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Adoption of artificial intelligence in healthcare: A strategic transformation framework

2026·0 Zitationen·Hong Kong Journal of Emergency MedicineOpen Access
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Abstract

Abstract Background Artificial intelligence (AI) holds significant promise for transforming healthcare delivery, yet many AI initiatives fail to progress beyond pilot stages or achieve sustained system‐wide impact. These failures are rarely due to technical limitations alone, but instead reflect deeper challenges related to organisational readiness, leadership, culture and behavioural change. Objective This educational article presents a strategic transformation framework for the adoption of AI in healthcare, emphasizing that successful implementation requires simultaneous attention to both performance outcomes and organisational health. Framework Drawing on the Beyond Performance 2.0 model, we propose a five‐frame approach—aspire, assess, architect, act and advance—to guide healthcare organisations across the full AI adoption journey. Key Components The framework begins with defining a clear and compelling aspiration for intelligent healthcare, aligned with organisational purpose and clinical priorities. It then emphasises systematic assessment of technical capabilities, governance structures, and underlying mindsets that may enable or impede change. The architect phase focuses on designing a balanced portfolio of AI initiatives alongside behavioural and cultural levers that foster understanding, role modelling, and reinforcement. Effective execution is supported through appropriate delivery models, strong governance, and continuous measurement of impact across initiatives, organisational health, performance, and enterprise value. Finally, the advance frame highlights the importance of institutionalising learning, ethical oversight and adaptive renewal to sustain long‐term transformation. Implications By integrating strategy, leadership and culture with technological innovation, this framework offers actionable guidance for healthcare leaders seeking to move beyond isolated AI deployments towards resilient learning health systems. Such systems enable continuous improvement, responsible innovation and the delivery of care that is both data‐driven and deeply human‐centred.

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Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIAI in Service Interactions
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