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From Data Silos to Health Records Without Borders: A Systematic Survey on Patient-Centered Data Interoperability
From Data Silos to Health Records Without Borders: A Systematic Survey on Patient-Centered Data Interoperability
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From Data Silos to Health Records Without Borders: A Systematic Survey on Patient-Centered Data Interoperability
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From Data Silos to Health Records Without Borders: A Systematic Survey on Patient-Centered Data Interoperability
From Data Silos to Health Records Without Borders: A Systematic Survey on Patient-Centered Data Interoperability

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From Data Silos to Health Records Without Borders: A Systematic Survey on Patient-Centered Data Interoperability
From Data Silos to Health Records Without Borders: A Systematic Survey on Patient-Centered Data Interoperability
Journal Article

From Data Silos to Health Records Without Borders: A Systematic Survey on Patient-Centered Data Interoperability

2025
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Overview
The widespread use of electronic health records (EHRs) and healthcare information systems (HISs) has led to isolated data silos across healthcare providers, and current interoperability standards like FHIR cannot address some scenarios. For instance, it cannot retrieve patients’ health records if they are stored by multiple healthcare providers with diverse interoperability standards or the same standard but different implementation guides. FHIR and similar standards prioritize institutional interoperability rather than patient-centered interoperability. We explored the challenges in transforming fragmented data silos into patient-centered data interoperability. This research comprehensively reviewed 56 notable studies to analyze the challenges and approaches in patient-centered interoperability through qualitative and quantitative analyses. We classified the challenges into four domains and categorized common features of the propositions to patient-centered interoperability into six categories: EMR integration, EHR usage, FHIR adaptation, blockchain application, semantic interoperability, and personal data retrieval. Our results indicated that “using blockchain” (48%) and “personal data retrieval” (41%) emerged as the most cited features. The Jaccard similarity analysis revealed a strong synergy between blockchain and personal data retrieval (0.47) and recommends their integration as a robust approach to achieving patient-centered interoperability. Conversely, gaps exist between semantic interoperability and personal data retrieval (0.06) and between FHIR adaptation and personal data retrieval (0.08), depicting research opportunities to develop unique contributions for both combinations. Our data-driven insights provide a roadmap for future research and innovation.