From product to knowledge: the life cycle of health technologies 2.0 as a framework for health system improvement.

BACKGROUND: Existing life cycle frameworks for health technologies have mainly adopted a product-oriented perspective. They treating evidence as a linear input to discrete regulatory and coverage decisions, and failing to capture the iterative, context-dependent, stakeholder-specific nature of knowledge generation needed for contemporary health-technology governance. OBJECTIVES: This article introduces the Life Cycle of Health Technologies 2.0 (LC 2.0), an updated conceptual framework building on the Life Cycle of Health Technologies (2017, LC 1.0). It traces the evolution from LC 1.0 to LC 2.0, conceptualises LC 2.0 as a knowledge life cycle, and outlines illustrative use cases. LC 2.0 integrates existing conceptual approaches to explore how knowledge about a health technology develops across stakeholder groups across different processes. In line with the Health for All Policies concept, it links this knowledge life cycle to societal, system and patient needs within and beyond the health sector. METHODS: A mixed-methods approach was applied comprising multi-stakeholder workshops and use-case validation across four technology types. RESULTS: Workshops identified revision requirements in LC 1.0 across macro, meso and micro levels, centring on structural inflexibility, implicit post-market learning and limited integration of contextual and stakeholder knowledge. The LC 2.0 framework addresses these through an infinity-loop structure with six phases and twelve subprocesses. It makes knowledge generation an explicit function at every phase and maps knowledge requirements to stakeholder roles. It also incorporates flexible, non-sequential pathways, including a cross-phase transition point (X) as governance decision point between research and development and between continued optimisation and more fundamental revision. CONCLUSION: LC 2.0 reconceives the health-technology life cycle as an iterative knowledge-generation and decision-supporting service, structuring what must be learned, by whom and when, to support appropriate technology use, needs-driven innovation and regulator-appropriate evidence generation, and to provide a shared reference framework for stakeholders inside and adjacent to healthcare and regulatory systems. This shared structure also facilitates easier exchange between different health-system contexts.