Special Series: Quality Care Symposium

Perspective

Big Data Infrastructure for Cancer Outcomes Research: Implications for the Practicing Oncologist By Anne-Marie Meyer, PhD, and Ethan Basch, MD The University of North Carolina at Chapel Hill, Chapel Hill, NC

Need for Big Data in Oncology The term big data has become a buzz word in many fields, including oncology. It represents many things, from next-generation DNA sequencing to electronic medical records to linked registry and insurance claims data.1 For outcomes research, specifically, the development of big data infrastructure holds immense promise because it enables research involving patients who are not typically represented in clinical trials. Although clinical trials often define treatment guidelines and influence practice, less than 3% of the cancer population is represented in these studies, and thus, clinical trials do not answer questions regarding treatment effectiveness in the real world.2,3 However, big data resources now allow researchers to observe large, retrospective, and heterogeneous cohorts of cancer patients from screening to end-of-life care though linkages between observational studies or data sets that are designed for purposes other than research (eg, administrative, delivery of care).4-7 The big data infrastructure, with data on real-world patients and practices, is essential for answering questions regarding treatment effectiveness and long-term outcomes.8-10

Challenges to Oncology Practice and Research in the Era of Big Data For practicing oncologists, big data contribute to increasingly complex treatment decision making because of a push for more personalized medicine at the bedside and the rapid pace of scientific discovery. This can be especially challenging for community oncologists without close ties to academic medical or research centers that help distill and disseminate evidence. In the late 20th century, study designs and sources of data remained relatively discrete, easy to quantify (eg, randomized trials, epidemiologic cohorts, and so on), and were well accepted within scientific and practice communities. The finite

and smaller sizes of these data sources made them easy to understand, interpret, and accept as evidence. Today, the size and diversity of clinical and population health data are changing how we design, analyze, and interpret research in many ways. New, interdisciplinary team science partnerships and tools are being developed to transform tera-, peta-, and exabytes of data into transparent, timely, and useful research data sets that represent discrete patient populations. Innovative study designs and analytic methods are being adapted to these new data sets to deal with their size and complexity (ie, machine learning). A logical consequence of these changes is a natural skepticism regarding the quality of the evidence and complicated messages emerging from big data studies regarding subgroups and patient heterogeneity.11 Therefore, improvements in data collection, standardization, transformation, and harmonization are required to facilitate transparency and increase confidence in big data research. Recent federal policies have encouraged data standardization, although extensive interoperability has not yet been achieved. Therefore, providers of clinical care who generate and collect data in their daily practices will play an increasingly crucial role in the future of big data. Practice and research communities must partner more closely regarding technologic infrastructure. The capacity for creating, storing, sharing, and manipulating data will continue to increase— only by working together will the resultant data and infrastructure enable a socalled learning health care system that benefits both research and practice.

New Opportunities for Big Data Several national policies have recently advanced the big data research agenda, including the American Recovery and Reinvestment Act of 2009 and the Patient Protection and Affordable Care Act. These policies have directly influenced the

Table 1. Key Big Data Infrastructure Initiatives Mini-Sentinel

FDA-funded initiative for active surveillance of regulated products (eg, drugs, vaccines, devices, and so on) through analyses of big data maintained by multiple collaborators via a distributed data network

PCORNet

A diverse nationally representative network designed to assemble clinical data that are standardized and interoperable for observational and experimental CER

APCD/MPCD

Large-scale claims databases that systematically collect and standardize health care claims data from multiple payers/sources; can be state mandated, voluntary, or private-industry supported

Flatiron Oncology Cloud and OncoAnalytics

Partnership between a technology company and multiple cancer centers to build cloud-based interoperable data system, with analytic tools to aid clinical decision making and research

CancerLinq

ASCO-sponsored IT platform designed as a learning health system for quality improvement and clinical decision support; includes multiple, diverse oncology practices nationwide

Abbreviations: APCD, all-payer claims database; ASCO, American Society of Clinical Oncology; CER, comparative effectiveness research; FDA, US Food and Drug Administration; IT, information technology; MPCD, multi-payer claims database; PCORNet, National Patient-Centered Clinical Research Network.

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activities and direction of the Institute of Medicine and Agency for Healthcare and Research Quality, and enabled the establishment of the Patient Centered Outcomes Research Institute. Both acts include initiatives that encourage states and other stakeholders to build collaborations that improve the collection and transparency of health care data. The policies have affected health care data in many ways: what is being captured (because of the emphasis on patient-reported outcomes); how patient data are captured and shared though meaningful use criteria for electronic health records; and how health care systems and states collect, store, and share population data via State Health Information Exchanges. These acts have also directly resulted in changes to information technology infrastructure, development of big data, and growth in methods research. Several ambitious and exciting investments in big data include the US Food and Drug Administration’s Mini-Sentinel,12 PCORNet (the National Patient-Centered Clinical Research Network),7 all- or multipayer claims data sets,13 Oncology Cloud (the cloud-based data repository of National Comprehensive Cancer Network data being developed in collaboration with Flatiron Health),14 and the American Society of Clinical Oncology’s CancerLinq15 (Table 1). In conclusion, there will continue to be rapid changes in data and infrastructure. Future successes will be defined by several criteria, including how oncology practices adopt and integrate data systems. It is critical that individual oncology providers, oncology care teams, and integrated health care systems support the systemic collection of data. Oncology providers (whether in community practice, academic centers, or employed by integrated networks) are the original source of information, and through effective partnerships can become the final beneficiaries of the resulting evidence and knowledge. It is therefore increasingly important to build a strong understanding between research and practice regarding big data. It is vital to work together and evaluate systems, not only on the basis of their initial intended use, but also with respect to how well they may be able to capitalize on and integrate more glob-

