title: Reconciling evidence-based medicine and precision medicine in the era of big data: challenges and opportunities.
abstract: This era of groundbreaking scientific developments in high-resolution, high-throughput technologies is allowing the cost-effective collection and analysis of huge, disparate datasets on individual health. Proper data mining and translation of the vast datasets into clinically actionable knowledge will require the application of clinical bioinformatics. These developments have triggered multiple national initiatives in precision medicine-a data-driven approach centering on the individual. However, clinical implementation of precision medicine poses numerous challenges. Foremost, precision medicine needs to be contrasted with the powerful and widely used practice of evidence-based medicine, which is informed by meta-analyses or group-centered studies from which mean recommendations are derived. This "one size fits all" approach can provide inadequate solutions for outliers. Such outliers, which are far from an oddity as all of us fall into this category for some traits, can be better managed using precision medicine. Here, we argue that it is necessary and possible to bridge between precision medicine and evidence-based medicine. This will require worldwide and responsible data sharing, as well as regularly updated training programs. We also discuss the challenges and opportunities for achieving clinical utility in precision medicine. We project that, through collection, analyses and sharing of standardized medically relevant data globally, evidence-based precision medicine will shift progressively from therapy to prevention, thus leading eventually to improved, clinician-to-patient communication, citizen-centered healthcare and sustained well-being.
authors.affiliation: Clinical Bioinformatics, SIB Swiss Institute of Bioinformatics, CH-1015, Lausanne, Switzerland. Jacques.Beckmann@unil.ch.
authors.forename: Jacques S
authors.initials: JS
authors.lastname: Beckmann
authors.affiliation: Clinical Bioinformatics, SIB Swiss Institute of Bioinformatics, CH-1015, Lausanne, Switzerland.
authors.forename: Daniel
authors.initials: D
authors.lastname: Lew
mesh_terms: D016208:Databases, Factual; D019317:Evidence-Based Medicine / Q000379:methods* / Q000639:trends*; D006801:Humans; D057285:Precision Medicine / Q000379:methods* / Q000639:trends*