Erratum to: Making sense of big data in health research: towards an EU action plan
Medicine and healthcare are undergoing profound changes. Whole genome sequencing and high resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of "big data" for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans. European healthcare systems and the potential for big data Medicine has traditionally been a science of observation and experience. For thousands of years, clinicians have integrated the knowledge of preceding generations with their own life long experiences to treat patients according to the oath of Hippocrates; mostly based on trial and Correspondence: cauffray@eisbm.org; rudi.balling@uni.lu European Institute for Systems Biology and Medicine, 1 avenue Claude Vellefaux, 75010 Paris, France Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7 Avenue des Hauts Fourneaux, 4362 Esch sur Alzette, Luxembourg Full list of author information is available at the end of the article (c) 2016 The Author(s) Open Access This artic International License (http:/creativecommons reproduction in any medium, provided you g the Creative Commons license, and indicate if (http:/creativecommons.org/publicdomain/ze error. Knowledge generation is changing dramatically. The digitalization of medicine allows the comparison of disease progression or treatment responses from patients worldwide. Whole genome sequencing allows searching and comparing one's own genome to millions and soon billions of other human genomes. Eventually, the entire world population could be used as a reference population in order to link genome information with many other types of physiological, clinical, environmental, and lifestyle data. For many, this is a vision full of opportunities, whereas for others it provides a wealth of technical le is distributed under the terms of the Creative Commons Attribution 4.0 .org/licenses/by/4.0/ which permits unrestricted use, distribution, and ive appropriate credit to the original author(s) and the source, provide a link to changes were made. The Creative Commons Public Domain Dedication waiver ro/1.0/ applies to the data made available in this article, unless otherwise stated. Auffray et al. Genome Medicine (2016) 8:71 Page 2 of 13 challenges, unanticipated consequences, and loss of privacy and autonomy. The quality of conclusions on the etiology of diseases follows a law of large numbers. Cross sectional cohort studies of 30,000 to 50,000 or more cases are required to separate the signal from noise and to detect genomic regions associated with a given trait in which diseaserelated genes or susceptibility factors are located [1, 2] Whole genome sequencing studies often identify only a few genomic regions that contain elements with large effects on the penetrance or expressivity of gene products but hundreds of genomic regions that have small effects and are highly dependent on genetic background, environmental factors, or social and lifestyle determinants [3] There is also a need to study disease pathogenesis on genome, epigenome, transcriptome, proteome, and metabolome levels and combine these dimensions through multi omics research. Furthermore, individual variation responsible for normal and disease phenotypes is high as a result of somatic mutations or variation in transcription, splicing, or allele specific gene expression between individuals [4 6] Vast amounts of temporal and spatial parameter data are now available. But what are we going to do with the data? It takes hard work to condense useful information from big data and turn this information into knowledge and action. The challenge will be to make a smart choice between situations when less is more versus less is less but also when more is more versus more is less. Here, we briefly describe the key challenges that result from making sense out of big data in health and using these data for the benefit of the patient and the healthcare system. We also highlight key technical, legal, and ethical issues that we face to develop evidence based personalized medicine. Finally, we put forward five recommendations for the European Union (EU) and member states' policy makers to serve as a framework for an EU action plan that could help to reach this ambitious goal. Making sense of big data in health research On 30 October 2015, the Health Directorate of the Directorate General for Research and Innovation at the European Commission (EC) the executive body of the EU, organized in Luxembourg a workshop entitled "Big data in health research: an EU action plan" [7] The aim of the workshop was to ask stakeholders in the "big data revolution" for their input on how European funding for health research should take into account the opportunities, limitations, and concerns of the anticipated developments in health and healthcare. Participants included bioinformaticians, computational biologists, genome scientists, drug developers, biobanking experts, experimental biologists, biostatisticians, information and communication technology (ICT) experts, public health researchers, clinicians, public policy experts, representatives of health services, patient advocacy groups, the pharmaceutical industry, and ICT companies. What do we mean by "big data" "Big data" has a wide range of definitions in health research [8, 9] and to create a single definition for all uses "one size fits all" approach) may be too abstract to be useful. However, a workable definition of what big data means for health research or at least a consensus of what this term means was proposed during the workshop in Luxembourg. "Big data in health" encompasses high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points. Big data can only be dealt with by adopting a strong governance model and best practices of new technologies, e.g. in large scale data production compliant with communitybased quality standards, coupled with interoperable data storage, data integration, and advanced analytics solutions [10] Another goal of the workshop was to develop an EU action plan for research funders towards the integration of big data into policy development, biomedical research, and clinical practice in health and wellness management. Big data comes from a variety of sources, such as clinical trials, electronic health records (EHRs) patient registries and databases, multidimensional data from genomic, epigenomic, transcriptomic, proteomic, metabolomic, and microbiomic measurements, and medical imaging. More recently, data are being integrated from social media, socioeconomic or behavioral indicators, occupational information, mobile applications, or environmental monitoring [11] Big data comes in a wide range of formats. Data streams have to be assessed and interpreted in a timely manner to benefit patients affected by diseases and to help citizens remain in good health [8, 12] Importance of patient registries Patient registries have for decades served as a key tool for assessing clinical outcome and clinical and health technology performance [13 15] Rare disease registries pool data to achieve a sufficient sample size for epidemiological and/ or clinical research [16, 17] The European Organization for Research and Treatment of Cancer (EORTC) [18] opened a prospective registry for patients with melanoma in June 2015 [19] The European Network of Cancer Registries (ENCR) [20] established within the framework of the Europe Against Cancer Programme of the EC, promotes collaboration between cancer registries, defines data collection standards, provides training for cancer registry personnel, and regularly disseminates information on incidence and mortality from cancer in the European Union and other European countries. Auffray et al. Genome Medicine (2016) 8:71 Page 3 of 13 Patient registries provide significant potential for research and public health improvements in the EU, owing to the large volume of patients in each registry and the variety of quality medical information related to each patient. Patient registries are increasingly important to monitor patients' treatments and for safety assessment and the identification of trends in translational medicine (e.g. registry based clinical trials, personalized medicine) [21] Patient registries allow informed policy decisions at the local, regional, national, and, in some cases, the international level. As a result, hundreds of registries have been set up that range from national to international rare disease initiatives, coupling clinical and genetic data and biobanks. However, for various reasons, including data protection and the fragmentation of regulatory frameworks, the combination of these disparate information so
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