{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:06:10Z","timestamp":1792785970854,"version":"4.5.0"},"reference-count":54,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Health Service England","id":[{"id":"https:\/\/ror.org\/02wnqcb97","id-type":"ROR","asserted-by":"crossref"}]},{"name":"National Health Service","id":[{"id":"https:\/\/ror.org\/02wnqcb97","id-type":"ROR","asserted-by":"crossref"}]},{"name":"National Health Service England","id":[{"id":"https:\/\/ror.org\/02wnqcb97","id-type":"ROR","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000276","name":"Department of Health and Social Care","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000276","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) is increasingly being deployed in health-care settings, yet health systems remain at an early stage in developing approaches to scale effective applications. Much of current activity is characterized by early-stage use cases and pilot projects. In this study, we explored how different health systems are approaching the development, adoption, implementation, and scaling of AI in healthcare to identify transferable lessons for system-level change.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>We conducted 4 qualitative case studies of health systems and their approaches to scaling AI in healthcare: Catalonia, Norway, Singapore, and Queensland. Data were generated through documents, qualitative interviews and focus groups with strategic decision-makers, policymakers, and lead clinicians. The study explored participants\u2019 perspectives on evolving strategies and needs, rationales for change, characteristics of approaches and technologies, expected and experienced benefits, lessons learned, perceived challenges, and elements considered transferable across contexts. Analysis was conducted in 2 stages: an initial within-case thematic analysis, followed by a cross-case analysis informed by the Technology, People, Organization, and Macroenvironment framework, while also allowing inductive themes to emerge.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The dataset consisted of 60 documents, 34 interviews, and 5 focus groups. We consulted a total of 50 different participants from case study sites across data collection activities. Our findings indicate that AI scaling trajectories in health-care systems are strongly shaped by existing digital strategies, historical and cultural contexts of digitalization, funding arrangements, and legacy technological infrastructures. Approaches to accelerating safe AI adoption at scale were characterized by efforts to orchestrate and coordinate change across multiple stakeholder groups with differing and evolving spheres of influence. Within this context, scaling was shaped by opportunities for experimentation, incentive structures, and the distribution of decision-making authority. Sustained use and wider spread of AI applications can be supported through ongoing post-deployment monitoring and the central collation and dissemination of emerging evidence. Crucially, these activities need to be embedded within a broader ecosystem that foregrounds learning, partnership, governance, and procurement as integral components of scaling.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our findings suggest that strategic decision-makers need to move beyond the conventional dichotomy of \u201ctop-down\u201d and \u201cbottom-up\u201d approaches to scaling AI and instead conceptualize scaling as a process of multilevel orchestration.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocag148","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T11:14:46Z","timestamp":1786706086000},"page":"2160-2176","source":"Crossref","is-referenced-by-count":0,"title":["International qualitative case studies of system-level approaches to promote the development, adoption, and implementation of artificial 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