CC BY-NC-ND 4.0 · Yearb Med Inform 2019; 28(01): 181-189
DOI: 10.1055/s-0039-1677916
Section 8: Bioinformatics and Translational Informatics
Georg Thieme Verlag KG Stuttgart

Personalized Medicine Implementation with Non-traditional Data Sources: A Conceptual Framework and Survey of the Literature

Casey Overby Taylor
1  Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
Peter Tarczy-Hornoch
2  Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
› Author Affiliations
Further Information

Publication History

Publication Date:
16 August 2019 (online)



Objectives: With the explosive growth in availability of health data captured using non-traditional sources, the goal for this work was to evaluate the current biomedical literature on theory- driven studies investigating approaches that leverage non- traditional data in personalized medicine applications.

Methods: We conducted a literature assessment guided by the personalized medicine unsolicited health information (pUHl) conceptual framework incorporating diffusion of innovations and task-technology fit theories.

Results: The assessment provided an oveiview of the current literature and highlighted areas for future research. In particular, there is a need for: more research on the relationship between attributes of innovation and of societal structure on adoption; new study designs to enable flexible communication channels; more work to create and study approaches in healthcare settings; and more theory-driven studies with data-driven interventions.

Conclusion: This work introduces to an informatics audience an elaboration on personalized medicine implementation with non-traditional data sources by blending it with the pUHl conceptual framework to help explain adoption. We highlight areas to pursue future theory-driven research on personalized medicine applications that leverage non-traditional data sources.