An HL7 FHIR® IG for lifestyle medicine in learning health systems: Multi-vendor wearable interoperability with documented terminology gaps

Background: Consumer wearable devices collect clinically useful physiological data from patients, yet less than 5% of this patient-generated health data (PGHD) reaches clinical systems. The European Health Data Space (EHDS) Regulation 2025/327 mandates standardized health data exchange, creating urgent need for validated transformation frameworks. Objective: To develop and validate a FHIR® R4 Implementation Guide enabling multi-vendor wearable data transformation for lifestyle medicine, documenting terminology gaps across seven vendor ecosystems. Methods: We developed FHIR® profiles, extensions, and ConceptMaps using a multi-layer architecture covering eleven domains (six lifestyle medicine pillars and five wearable-specific). Technical validation included HL7 IG Publisher compilation, FHIR® Validator conformance testing, and systematic terminology verification. Results: The IG comprises 74 profiles, 50 extensions, 14 CodeSystems, 174 ValueSets, and 28 ConceptMaps (546 artifacts). A two-phase terminology audit refined 1173 custom codes to 718 without standard direct equivalents, including genuine gaps (RMSSD, pNN50), wearable-specific metrics, and granularity not yet addressed by LOINC or SNOMED CT. Build validation produces 23 errors (all IPS upstream) and 73 warnings (97% non-actionable). Of six core HRV metrics, only SDNN has a LOINC code (80404-7). The multi-layer architecture achieved 75% weighted reuse across seven vendor ecosystems. Conclusions: This work extends the HL7 Physical Activity IG’s “Temporary Codes” pattern into a semantic convergence buffer—a vendor-neutral intermediary CodeSystem with outbound ConceptMap coverage to LOINC, SNOMED CT, and OMOP enabling multi-vendor semantic convergence. The 75% weighted reuse and iterative terminology audits progressively reduce custom codes as standards mature. All profiles are vendor-agnostic and EHDS-compliant. • IPS-compatible, EHDS-ready FHIR® IG for patient-generated wearable data. • Multi-vendor semantic convergence buffer spanning 7 wearable vendors and 11 domains across lifestyle medicine and wearable biometrics. • Documents critical wearable terminology gaps: RMSSD and pNN50 lack LOINC codes; all SNOMED CT HRV concepts are fetal/obstetric. • 718 custom codes with 28 ConceptMaps providing declarative migration paths to LOINC, SNOMED CT, and OMOP via OCL governance. • Iterative terminology audit: 1173 codes refined to 718, with 19 LOINC substitutions across recurrent audit cycles.

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Publication Details

Journal
International Journal of Medical Informatics
Published
2026-05-01
DOI
https://doi.org/10.1016/j.ijmedinf.2026.106465
Primary Topic
Mobile Health and mHealth Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

An HL7 FHIR® IG for lifestyle medicine in learning health systems: Multi-vendor wearable interoperability with documented terminology gaps

Lourenço dos Santos Ricardo, João Cruz-Correia Ricardo
International Journal of Medical Informatics
Mobile Health and mHealth Applications
article

An HL7 FHIR® IG for lifestyle medicine in learning health systems: Multi-vendor wearable interoperability with documented terminology gaps

Lourenço dos Santos Ricardo, João Cruz-Correia Ricardo
article en

Abstract

Background: Consumer wearable devices collect clinically useful physiological data from patients, yet less than 5% of this patient-generated health data (PGHD) reaches clinical systems. The European Health Data Space (EHDS) Regulation 2025/327 mandates standardized health data exchange, creating urgent need for validated transformation frameworks. Objective: To develop and validate a FHIR® R4 Implementation Guide enabling multi-vendor wearable data transformation for lifestyle medicine, documenting terminology gaps across seven vendor ecosystems. Methods: We developed FHIR® profiles, extensions, and ConceptMaps using a multi-layer architecture covering eleven domains (six lifestyle medicine pillars and five wearable-specific). Technical validation included HL7 IG Publisher compilation, FHIR® Validator conformance testing, and systematic terminology verification. Results: The IG comprises 74 profiles, 50 extensions, 14 CodeSystems, 174 ValueSets, and 28 ConceptMaps (546 artifacts). A two-phase terminology audit refined 1173 custom codes to 718 without standard direct equivalents, including genuine gaps (RMSSD, pNN50), wearable-specific metrics, and granularity not yet addressed by LOINC or SNOMED CT. Build validation produces 23 errors (all IPS upstream) and 73 warnings (97% non-actionable). Of six core HRV metrics, only SDNN has a LOINC code (80404-7). The multi-layer architecture achieved 75% weighted reuse across seven vendor ecosystems. Conclusions: This work extends the HL7 Physical Activity IG’s “Temporary Codes” pattern into a semantic convergence buffer—a vendor-neutral intermediary CodeSystem with outbound ConceptMap coverage to LOINC, SNOMED CT, and OMOP enabling multi-vendor semantic convergence. The 75% weighted reuse and iterative terminology audits progressively reduce custom codes as standards mature. All profiles are vendor-agnostic and EHDS-compliant. • IPS-compatible, EHDS-ready FHIR® IG for patient-generated wearable data. • Multi-vendor semantic convergence buffer spanning 7 wearable vendors and 11 domains across lifestyle medicine and wearable biometrics. • Documents critical wearable terminology gaps: RMSSD and pNN50 lack LOINC codes; all SNOMED CT HRV concepts are fetal/obstetric. • 718 custom codes with 28 ConceptMaps providing declarative migration paths to LOINC, SNOMED CT, and OMOP via OCL governance. • Iterative terminology audit: 1173 codes refined to 718, with 19 LOINC substitutions across recurrent audit cycles.

International Journal of Medical Informatics
Universidade do Porto (PT), Centre for Health Technology and Services Research (PT)
Fundação para a Ciência e a Tecnologia
Openalex Percentile: Top 8%
Mobile Health and mHealth Applications
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