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A New Framework and Prototype Solution for Clinical Decision Support and Research in Genomics and Other Data-intensive Fields of Medicine.

Evans JP, Wilhelmsen KC, Berg J, Schmitt CP, Krishnamurthy A, Fecho K, Ahalt SC - EGEMS (Wash DC) (2016)

Bottom Line: However, it is not clear if the routine accumulation of massive amounts of (largely uninterpretable) data will yield any health benefits to patients.We propose a two-pronged Genomic Clinical Decision Support System (CDSS) that encompasses the concept of the "Clinical Mendeliome" as a patient-centric list of genomic variants that are clinically actionable and introduces the concept of the "Archival Value Criterion" as a decision-making formalism that approximates the cost-effectiveness of capturing, storing, and curating genome-scale sequencing data.The proposed framework and prototype solution are designed to address the perspectives of stakeholders, stimulate effective clinical use of genomic data, drive genomic research, and meet current and future needs.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, University of North Carolina at Chapel Hill; Department of Medicine, University of North Carolina at Chapel Hill.

ABSTRACT

Introduction: In genomics and other fields, it is now possible to capture and store large amounts of data in electronic medical records (EMRs). However, it is not clear if the routine accumulation of massive amounts of (largely uninterpretable) data will yield any health benefits to patients. Nevertheless, the use of large-scale medical data is likely to grow. To meet emerging challenges and facilitate optimal use of genomic data, our institution initiated a comprehensive planning process that addresses the needs of all stakeholders (e.g., patients, families, healthcare providers, researchers, technical staff, administrators). Our experience with this process and a key genomics research project contributed to the proposed framework.

Framework: We propose a two-pronged Genomic Clinical Decision Support System (CDSS) that encompasses the concept of the "Clinical Mendeliome" as a patient-centric list of genomic variants that are clinically actionable and introduces the concept of the "Archival Value Criterion" as a decision-making formalism that approximates the cost-effectiveness of capturing, storing, and curating genome-scale sequencing data. We describe a prototype Genomic CDSS that we developed as a first step toward implementation of the framework.

Conclusion: The proposed framework and prototype solution are designed to address the perspectives of stakeholders, stimulate effective clinical use of genomic data, drive genomic research, and meet current and future needs. The framework also can be broadly applied to additional fields, including other '-omics' fields. We advocate for the creation of a Task Force on the Clinical Mendeliome, charged with defining Clinical Mendeliomes and drafting clinical guidelines for their use.

No MeSH data available.


Related in: MedlinePlus

Schematic Showing the Workflows Managed by the GMW EngineNotes: Arrows depict the information flow. AnnoBot = Annotation Bot; BSP lab = BioSpecimen Processing laboratory; CANVAS = CAroliNa Variant Annotation Store; CLIA lab = a laboratory certified to meet United States Congressional Clinical Laboratory Improvements Amendments; Dx = diagnostic; ELSI Researcher = Ethical, Legal, and Social Implications Researcher; EMR = electronic medical record; GMW Engine = Genetic Medical Workflow Engine; iRODS = integrated Rule-Oriented Data System; MaPSeq = Massively Parallel Sequencing system; PHI = protected health information; QC = Quality Control; Wet lab = basic science laboratory.Source: Image reprinted with permission from Owen et al.28
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f3-egems1198: Schematic Showing the Workflows Managed by the GMW EngineNotes: Arrows depict the information flow. AnnoBot = Annotation Bot; BSP lab = BioSpecimen Processing laboratory; CANVAS = CAroliNa Variant Annotation Store; CLIA lab = a laboratory certified to meet United States Congressional Clinical Laboratory Improvements Amendments; Dx = diagnostic; ELSI Researcher = Ethical, Legal, and Social Implications Researcher; EMR = electronic medical record; GMW Engine = Genetic Medical Workflow Engine; iRODS = integrated Rule-Oriented Data System; MaPSeq = Massively Parallel Sequencing system; PHI = protected health information; QC = Quality Control; Wet lab = basic science laboratory.Source: Image reprinted with permission from Owen et al.28

Mentions: Figure 3 provides a more detailed view of the GMW Engine, which drives the overall system. The GMW Engine is a highly sophisticated and powerful workflow engine. While much of the workflow processes and technologies, such as the SMW and iRODS, were already in place at our institution before NCGENES was implemented, they lacked coordination and the sophistication needed to bring NCGENES to fruition. The GMW Engine provides a seamless integration and orchestration of disparate and distributed systems, processes, samples, data, and people. In addition to the GMW Engine, the NCGENES team developed and implemented two new technologies that have since proven to be fundamental to the success of the project: MaPSeq and CANVAS/AnnoBot. MaPSeq securely manages and executes the complex downstream computational and analytical workflow steps required for genomic sequencing, including the opportunistic use of distributed compute resources. CANVAS and AnnoBot together provide version-controlled annotation and metadata on genomic variant data, with continuous, automated updates from monitored databases and national repositories. While each of these technologies is being refined over time, all have proven effective and efficient in the implementation of NCGENES.


