Clinical Data Integration Model

J. -F. Ethier; V. Curcin; A. Barton; M. M. McGilchrist; H. Bastiaens; A. Andreasson; J. Rossiter; L. Zhao; T. N. Arvanitis; A. Taweel; B. C. Delaney; A. Burgun

doi:10.3414/ME13-02-0024

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2015; 54(01): 16-23
DOI: 10.3414/ME13-02-0024

Focus Theme – Original Articles

Schattauer GmbH

Clinical Data Integration Model

Core Interoperability Ontology for Research Using Primary Care Data

J. -F. Ethier

¹INSERM UMR 1138 team 22 Centre de Recherche des Cordeliers, Faculté de médecine, Université Paris Descartes – Sorbonne Paris Cité, Paris, France

,

V. Curcin

²Department of Primary Care and Public Health, Imperial College London, London, United Kingdom

,

A. Barton

¹INSERM UMR 1138 team 22 Centre de Recherche des Cordeliers, Faculté de médecine, Université Paris Descartes – Sorbonne Paris Cité, Paris, France

,

M. M. McGilchrist

³Public Health Sciences, University of Dundee, Dundee, United Kingdom

,

H. Bastiaens

⁴Department of Primary and Interdisciplinary Care, University of Antwerp, Antwerp, Belgium

,

A. Andreasson

⁵Centre for Family Medicine, Karolinska Institute, Stockholm, Sweden and Stress Research Institute, Stockholm University, Stockholm, Sweden

,

J. Rossiter

⁶Institute of Digital Healthcare, WMG, University of Warwick, Coventry, United Kingdom

,

L. Zhao

⁶Institute of Digital Healthcare, WMG, University of Warwick, Coventry, United Kingdom

,

T. N. Arvanitis

⁶Institute of Digital Healthcare, WMG, University of Warwick, Coventry, United Kingdom

,

A. Taweel

⁷Department of Informatics, King’s College London, London, United Kingdom

,

B. C. Delaney

⁸NIHR Biomedical Research Centre at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London, London, United Kingdom

,

A. Burgun

¹INSERM UMR 1138 team 22 Centre de Recherche des Cordeliers, Faculté de médecine, Université Paris Descartes – Sorbonne Paris Cité, Paris, France

› Author Affiliations

Abstract

Summary

Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Managing Interoperability and Complexity in Health Systems”.

Background: Primary care data is the single richest source of routine health care data. However its use, both in research and clinical work, often requires data from multiple clinical sites, clinical trials databases and registries. Data integration and interoperability are therefore of utmost importance.

Objectives: TRANSFoRm’s general approach relies on a unified interoperability framework, described in a previous paper. We developed a core ontology for an interoperability framework based on data mediation. This article presents how such an ontology, the Clinical Data Integration Model (CDIM), can be designed to support, in conjunction with appropriate terminologies, biomedical data federation within TRANSFoRm, an EU FP7 project that aims to develop the digital infrastructure for a learning healthcare system in European Primary Care.

Methods: TRANSFoRm utilizes a unified structural / terminological interoperability frame work, based on the local-as-view mediation paradigm. Such an approach mandates the global information model to describe the domain of interest independently of the data sources to be explored. Following a requirement analysis process, no ontology focusing on primary care research was identified and, thus we designed a realist ontology based on Basic Formal Ontology to support our framework in collaboration with various terminologies used in primary care.

Results: The resulting ontology has 549 classes and 82 object properties and is used to support data integration for TRANSFoRm’s use cases. Concepts identified by researchers were successfully expressed in queries using CDIM and pertinent terminologies. As an example, we illustrate how, in TRANSFoRm, the Query Formulation Workbench can capture eligibility criteria in a computable representation, which is based on CDIM.

Conclusion: A unified mediation approach to semantic interoperability provides a flexible and extensible framework for all types of interaction between health record systems and research systems. CDIM, as core ontology of such an approach, enables simplicity and consistency of design across the heterogeneous software landscape and can support the specific needs of EHR-driven phenotyping research using primary care data.

Keywords

Translational medical research - interoperability - phenotyping - ontology - primary care

Full Text

References

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