Ontology-Driven Clinical Intelligence: A Path from the Biobank to Cross-Disease Research
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Ontology-Driven Clinical Intelligence: A Path from the Biobank to Cross-Disease Research

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The discovery of clinical insights through effective management and reuse of data requires several conditions to be optimized: Data need to be digital, data need to be structured, and data need to be ...

The discovery of clinical insights through effective management and reuse of data requires several conditions to be optimized: Data need to be digital, data need to be structured, and data need to be standardized in terms of metadata and ontology. This presentation describes a bioinformatics system that combines a next-generation biobank management model mapped to applicable international standards and guidelines with a master ontology that controls all input and output and is able to add unique properties to meet the specialized needs of clinicians for cross-disease research.

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Ontology-Driven Clinical Intelligence: A Path from the Biobank to Cross-Disease Research Ontology-Driven Clinical Intelligence: A Path from the Biobank to Cross-Disease Research Presentation Transcript

  • Ontology-Driven Clinical Intelligence A Path from the Biobank to Cross-Disease Research   Bruce Pharr | Vice President, Bioinformatics Systems Molecular Medicine Tri-Conference | February 11, 2014 1  
  • Data Barriers to Clinical Research Critical Data is Dispersed in Separate Systems Disease A Disease B Considering the vast stores of clinical data available to potential investigators, the actual amount of clinical research performed has been quite modest. At many medical centers, the data are dispersed in separate systems that have evolved independently of one another. Source: Obstacles and Approaches to Clinical Database Research: Experience at the University of California, San Francisco
  • Removing the Data Barriers Structured Digital Data with Standardized Metadata and Ontology Disease A Disease B The discovery of scientific insights through effective management and reuse of data requires several conditions to be optimized: •  Data need to be digital; •  Data need to be structured; •  Data need to be standardized in terms of metadata and ontology. Source: Anne E. Thessen and David J. Patterson, Data issues in life sciences, PMC (NIH/NLM) (November 28, 2011).
  • Ontology-Driven Clinical Intelligence Structured Data with Standardized Metadata and Ontology New Patient Biobank Lab Test & Analysis Disease Registry Pre-analytical Data Analytical Data Mosaic™ Ontology-Based Platform Legacy Data Patient Data Legacy Disease Database Patient Data
  • Ontology-Driven Clinical Intelligence Remedy Informatics Architecture Patient Data New Data Patient Data Remedy Bioinformatics RemedyAMH™ Biobank Management Informatics Aggregate, Map & Harmonize   Legacy Data   Mosaic Builder Applications Patient Data Content and Registry Development   Mosaic Engine Functional Layers: Physical, Data Model, Information Model, Ontology, Representation Model Mosaic™ Platform Remedy Informatics   Disease Registry
  • Next-Gen Biobank A Path from the Biobank to Cross-Disease Research Patient Data New Data Remedy Bioinformatics Biobank Management Informatics   Remedy Informatics
  • Biobank Growth and Upgrade Cycle Drivers for Next-Gen Biobanks Growth 33% of all biobanks have been installed since the early 2000s (HGP) •  •  •  Increase in population genetics studies Personalized medicine Genetic information in food safety, forensics and disease surveillance Upgrade The Cancer Genome Atlas (TCGA) project (2006-8) exposed deficiencies •  Many biobank managers didn’t know exactly what was in their freezers •  Some specimens were unfit for analysis •  •  Others had been obtained from patients without adequate consent The rate of unacceptable shipments from some institutions was 99% Source:  The  Future  of  Biobanking,  Laboratory  Focus,  January  2013  
  • Next-Gen Biobank Management Best Practices Model Mapped to Applicable Global Standards Patient Biobank Manage all information about: 1.  Specimens, 2.  Patients, and 3.  Operations throughout: •  Collection •  Processing •  Storage and Inventory •  Distribution
  • Best Practices Biobank Management Informatics Requirements •  •  •  •  •  •  •  •  •  •  •  •  •  Metadata Entity Types Sample Acquisition Sample and Data Management Sample Retention and Distribution Support of Laboratory Processes User Management Search Presentation of Entities Printing Reports and Audits Non-functional Requirements External Interface Requirements
