as challenges with setting up, running, and evaluating the networks activities. Nurses are also involved in broader multidisciplinary research networks that focus on specific research methods such as clinical trials (Gurwitz et al. 2022; Toups et al. 2023) or health-related topics like COVID-19 (DeVoe et al. 2020).
Original languageEnglish
JournalJournal of Nursing Scholarship
Early online date1 Aug 2025
DOIs
Publication statusPublished - 1 Aug 2025

Keywords

  • artificial intelligence
  • clinical research network
  • natural language processing
  • nursing
  • machine learning

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