1Post-doctoral Master of Science in Clinical and Translational Research (MSc) and Hispanics in Research Capability (HiREC) Endowment Programs, School of Health Professions, Medical Sciences Campus-University of Puerto Rico.
2Post-doctoral Master of Science in Clinical and Translational Research (MSc), School of Health Professions, and the Puerto Rico Clinical and Translational Research Consortium, Medical Sciences Campus - University of Puerto Rico.
3Post-doctoral Master of Science in Clinical and Translational Research (MSc) School of Health Professions, Medical Sciences Campus-University of Puerto Rico.
Personalized medicine is the development of 'tailored' therapies that reflect traditional medical approaches, with the incorporation of the patient's unique genetic profile and the environmental basis of the disease. These individualized strategies encompass disease prevention, diagnosis, as well as treatment strategies. Today's healthcare workforce is faced with the availability of massive amounts of patient- and disease-related data. When mined effectively, these data will help produce more efficient and effective diagnoses and treatment, leading to better prognoses for patients at both the individual and population level. Designing preventive and therapeutic interventions for those patients who will benefit most while minimizing side effects and controlling healthcare costs, requires bringing diverse data sources together in an analytic paradigm. A resource to clinicians in the development and application of personalized medicine is largely facilitated, perhaps even driven, by the analysis of "big data". For example, the availability of clinical data warehouses is a significant resource for clinicians in practicing personalized medicine. These "big data" repositories can be queried by clinicians, using specific questions, with data used to gain an understanding of challenges in patient care and treatment. Health informaticians are critical partners to data analytics including the use of technological infrastructures and predictive data mining strategies to access data from multiple sources, assisting clinicians' interpretation of data and development of personalized, targeted therapy recommendations. In this paper, we look at the concept of personalized medicine, offering perspectives in four important, influencing topics: 1) the availability of 'big data' and the role of biomedical informatics in personalized medicine, 2) the need for interdisciplinary teams in the development and evaluation of personalized therapeutic approaches, and 3) the impact of electronic medical record systems and clinical data warehouses on the field of personalized medicine. In closing, we present our fourth perspective, an overview to some of the ethical concerns related to personalized medicine and health equity.