ally into the cancer research landscape. Future successes will be defined by how well data infrastructure and systems are able to integrate structured data like insurance claims and registry data with unstructured patient-reported outcomes and electronic medical records data from practices. There is an immediate need to define methods and standards for how to systematically collect and combine multiple sources of patient information. Practicing oncologists are experts in collecting patient information and have important perspectives on how data can be more efficiently and validly collected without increasing burden. Working together, interoperable data systems can be developed that facilitate a learning health care system.3 This will help validate the existing federal policies that make these systems possible and address future challenges to the development and maintenance of these investments in cancer research. More crucially, big data infrastructure will make it easier to answer questions of clinical and public health importance and deliver the answers back in a timely and meaningful way that helps oncologists and improves patient care. Authors’ Disclosures of Potential Conflicts of Interest Disclosures provided by the authors are available with this article at jop.ascopubs.org.

Author Contributions Conception and design: Anne-Marie Meyer, Ethan Basch Collection and assembly of data: Anne-Marie Meyer Data analysis and interpretation: Anne-Marie Meyer Manuscript writing: All authors Final approval of manuscript: All authors Corresponding author: Anne-Marie Meyer, PhD, Lineberger Cancer Center, University of North Carolina at Chapel Hill, CB #7293, Chapel Hill, NC 27599-7293; e-mail: [email protected].

DOI: 10.1200/JOP.2015.004432; published online ahead of print at jop.ascopubs.org on April 14, 2015.

References 1. Ward JS, Barker A: Undefined by data: A survey of big data definitions. http:// arXiv.org/abs/1309.5821

8. Sox HC, Goodman SN: The methods of comparative effectiveness research. Annu Rev Public Health 33:425-445, 2012

2. Lara PN Jr, Higdon R, Lim N, et al: Prospective evaluation of cancer clinical trial accrual patterns: Identifying potential barriers to enrollment. J Clin Oncol 19:17281733, 2001

9. Sox HC, Greenfield S: Comparative effectiveness research: A report from the Institute of Medicine. Ann Intern Med 151:203-205, 2009

3. Institute of Medicine: Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. Washington, DC, National Academies Press, 2012. http://www.iom.edu/Reports/2012/Best-Care-at-Lower-Cost-ThePath-to-Continuously-Learning-Health-Care-in-America.aspx 4. Meyer AM, Olshan AF, Green L: Big data for population-based cancer research: The integrated cancer information and surveillance system. N C Med J 75:265-269, 2014 5. Hershman DL, Wright JD: Comparative effectiveness research in oncology methodology: Observational data. J Clin Oncol 30:4215-4222, 2012 6. Curtis LH, Brown J, Platt R: Four health data networks illustrate the potential for a shared national multipurpose big-data network. Health Aff (Millwood) 33:11781186, 2014 7. Fleurence RL, Curtis LH, Califf RM, et al: Launching PCORnet, a national patient-centered clinical research network. J Am Med Inform Assoc 21:578-582, 2014

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10. Institute of Medicine: Initial National Priorities for Comparative Effectiveness Research. Washington, DC, National Academies Press, 2009. http:// www.iom.edu/Reports/2009/ComparativeEffectivenessResearchPriorities.aspx 11. [No authors listed]: Shades of grey. Nature 497:410, 2013 12. Platt R, Carnahan RM, Brown JS, et al: The U.S. Food and Drug Administration’s Mini-Sentinel program: Status and direction. Pharmacoepidemiol Drug Saf 21:1-8, 2012 (suppl 1) 13. Porter J, Love D, Costello A, et al: All-Payer Claims Database Development Manual: Establishing a Foundation for Health Care Transparency and Informed Decision Making. Durham, NH, APCD Council and West Health Policy Center, 2015 14. NCCN and Flatiron Health announce collaboration to launch novel oncology outcomes database. http://www.flatiron.com/about/press/NCCN 15. Schilsky RL, Michels DL, Kearbey AH, et al: Building a rapid learning health care system for oncology: The regulatory framework of CancerLinQ. J Clin Oncol 32:2373-2379, 2014

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AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST Big Data Infrastructure for Cancer Outcomes Research: Implications for the Practicing Oncologist The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated. Relationships are self-held unless noted. I ⫽ Immediate Family Member, Inst ⫽ My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO’s conflict of interest policy, please refer to www.asco.org/rwc or jop.ascopubs.org/site/misc/ifc.xhtml. Anne-Marie Meyer Consulting or Advisory Role: Merck

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Ethan Basch No relationship to disclose

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