Abstract
Big data and comparative effectiveness research methodologies can be applied within the framework of a rapid-learning health-care system (RLHCS) to accelerate discovery and to help turn the dream of fully personalized medicine into a reality. We synthesize recent advances in genomics with trends in big data to provide a forward-looking perspective on the potential of new advances to usher in an era of personalized radiation therapy, with emphases on the power of RLHCS to accelerate discovery and the future of individualized radiation treatment planning.
Highlights
Big data and comparative effectiveness research methodologies can be applied within the framework of a rapid-learning health-care system (RLHCS) to accelerate discovery and to help turn the dream of fully personalized medicine into a reality
The integration of COMPARATIVE EFFECTIVENESS RESEARCH (CER), Big Data, and Big Data Analytics (BDA) is especially important in the field of Oncology where multiple groups are investing significant time and resources in efforts to expand the availability of data and advance the methods used to extract meaningful information from that data [4, 10,11,12,13,14]
Multiple studies have already begun to look at how BDA methods such as machine learning and neural networks can be used to aid in dose optimization and toxicity prediction modeling in radiation oncology [17, 25,26,27], which could provide more optimal treatment plan alternatives for individual patients
Summary
Effectiveness Research, and Rapid-Learning Health-Care Systems Can Transform Patient Care in Radiation Oncology. As RLHCS are created and their data sets are expanded, we will continue to identify specific genomic and proteomic data to help define cohorts and stratify patients into risk groups, treatment response groups, and potentially to help design highly tailored therapy regimens [9]. In this sense, RLCHS would usher in a more fertile era for improving bio medical research than ever before. BDA and CER provide the research methodologies needed to rapidly generate evidence using RLHCS It should be noted, that there are substantial practical obstacles that must be addressed to achieve the vision of RLHSC. These include patient concerns regarding privacy and security of sensitive information, interconnectivity among different health records, and regulatory barriers to the exchange of health information
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