The development and the implementation of personalized medicine will occur in
three precisely defined phases. In the first phase, next to education and regulatory
framework, an important role will be played by the dialogue with the users,
stakeholder participation, standardization, and proof of principle.
The second phase will be marked by action harmonization, creation of an
interacting network (molecularly as well as environmentally), data integration,
and monitoring.^26
The third phase will be labeled byin silicomodels, systematic collecting of data,
and nanomedicine implementation. All the while, one has to keep in mind possible
issues and key factors that could influence the development and implementation of
personalized medicine, e.g., education, participation of a third party,
multidisciplinarity, infrastructure, revised disease classification, regulatory frame-
work, models for compensating the costs of medical care, ethical, social and legal
questions (Figs. 1 and 2 ).
Acknowledgements This text is supported by the Croatian Science Foundation project “5709 –
Perspectives of maintaining the social state: towards the transformation of social security systems
for individuals in personalized medicine” and University of Rijeka research grants 13.11.1.1.11
and 13.11.1.2.01. We greatly acknowledge the project RISK “Development of University of
Rijeka campus laboratory research infrastructure”, financed by European Regional Development
Fund (ERDF).
Table 2Timeline for the development and implementation of personalized medicine (modified
from ESF Forward Look 2012 )
Phase 1 Phase 2 Phase 3
Education Applicaton of metrics In silicomodels
Regulatory
frameworksResponsible governance frameworks Remote sensingPublic dialogue Patient-centered partnerships E-learning and adaptable
interfaces
Infrastructure
planningHarmonization of procedures Real-time monitoringCollection of refer-
ence dataInteraction networks (molecular and
environmetal)Systematic data collectionStakeholder
participationInfrastructure testingProof of principle Data integration
Biomarker validation Data sharing
Data standardization Dynamic monitoring(^26) Huser et al. ( 2014 ).
16 K. Pavelic ́et al.