REAL WORLD DATA IN FRANCE – STATE OF THE ART

Author(s)

Gurnot S1, Tardu J2, Hirtz B2, Soudani S2, Defrance M2
1Paris-Dauphine, Paris, France, 2Paris-dauphine, Paris, France

Objectives: So far, the use of real world data (RWD) in France is limited. The aim of the study is to make an inventory of RWD and the way they are used by the different stakeholders of the health care system in France (hospitals, pharmaceutical companies, public health agencies, doctors…). We want to compare the existing RWD in France and the new ‘SNDS’ (Système national des données de santé) and investigate the limitations within the use of RWD in France and how the creation of the SNDS could enable a better use of the RWD. Methods: We collected various forms of RWD by searching in the medicines recommendations made by the HAS (Haute Autorité de Santé), the database of the ATIH (technical agency on hospitalization’s information) and of the French national health insurance. Results: We built a table that summarized this information and that contained six columns: Type of RWD, content, access, limits, cost and an example. Two different tables have been created: the first one represents the various existing types of RWD (register, observational study, PMSI, SNIIR-AM) study and the second one shows the new perspectives open by the creation of the SNDS. We also found new kinds of data that could be incorporated in the future into real world studies (health application, connected devices) and we listed new potential stakeholders that could enter the domain of RWD (Google, phone operators, startups…). Conclusion: The objectives of the SNDS helped us to conceptualize and synthetize what the RWD are in France and how they should evolve in the next few yearsRWD are growing and should increase even more with the opening of the SNDS. The SNDS have an important potential and may help to go over the limitations that the French system is facing so far with RWD.

Conference/Value in Health Info

2017-11, ISPOR Europe 2017, Glasgow, Scotland

Value in Health, Vol. 20, No. 9 (October 2017)

Code

PRM247

Topic

Methodological & Statistical Research

Topic Subcategory

Confounding, Selection Bias Correction, Causal Inference

Disease

Multiple Diseases

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