Clinical decision-making frameworks for pandemic preparedness and response: A scoping review

Background: Pandemic management demands a multifaceted strategy that integrates disease transmission control, resource allocation, and effective public health interventions. This study explores how combining clinical decision-making frameworks (CDMFs) can improve decision-making and adaptive strategies during public health emergencies. The goal is to provide a synergistic approach that enhances the speed, effectiveness, and equity of pandemic responses. Methods: We conducted a review of literature published up to January 2025 to evaluate the contributions and limitations of these frameworks in pandemic preparedness and response. The review emphasizes how each framework supports adaptability, risk identification, and strategic planning, while also addressing challenges related to equity and data quality. Results: The SOAR framework fosters adaptability and creativity, while risk assessment provides a systematic method for threat identification and mitigation. Artificial intelligence (AI)-driven decision support system (DSS) leverage machine learning and predictive analytics to provide immediate insights and improve strategic planning, although issues of data quality and equity must be addressed. The DECIDE framework ensures comprehensive decision-making, balancing strategic planning with the urgency of a crisis. The review highlights the potential of AI to improve decision-making efficiency, while underscoring the need for careful oversight to maintain transparency and prevent the perpetuation of health inequalities. Conclusion: Integrating AI into CDMFs offers significant opportunities to improve future pandemic responses. Evolving these frameworks and incorporating AI-DSS, while carefully addressing ethical considerations and data quality, will lead to more scientifically sound, practical, and equitable solutions to global health problems, enhancing overall pandemic preparedness and resilience.