A patient’s diagnosis and medical treatment is frequently guided by where that patient’s laboratory values fall along a range of established “normal” values. A new “Viewpoint” (In the Era of Precision Medicine and Big Data, Who Is Normal?) in the Journal of the American Medical Association (JAMA) raises an important question in the context of laboratory values and precision medicine: if medicine is personalized, to what do we compare an individual’s laboratory values if “normal” is actually relative? The authors consider solutions to ensure that test results “be interpreted in reference to a population of ‘similar’, ‘healthy’ individuals”. For example, the authors propose 1) that longitudinal data on individual outcomes be accessible to researchers to determine whether selected reference values are truly useful 2) that large-scale analyses be carried out across data sets 3) that reference values be tailored to patients and delivered at point of care, and 3) that “computationally derived genetic ancestry” be linked to laboratory test values, so that race is not used as a proxy.
Monday, May 28, 2018
Curators' Favorite Papers
A critical component of pharmacogenetic/pharmacogenomics (PGx) implementation into clinical care is the integration of PGx data in clinical decision support (CDS) and electronic health records (EHRs). A new article in the journal Human Molecular Genetics (Genomics and electronic health record systems) discusses the benefits of, and current challenges to, the integration of genomics and PGx data, into EHRs. In particular, the article discusses how PGx data in EHRs relates to questions of standards and evidence generation, and how data should be represented so as not to be overwhelming for clinicians. The article adeptly outlines specific components of CDS pertaining to whole genome sequencing (WGS) (e.g. ordering and interpreting a test, importing data, trigger alerts and warning in EHRs, and evaluating outcomes) and describes existing tools, such as application programming interfaces (APIs), that link knowledgebases (including PharmGKB) to EHRs to assist with CDS. The authors conclude that with continued developments in technology, and ambitious research programs with large, diverse cohorts, such as the NIH-sponsored All of Us program, the “goals of generating new knowledge and clinically relevant discoveries using population-based genomics data can someday be achieved by using EHRs”.
A patient’s diagnosis and medical treatment is frequently guided by where that patient’s laboratory values fall along a range of established “normal” values. A new “Viewpoint” (In the Era of Precision Medicine and Big Data, Who Is Normal?) in the Journal of the American Medical Association (JAMA) raises an important question in the context of laboratory values and precision medicine: if medicine is personalized, to what do we compare an individual’s laboratory values if “normal” is actually relative? The authors consider solutions to ensure that test results “be interpreted in reference to a population of ‘similar’, ‘healthy’ individuals”. For example, the authors propose 1) that longitudinal data on individual outcomes be accessible to researchers to determine whether selected reference values are truly useful 2) that large-scale analyses be carried out across data sets 3) that reference values be tailored to patients and delivered at point of care, and 3) that “computationally derived genetic ancestry” be linked to laboratory test values, so that race is not used as a proxy.
A patient’s diagnosis and medical treatment is frequently guided by where that patient’s laboratory values fall along a range of established “normal” values. A new “Viewpoint” (In the Era of Precision Medicine and Big Data, Who Is Normal?) in the Journal of the American Medical Association (JAMA) raises an important question in the context of laboratory values and precision medicine: if medicine is personalized, to what do we compare an individual’s laboratory values if “normal” is actually relative? The authors consider solutions to ensure that test results “be interpreted in reference to a population of ‘similar’, ‘healthy’ individuals”. For example, the authors propose 1) that longitudinal data on individual outcomes be accessible to researchers to determine whether selected reference values are truly useful 2) that large-scale analyses be carried out across data sets 3) that reference values be tailored to patients and delivered at point of care, and 3) that “computationally derived genetic ancestry” be linked to laboratory test values, so that race is not used as a proxy.
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