Towards an Integrated Framework for Health Surveillance Systems: A Systematic Literature Review of Design Components and Implementation Challenges.

Background and Aims: Health surveillance systems play a critical role in early detection, timely response, and evidence-based policymaking. Despite significant technological progress, challenges such as data fragmentation, limited interoperability, and inconsistent governance continue to undermine system effectiveness. This study aims to advance the understanding of surveillance design by developing an integrated framework that consolidates technical, organizational, and ethical dimensions. Methods: A systematic literature review (SLR) was conducted following PRISMA 2020 guidelines, covering studies indexed in Scopus and Web of Science up to April 2025. From 191 initially retrieved records, 36 studies met the inclusion criteria and were thematically analyzed. Results: Through thematic analysis, ten key design components were identified. These findings offer actionable insights for policymakers, healthcare administrators, and system developers. Based on these findings, an integrated, evidence-based framework is proposed to support effective system implementation. These key components are: leadership and management, stakeholder engagement, education and awareness, workforce competence, finance and insurance, data privacy and security, patient comfort, risk assessment, infrastructure readiness, and cultural/ethical considerations. Conclusion: The synthesis revealed recurring implementation challenges, including fragmented data systems, inadequate stakeholder participation, and limited financial or technical capacity. Based on the findings, this study proposes an evidence-based integrated framework to guide the design and implementation of adaptive, efficient, and ethically grounded surveillance systems. The proposed framework provides actionable insights for policymakers, healthcare administrators, and digital health architects seeking to strengthen surveillance infrastructures across diverse health settings and promote sustainable, data-driven decision-making.