Charles S. Mayo, PhD, Marc L

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Presentation transcript:

The big data effort in radiation oncology: Data mining or data farming? Charles S. Mayo, PhD, Marc L. Kessler, PhD, Avraham Eisbruch, MD, Grant Weyburne, BS, Mary Feng, MD, James A. Hayman, MD, Shruti Jolly, MD, Issam El Naqa, PhD, Jean M. Moran, PhD, Martha M. Matuszak, PhD, Carlos J. Anderson, PhD, Lynn P. Holevinski, BS, Daniel L. McShan, PhD, Sue M. Merkel, MSA RT(R)(T), Sherry L. Machnak, MBA RT(T), Theodore S. Lawrence, MD PhD, Randall K. Ten Haken, PhD Advances in Radiation Oncology Volume 1, Issue 4, Pages 260-271 (October 2016) DOI: 10.1016/j.adro.2016.10.001 Copyright © 2016 The Authors on behalf of the American Society for Radiation Oncology Terms and Conditions

Figure 1 The systems required for construction of a knowledge-guided radiation therapy system that supports machine learning, reporting, and participation in trials and other clinical efforts can be conceptualized in 4 tiers. The foundational clinical processes and aggregation tiers enable the benefits of the analytics tier. The integration tier promotes interoperability even when multiple technologies are used. Advances in Radiation Oncology 2016 1, 260-271DOI: (10.1016/j.adro.2016.10.001) Copyright © 2016 The Authors on behalf of the American Society for Radiation Oncology Terms and Conditions

Figure 2 Farming is a useful metaphor for envisioning the issues in creating outcomes databases in health care. Advances in Radiation Oncology 2016 1, 260-271DOI: (10.1016/j.adro.2016.10.001) Copyright © 2016 The Authors on behalf of the American Society for Radiation Oncology Terms and Conditions

Figure 3 Evolution of practical Big Data systems progresses from smaller highly skilled groups to large vendor-based systems as the multidisciplinary village of staked holders (physicians, physicists, administration, RTT, dosimetry, nursing) finds and demonstrates value for these systems. Value drives willingness to modify clinical practices to reduce data variability. Advances in Radiation Oncology 2016 1, 260-271DOI: (10.1016/j.adro.2016.10.001) Copyright © 2016 The Authors on behalf of the American Society for Radiation Oncology Terms and Conditions

Figure 4 A self-service dashboard from M-ROAR illustrating high-velocity output from a large volume of data, value for supporting PQI, and research effort and means to improve veracity by bringing the consequences of “dirty data” directly into the view of end-users. PQI, practice quality improvement. Advances in Radiation Oncology 2016 1, 260-271DOI: (10.1016/j.adro.2016.10.001) Copyright © 2016 The Authors on behalf of the American Society for Radiation Oncology Terms and Conditions