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A "pivot" Model to set up Large Scale Rare Diseases Information Systems: Application to the Fibromuscular Dysplasia Registry.

Author information

1
INSERM, U1142, LIMICS, F-75006, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, F-75006, Paris, France; Université Paris 13, Sorbonne Paris Cité, F-93430, Villetaneuse, France.
2
Hypertension unit, department of genetics and rare vascular diseases reference center; Hopital Europeen G Pompidou (HEGP); Paris-Descartes University.

Abstract

The SIR-FMD project is a partnership between the Department of Genetics and Reference Centre for Rare Vascular Diseases at the Georges Pompidou European Hospital in Paris and the Medical Informatics and Knowledge Engineering Laboratory of Inserm. Its aim is to use an ontological approach to implement an information system for the French Fibromuscular Dysplasia Registry. The existing data was dispersed in numerous databases, which had been created independently. These databases have different structures and contain data of diverse quality. The project aims to provide generic solutions for the management of the communication of medical data. The secondary objective is to demonstrate the applicability of these generic solutions in the field of rare diseases (RD) in an operational context. The construction of the French FMD registry was a multistep process. A secure platform has been available since the beginning of November 2013. The medical records of 471 patients from the initial dataset provided by the HEGP-Paris, France have been included, and are accessible from a secure user account. Users are organized into a collaborative group, and can access patient groups. Each electronic patient record contains more than 2,200 items. The problem of semantic interoperability has become one of the major challenges for the development of applications requiring the sharing and reuse of data. The information system component of the SIR-FMD project has a direct impact on the standardisation of coding of rare diseases and thereby contributes to the development of e-Health.

PMID:
25991283
[Indexed for MEDLINE]

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