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Methods Inf Med. 2015;54(1):16-23. doi: 10.3414/ME13-02-0024. Epub 2014 Jun 18.

Clinical data integration model. Core interoperability ontology for research using primary care data.

Author information

1
Jean-François Ethier, INSERM UMR_S 872 team 22, Information Sciences to support Personalized Medicine, Centre de Recherche des Cordeliers, Rue de l'Ecole de Médecine, 75006 Paris, France, E-mail: ethierj@gmail.com.

Abstract

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 framework, 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; ontology; phenotyping; primary care

PMID:
24954896
DOI:
10.3414/ME13-02-0024
[Indexed for MEDLINE]

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