Patient driven adaptive technologies has the potential to improve clinical decision-making for personalized risk estimation of patients (Jiang, Boxwala, El-Kareh, Kim, & Ohno-Machado, 2012). Within the health care industry, data is driving new discoveries, reimbursement, outcomes, systems design as well as the decisional making process. Patient data sources are used to perform clinical interpretation and evidence based reporting having the potential to minimize risk, promote health and encourage patient engagement in their care across the care continuum. In cancer care, quality care has been challenged by the multifactorial nature of cancer disease and patient systems. Patient driven adaptive technologies is driving the fight against cancer and is promising in the development of new drug discoveries and clinical trials (Taglang & Jackson, 2016). Clinical decision making can potentially mean the difference between toxic and effective cancer treatment choices, thus affecting patient quality of life and longevity. Patient safety can be improved with the detection of early adverse drug event using spontaneous reporting systems database such as the Food and Drug Administration’s Adverse Event Reporting System. The use of patient driven adaptive technologies has the potential to shift from providing standard care to providing a personalized approach in care of that patient (Jiang, Boxwala, El-Kareh, Kim, & Ohno-Machado, 2012).
Jiang, X., Boxwala, A. A., El-Kareh, R., Kim, J., & Ohno-Machado, L. (2012). A patient-driven adaptive prediction technique to improve personalized risk estimation for clinical decision support. Journal of the American Medical Informatics Association, 19(e1), e137-e144. doi:10.1136/amiajnl-2011-000751
Taglang, G., & Jackson, D. B. (2016). Use of “big data” in drug discovery and clinical trials. Gynecologic Oncology, 141(1), 17-23. doi:10.1016/j.ygyno.2016.02.022