Implementation Science Team, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, MD, United States.
Front Public Health. 2021 Jun 17;9:681602. doi: 10.3389/fpubh.2021.681602. eCollection 2021.
Many health policies are designed with the intention of improving health outcomes for all. Yet implementation of policies are variable across contexts, potentially limiting its impact on population health outcomes. The potential impact of a policy to advance health equity depends both on the design and its implementation, requiring ongoing evaluation and stakeholder engagement. Despite the importance of health policies in shaping public health, health care policy implementation science remains underrepresented in research. We argue that enhanced integration of policy questions within implementation science could reduce the time lag from policy to practice and improve population health outcomes to build a body of evidence on effective policy implementation. In this commentary, we argue that approaches to studying policy implementation science should reflect the dynamic and evolving policy context, analogous to the "learning healthcare system," to better understand and respond to systematic and multilevel impacts of policy. Several example opportunities for a learning health policy system are posed in building a broader agenda toward research and practice in policy implementation science in public health.
许多卫生政策的制定初衷都是为了改善所有人的健康结果。然而,政策在不同背景下的实施情况存在差异,这可能限制了其对人群健康结果的影响。一项旨在促进健康公平的政策的潜在影响既取决于其设计,也取决于其实施情况,这需要不断进行评估和利益攸关方的参与。尽管卫生政策在塑造公共卫生方面具有重要意义,但卫生保健政策实施科学在研究中仍然代表性不足。我们认为,在实施科学中更加强调政策问题的整合,可以减少政策到实践的时间滞后,并改善人群健康结果,从而建立关于有效政策实施的证据基础。在这篇评论中,我们认为,研究政策实施科学的方法应该反映政策的动态和不断变化的背景,类似于“学习型医疗保健系统”,以更好地理解和应对政策的系统性和多层次影响。在公共卫生领域的政策实施科学的研究和实践中,我们提出了一些学习型卫生政策系统的范例机会,以构建更广泛的议程。
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