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==Rapid-learning health care system (RLHCS) and personalized medicine==
==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.<ref name="deLaTorreDíezBig16">{{cite journal |title=Big Data in Health: a Literature Review from the Year 2005 |journal=Journal of Medical Systems |author=de la Torre Díez, I.; Cosgava, H.M.; Garcia-Zapirain, B.; López-Coronado, M. |volume=40 |issue=9 |pages=209 |year=2016 |doi=10.1007/s10916-016-0565-7 |pmid=27520614}}</ref> 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.<ref name="GinsburgCompar12">{{cite journal |title=Comparative effectiveness research, genomics-enabled personalized medicine, and rapid learning health care: A common bond |journal=Journal of Clinical Oncology |author=Ginsburg, G.S.; Kuderer, N.M. |volume=30 |issue=34 |pages=4233-42 |year=2012 |doi=10.1200/JCO.2012.42.6114 |pmid=23071236 |pmc=PMC3504328}}</ref> 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.<ref name="GinsburgCompar12" /><ref name="GinsburgAcademic11">{{cite journal |title=Academic medical centers: Ripe for rapid-learning personalized health care |journal=Science Translational Medicine |author=Ginsburg, G.S.; Staples, J.; Abernethy, A.P. |volume=3 |issue=101 |pages=101cm27 |year=2011 |doi=10.1126/scitranslmed.3002386 |pmid=21937754}}</ref><ref name="AbernathyRapid10">{{cite journal |title=Rapid-learning system for cancer care |journal=Journal of Clinical Oncology |author=Abernethy, A.P.; Etheredge, L.M.; Ganz, P.A. et al. |volume=28 |issue=27 |pages=4268-74 |year=2010 |doi=10.1200/JCO.2010.28.5478 |pmid=20585094 |pmc=PMC2953977}}</ref> 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.<ref name="RamseyHow11">{{cite journal |title=How comparative effectiveness research can help advance 'personalized medicine' in cancer treatment |journal=Health Affairs |author=Ramsey, S.D.; Veenstra, D.; Tunis, S.R. et al. |volume=30 |issue=12 |pages=2259–68 |year=2011 |doi=10.1377/hlthaff.2010.0637 |pmid=22147853 |pmc=PMC3477796}}</ref> 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.
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.<ref name="deLaTorreDíezBig16">{{cite journal |title=Big Data in Health: a Literature Review from the Year 2005 |journal=Journal of Medical Systems |author=de la Torre Díez, I.; Cosgava, H.M.; Garcia-Zapirain, B.; López-Coronado, M. |volume=40 |issue=9 |pages=209 |year=2016 |doi=10.1007/s10916-016-0565-7 |pmid=27520614}}</ref> 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.<ref name="GinsburgCompar12">{{cite journal |title=Comparative effectiveness research, genomics-enabled personalized medicine, and rapid learning health care: A common bond |journal=Journal of Clinical Oncology |author=Ginsburg, G.S.; Kuderer, N.M. |volume=30 |issue=34 |pages=4233-42 |year=2012 |doi=10.1200/JCO.2012.42.6114 |pmid=23071236 |pmc=PMC3504328}}</ref> 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.<ref name="GinsburgCompar12" /><ref name="GinsburgAcademic11">{{cite journal |title=Academic medical centers: Ripe for rapid-learning personalized health care |journal=Science Translational Medicine |author=Ginsburg, G.S.; Staples, J.; Abernethy, A.P. |volume=3 |issue=101 |pages=101cm27 |year=2011 |doi=10.1126/scitranslmed.3002386 |pmid=21937754}}</ref><ref name="AbernathyRapid10">{{cite journal |title=Rapid-learning system for cancer care |journal=Journal of Clinical Oncology |author=Abernethy, A.P.; Etheredge, L.M.; Ganz, P.A. et al. |volume=28 |issue=27 |pages=4268-74 |year=2010 |doi=10.1200/JCO.2010.28.5478 |pmid=20585094 |pmc=PMC2953977}}</ref> 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.<ref name="RamseyHow11">{{cite journal |title=How comparative effectiveness research can help advance 'personalized medicine' in cancer treatment |journal=Health Affairs |author=Ramsey, S.D.; Veenstra, D.; Tunis, S.R. et al. |volume=30 |issue=12 |pages=2259–68 |year=2011 |doi=10.1377/hlthaff.2010.0637 |pmid=22147853 |pmc=PMC3477796}}</ref> 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.
==Integrating a RLHCS with oncology==
The integration of CER, big data, and 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.<ref name="MargolisTheNat14" /><ref name="HelftCanBig14">{{cite journal |title=Can big data cure cancer? |journal=Fortune |author=Helft, M. |volume=170 |issue=2 |pages=70–4, 76, 78 |year=2014 |pmid=25318238}}</ref><ref name="WilliamsArtificial18">{{cite journal |title=Artificial intelligence, physiological genomics, and precision medicine |journal=Physiological Genomics |author=Williams, A.M.; Liu, Y.; Regner, K.R. et al. |volume=50 |issue=4 |pages=237–43 |year=2018 |doi=10.1152/physiolgenomics.00119.2017 |pmid=29373082 |pmc=PMC5966805}}</ref><ref name="SavageBigData14">{{cite journal |title=Big data versus the big C |journal=Scientific American |author=Savage, N. |volume=311 |issue=1 |pages=S20–1 |year=2014 |pmid=24974705}}</ref><ref name="ShahBuilding16">{{cite journal |title=Building a rapid learning health care system for oncology: Why CancerLinQ collects identifiable health information to achieve its vision |journal=Journal of Clinical Oncology |author=Shah, A.; Stewart, A.K.; Kolacevski, A. et al. |volume=34 |issue=7 |pages=756–63 |year=2016 |doi=10.1200/JCO.2015.65.0598 |pmid=26755519}}</ref><ref name="TrifilettiBigData15">{{cite journal |title=Big Data and Comparative Effectiveness Research in Radiation Oncology: Synergy and Accelerated Discovery |journal=Frontiers in Oncology |author=Trifiletti, D.M.; Showalter, T.N. |volume=5 |pages=274 |year=2015 |doi=10.3389/fonc.2015.00274 |pmid=26697409 |pmc=PMC4672039}}</ref> The American Society of Clinical Oncology started their own RLHCS, CancerLinQ, to overcome the lack of interoperability between EMRs and accomplish their goal of being able to “analyze and share data on every patient with cancer.”<ref name="ASCOShaping11">{{cite web |url=https://www.asco.org/sites/default/files/shapingfuture-lowres.pdf |title=Shaping the Future of Oncology: Envisioning Cancer Care in 2030: Outcomes of the ASCO Board of Directors Strategic Planning and Visioning Process, 2011-2012 |publisher=American Society of Clinical Oncology |date=2011}}</ref> While the vision of RLCHS has not yet been fully achieved, the potential impact on society has stimulated enthusiasm toward this effort.


