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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Data Barriers to Clinical Research Critical Data is Dispersed in Separate Systems

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

Disease A Disease B

Removing the Data Barriers Structured Digital Data with Standardized Metadata and Ontology

Source: Anne E. Thessen and David J. Patterson, Data issues in life sciences, PMC (NIH/NLM) (November 28, 2011).

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.

Ontology-Driven Clinical Intelligence Structured Data with Standardized Metadata and Ontology

Mosaic™ Ontology-Based Platform

Pre-analytical Data Analytical Data

Lab Test & Analysis

Disease Registry

New Patient

Legacy Disease Database

Legacy Data

Biobank

Patient Data

Patient Data

Remedy Informatics Mosaic™ Platform

Ontology-Driven Clinical Intelligence Remedy Informatics Architecture

Mosaic Engine Functional Layers: Physical, Data Model, Information Model, Ontology, Representation Model

 

Mosaic Builder Applications Content and Registry Development

 

Remedy Bioinformatics Biobank Management Informatics

 

RemedyAMH™ Aggregate, Map & Harmonize

 

Patient Data

Legacy Data

Patient Data

Disease Registry

New Data Patient

Data

Remedy Informatics

Next-Gen Biobank A Path from the Biobank to Cross-Disease Research

Remedy Bioinformatics Biobank Management Informatics

 

New Data Patient

Data

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.

Remedy Informatics Mosaic Platform

Mosaic Ontology Purpose-Specific Structured Data Model

Mosaic Engine Functional Layers: Physical, Data Model, Information Model, Ontology, Representation Model

 

Mosaic Builder Applications Content and Registry Development

 

RemedyAMH™ Aggregate, Map & Harmonize

 

Patient Data

Legacy Data

Patient Data

Disease Registry

1.  Predefined, Standardized Terminology 2.  Domain-Specific Mapped Relationships 3.  Permissible Values and Validation Rules

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

Has Result

Response to Tx

Evidence for

Cause

Mosaic Ontology Domain-Specific Mapped Relationships

Lab Result LOINC

Subject

Units

High End of Normal

Low End of Normal

Confidentiality

Validation Status

Validator

Supplier of Data

Disorder SNOMED

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

Procedure SNOMED

Subject

Operator

Facility

Start-Stop Time

Urgency Status

Intent

Confidentiality

Source

Date of Entry

Validation Status

Validator

Supplier of Data

Indication

Contraindication

Mild

Moderate

Severe Screening

Diagnostic

Prevention

Therapeutic

Palliation

End-of-Life

Mosaic Ontology Permissible Value and Validation Rules

Disorder SNOMED

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

Procedure SNOMED

Subject

Operator

Facility

Start-Stop Time

Urgency Status

Intent

Confidentiality

Source

Date of Entry

Validation Status

Validator

Supplier of Data

Mosaic Ontology Standardized, Extensible Disease Registry Implementation

Ontology-Driven Clinical Intelligence Cross-Disease Research

•  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.

Remedy Informatics

Thanks! – Questions?

Bruce Pharr Vice President, Bioinformatics Systems bruce.pharr@remedyinformatics.com Remedy Informatics www.remedyinformatics.com

Booth 406