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.