Complexity science has emerged as a powerful interdisciplinary framework for understanding systems characterized by nonlinearity, emergence, adaptation, and uncertainty. Health care organizations, governance structures, and social systems increasingly exhibit these properties, challenging traditional linear, reductionist models of design, management, and evaluation. This article offers an extensive theoretical and conceptual exploration of complexity science as applied to health care and organizational systems, drawing strictly from established foundational and applied literature. By synthesizing perspectives from complex adaptive systems theory, systems thinking, network science, and organizational studies, the paper examines how interactions among heterogeneous agents generate emergent patterns that cannot be fully predicted or controlled. The analysis situates health care as a paradigmatic complex adaptive system, where outcomes arise from dynamic relationships among patients, professionals, technologies, policies, and sociocultural contexts. Methodologically, the article adopts an integrative, theory-driven narrative review approach grounded in systematic literature review principles to ensure conceptual rigor and coherence. The results are presented as a descriptive synthesis of recurring theoretical constructs, empirical insights, and practical implications across health care, primary care, palliative care, integrated care, and governance domains. The discussion critically interrogates the implications of complexity thinking for leadership, quality improvement, accountability, and policy design, while also addressing tensions between complexity-informed approaches and conventional managerial paradigms. Limitations related to operationalization, measurement, and translation into practice are examined in depth, alongside future research directions emphasizing agent-based modeling, reflective practice, and adaptive governance. The article concludes that embracing complexity science does not imply abandoning structure or standards, but rather reframing them as enabling constraints that support learning, resilience, and sustained improvement in complex systems.
