Clinical Trial Schedule of Activities Specification Using Fast Healthcare Interoperability Resources Definitional Resources: Mixed Methods Study

This study demonstrates that graph-based modeling, combined with targeted FHIR PlanDefinition extensions, enables an accurate and comprehensive representation of clinical study SoAs, including complex scheduling features that are not supported by current standards. These methods improve interoperability, reduce reliance on manual interpretation, and provide a basis for the automated integration of study protocols with electronic health records. While further tooling (eg, FHIRPath and clinical quality language) is needed for operational deployment, this approach offers a more precise and extensible solution for digital protocol implementation.

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Publication Details

Journal
JMIR Medical Informatics
Published
2025-09-16
DOI
https://doi.org/10.2196/71430
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
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article

Clinical Trial Schedule of Activities Specification Using Fast Healthcare Interoperability Resources Definitional Resources: Mixed Methods Study

Andrew Richardson, Patrick Genyn
JMIR Medical Informatics
Machine Learning in Healthcare
article

Clinical Trial Schedule of Activities Specification Using Fast Healthcare Interoperability Resources Definitional Resources: Mixed Methods Study

Andrew Richardson, Patrick Genyn
article en

Abstract

This study demonstrates that graph-based modeling, combined with targeted FHIR PlanDefinition extensions, enables an accurate and comprehensive representation of clinical study SoAs, including complex scheduling features that are not supported by current standards. These methods improve interoperability, reduce reliance on manual interpretation, and provide a basis for the automated integration of study protocols with electronic health records. While further tooling (eg, FHIRPath and clinical quality language) is needed for operational deployment, this approach offers a more precise and extensible solution for digital protocol implementation.

JMIR Medical InformaticsVol. 13
CAB International (GB), Berkshire College of Agriculture (GB), RDA (United States) (US), ALS Biopharma (United States) (US), Doylestown Hospital (US)
Openalex Percentile: Top 87%
Machine Learning in Healthcare
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