ontology-driven clinical intelligence: removing data barriers for cross-discipline research
DESCRIPTION
The presentation describes how Remedy Informatics is advocating and innovating "flexible standardization" through an ontology-driven approach to clinical research. You will see in greater detail how a foundational, standardized Mosaic Ontology can be extended for more specific research applications and even more specific and focused disease research.TRANSCRIPT
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
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
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)
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
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
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
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
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.
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
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
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
Mosaic Ontology Standardized, Extensible Disease Registries…
Mosaic Ontology …Enable Cross-Discipline Research
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.
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™
Thanks! – Questions?
Bruce Pharr Vice President, Life Sciences Research Systems [email protected] Remedy Informatics www.remedyinformatics.com