Data-Driven Decision Support Systems for Public Health Policy and Crisis Management

Emma Brunskill

Data-Driven Decision Support Systems for Public Health Policy and Crisis Management

Author(s)	Emma Brunskill
Country	United States
Abstract	Public health systems worldwide face increasingly complex challenges due to emerging infectious diseases, pandemics, climate-related health emergencies, population growth, healthcare inequalities, and rapidly changing disease patterns. Traditional decision-making approaches often rely on delayed reporting systems and fragmented information sources, limiting the ability of policymakers to respond effectively during health crises. Data-Driven Decision Support Systems (DDSS) have emerged as transformative tools that integrate big data analytics, Artificial Intelligence (AI), Machine Learning (ML), Geographic Information Systems (GIS), Internet of Things (IoT), cloud computing, and predictive modelling to support evidence-based public health planning and crisis management. This study examines the role of data-driven decision support systems in public health policy and emergency management using a qualitative and analytical research methodology based on secondary data from scientific literature, healthcare reports, government frameworks, and international case studies. The study explores applications of DDSS in disease surveillance, epidemic prediction, resource allocation, healthcare capacity planning, vaccination strategies, risk assessment, emergency response coordination, and policy evaluation. The findings indicate that data-driven systems significantly improve public health decision-making by enabling real-time monitoring, predictive forecasting, early warning mechanisms, and efficient allocation of healthcare resources. Artificial Intelligence-based models enhance the ability to identify disease patterns, predict outbreaks, analyse population health trends, and support rapid policy interventions. Integration with digital health technologies, electronic health records, wearable devices, and geospatial analytics further strengthens public health preparedness and resilience. However, challenges related to data privacy, cybersecurity, interoperability, algorithmic bias, data quality, ethical concerns, and unequal access to digital infrastructure remain significant barriers. Effective implementation requires transparent governance frameworks, secure data management practices, interdisciplinary collaboration, and responsible use of Artificial Intelligence. The study concludes that Data-Driven Decision Support Systems provide a critical foundation for modern public health governance by transforming large-scale health information into actionable intelligence. Future healthcare systems must adopt human-centred, ethical, and adaptive data-driven approaches to improve crisis preparedness, strengthen health policy decisions, and enhance population health outcomes.
Keywords	Data-Driven Decision Support Systems, Public Health Policy, Crisis Management, Artificial Intelligence, Big Data Analytics, Predictive Modelling, Healthcare Analytics, Digital Health.
Field	Engineering
Published In	Volume 1, Issue 2, March-April 2019
Published On	2019-04-30

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American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Data-Driven Decision Support Systems for Public Health Policy and Crisis Management

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