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Implementing AI in Hospitals to Achieve a Learning Health System: Systematic Review of Current Enablers and Barriers.

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العنوان: Implementing AI in Hospitals to Achieve a Learning Health System: Systematic Review of Current Enablers and Barriers.
المؤلفون: Kamel Rahimi A; Queensland Digital Health Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia.; Digital Health Cooperative Research Centre, Australian Government, Sydney, Australia., Pienaar O; The School of Mathematics and Physics, The University of Queensland, Brisbane, Australia., Ghadimi M; The School of Mathematics and Physics, The University of Queensland, Brisbane, Australia., Canfell OJ; Queensland Digital Health Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia.; Digital Health Cooperative Research Centre, Australian Government, Sydney, Australia.; Business School, The University of Queensland, Brisbane, Australia.; Department of Nutritional Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom., Pole JD; Queensland Digital Health Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia.; Dalla Lana School of Public Health, The University of Toronto, Toronto, ON, Canada.; ICES, Toronto, ON, Canada., Shrapnel S; Queensland Digital Health Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia.; The School of Mathematics and Physics, The University of Queensland, Brisbane, Australia., van der Vegt AH; Queensland Digital Health Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia., Sullivan C; Queensland Digital Health Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia.; Metro North Hospital and Health Service, Department of Health, Queensland Government, Brisbane, Australia.
المصدر: Journal of medical Internet research [J Med Internet Res] 2024 Aug 02; Vol. 26, pp. e49655. Date of Electronic Publication: 2024 Aug 02.
نوع المنشور: Journal Article; Systematic Review; Review
اللغة: English
بيانات الدورية: Publisher: JMIR Publications Country of Publication: Canada NLM ID: 100959882 Publication Model: Electronic Cited Medium: Internet ISSN: 1438-8871 (Electronic) Linking ISSN: 14388871 NLM ISO Abbreviation: J Med Internet Res Subsets: MEDLINE
أسماء مطبوعة: Publication: <2011- > : Toronto : JMIR Publications
Original Publication: [Pittsburgh, PA? : s.n., 1999-
مواضيع طبية MeSH: Learning Health System* , Artificial Intelligence*, Humans ; Electronic Health Records ; Hospitals
مستخلص: Background: Efforts are underway to capitalize on the computational power of the data collected in electronic medical records (EMRs) to achieve a learning health system (LHS). Artificial intelligence (AI) in health care has promised to improve clinical outcomes, and many researchers are developing AI algorithms on retrospective data sets. Integrating these algorithms with real-time EMR data is rare. There is a poor understanding of the current enablers and barriers to empower this shift from data set-based use to real-time implementation of AI in health systems. Exploring these factors holds promise for uncovering actionable insights toward the successful integration of AI into clinical workflows.
Objective: The first objective was to conduct a systematic literature review to identify the evidence of enablers and barriers regarding the real-world implementation of AI in hospital settings. The second objective was to map the identified enablers and barriers to a 3-horizon framework to enable the successful digital health transformation of hospitals to achieve an LHS.
Methods: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were adhered to. PubMed, Scopus, Web of Science, and IEEE Xplore were searched for studies published between January 2010 and January 2022. Articles with case studies and guidelines on the implementation of AI analytics in hospital settings using EMR data were included. We excluded studies conducted in primary and community care settings. Quality assessment of the identified papers was conducted using the Mixed Methods Appraisal Tool and ADAPTE frameworks. We coded evidence from the included studies that related to enablers of and barriers to AI implementation. The findings were mapped to the 3-horizon framework to provide a road map for hospitals to integrate AI analytics.
Results: Of the 1247 studies screened, 26 (2.09%) met the inclusion criteria. In total, 65% (17/26) of the studies implemented AI analytics for enhancing the care of hospitalized patients, whereas the remaining 35% (9/26) provided implementation guidelines. Of the final 26 papers, the quality of 21 (81%) was assessed as poor. A total of 28 enablers was identified; 8 (29%) were new in this study. A total of 18 barriers was identified; 5 (28%) were newly found. Most of these newly identified factors were related to information and technology. Actionable recommendations for the implementation of AI toward achieving an LHS were provided by mapping the findings to a 3-horizon framework.
Conclusions: Significant issues exist in implementing AI in health care. Shifting from validating data sets to working with live data is challenging. This review incorporated the identified enablers and barriers into a 3-horizon framework, offering actionable recommendations for implementing AI analytics to achieve an LHS. The findings of this study can assist hospitals in steering their strategic planning toward successful adoption of AI.
(©Amir Kamel Rahimi, Oliver Pienaar, Moji Ghadimi, Oliver J Canfell, Jason D Pole, Sally Shrapnel, Anton H van der Vegt, Clair Sullivan. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 02.08.2024.)
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فهرسة مساهمة: Keywords: artificial intelligence; clinical; decision support system; electronic health records; life cycle; machine learning; medical informatics; routinely collected health data
تواريخ الأحداث: Date Created: 20240802 Date Completed: 20240802 Latest Revision: 20240819
رمز التحديث: 20240819
مُعرف محوري في PubMed: PMC11329852
DOI: 10.2196/49655
PMID: 39094106
قاعدة البيانات: MEDLINE
الوصف
تدمد:1438-8871
DOI:10.2196/49655