Date of Award

Spring 5-16-2016

Degree Type


Degree Name

PhD Health Sciences


Health and Medical Sciences


Deborah A. DeLuca, J.D.

Committee Member

Terrence Cahill, Ed.D.

Committee Member

Genevieve Pinto-Zipp, Ed.D.


pharmacogenomic, precision medicine, healthcare technologyadoption, genetic discrimination, clinical setting, patient data protection


The changing landscape of healthcare in the US has created new questions about how to best provide cost-effective, individualized care. Personalized medicine and more specifically, pharmacogenomic technology have offered new tools for healthcare providers to use to increase the efficacy, safety, and cost-effectiveness of care. However, these tools are not being utilized to their predicted extent in the clinical setting. This study utilized Rogers’ Diffusion of Innovations theory to investigate some of the reasons why. A multi-question survey, the PI-created, Pharmacogenomic Adoption Instrument (PAI) ©, was developed to assess the knowledge, attitudes and experience concerning pharmacogenetic technology in a spectrum of different healthcare providers and types, and was administered online. This study found both knowledge and attitude, overall, to be highly correlated to adoption likelihood. Lack of knowledge was the most frequently cited barrier to adoption. This study also found that the perception of clinical benefit, the potential for misuse and genetic discrimination, and the ability of providers to effectively explain, and patients to understand test results, were significant factors in making decisions about utilizing pharmacogenomic technology. Further, the study found that clinical setting and the availability of clinical training may affect the perceptions of compatibility and trialability. These findings suggest that knowledge may be a key requisite, but the most influential factors on the adoption process are likely related to direct observation of a benefit in the clinic, including successful patient communication and a positive perception of protections from misuse of patient data. Therefore, focusing on improvement of the mechanisms for these processes may help to improve the rate of clinical adoption.