Electronic Health Records (EHRs): Supporting ASCO's Vision of Cancer Care
Author(s) -
Peter Paul Yu,
David Artz,
Jeremy L. Warner
Publication year - 2014
Publication title -
american society of clinical oncology educational book
Language(s) - English
Resource type - Journals
eISSN - 1548-8756
pISSN - 1548-8748
DOI - 10.14694/edbook_am.2014.34.225
Subject(s) - clinical decision support system , construct (python library) , systems biology , agency (philosophy) , data science , health care , computer science , big data , systems medicine , decision support system , artificial intelligence , bioinformatics , data mining , biology , philosophy , epistemology , economics , programming language , economic growth
ASCO's vision for cancer care in 2030 is built on the expanding importance of panomics and big data, and envisions enabling better health for patients with cancer by the rapid transformation of systems biology knowledge into cancer care advances. This vision will be heavily dependent on the use of health information technology for computational biology and clinical decision support systems (CDSS). Computational biology will allow us to construct models of cancer biology that encompass the complexity of cancer panomics data and provide us with better understanding of the mechanisms governing cancer behavior. The Agency for Healthcare Research and Quality promotes CDSS based on clinical practice guidelines, which are knowledge bases that grow too slowly to match the rate of panomic-derived knowledge. CDSS that are based on systems biology models will be more easily adaptable to rapid advancements and translational medicine. We describe the characteristics of health data representation, a model for representing molecular data that supports data extraction and use for panomic-based clinical research, and argue for CDSS that are based on systems biology and are algorithm-based.
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