Streamlining wearable data integration for EHDS: a case study on advancing healthcare interoperability using Garmin devices and FHIR
Bibliographic record
Abstract
Introduction: Patient-Generated Health Data (PGHD) collected through wearable devices such as smartwatches offers new opportunities for personalized care, chronic disease management, and preventive health. Despite this potential, technical, regulatory, and interoperability challenges still limit the integration of PGHD into healthcare systems, especially in relation to standards such as Fast Healthcare Interoperability Resources (FHIR) and the European Health Data Space (EHDS). Methods: This study used the Garmin Vívoactive 4 smartwatch to collect PGHD and integrate it into a FHIR server via the Fitrockr hub and API. The Kodjin FHIR server was deployed to enable standardized data storage and transfer. In parallel, data from the Modular Open Research Platform (MORE) were examined for compatibility with FHIR resources. The process included device enrolment, data collection, mapping to FHIR specifications, and evaluation of compliance with General Data Protection Regulation (GDPR) requirements. Results: The prototype demonstrated that data from Garmin devices could be securely collected, mapped, and transferred into a FHIR environment. Integration through the Fitrockr hub ensured structured data formatting and reliability. The analysis of PGHD from the MORE platform confirmed that heterogeneous data types, including physiological measures and survey responses, could be represented with appropriate FHIR resources. These findings highlight the technical feasibility and scalability of PGHD integration. Discussion: The results confirm that PGHD from wearable devices can be standardized and transferred into healthcare systems in compliance with international standards and European regulations. This approach contributes to bridging the gap between personal health data and medical decision-making, supporting the objectives of the EHDS and enabling further use of PGHD in research and innovation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".