Skip to content

Advertisement

Orphanet Journal of Rare Diseases

What do you think about BMC? Take part in

Open Access

The French national registry for rare diseases: an integrated model from care to epidemiology and research

  • Rémy Choquet1, 2 and
  • Paul Landais1, 3
Orphanet Journal of Rare Diseases20149(Suppl 1):O7

https://doi.org/10.1186/1750-1172-9-S1-O7

Published: 11 November 2014

The first national plan for rare diseases (2005-2008) set the network for care and research in the rare diseases (RD) field across all French hospitals. The 131 RD centers of expertise (CE) initiated then various IT projects to register electronically their RD patients. The CEMARA project [1] up to now registered 235,000 RD patients, from 62 RDCE (out of 131), 383 units of care and described over 4000 rare diseases. The identified limits of the CEMARA model were: i) data collection was not incorporated in the care setting, ii) exposed to data re-entry, iii) coping with data privacy new regulations and iv) gaining a wide national consensus on the data to collect for all RDs and from all CEs. To overcome these limits, the 2nd national plan for rare diseases (2010-2014) promoted the creation of a national data repository for all rare diseases (BNDMR) based on the CEMARA model with the following objectives: i) identifying RD patients within the health information systems in care setting, ii) describing the RD demand of care, and the adequacy of the supply and iii) identifying patients eligible for clinical trials or cohorts.

The proposed national architecture incorporates a national minimum data set for all rare diseases (F-MDS-RD) into hospital information care systems to enable electronic patient files re-use for epidemiological studies or research [2]. To ensure a full interoperability between local hospital information systems and the BNDMR, a national interoperability framework is defined. It is set on 3 pillars: i) defining a national patient ID for rare diseases that will help to identify patients across different health IS, ii) defining a common data format for all rare diseases, compatible with EHR standards such as HL7, and iii) setting the necessary technical data flows complying with strict security rules for data privacy [3] and security.

The minimum data set for rare diseases is now being implemented in several local systems, and connectors are being rolled out with several database suppliers. Data re-entry is nowadays a major concern for clinicians. Enabling data re-use is not only an interoperability problem; data must be structured, qualified, standardized [4] and suitable for research [5]. The structuration of the data collected is set on a variety of standard terminologies such as Orphanet or the Human Phenotype Ontology which requires to be accompanied with the necessary IT tools to help clinicians coding the data [6].

Authors’ Affiliations

(1)
BNDMR, Assistance Publique Hôpitaux de Paris, Hôpital Necker Enfants Malades
(2)
INSERM, U1142
(3)
EA2415, Clinical Research University Institute, Faculty of Medicine, Montpellier University & BESPIM, Nîmes University hospital

References

  1. Landais P, Messiaen C, Rath A, Le Mignot L, Dufour E, Ben Said M, Jais J-P, Toubiana L, Baujat G, Bourdon-Lanoy E, Gérard-Blanluet M, Bodemer C, Salomon R, Aymé S, Le Merrer M, Verloes A: “CEMARA an information system for rare diseases.,”. Stud. Health Technol. Inform. 2010, 160 (Pt 1): 481-5.PubMedGoogle Scholar
  2. Coorevits P, Sundgren M, Klein G O, Bahr a, Claerhout B, Daniel C, Dugas M, Dupont D, Schmidt a, Singleton P, De Moor G, Kalra D: “Electronic health records: new opportunities for clinical research.,”. J. Intern. Med. 2013, 274 (6): 547-60. 10.1111/joim.12119.View ArticlePubMedGoogle Scholar
  3. Atreya RV, Smith JC, McCoy AB, Malin B, Miller RA: “Reducing patient re-identification risk for laboratory results within research datasets,”. J Am Med Inf. Assoc. 2013, 20 (1): 95-101. 10.1136/amiajnl-2012-001026.View ArticleGoogle Scholar
  4. Choquet R, Qouiyd S, Ouagne D, Pasche E, Daniel C, Boussaïd O, Jaulent M-C: “The Information Quality Triangle: a methodology to assess clinical information quality.,”. Stud. Health Technol. Inform. 2010, 160: 699-703.PubMedGoogle Scholar
  5. Hripcsak G, Albers DJ: “Next-generation phenotyping of electronic health records,”. J Am Med Inf. Assoc. 2013, 20 (1): 117-121. 10.1136/amiajnl-2012-001145.View ArticleGoogle Scholar
  6. Choquet R, Fonjallaz Y, De Carrara A, Maaroufi M, Vandenbussche P, Dhombres F, Landais P: “Coding rare diseases in health information systems?: a tool for visualizing classifications and integrating phenotypic and genetic data,”. Journées Francofones de l’Informatique Médicale (JFIM). 2014Google Scholar

Copyright

© Choquet and Landais; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Advertisement