From Data Silos to Health Records Without Borders: A Systematic Survey on Patient-Centered Data Interoperability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it