1.
Ontology-Driven Clinical Intelligence
Removing Data Barriers for Cross-Discipline Research
Bruce Pharr | Vice President, Life Sciences Research Systems
Medical Informatics World | April 29, 2014
1

2.
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 is dispersed in
separate systems that have evolved independently of one another.
Obstacles and Approaches to Clinical Database Research:
Experience at the University of California, San Francisco
Disease A Disease B

3.
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 needs to be digital;
• Data needs to be structured;
• Data needs to be standardized in terms of metadata and ontology.
Data Issues in Life Sciences, Data Conservancy (Life Sciences)

4.
Ontology-Driven Clinical Intelligence
Structured Data with Standardized Metadata and Ontology
Siloed Legacy Patient/Disease Databases
Clinical
Research
Mosaic™ Ontology-Driven Platform
Analytical Lab
Biobank
New Data
Patient
Pre-analytical Data
Post-analytical Data
Legacy Data
Patient/Disease
Registry
Harmonized,
Mapped New
and Legacy Data
Cross-Discipline Research
Intuitive
Cross-Registry
Queries

5.
Ontology-Driven Clinical Intelligence
Remedy Informatics Architecture
Remedy Informatics
Mosaic™ Platform
Mosaic Engine
Functional Layers: Physical, Data Model, Information Model, Ontology, Representation Model
Mosaic Applications
Content and Registry Development
Specimen Track™ & Study Manager™
Research Management System
Remedy AMH™
Aggregate, Map & Harmonize
Legacy
Patient/Disease
Data
Ontology Manager™
Registry Builder™
Harmonized
Patient/Disease
Data
Cross-Discipline
Research
Patient
Biobank
Analytical Lab
Clinical
Research
New Pre- and
Post-Analytical
Data

6.
Ontology
What is it?
Ontology is an explicitly defined reference model of application
domains with the purpose of improving information consistency and
reusability, systems interoperability, and knowledge sharing. Ontology
formally represents knowledge as a set of concepts within a domain,
and the relationships between pairs of concepts. It provides a shared
vocabulary, which can be used to model a domain.
A Novel Method to Transform Relational Data into Ontology in the Biomedical Domain,
International Journal of Engineering and Technology

7.
Mosaic Ontology
A Purpose-Specific Structured Data Model
1. Predefined, standardized terminology
2. Domain-specific mapped relationships
3. Permissible values and validation rules
Mosaic™ Platform
Mosaic Engine
Functional Layers: Physical, Data Model, Information Model, Ontology, Representation Model
Mosaic Applications
Content and Registry Development

8.
Mosaic Ontology
Predefined, Standardized Terminology (Vocabulary)
Domain Standards for Terminology
Acronym Standard Description
CDISC
Clinical Data Interchange
Standards Consortium
Data standards for information system interoperability to improve
medical research.
GO Gene Ontology
Standardize the representation of gene and gene product
attributes across species and databases.
ICD
International Statistical
Classification of Diseases
International health care classification system of diagnostic codes
for classifying disease.
LOINC
Logical Observation Identifiers
Names and Codes
A database and universal standard for identifying medical
laboratory observations.
RxNorm Prescription Normalization
Normalized names for clinical drugs with drug vocabularies used in
pharmacy management and drug interaction software.
SNOMED CT
Systematized Nomenclature of
Medicine—Clinical Terms
Computable collection of medical codes, terms, synonyms, and
definitions used in clinical documentation and reporting.

9.
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
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
Procedure
SNOMED CT
Subject
Operator
Facility
Start-Stop Time
Urgency Status
Intent
Confidentiality
Source
Date of Entry
Validation Status
Validator
Supplier of Data

10.
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 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
Procedure
SNOMED CT
Subject
Operator
Facility
Start-Stop Time
Urgency Status
Intent
Confidentiality
Source
Date of Entry
Validation Status
Validator
Supplier of Data
Indication
Contraindication

11.
Mild
Moderate
Severe Screening
Diagnostic
Prevention
Therapeutic
Palliation
End-of-Life
Mosaic Ontology
Permissible Value and Validation Rules
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
Procedure
SNOMED CT
Subject
Operator
Facility
Start-Stop Time
Urgency Status
Intent
Confidentiality
Source
Date of Entry
Validation Status
Validator
Supplier of Data

12.
Mosaic Ontology
Standardized, Extensible Disease Registries…

13.
Mosaic Ontology
…Enable Cross-Discipline Research

14.
Remedy Informatics
A Clinical Intelligence Company
Remedy Informatics is a clinical intelligence company
that is transforming global biomedical research and healthcare.
Our clients include:
Academic medical centers
Biopharmaceutical companies
Biomedical research organizations
Our solutions enable clients to:
Collect, harmonize, and analyze data across disciplines
Detect patterns in clinical data
Accelerate disease and therapeutic research
Ultimately, our solutions enable you to bring safe, effective, increasingly
personalized treatments to market faster and more efficiently.

15.
Remedy Informatics
Systems and Solutions
RESEARCH SYSTEMS
Specimen Track™ Biobank Management System
Study Manager™ Clinical Research Management System
TECHNOLOGY SOLUTIONS
Mosaic™ Platform
Mosaic Ontology
Mosaic Engine
Mosaic Builder
TIMe™—The Informatics Marketplace™
CLINICAL REGISTRIES
Comprehensive Blood Cancer™
Comprehensive BMT™
Comprehensive Heart & Vascular™
Comprehensive Orthopedics™
Comprehensive Solid Tumor™

16.
Thanks! – Questions?
Bruce Pharr
Vice President, Life Sciences Research Systems
bruce.pharr@remedyinformatics.com
Remedy Informatics
www.remedyinformatics.com