Table 1 Examples of challenges and opportunities of evidence-based precision medicine

•Multiplicity of stakeholders and disciplines

•Analyses of big data

•Heterogeneity of complex, multilayered data types, and formats

•Harmonization of data semantics (clinical, laboratory, and others): vocabularies, terminologies, classification and coding systems, ontologies

•Standardization of data entry and storage

•Integration of multiple data types (such as laboratory, clinical, behavioral, lifestyle, environmental)

•Secure, sustainable, and effective data storage and sharing

•Necessity for new analytic tools and algorithms

•Multiplicity and lack of semantic and technical interoperability of electronic health record systems

•Extremely dynamic and fast-changing field, with new tools constantly emerging

•Training and education of the different stakeholders (medical staff, patients, and decision-makers)

•Ethical, legal, social, and consent issues

•Uberization of medicine

•Improved disease delineation, classification, and stratification

•Detection and monitoring of disease symptoms as early as possible

•Non-invasive prenatal or cancer testing

•Identification of pre- or asymptomatic individuals

•Identification of new disease mechanisms and treatment modalities

•Monitoring and modeling the dynamics of disease evolution

•Improved, personalized surveillance and management of disease and therapies

•Significant delay of disease onset and, whenever possible, prevention

•Development of evidence-based precision medicine

•Shifting emphasis of medicine more from therapy to prevention, and from disease to wellness

•Systemic view of medicine

•Patient participation

•Patient-centered medicine