Contextual factors in value-based decision support to enhance health technologies adoption: the case of biosimilars

价值导向决策支持中的情境因素如何促进医疗技术采纳:以生物类似药为例

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Abstract

INTRODUCTION: Biosimilar medicines play a critical role in enhancing global health outcomes by improving access to effective biologic treatments. However, their acceptance and implementation, particularly in emerging markets, depend not only on clinical evidence but also on the integration of societal, individual, and cultural values. This paper explores how value-based decision-making can support the adoption of biosimilars across diverse contexts. METHODS: A multi-stakeholder workshop was conducted with participants from various countries, focusing on decision-making processes for biosimilars in emerging health systems. Discussions addressed stakeholder roles, contextual influences, and the alignment of evidence with values. A Multi-Criteria Decision Analysis (MCDA) framework was proposed as a tool to systematically integrate measurable outcomes and intangible factors such as trust, perceived quality, and cultural acceptance. RESULTS: Key barriers identified included regulatory uncertainties, limited local evidence, regional data protection constraints, and patient preferences for originator biologics. Participants emphasized the importance of adaptable frameworks that reflect local cultural, economic, and systemic conditions. The proposed MCDA approach was viewed as a promising method for capturing complex value dimensions and facilitating transparent, inclusive decision-making. Broader societal benefits of biosimilars, such as economic development through local production, were also highlighted. DISCUSSION: The workshop underscored the need for value-sensitive implementation strategies that go beyond clinical effectiveness. Integrating context-specific values into evidence-based decision-making can foster trust and support the sustainable adoption of biosimilars. The MCDA framework offers a structured approach to operationalize these principles. Future research should test and refine this model in varied health system settings to support its practical application by policymakers, healthcare providers, and industry stakeholders.

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