Challenges and issues with information sharing: The four pillars of semantic interoperability Douglas B. Fridsma, MD, PhD, FACP University of Pittsburgh School of Medicine Center for Biomedical Informatics

R&D Expenditures ($B) Investigational New Drug Applications New Chemical Entities Approved BioPharma R&D Conundrum Courtesy of Charles Jaffe

“the nice thing about standards is that everyone has one”

Clinic Research back office

Cancer Research: Two Different Worlds Patient Care World Multiple data sources and types HL7 is a pervasive standard Data are organized around the patient Clinical Research World Protocol defines define elements Linear data flow CDISC is the emerging standard Data are organized around a trial Patient Data in Proprietary Format Acknowledgements: Landen Bain, CDISC

Challenges to Interoperability Multitude of information systems No common data model No common data formats Few common vocabularies No infrastructure for data sharing Informatics Tower of Babel

So what do we need?

The Pillars of (Semantic) Interoperability Necessary but not Sufficient Common object and information models across all domains-of-interest –BRIDG –caBIO Model grounded on robust data type specification –Common data elements –Complex data types Methodology for binding terms from concept-based terminologies –Vocabulary services –Thesaurus A formally defined process for defining specific structures to be exchanged between machines, i.e. a “messaging standard” –HL7 and implementation specifications –Unified process for software development

“Protocol” and the Semiotic Triangle Symbol “ Protocol ” “We need to sign off on the protocol by Friday” Concept 1 Thing 1 Document Study “Protocol XYZ has enrolled 73 patients” Thing 2 Concept 2 “Per the protocol, you must be at least 18 to be enrolled” Concept 3 Thing 3 Plan Source: John Speakman/Charlie Mead

Interchange vs Interoperability Main Entry: in·ter·op·er·a·bil·i·ty : ability of a system... to use the parts or equipment of another system Source: Merriam-Webster web site interoperability : ability of two or more systems or components to exchange information and to predictably use the information that has been exchanged. Source: IEEE Standard Computer Dictionary: A Compilation of IEEE Standard Computer Glossaries, IEEE, 1990] Syntax  Structure Semantics  Meaning Source: Charlie Mead Semantic interoperability Syntactic interoperability (interchange)

caBIG™ Solution to Problem Common, widely distributed infrastructure Shared, harmonized set of terminology, data elements, and data models that facilitate information exchange Leverage existing international standards wherever possible Work towards convergence of existing international standards and data models

This isn’t rocket science A lot of it isn’t even computer science –Most industries did this years ago But it is hard caBIG™’s goal (oversimplified): facilitate the exchange of data useful for cancer research –Between research domains, systems, investigators, organizations

SYNTACTIC SEMANTIC caBIG Compatibility Guidelines

The glue that binds parts together is metadata infrastructure Shape of boundary is defined in APIs Getting to Interoperability Focus on boundaries, interfaces, and how things fit together Not focused on the internal details of how systems are built: assume that will be diverse & changing

The Pillars of (Semantic) Interoperability Necessary but not Sufficient Common object and information models across all domains-of-interest –BRIDG –caBIO Model grounded on robust data type specification –Common data elements –Complex data types Methodology for binding terms from concept-based terminologies –Vocabulary services –Thesaurus A formally defined process for defining specific structures to be exchanged between machines, i.e. a “messaging standard” –HL7 and implementation specifications –Unified process for software development

caCORE Common Ontologic Representation Environment Bioinformatics Objects Enterprise Vocabulary Common Data Elements SECURITYSECURITYApplications

The Pillars of (Semantic) Interoperability Necessary but not Sufficient Common object and information models across all domains-of-interest –BRIDG –caBIO Model grounded on robust data type specification –Common data elements –Complex data types Methodology for binding terms from concept-based terminologies –Vocabulary services –Thesaurus A formally defined process for defining specific structures to be exchanged between machines, i.e. a “messaging standard” –HL7 and implementation specifications –Unified process for software development

The BRIDG Model What is it? A collaborative effort between CDISC, HL7, caBIG TM, NCI, FDA, and Industry to produce one common, shared data exchange standard –The official domain analysis model of RCRIM –The semantic foundation for harmonization of CDISC message specifications –Plans underway for the FDA Janus data base to be harmonized around BRIDG –An open community of stakeholders interested in developing standards for exchanging information about clinical trials

The BRIDG Model Vision: Create a domain analysis model for the clinical research domain to harmonize clinical research standards among each other and to harmonize standards between clinical research and healthcare –A bridge between different models of clinical trials information –A formal model of the shared semantics of regulated clinical trials research

The BRIDG Model Key Goal: Define a structured computable protocol representation that supports the entire life-cycle of clinical trials protocol to achieve syntactic and semantic interoperability The semantic foundation for application and message development in HL7, caBIG, and CDISC

Protocol Authoring and Documentation Clinical Trial Design Structured Statistical Analysis Clinical Trial Registration Eligibility Determination Protocol activities and Safety monitoring (AE) BRIDG

