A conceptual model for translating omic data into clinical action

Herr, Timothy M.; Bielinski, Suzette J; Böttinger, Erwin P.; Brautbar, Ariel; Brilliant, Murray H.; Chute, Christopher G.; Denny, Joshua C.; Freimuth, Robert R.; Hartzler, Andrea L.; Kannry, Joseph L.; Kohane, Isaac S.; Kullo, Iftikhar J.; Lin, Simon; Pathak, Jyotishman; Peissig, Peggy L.; Pulley, Jill M.; Ralston, James D.; Rasmussen, Luke V.; Roden, Dan M.; Tromp, Gerard; Williams, Marc S.; Starren, Justin

doi:10.4103/2153-3539.163985

Cited by 18 publications

(10 citation statements)

References 22 publications

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“…However, there remain challenges in extracting phenotypic data that sometimes need to be inferred from multiple sources. But, recent work has made progress in translating "omic" data into patient care and CDS using pharmacogenomics data [107,108]. Biobanks are now linked to personal and family health information in EHRs to accurately identify subjects with specific diseases and phenotypes and to identify genotype-phenotype associations [109].…”

Section: New Functionality and Usementioning

confidence: 99%

Electronic Health Records: Then, Now, and in the Future

2016

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The current use of EHRs initiated by new technology would have been hard to foresee. Current and new EHR technology will help to provide international standards for interoperable applications that use health, social, economic, behavioral, and environmental data to communicate, interpret, and act intelligently upon complex healthcare information to foster precision medicine and a learning health system.

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Section: New Functionality and Usementioning

confidence: 99%

Electronic Health Records: Then, Now, and in the Future

2016

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“…Although technological barriers are continuing to be reduced, ethical considerations increasingly are becoming the rate‐limiting process. The transmission of these results also remains challenging because most clinical information systems are currently not set up to manage NGS data …”

Section: Reporting Resultsmentioning

confidence: 99%

“…The transmission of these results also remains challenging because most clinical information systems are currently not set up to manage NGS data. 52,53…”

mentioning

confidence: 99%

Big data from small samples: Informatics of next‐generation sequencing in cytopathology

Roy‐Chowdhuri

Routbort

Pantanowitz

2016

Cancer Cytopathology

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The rapid adoption of next-generation sequencing (NGS) in clinical molecular laboratories has redefined the practice of cytopathology. Instead of simply being used as a diagnostic tool, cytopathology has evolved into a practice providing important genomic information that guides clinical management. The recent emphasis on maximizing limited-volume cytology samples for ancillary molecular studies, including NGS, requires cytopathologists not only to be more involved in specimen collection and processing techniques but also to be aware of downstream testing and informatics issues. For the integration of molecular informatics into the clinical workflow, it is important to understand the computational components of the NGS workflow by which raw sequence data are transformed into clinically actionable genomic information and to address the challenges of having a robust and sustainable informatics infrastructure for NGS-based testing in a clinical environment. Adapting to needs ranging from specimen procurement to report delivery is crucial for the optimal utilization of cytology specimens to accommodate requests from clinicians to improve patient care. This review presents a broad overview of the various aspects of informatics in the context of NGS-based testing of cytology specimens. Cancer Cytopathol 2017;125:236-244. © 2016 American Cancer Society.

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“…Details of the solutions are beyond the scope of this article, but it must be emphasized that the processes discussed below are dependent on a robust institutional informatics infrastructure. 15 While not all organizations have such an infrastructure, the increase in use of fully functional EHR systems coupled with international efforts to develop and implement standards to support the use of genomic data in the clinic should, in time, reduce reliance on local solutions to store and communicate genomic information and improve generalizability across health care information systems.…”

Section: Initial Implementation Of a Genomic Medicine And Precision Hmentioning

confidence: 99%

Patient-Centered Precision Health In A Learning Health Care System: Geisinger’s Genomic Medicine Experience

Williams

Buchanan²,

Davis

et al. 2018

Health Affairs

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Health care delivery is increasingly influenced by the emerging concepts of precision health and the learning health care system. Although not synonymous with precision health, genomics is a key enabler of individualized care. Delivering patient-centered, genomics-informed care based on individual-level data in the current national landscape of health care delivery is a daunting challenge. Problems to overcome include data generation, analysis, storage, and transfer; knowledge management and representation for patients and providers at the point of care; process management; and outcomes definition, collection, and analysis. Development, testing, and implementation of a genomics-informed program requires multidisciplinary collaboration and building the concepts of precision health into a multilevel implementation framework. Using the principles of a learning health care system provides a promising solution. This article describes the implementation of population-based genomic medicine in an integrated learning health care system-a working example of a precision health program.

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A conceptual model for translating omic data into clinical action

Cited by 18 publications

References 22 publications

Electronic Health Records: Then, Now, and in the Future

Electronic Health Records: Then, Now, and in the Future

Big data from small samples: Informatics of next‐generation sequencing in cytopathology

Patient-Centered Precision Health In A Learning Health Care System: Geisinger’s Genomic Medicine Experience

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