Question 1: Big Data will have what impact on Patient Health Information:
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Reference: | Kevin L. Moore, George C. Kagadis, Todd R. McNutt, Vitali Moiseenko, and Sasa Mutic, “Automation and Advanced Computing in Clinical Radiation Oncology” Med Phys 41, 010901 (2014)
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Choice A: | Resolve all of the PHI issues by providing more open access. |
Choice B: | Partition PHI from institutional access by use of cloud computing. |
Choice C: | Remove PHI as an issue of concern. |
Choice D: | Provide an increase in complexity for PHI that will require greater diligence to ensure patient privacy. |
Question 2: Development of the Radiation Oncology Incident learning System (ROILS) has several advantages, EXCEPT:
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Reference: | Overview of the ASTRO-NIH-AAPM Workshop 2015: Exploring Opportunities for Radiation Oncology in the Era of Big Data, Stanley H. Benedict, PhD (Chair), Karen Hoffman, MD (Co-Chair),Mary K. Martel, PhD, et al, In Press Int Jo Radiat Oncol Biol Phys
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Choice A: | It allows institutions to share their “close calls” in potential misadministrations. |
Choice B: | Provides a means to review actual incidents that have led to unintended radiation doses to the patient. |
Choice C: | Is a searchable database that can identify issues with specific technology and techniques. |
Choice D: | Ensures that PHI is secure and confidential according to established guidelines. |
Question 3: The proposed national radiation oncology registry has several advantages, EXCEPT:
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Reference: | Overview of the ASTRO-NIH-AAPM Workshop 2015: Exploring Opportunities for Radiation Oncology in the Era of Big Data, Stanley H. Benedict, PhD (Chair), Karen Hoffman, MD (Co-Chair),Mary K. Martel, PhD, et al, In Press Int Jo Radiat Oncol Biol Phys
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Choice A: | Provide a wider opportunity to pool patients for clinical research analysis from diverse patient populations, geography, and institutions. |
Choice B: | Potentially allows a mechanism for data mining and “virtual” clinical research analysis. |
Choice C: | Helps to foster the mission of learning as much as possible from our patients in the goal of providing precision medicine. |
Choice D: | Allows patients to enroll themselves into clinical protocols. |
Question 4: Knowledge based treatment planning has many advantages, EXCEPT:
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Reference: | McNutt T., Moore K., Quon H. “Needs and Challenges for Big Data in Radiation Oncology,” Int’l J. of Radiation Oncology, Biology, Physics. Published online: November 27 2015
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Choice A: | Allows treatment plans for various disease sites to be compared to many other similar plans. |
Choice B: | Is a personalized method for treatment planning. |
Choice C: | Can include adverse effects of radiation on normal tissues. |
Choice D: | Ensures that PHI is secure and confidential according to established guidelines. |
Question 5: Which answer is not a major challenge for the implementation of Learning Health Systems for Radiation Oncology?
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Reference: | McNutt T., Moore K., Quon H. “Needs and Challenges for Big Data in Radiation Oncology,” Int’l J. of Radiation Oncology, Biology, Physics. Published online: November 27 2015
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Choice A: | Clinical culture and documentation to support the quantification of the patient condition. |
Choice B: | Database technology to enable large scale access to complex RT data. |
Choice C: | Data science models that can predict outcomes and are consistent with existing knowledge. |
Choice D: | Data completeness and integrity . |
Question 6: Radiomics is an emerging field of research and promises to advance significantly patient care. All of the following are true, EXCEPT
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Reference: | Gillies R.J., Kinahan P.E., Hricak H., Radiomics: Images Are More than Pictures, They Are Data, Radiology. 2016 Feb;278(2):563-577.
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Choice A: | It uses a large amount of imaging data for prognostic prediction of outcome of the patients. |
Choice B: | It quantifies tumor phenotypic characteristics by applying feature algorithms to imaging data. |
Choice C: | It is used in current knowledge based treatment planning. |
Choice D: | It represents a significant component of big data initiatives aimed at drawing inferences from large data sets that are not derived from carefully controlled experiments. |