  • Best Practices Applicable International Standards and Guidelines ISBER International Society for Biological and Environmental Repositories. Best Practices for Repositories: Collection, Storage, Retrieval, and Distribution of Biological Materials for Research. NCI National Cancer Institute. First-generation guidelines for NCI-supported Biorepositories. BAP Biorepository Accreditation Program (BAP) Checklist – College of American Pathologists (CAP) 21 CFR Part 11 US FDA – Guidelines on electronic records and electronic signatures. 45 CFR § 164.514 US HHS – Other requirements relating to uses and disclosures of protected health information. ISO 15189 Medical laboratories – Particular requirements for quality and competence. ISO 17025 General requirements for the competence of testing and calibration laboratories. MoReq2 European Commission. Model Requirements for the management of electronic records. OECD Best Practice Guidelines for biological resource centres. Rec(2006)4 Council of Europe, Committee of Ministers. Recommendation of the Committee of Ministers to member states on research on biological materials of human origin.
  • Mosaic Ontology Purpose-Specific Structured Data Model 1.  Predefined, Standardized Terminology 2.  Domain-Specific Mapped Relationships 3.  Permissible Values and Validation Rules Patient Data Legacy Data RemedyAMH™ Aggregate, Map & Harmonize   Mosaic Builder Applications Patient Data Content and Registry Development   Mosaic Engine Functional Layers: Physical, Data Model, Information Model, Ontology, Representation Model Mosaic Platform Remedy Informatics   Disease Registry
  • Mosaic Ontology Predefined, Standardized Terminology Lab Result LOINC Subject Units High End of Normal Low End of Normal Confidentiality Validation Status Validator Supplier of Data LOINC Medical Laboratory and Clinical Observations
  • Mosaic Ontology Predefined, Standardized Terminology Disorder SNOMED CT Assertion Subject Severity Stage Response to Treatment Active State Onset Date Resolved State First Diagnosed Date Confidentiality Source Date of Entry Validation Status Validator Supplier of Data LOINC Medical Laboratory and Clinical Observations SNOMED CT Clinical Codes, Terms, Synonyms and Definitions
  • Mosaic Ontology Predefined, Standardized Terminology LOINC Medical Laboratory and Clinical Observations SNOMED CT Clinical Codes, Terms, Synonyms and Definitions ICD Disease Classifications Gene Ontology Gene Product Characteristics and Annotation RxNorm Clinical Drug Classifications CDISC Clinical Protocol, Analysis and Reporting
  • Mosaic Ontology Domain-Specific Mapped Relationships Lab Result Disorder Procedure LOINC SNOMED SNOMED Subject Response to Tx Cause Subject Units High End of Normal Assertion Evidence for Severity Subject Operator Indication Facility Low End of Normal Stage Confidentiality Response to Treatment Validation Status Active State Intent Onset Date Confidentiality Resolved State Source First Diagnosed Date Date of Entry Confidentiality Validation Status Source Validator Date of Entry Supplier of Data Validator Supplier of Data Has Result Validation Status Validator Supplier of Data Start-Stop Time Contraindication Urgency Status
  • Mosaic Ontology Permissible Value and Validation Rules Disorder Procedure SNOMED SNOMED Assertion Subject Mild Subject Operator Moderate Severity Facility Severe Stage Screening Start-Stop Time Response to Treatment Diagnostic Urgency Status Active State Prevention Intent Onset Date Therapeutic Confidentiality Resolved State Palliation Source First Diagnosed Date End-of-Life Date of Entry Confidentiality Validation Status Source Validator Date of Entry Supplier of Data Validation Status Validator Supplier of Data
  • Mosaic Ontology Standardized, Extensible Disease Registry Implementation
  • Ontology-Driven Clinical Intelligence Cross-Disease Research
  • Remedy Informatics •  Founded in 2003, privately held. •  U.S. headquarters in Salt Lake City, Utah. Development offices in Menlo Park, California. •  Satellite offices in London, England; Sao Paulo, Brazil; and Munich, Germany. •  More than 120 employees. •  Strategic partnerships with Merck and IMS. •  Developed proprietary Mosaic Platform, an ontology-driven clinical intelligence system scalable to any size enterprise. •  Delivered more than 120 registries to wide range of leading life sciences research and healthcare delivery organizations.
  • Thanks! – Questions? Bruce Pharr Vice President, Bioinformatics Systems bruce.pharr@remedyinformatics.com Remedy Informatics www.remedyinformatics.com Booth 406