Rich semantics captured in BRIDG CDISC glossary of terms Semantics and examples at the class and attribute level Datatypes help to clarify the semantics around the classes and attributes

caBIO: Objects for Data Access Set of common, cross-domain objects used to obtain information Provides interfaces to access information without worrying about backend data sources Development follows open, vendor-neutral OMG Model Driven Architecture (MDA) standards Supports “syntactic interoperability”

Bioinformatics Objects Domain Information models expressed as Unified Modeling Language (UML) Class Diagrams

The Pillars of (Semantic) Interoperability Necessary but not Sufficient Common object and information models across all domains-of-interest –BRIDG –caBIO Model grounded on robust data type specification –Common data elements –Complex data types Methodology for binding terms from concept-based terminologies –Vocabulary services –Thesaurus A formally defined process for defining specific structures to be exchanged between machines, i.e. a “messaging standard” –HL7 and implementation specifications –Unified process for software development

caDSR: Metadata Repository Provides support for “semantic interoperability” ISO Registry for Common Data Elements (CDEs) Addresses the need for consistency in data descriptions CDEs precisely define the questions and answers –What question are you asking, exactly? –What are the possible answers, and what do they mean? Provides run-time access to metadata and semantics

Common Data Elements (CDEs) Data descriptors or “metadata” for cancer research Precisely defining the questions and answers –What question are you asking, exactly? –What are the possible answers, and what do they mean? –Using controlled vocabularies from EVS Describing data objects and attributes used for presentation to scientific applications Ongoing projects covering various domains –Clinical Trials –Imaging –Biomarkers –Genomics

Cancer Data Standards Repository Registry for Common Data Elements Follows international standard format (ISO/IEC extensions) Public ID + Version = Unique Identifier Interactive Tools: –CDE Curation – Integrated with EVS to draw terms from controlled vocabulary –CDE Browser – search and export –Form Compliance with CDEs

The Pillars of (Semantic) Interoperability Necessary but not Sufficient Common object and information models across all domains-of-interest –BRIDG –caBIO Model grounded on robust data type specification –Common data elements –Complex data types Methodology for binding terms from concept-based terminologies –Vocabulary services –Thesaurus A formally defined process for defining specific structures to be exchanged between machines, i.e. a “messaging standard” –HL7 and implementation specifications –Unified process for software development

Enterprise Vocabulary Services Provides underlying semantic content and vocabulary control for data objects and CDEs Vocabulary Products and Services –NCI Thesaurus: Cancer ontology 46,000 concepts, 92 named inter-concept relations –NCI Metathesaurus: UMLS enhanced with cancer-oriented concepts 930,000+ concepts, 2,200,000 terms and phrases Mappings among over 50 vocabularies Rich synonymy: Over 40,000 terms for “cancer” mapped to 7,000 concepts –External Vocabularies – over 70 including SNOMED, MedDRA, GO, LOINC, the MGED Ontology and many others Formal collaborations with FDA, NHLBI, NIAID, VA, CDC, caBIG TM

The Pillars of (Semantic) Interoperability Necessary but not Sufficient Common object and information models across all domains-of-interest –BRIDG –caBIO Model grounded on robust data type specification –Common data elements –Complex data types Methodology for binding terms from concept-based terminologies –Vocabulary services –Thesaurus A formally defined process for defining specific structures to be exchanged between machines, i.e. a “messaging standard” –HL7 and implementation specifications –Unified process for software development

The Importance of Process Processes to develop interoperable applications –Model-driven architecture, Unified Process Processes to develop interchange standards –HL7 and CDISC Processes to maintain the models –Developing methods of collaboration and versioning control, based on open-source architectures and methods –Developing methods of model harmonization based on HL7 frameworks Goal is to create a community of stakeholders who value the quality of the shared model of clinical research

Tools to Support the People Process –Open source –Used for projects like firefox and mozilla –Bug and feature tracking –Discussion forums –Source control with CVS check-in/check-out –Project administration – and listservs with notification of code, discussion group, or document changes –Ability to link and synchronize CVS repositories across sites

Existing Standards Harmonization Process A –Harmonize existing HL7 DMIM for regulated clinical research Process B –“extract” domain knowledge from existing messages and systems into formal UML models Process C –Harmonize and merge UML models into BRIDG Existing CDISC message BRIDG model UML submodel Existing object model (caBIO) UML submodel Existing HL7 message CC B\B\ B A

New Message and Application Development Process D –Generate a “release” of BRIDG for formal requirements traceability with vetting within stakeholder organizations Process E –HL7 message generation using HL7 mechanisms Process F –caBIG application development with annotation, semantic connector, UML loader DMIM BRIDG model BRIDG model release version X E D HL7 message generation F Annotated UML model caBIG App Development

Take Home Messages Interoperability is hard – it is more than shared data, it requires shared meaning –Four pillars of interoperability Shared information models Common data elements Rich vocabularies Repeatable processes Interoperability requires support tools- –Collaborative websites for modeling ( –caDSR for Common data elements –EVS for vocabularies Interoperability is collaborative--The collaboration and processes are perhaps the most important part of the effort in caBIG –Collaborate until it hurts

Douglas B. Fridsma