OPEN: A Community-Informed Metadata Framework for Interoperable Computable Phenotypes
- 1VA Boston Healthcare System
Abstract
Objective: To identify community - prioritized metadata for reporting and sharing computable phenotypes (CPs), develop a cross-library candidate framework, assess alignment between user requirements and library metadata, and test the feasibility of metadata exchange. Materials and Methods: We combined an international, library - led effort by the Open PhEnomics Network (OPEN) consortium with a UK community consensus-building exercise aimed at improving the FAIRness (Findable, Accessible, Interoperable, Reusable) of CPs. The consortium performed a cross-library field comparison and literature review to inform candidate fields, refined through consortium consensus. The community approach was developed through stakeholder interviews, a survey, and workshop, guided by a working group. Community requirements were interpretively mapped to the resulting framework and classified into conformance tiers. Interoperability was tested by manually and programmatically mapping diabetes-related phenotypes across libraries. Results: The library - led process produced 23 candidate fields spanning phenotype description, data/technical details, and performance domains. Thirteen fields aligned across all five libraries, and 22 fields aligned with published CP library desiderata. Interoperability testing successfully catalogued phenotypes through manual mapping, with API - mediated mapping achieving 63 - 76% metadata field completeness across libraries. A community survey (n=124 respondents) identified six metadata elements with majority support as mandatory: description, coding terminology, phenotype type, version history, unique identifier, and data source. Logic representation and validation requirements were rated as mostly context-dependent. Community - identified priorities showed strong alignment with the OPEN framework, though some requirements were only partially represented. Discussion: Through a consensus - based, community - driven approach, we established a CP framework enabling interoperability across libraries. Conclusion: OPEN provides a credible, community - informed foundation for a FAIR CP metadata standard.
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BibTeX
@misc{honerlaw2026open,
title = {{OPEN: A Community-Informed Metadata Framework for Interoperable Computable Phenotypes}},
author = {J. Honerlaw and J. A. L. MacArthur and Y.-L. Ho and S. Gangireddy and A. Galloway and K. Hilty and G. Rao and L. Li and A. Shoaibi and S. Denaxas and K. Cho and W.-Q. Wei},
howpublished = {medRxiv},
year = {2026},
month = nov,
doi = {10.64898/2026.11.06.26367456},
url = {https://www.medrxiv.org/content/10.64898/2026.11.06.26367456v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - OPEN: A Community-Informed Metadata Framework for Interoperable Computable Phenotypes
AU - J. Honerlaw
AU - J. A. L. MacArthur
AU - Y.-L. Ho
AU - S. Gangireddy
AU - A. Galloway
AU - K. Hilty
AU - G. Rao
AU - L. Li
AU - A. Shoaibi
AU - S. Denaxas
AU - K. Cho
AU - W.-Q. Wei
PY - 2026
DA - 2026/11/06
PB - medRxiv
DO - 10.64898/2026.11.06.26367456
UR - https://www.medrxiv.org/content/10.64898/2026.11.06.26367456v1
AB - Objective: To identify community - prioritized metadata for reporting and sharing computable phenotypes (CPs), develop a cross-library candidate framework, assess alignment between user requirements and library metadata, and test the feasibility of metadata exchange. Materials and Methods: We combined an international, library - led effort by the Open PhEnomics Network (OPEN) consortium with a UK community consensus-building exercise aimed at improving the FAIRness (Findable, Accessible, Interoperable, Reusable) of CPs. The consortium performed a cross-library field comparison and literature review to inform candidate fields, refined through consortium consensus. The community approach was developed through stakeholder interviews, a survey, and workshop, guided by a working group. Community requirements were interpretively mapped to the resulting framework and classified into conformance tiers. Interoperability was tested by manually and programmatically mapping diabetes-related phenotypes across libraries. Results: The library - led process produced 23 candidate fields spanning phenotype description, data/technical details, and performance domains. Thirteen fields aligned across all five libraries, and 22 fields aligned with published CP library desiderata. Interoperability testing successfully catalogued phenotypes through manual mapping, with API - mediated mapping achieving 63 - 76% metadata field completeness across libraries. A community survey (n=124 respondents) identified six metadata elements with majority support as mandatory: description, coding terminology, phenotype type, version history, unique identifier, and data source. Logic representation and validation requirements were rated as mostly context-dependent. Community - identified priorities showed strong alignment with the OPEN framework, though some requirements were only partially represented. Discussion: Through a consensus - based, community - driven approach, we established a CP framework enabling interoperability across libraries. Conclusion: OPEN provides a credible, community - informed foundation for a FAIR CP metadata standard.
ER -