==References==
==References==

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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)
This article should not be considered complete until this message box has been removed. This is a work in progress.

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.”[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.

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.[5] 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.[6] 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.[6][7][8] 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.[9] 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.

Integrating a RLHCS with oncology

The integration of CER, big data, and 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] The American Society of Clinical Oncology started their own RLHCS, CancerLinQ, to overcome the lack of interoperability between EMRs and accomplish their goal of being able to “analyze and share data on every patient with cancer.”[15] While the vision of RLCHS has not yet been fully achieved, the potential impact on society has stimulated enthusiasm toward this effort.

References

  1. ↑ 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. 
  2. ↑ 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. 
  3. ↑ 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. 
  4. ↑ 4.0 4.1 4.2 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. 
  5. ↑ de la Torre Díez, I.; Cosgava, H.M.; Garcia-Zapirain, B.; López-Coronado, M. (2016). "Big Data in Health: a Literature Review from the Year 2005". Journal of Medical Systems 40 (9): 209. doi:10.1007/s10916-016-0565-7. PMID 27520614. 
  6. ↑ 6.0 6.1 Ginsburg, G.S.; Kuderer, N.M. (2012). "Comparative effectiveness research, genomics-enabled personalized medicine, and rapid learning health care: A common bond". Journal of Clinical Oncology 30 (34): 4233-42. doi:10.1200/JCO.2012.42.6114. PMC PMC3504328. PMID 23071236. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3504328. 
  7. ↑ Ginsburg, G.S.; Staples, J.; Abernethy, A.P. (2011). "Academic medical centers: Ripe for rapid-learning personalized health care". Science Translational Medicine 3 (101): 101cm27. doi:10.1126/scitranslmed.3002386. PMID 21937754. 
  8. ↑ Abernethy, A.P.; Etheredge, L.M.; Ganz, P.A. et al. (2010). "Rapid-learning system for cancer care". Journal of Clinical Oncology 28 (27): 4268-74. doi:10.1200/JCO.2010.28.5478. PMC PMC2953977. PMID 20585094. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2953977. 
  9. ↑ Ramsey, S.D.; Veenstra, D.; Tunis, S.R. et al. (2011). "How comparative effectiveness research can help advance 'personalized medicine' in cancer treatment". Health Affairs 30 (12): 2259–68. doi:10.1377/hlthaff.2010.0637. PMC PMC3477796. PMID 22147853. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3477796. 
  10. ↑ Helft, M. (2014). "Can big data cure cancer?". Fortune 170 (2): 70–4, 76, 78. PMID 25318238. 
  11. ↑ Williams, A.M.; Liu, Y.; Regner, K.R. et al. (2018). "Artificial intelligence, physiological genomics, and precision medicine". Physiological Genomics 50 (4): 237–43. doi:10.1152/physiolgenomics.00119.2017. PMC PMC5966805. PMID 29373082. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5966805. 
  12. ↑ Savage, N. (2014). "Big data versus the big C". Scientific American 311 (1): S20–1. PMID 24974705. 
  13. ↑ Shah, A.; Stewart, A.K.; Kolacevski, A. et al. (2016). "Building a rapid learning health care system for oncology: Why CancerLinQ collects identifiable health information to achieve its vision". Journal of Clinical Oncology 34 (7): 756–63. doi:10.1200/JCO.2015.65.0598. PMID 26755519. 
  14. ↑ Trifiletti, D.M.; Showalter, T.N. (2015). "Big Data and Comparative Effectiveness Research in Radiation Oncology: Synergy and Accelerated Discovery". Frontiers in Oncology 5: 274. doi:10.3389/fonc.2015.00274. PMC PMC4672039. PMID 26697409. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4672039. 
  15. ↑ "Shaping the Future of Oncology: Envisioning Cancer Care in 2030: Outcomes of the ASCO Board of Directors Strategic Planning and Visioning Process, 2011-2012". American Society of Clinical Oncology. 2011. https://www.asco.org/sites/default/files/shapingfuture-lowres.pdf. 

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.

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