Risk-Based Decision Support-Bowtie and AHP Supporting Information

Healthcare decision-making requires collaboration across politicians, health providers, and managers. During the COVID-19 pandemic, a key challenge in Mindanao, Southern Philippines, was translating science-based recommendations into coordinated government action. This study developed a risk-based decision support system to improve the prioritization of health interventions during the pandemic. A mixed-method study design, specifically an exploratory-sequential approach, was employed to guide the process. We applied Bowtie analysis to visualize how COVID-19 risks could unfold and identify potential controls and used the Analytic Hierarchy Process (AHP) to systematically rank interventions based on inputs from diverse stakeholders. Findings revealed that while stakeholders shared common concerns, their priorities were often mismatched, creating barriers to unified and timely responses. The integrated Bowtie–AHP approach, implemented within this mixed-method framework, helped bridge these gaps by providing a transparent, structured, and collaborative way to evaluate interventions. The resulting decision support system enabled decision-makers to better understand the pathways of disease spread and assess the relative effectiveness of interventions. By aligning scientific evidence with stakeholder perspectives, this study demonstrates how risk-based tools can strengthen collaboration, improve prioritization, and support more effective public health responses in resource-limited settings during health emergencies.

医疗决策需要政界、医疗服务提供者与管理者多方协同配合。新冠疫情（COVID-19 pandemic）期间，菲律宾南部棉兰老岛（Mindanao）面临的一项核心挑战，是如何将基于科学的防控建议转化为协同一致的政府行动。本研究开发了一套基于风险的决策支持系统，以优化疫情期间公共卫生干预措施的优先级排序流程。本研究采用混合方法研究设计，具体为探索性序列研究法，来指导整个研究实施过程。我们运用蝴蝶结分析法（Bowtie analysis）可视化新冠疫情的风险演化路径并识别潜在防控手段，同时借助层次分析法（Analytic Hierarchy Process, AHP），基于多方利益相关者的输入信息对各项干预措施进行系统性排序。研究结果显示，尽管利益相关者存在共同关切，但他们的优先级往往存在错位，这为形成统一且及时的疫情应对措施制造了障碍。本研究整合的蝴蝶结分析-层次分析法框架，在混合方法研究体系下得以落地实施，通过提供透明、结构化且协作式的干预措施评估方式，有效弥合了这些分歧。最终构建的决策支持系统，可帮助决策者更好地理解疾病传播路径，并评估各项干预措施的相对有效性。本研究将科学证据与利益相关者视角相结合，证明了在卫生紧急事件期间，基于风险的工具能够如何强化资源有限地区的多方协作、优化优先级排序，并助力更高效的公共卫生应对。