Using oncology real-world evidence for quality improvement and discovery: the case for ASCO's CancerLinQ
In ‘Shaping the Future of Oncology: Envisioning Cancer Care in 2030’ (1), the board proposed that three key drivers would have the biggest impact: ‘Big data’ Cancer panomics Delivering value Advances in health information technology would enable clinical, administrative and patient-generated data on...
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Veröffentlicht in: | Future oncology (London, England) England), 2018-01, Vol.14 (1), p.5-8 |
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description | In ‘Shaping the Future of Oncology: Envisioning Cancer Care in 2030’ (1), the board proposed that three key drivers would have the biggest impact: ‘Big data’ Cancer panomics Delivering value Advances in health information technology would enable clinical, administrative and patient-generated data on virtually every patient with cancer to be systematically collected, analyzed and shared, in near real time, to immediately impact patient care and contribute to the generation of new knowledge. Unlike clinical trial data which conform to the strict conditions enabled by prespecified patient eligibility criteria, treatment regimens and diagnostic assessments, transactional EHR data are inherently less organized and frequently not recorded in discrete fields, especially for many critical oncologic variables such as cancer stage, biomarkers, adverse event documentation and disease progression. [...]important gaps in data will continue to exist. In order to offer a more representative, holistic view of the cancer patient's journey to support quality improvement efforts and discovery, CancerLinQ is tapping into information that exists beyond the limited cohort of data within traditional clinical trials and the lens of medical oncology alone (11). [...]CancerLinQ has engaged the broader oncology community beyond ASCO's membership to incorporate the integral perspectives of the entire oncology care team, all in an effort to create one of the largest, most robust sources of RWE in oncology. |
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Unlike clinical trial data which conform to the strict conditions enabled by prespecified patient eligibility criteria, treatment regimens and diagnostic assessments, transactional EHR data are inherently less organized and frequently not recorded in discrete fields, especially for many critical oncologic variables such as cancer stage, biomarkers, adverse event documentation and disease progression. [...]important gaps in data will continue to exist. In order to offer a more representative, holistic view of the cancer patient's journey to support quality improvement efforts and discovery, CancerLinQ is tapping into information that exists beyond the limited cohort of data within traditional clinical trials and the lens of medical oncology alone (11). 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Unlike clinical trial data which conform to the strict conditions enabled by prespecified patient eligibility criteria, treatment regimens and diagnostic assessments, transactional EHR data are inherently less organized and frequently not recorded in discrete fields, especially for many critical oncologic variables such as cancer stage, biomarkers, adverse event documentation and disease progression. [...]important gaps in data will continue to exist. In order to offer a more representative, holistic view of the cancer patient's journey to support quality improvement efforts and discovery, CancerLinQ is tapping into information that exists beyond the limited cohort of data within traditional clinical trials and the lens of medical oncology alone (11). 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Unlike clinical trial data which conform to the strict conditions enabled by prespecified patient eligibility criteria, treatment regimens and diagnostic assessments, transactional EHR data are inherently less organized and frequently not recorded in discrete fields, especially for many critical oncologic variables such as cancer stage, biomarkers, adverse event documentation and disease progression. [...]important gaps in data will continue to exist. In order to offer a more representative, holistic view of the cancer patient's journey to support quality improvement efforts and discovery, CancerLinQ is tapping into information that exists beyond the limited cohort of data within traditional clinical trials and the lens of medical oncology alone (11). 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subjects | ASCO Bias Big Data Cancer CancerLinQ Clinical medicine Clinical trials Data models Electronic health records Initiatives Medicine Oncology Quality Quality control Quality improvement real-world evidence |
title | Using oncology real-world evidence for quality improvement and discovery: the case for ASCO's CancerLinQ |
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