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| Full article title |
How big data, comparative effectiveness research, and rapid-learning healthcare 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) |
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Introduction
Big data and comparative effectiveness research methodologies can be applied within the framework of a rapid-learning healthcare 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.”[1] In essence, the goal of CER is to identify "which treatment will work best, in which patient, under what circumstances.”[2] 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.[3] 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 (4). Additionally, the BD2K program will move to make sure that biomedical big data is “findable, accessible, interoperable, and reusable” (FAIR).[4] Over the past decade, CER methodologies have become increasingly prevalent in radiation oncology research, and there is much enthusiasm surrounding BDA.
References
- ↑ Institute of Medicine of the National Academies (2009). Initial National Priorities for Comparative Effectiveness Research. National Academies Press. ISBN 9780309138369. https://www.nap.edu/catalog/12648/initial-national-priorities-for-comparative-effectiveness-research.
- ↑ Greenfield, S.; Rich, E. (2012). "Welcome to the Journal of Comparative Effectiveness Research". Journal of Comparative Effectiveness Research 1 (1): 1–3. doi:10.2217/cer.11.13. PMID 24237290.
- ↑ Sivarajah, U.; Kamal, M.M.; Irani, Z.; Weerakkody, V. (2017). "Critical analysis of Big Data challenges and analytical methods". Journal of Business Research 70: 263–86. doi:10.1016/j.jbusres.2016.08.001.
- ↑ Margolis, R.; Derr, L.; Dunn, M. et al. (2014). "The National Institutes of Health's Big Data to Knowledge (BD2K) initiative: Capitalizing on biomedical big data". JAMIA 21 (6): 957–8. doi:10.1136/amiajnl-2014-002974. PMC PMC4215061. PMID 25008006. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4215061.
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.
