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Challenges and strategies in building a foundational digital health data integration ecosystem: a systematic review and thematic synthesis

2025·20 Zitationen·Frontiers in Health ServicesOpen Access
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20

Zitationen

6

Autoren

2025

Jahr

Abstract

Background: Chronic conditions require robust healthcare data integration to support personalized care, real-time decision-making, and secure information exchange. However, fragmented data ecosystems disrupt interoperability, complicate patient-centered care (PCC), and present challenges for incorporating genomic data into clinical workflows. Objective: This systematic review with thematic synthesis aims to identify key challenges and synthesize existing strategies from the literature to inform the development of a foundational digital health data integration ecosystem. Methods: Following PRISMA guidelines, we systematically screened literature across multiple databases. A thematic synthesis approach was used to categorize findings into three primary themes: interoperability, PCC, and genomic data integration. Results: A total of 161 studies were included. Key challenges identified include semantic misalignment across commonly used healthcare standards such as HL7 FHIR and SNOMED CT, limited cross-system data exchange, inadequate patient engagement features in EHRs, and concerns regarding the security and clinical utility of genomic data. Strategies described across the literature include ontology-based interoperability models, AI-supported PCC frameworks, and blockchain-enabled genomic data governance. Conclusion: By analyzing current methodologies, research gaps, and implementation challenges, this review offers an evidence-based foundation to guide future advancements in healthcare data integration. It supports the development of scalable, privacy-preserving, and ethically governed data-sharing infrastructures that enable personalized medicine and real-time clinical interventions. Systematic Review Registration: https://osf.io/c2xvw.

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Themen

Electronic Health Records SystemsArtificial Intelligence in Healthcare and EducationMachine Learning in Healthcare
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