"Big data" and "open data": What kind of access should researchers enjoy?

The healthcare sector is currently facing a new paradigm, the explosion of "big data". Coupled with advances in computer technology, the field of "big data" appears promising, allowing us to better understand the natural history of diseases, to follow-up new technologies (devices, drugs) implementation and to participate in precision medicine, etc. Data sources are multiple (medical and administrative data, electronic medical records, data from rapidly developing technologies such as DNA sequencing, connected devices, etc.) and heterogeneous while their use requires complex methods for accurate analysis. Moreover, faced with this new paradigm, we must determine who could (or should) have access to which data, how to combine collective interest and protection of personal data and how to finance in the long-term both operating costs and databases interrogation. This article analyses the opportunities and challenges related to the use of open and/or "big data", from the viewpoint of pharmacologists and representatives of the pharmaceutical and medical device industry.

[1]  Mohamed Ismail Nounou,et al.  Are Currently Available Wearable Devices for Activity Tracking and Heart Rate Monitoring Accurate, Precise, and Medically Beneficial? , 2015, Healthcare informatics research.

[2]  Douglas MacFadden,et al.  SHRINE: Enabling Nationally Scalable Multi-Site Disease Studies , 2013, PloS one.

[3]  B. Collins Big Data and Health Economics: Strengths, Weaknesses, Opportunities and Threats , 2016, PharmacoEconomics.

[4]  Frank Pétavy,et al.  Access to patient-level trial data--a boon to drug developers. , 2013, The New England journal of medicine.

[5]  Patrice Degoulet,et al.  Translational research platforms integrating clinical and omics data: a review of publicly available solutions , 2014, Briefings Bioinform..

[6]  Douglas MacFadden,et al.  Application of Information Technology The Shared Health Research Information Network ( SHRINE ) : A Prototype Federated Query Tool for Clinical Data Repositories , 2014 .

[7]  R. Lanfear,et al.  The Extent and Consequences of P-Hacking in Science , 2015, PLoS biology.

[8]  Cyril Grouin,et al.  De-identification of clinical notes in French: towards a protocol for reference corpus development , 2014, J. Biomed. Informatics.

[9]  Yudhijit Bhattacharjee,et al.  Biomedicine. Pharma firms push for sharing of cancer trial data. , 2012, Science.

[10]  I. Kohane,et al.  Finding the missing link for big biomedical data. , 2014, JAMA.

[11]  D Kalra,et al.  Electronic health records: new opportunities for clinical research , 2013, Journal of internal medicine.

[12]  G. Nolan,et al.  Computational solutions to large-scale data management and analysis , 2010, Nature Reviews Genetics.

[13]  K Furu,et al.  The Nordic prescription databases as a resource for pharmacoepidemiological research—a literature review , 2013, Pharmacoepidemiology and drug safety.

[14]  Joseph Finkelstein,et al.  Activity Trackers: A Critical Review , 2014, MIE.

[15]  Fiona Godlee,et al.  Goodbye PubMed, hello raw data , 2011, BMJ : British Medical Journal.

[16]  Y. Benjamini,et al.  Sharing clinical trial data on patient level: Opportunities and challenges , 2014, Biometrical journal. Biometrische Zeitschrift.

[17]  Mark Walport,et al.  Sharing research data to improve public health , 2011, The Lancet.

[18]  George Hripcsak,et al.  Viewpoint Paper: Large Datasets in Biomedicine: A Discussion of Salient Analytic Issues , 2009, J. Am. Medical Informatics Assoc..