A New Framework and Prototype Solution for Clinical Decision Support and Research in Genomics and Other Data-intensive Fields of Medicine.

Evans JP, Wilhelmsen KC, Berg J, Schmitt CP, Krishnamurthy A, Fecho K, Ahalt SC - EGEMS (Wash DC) (2016)

Schematic Showing the Workflows Managed by the GMW EngineNotes: Arrows depict the information flow. AnnoBot = Annotation Bot; BSP lab = BioSpecimen Processing laboratory; CANVAS = CAroliNa Variant Annotation Store; CLIA lab = a laboratory certified to meet United States Congressional Clinical Laboratory Improvements Amendments; Dx = diagnostic; ELSI Researcher = Ethical, Legal, and Social Implications Researcher; EMR = electronic medical record; GMW Engine = Genetic Medical Workflow Engine; iRODS = integrated Rule-Oriented Data System; MaPSeq = Massively Parallel Sequencing system; PHI = protected health information; QC = Quality Control; Wet lab = basic science laboratory.Source: Image reprinted with permission from Owen et al.28
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4862762&req=5

f3-egems1198: Schematic Showing the Workflows Managed by the GMW EngineNotes: Arrows depict the information flow. AnnoBot = Annotation Bot; BSP lab = BioSpecimen Processing laboratory; CANVAS = CAroliNa Variant Annotation Store; CLIA lab = a laboratory certified to meet United States Congressional Clinical Laboratory Improvements Amendments; Dx = diagnostic; ELSI Researcher = Ethical, Legal, and Social Implications Researcher; EMR = electronic medical record; GMW Engine = Genetic Medical Workflow Engine; iRODS = integrated Rule-Oriented Data System; MaPSeq = Massively Parallel Sequencing system; PHI = protected health information; QC = Quality Control; Wet lab = basic science laboratory.Source: Image reprinted with permission from Owen et al.28
Mentions: Figure 3 provides a more detailed view of the GMW Engine, which drives the overall system. The GMW Engine is a highly sophisticated and powerful workflow engine. While much of the workflow processes and technologies, such as the SMW and iRODS, were already in place at our institution before NCGENES was implemented, they lacked coordination and the sophistication needed to bring NCGENES to fruition. The GMW Engine provides a seamless integration and orchestration of disparate and distributed systems, processes, samples, data, and people. In addition to the GMW Engine, the NCGENES team developed and implemented two new technologies that have since proven to be fundamental to the success of the project: MaPSeq and CANVAS/AnnoBot. MaPSeq securely manages and executes the complex downstream computational and analytical workflow steps required for genomic sequencing, including the opportunistic use of distributed compute resources. CANVAS and AnnoBot together provide version-controlled annotation and metadata on genomic variant data, with continuous, automated updates from monitored databases and national repositories. While each of these technologies is being refined over time, all have proven effective and efficient in the implementation of NCGENES.

Bottom Line: However, it is not clear if the routine accumulation of massive amounts of (largely uninterpretable) data will yield any health benefits to patients.We propose a two-pronged Genomic Clinical Decision Support System (CDSS) that encompasses the concept of the "Clinical Mendeliome" as a patient-centric list of genomic variants that are clinically actionable and introduces the concept of the "Archival Value Criterion" as a decision-making formalism that approximates the cost-effectiveness of capturing, storing, and curating genome-scale sequencing data.The proposed framework and prototype solution are designed to address the perspectives of stakeholders, stimulate effective clinical use of genomic data, drive genomic research, and meet current and future needs.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, University of North Carolina at Chapel Hill; Department of Medicine, University of North Carolina at Chapel Hill.

ABSTRACT

Introduction: In genomics and other fields, it is now possible to capture and store large amounts of data in electronic medical records (EMRs). However, it is not clear if the routine accumulation of massive amounts of (largely uninterpretable) data will yield any health benefits to patients. Nevertheless, the use of large-scale medical data is likely to grow. To meet emerging challenges and facilitate optimal use of genomic data, our institution initiated a comprehensive planning process that addresses the needs of all stakeholders (e.g., patients, families, healthcare providers, researchers, technical staff, administrators). Our experience with this process and a key genomics research project contributed to the proposed framework.

Framework: We propose a two-pronged Genomic Clinical Decision Support System (CDSS) that encompasses the concept of the "Clinical Mendeliome" as a patient-centric list of genomic variants that are clinically actionable and introduces the concept of the "Archival Value Criterion" as a decision-making formalism that approximates the cost-effectiveness of capturing, storing, and curating genome-scale sequencing data. We describe a prototype Genomic CDSS that we developed as a first step toward implementation of the framework.

Conclusion: The proposed framework and prototype solution are designed to address the perspectives of stakeholders, stimulate effective clinical use of genomic data, drive genomic research, and meet current and future needs. The framework also can be broadly applied to additional fields, including other '-omics' fields. We advocate for the creation of a Task Force on the Clinical Mendeliome, charged with defining Clinical Mendeliomes and drafting clinical guidelines for their use.

No MeSH data available.


Related in: MedlinePlus