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Full article title	How big data, comparative effectiveness research, and rapid-learning health care systems can transform patient care in radiation oncology
Journal	Frontiers in Oncology
Author(s)	Sanders, Jason C.; Showalter, Timothy N.
Author affiliation(s)	University of Virginia School of Medicine
Primary contact	Email: tns3b@virginia.edu
Editors	Deng, Jun
Year published	2018
Volume and issue	8
Page(s)	155
DOI	10.3389/fonc.2018.00155
ISSN	2234-943X
Distribution license	Creative Commons Attribution 4.0 International
Website	https://www.frontiersin.org/articles/10.3389/fonc.2018.00155/full
Download	https://www.frontiersin.org/articles/10.3389/fonc.2018.00155/pdf (PDF)

Introduction

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.

Comparative effectiveness research (CER) and big data

The Committee on CER Prioritization was created by the Institute of Medicine in 2009. They defined CER as “a strategy that focuses on the practical comparison of two or more health intervention to discern what works best for which patients and populations.” In essence, the goal of CER is to identify "which treatment will work best, in which patient, under what circumstances.” Big data refers to data sets that are so large that they cannot be analyzed directly by individuals or traditional processing software. Big data analytics (BDA) is a growing field with a multitude of methods that is being utilized in various sectors from business to medicine. The advent of the electronic medical record (EMR) has resulted in the digitization of massive data sets of medical information, including clinic encounters, laboratory values, imaging data sets and reports, pathology reports, patient outcomes, and family history, as well as genomic and biological data, etc.

To help with the analysis of big data, the National Institutes of Health (NIH) has created the Big Data to Knowledge (BD2K) program, which has invested over $200 million in grant awards to foster the development of methods and tools to analyze big data in biomedical research. Additionally, the BD2K program will move to make sure that biomedical big data is “findable, accessible, interoperable, and reusable” (FAIR). Over the past decade, CER methodologies have become increasingly prevalent in radiation oncology research, and there is much enthusiasm surrounding BDA.

Rapid-learning health care system (RLHCS) and personalized medicine

The number of articles on big data in health care has increased exponentially from under 500 articles in 2005 to over 2500 articles in 2015. As the amount of biomedical big data and our ability to analyze these data continues to advance, so will the implications and use of the information we are able to extract. One of the most important steps toward advancing our ability to analyze these big data for biomedical discovery is the creation of RLHCS, which will allow for the sharing of patient data between EMRs, ideally in real-time. An ideal RLHCS would take patient data that was routinely generated as part of standard patient care and compile that data into a large data system. This aggregate data would then be available for both BDA to accelerate identification of new hypotheses and CER to rapidly generate evidence through hypothesis-testing studies. Clinical data from patient records can be used readily to identify novel relationships among clinical factors and patient outcomes, or to evaluate treatment effectiveness in specific subgroups, that cannot be studied adequately in randomized, controlled trials. The extreme power of RLCHS, though, is even more exciting when one considers the possibility of adding biospecimens to accelerate discovery in genomics and proteomics. As RLHCSs 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 and treatment response groups, and potentially to help design highly tailored therapy regimens. In this sense, the RLHCS would usher in a more fertile era for improving biomedical research than ever before. BDA and CER provide the research methodologies needed to rapidly generate evidence from the RLHCS. It should be noted, however, that there are substantial practical obstacles that must be addressed to achieve the vision of the RLHCS. 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.

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.