title: Electronic health records (EHRs): supporting ASCO's vision of cancer care.
abstract: 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.
authors.affiliation: From the Department of Hematology and Department of Oncology, Palo Alto Medical Foundation, Mountain View, CA; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY; Department of Medicine and Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN.
authors.forename: Peter
authors.initials: P
authors.lastname: Yu
authors.affiliation: From the Department of Hematology and Department of Oncology, Palo Alto Medical Foundation, Mountain View, CA; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY; Department of Medicine and Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN.
authors.forename: David
authors.initials: D
authors.lastname: Artz
authors.affiliation: From the Department of Hematology and Department of Oncology, Palo Alto Medical Foundation, Mountain View, CA; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY; Department of Medicine and Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN.
authors.forename: Jeremy
authors.initials: J
authors.lastname: Warner
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism; D066264:Datasets as Topic; D003661:Decision Support Techniques; D004059:Diffusion of Innovation; D057286:Electronic Health Records; D006801:Humans; D008495:Medical Oncology / Q000379:methods*; D008498:Medical Record Linkage; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics / Q000378:metabolism / Q000401:mortality / Q000628:therapy*; D011237:Predictive Value of Tests; D011379:Prognosis; D018570:Risk Assessment; D012307:Risk Factors; D049490:Systems Biology
chemical_list: D014408:Biomarkers, Tumor