Despite the benefits of conducting evidence‐based practice, many public health initiatives remain unsupported by evidence1 and public health policies and practices that have been shown to improve health outcomes are not routinely implemented.2, 3 Maximising the impact of public health interventions requires policy‐makers and practitioners to use robust evidence to consider both ‘what’ interventions are effective in addressing public health issues and ‘how’ such interventions can best be implemented into practice. However, organisations that deliver public health initiatives face a range of barriers including a lack of skills and capacity when using and generating evidence to aid such decision‐making.4,5 The development of decision support tools has been suggested as a useful strategy to help overcome such barriers.6

Decision trees are frequently used tools in health care to assist clinicians to make evidence‐based diagnostic and therapeutic decisions.7 Such tools may also be useful for public health policy and service delivery organisations to aid their selection of evidence‐based interventions and implementation strategies, and also to identify where further evidence needs to be generated. While a number of process models and decision trees for the medical and nursing field have been published,3, 8 few of these address consideration of evidence‐informed implementation strategies or evidence generation needs.

In this editorial, we describe a decision tree (Figure 1) developed and utilised in a large public health organisation in NSW, Australia.9 The decision tree tool aims to assist in the application of research evidence to maximise the impact of public health programs and services. The tool helps identify when there is sufficient evidence to support the delivery of particular services, and when there is not. The latter outcome provides an indication of where further research may be needed, identifying opportunities to undertake policy and practice relevant research. At each step in the decision tree, users are posed a question, and based on their response, a service and/or research action is suggested. Health services may have the capacity and expertise to conduct research actions or they may need to commission, partner or collaborate with researchers to do so. This tool should be used with other resources such as the Intervention Scalability and Assessment tool to determine intervention suitability for scaling up.10 Such assessments need to consider end‐user values, resource, capability and context.

尽管进行循证实践有好处，但许多公共卫生举措仍然没有证据1的支持，并且已被证明可以改善健康结果的公共卫生政策和实践并未得到常规实施。2, 3最大限度地发挥公共卫生干预措施的影响需要政策制定者和从业者使用强有力的证据来考虑“哪些”干预措施可以有效解决公共卫生问题以及“如何”最好地将此类干预措施付诸实践。然而，提供公共卫生举措的组织面临一系列障碍，包括在使用和生成证据来帮助此类决策时缺乏技能和能力。4 , 5有人建议开发决策支持工具作为帮助克服此类障碍的有用策略。6

决策树是医疗保健领域常用的工具，可帮助临床医生做出基于证据的诊断和治疗决策。7这些工具也可能对公共卫生政策和服务提供组织有用，帮助他们选择基于证据的干预措施和实施策略，并确定需要在哪里生成进一步的证据。虽然医疗和护理领域的许多流程模型和决策树已经发表，但其中3, 8很少涉及对证据知情的实施策略或证据生成需求的考虑。

在这篇社论中，我们描述了澳大利亚新南威尔士州一家大型公共卫生组织开发和使用的决策树（图 1）。9决策树工具旨在协助研究证据的应用，以最大限度地发挥公共卫生计划和服务的影响。该工具有助于确定何时有足够的证据支持特定服务的提供，何时没有。后一个结果表明可能需要进一步研究的地方，确定进行政策和实践相关研究的机会。在决策树的每个步骤中，都会向用户提出一个问题，并根据他们的回答建议服务和/或研究行动。卫生服务机构可能拥有开展研究行动的能力和专业知识，或者可能需要委托研究人员、与研究人员合作或合作来开展研究行动。10此类评估需要考虑最终用户的价值、资源、能力和背景。