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Objectives: Despite significant investment in social prescribing in England over the last decade, we still do not know if it works, or how models of social prescribing fit within wider health and care policy and practice. This study explores current service delivery structures and assesses the feasibility of a national evaluation of the link worker model. Methods: Semi-structured interviews were conducted between May and September 2020, with 25 key informants from across social prescribing services in England. Participants included link workers, voluntary, community and social enterprise staff, and those involved in policy and decision-making for social prescribing services. Interview and workshop transcripts were analysed thematically, adopting a framework approach. Results: We found differences in how services are provided, including by individual link workers, and between organisations and regions. Standards, referral pathways, reporting, and monitoring structures differ or are lacking in voluntary services as compared to clinical services. People can self-refer to a link worker or be referred by a third party, but the lack of standardised processes generated confusion in both public and professional perceptions of the link worker model. We identified challenges in determining the appropriate outcomes and outcome measures needed to assess the impact of the link worker model. Conclusions: The current varied service delivery structures in England poses major challenges for a national impact evaluation. Any future rigorous evaluation needs to be underpinned with national standardised outcomes and process measures which promote uniform data collection. [ABSTRACT FROM AUTHOR]

Background: There has been a substantial increase in the development of artificial intelligence (AI) tools for clinical decision support. Historically, these were mostly knowledge-based systems, but recent advances include non-knowledge-based systems using some form of machine learning. The ability of health care professionals to trust technology and understand how it benefits patients or improves care delivery is known to be important for their adoption of that technology. For non-knowledge-based AI tools for clinical decision support, these issues are poorly understood., Objective: The aim of this study is to qualitatively synthesize evidence on the experiences of health care professionals in routinely using non-knowledge-based AI tools to support their clinical decision-making., Methods: In June 2023, we searched 4 electronic databases, MEDLINE, Embase, CINAHL, and Web of Science, with no language or date limit. We also contacted relevant experts and searched reference lists of the included studies. We included studies of any design that reported the experiences of health care professionals using non-knowledge-based systems for clinical decision support in their work settings. We completed double independent quality assessment for all included studies using the Mixed Methods Appraisal Tool. We used a theoretically informed thematic approach to synthesize the findings., Results: After screening 7552 titles and 182 full-text articles, we included 25 studies conducted in 9 different countries. Most of the included studies were qualitative (n=13), and the remaining were quantitative (n=9) and mixed methods (n=3). Overall, we identified 7 themes: health care professionals' understanding of AI applications, level of trust and confidence in AI tools, judging the value added by AI, data availability and limitations of AI, time and competing priorities, concern about governance, and collaboration to facilitate the implementation and use of AI. The most frequently occurring are the first 3 themes. For example, many studies reported that health care professionals were concerned about not understanding the AI outputs or the rationale behind them. There were issues with confidence in the accuracy of the AI applications and their recommendations. Some health care professionals believed that AI provided added value and improved decision-making, and some reported that it only served as a confirmation of their clinical judgment, while others did not find it useful at all., Conclusions: Our review identified several important issues documented in various studies on health care professionals' use of AI tools in real-world health care settings. Opinions of health care professionals regarding the added value of AI tools for supporting clinical decision-making varied widely, and many professionals had concerns about their understanding of and trust in this technology. The findings of this review emphasize the need for concerted efforts to optimize the integration of AI tools in real-world health care settings., Trial Registration: PROSPERO CRD42022336359; https://tinyurl.com/2yunvkmb., (©Abimbola Ayorinde, Daniel Opoku Mensah, Julia Walsh, Iman Ghosh, Siti Aishah Ibrahim, Jeffry Hogg, Niels Peek, Frances Griffiths. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.10.2024.)