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.

1. 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
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2. 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

3. 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).

4. 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

5. 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

6. Next-Gen Biobank
A Path from the Biobank to Cross-Disease Research
Patient
Data
New
Data
Remedy Bioinformatics
Biobank Management Informatics
Remedy Informatics

7. 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

8. 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

9. 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

10. 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.

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. Mosaic Ontology
Standardized, Extensible Disease Registry Implementation

18. Ontology-Driven Clinical Intelligence
Cross-Disease Research

19. 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.

20. Thanks! – Questions?
Bruce Pharr
Vice President, Bioinformatics Systems
bruce.pharr@remedyinformatics.com
Remedy Informatics
www.remedyinformatics.com
Booth 406