Enhancing Healthcare Knowledge With AI: Key Insights and the Strategic Framework

Abstract

Abstract Knowledge management (KM) has emerged as a strategic necessity in health care, facilitating innovation in both clinical research and operational practices. While KM can streamline workflows, its impact on patient outcomes remains paramount. Given health care’s inherent complexity, involving diverse disciplines and numerous stakeholders, Artificial Intelligence (AI)-supported KM strategies introduce a transformative dimension that redefines traditional boundaries. This study highlights how contemporary KM, bolstered by AI, demands an organizational culture and knowledge-sharing practices that foster collaboration among professionals, institutions, policy frameworks, and ecosystem stakeholders. Co-occurrence analysis and clustering reveal a trend toward AI integration, digital transformation, and sustainable innovation. To guide this progression, a proposed taxonomy provides a structured framework for organizing knowledge and AI applications in health care across various dimensions, thereby advancing research, policy, and practice. The study formalizes these findings in a comprehensive decision-making framework centered on AI in healthcare knowledge practices and spanning seven strategic areas. This framework balances technological and human aspects, beginning with AI and technology impact assessment and extending to public health policy, workforce well-being, data analytics, disruptive technologies, healthcare management, ethics, innovation, and sustainability. Each decision pathway includes a conditional assessment to enhance adaptability, promoting sustainable, ethically aligned innovation. As a roadmap for decision-makers, this framework ensures all critical aspects are addressed in healthcare KM, offering flexibility to adapt to organizational needs and fostering a balanced focus on quality, compliance, and long-term sustainability.

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