1. WHIT 3.0 December 11, 2007 Christopher Pierce and Chimezie Ogbuji
Applications of Semantic Technologies to Patient Medical Data at Cleveland Clinic
WHIT 3.0
December 11, 2007
Christopher Pierce and Chimezie Ogbuji

2. Current Challenges Explosion of Need for Structured Clinical Data
Mandated and voluntary reporting
Research, quality management and decision support
No Hospital-Wide System for Capturing Structured Data
EMRs primarily narrative accounts rather than structured data
Although most clinical information is documented in unstructured narrative descriptions of medical encounters, increases in mandated reporting and the use of routine clinical data in outcomes research has led to an explosion of structured data captured as values for variables.

3. Current Challenges
Structured Data Captured in Numerous Single-Purpose Applications
Cleveland Clinic has over 500 IRB approved database applications and growing
Just tip of the iceberg
Islands of Data
Idiosyncratic terms and definitions
Overlapping content
Incomplete and inconsistent patient data

4. Consequences Data and Process Fragmentation Data Fidelity Issues
Semantic Dissonance
Poor Accessibility and Usability across Data Silos

5. Semantic Solutions Point-to-Point Mappings Interlingua Ontologies
Hard-coded by human or ontology
No a priori agreement required
Interlingua Ontologies
Mediated by one or more ontologies
Requires agreement
Semantic Database
No mediation required
Works best with agreement, but not required
Decreasing cost and brittleness

6. Cleveland Clinic Applications
SemanticDB™ Database/Knowledgebase
For patient clinical data collection and storage
Semantic Query
To facilitate investigators identifying patient cohorts for clinical research and reporting
Semantic Search Engine
For intelligent common sense search of healthcare information by patients

7. SemanticDB

8. SemanticDB Features
Extensible - Accept any kind of data without refactoring of data store. Store has no knowledge of content.
Expressive - Formal knowledge representation with transforms between KR dialects
Automated - Model and metadata-driven
Accessible - Highly distributable
Scaleable - Handles enterprise-scale data management needs
Standard - Based on emerging W3C standards

9. SemanticDB Overview

10. SemanticDB Architecture
Ad Hoc Query
SPARQL &
Natural Language
with Cyc
Facilitated Data Entry
Data Entry
Screen Compiler
User Interface Plan
Domain Model
Templates
XML
Data Dictionary
Stored Queries
Report Templates
Data Mason
Domain RDF Store
CCF SQL DB
RDF Triple-store
SPARQL Interface
XML
Schema
OWL
Ontology
XML
XSLT
‘Throttled’ Dual Representation
Domain Instance

13. Timeline Browse View

15. Semantic Query

16. Complex Query for Outcomes
IDENTIFY PATIENT POPULATION
FIND all native aortic valve replacements performed at CCF between January 1, 2000 and December 31, 2004 with a pre-operative diagnosis, as determined by echocardiogram, of moderately severe or severe aortic stenosis and moderate to severe left ventricular impairment.
INCLUDE operations in which concomitant primary CABG or concomitant mitral or tricuspid valve repair was performed.
EXCLUDE all patients with any prior valve repair or replacement; or with concomitant pulmonary valve repair; or with concomitant mitral, tricuspid, or pulmonary valve replacement; or with aortic regurgitation greater than moderate degree.

17. Cyc Ontology & Knowledge Base
Semantic Query Architecture
Reasoning
Modules
Query Formulation
Cyc Ontology & Knowledge Base
Answer Exploration
Semantic Knowledge-Source Integration
CCF SemanticDB™
SPARQL Interface
Refine,
Convert,
Integrate
Registry
Data
RDF Triple-store
CCF SQL DB

18. The Analytic Environment
Simple English sentences are typed into query search box
System extracts entities, concepts, and relations from the text and instantiates them according to rules and constraints placed on the concepts and relations

19. The Analytic Environment
User selects relevant query fragments
They then use a menu option to combine automatically the fragments into a single query

20. Full query appears in query construction screen
The system combines the fragments into a single query

21. Terms that can be temporally qualified are referenced here.
The system combines the fragments into a single query
Terms that can be temporally qualified are referenced here.

22. User can drag and drop these to form temporal sequences
The user can drag and drop boxes representing various events to designate temporal ordering
User can drag and drop these to form temporal sequences

23. Here the user has specified that the infection comes after the pericardial window procedure
Here user has specified that pericardial procedure precedes the infection

24. At that point, constraint is automatically added to query
Here the user has specified that the infection comes after the pericardial window procedure
At that point, constraint is automatically added to query

25. When the answers come back we paraphrase the event and procedure nodes with information from the justification.
User can also specify a range of dates within which condition or procedure must occur.

26. This is the SPARQL query that is dispatched to SemanticDB service.

27. Answers are displayed
When the answers come back we paraphrase the event and procedure nodes with information from the justification.

28. A full English justification can be provided for any answer (by any of several “drill down” gestures by the user)
We can generate a full justification of why the system returned that answer

29. Semantic Technology Implementation Model
Enterprise-wide patient-centric clinical
information supporting patient care
Application Development & Integration:
Web 2.0 (SAAS, SOA),
Web 3.0 (Semantic Web)
EMR
User
Interface
Demographic data
Patient treatment specific
departmental system
Patient Registry data source
Patient treatment specific
departmental module
Patient Registry
data source
Patient-centric
Departmental
Patient centric
database
SemanticDB
Enterprise-wide population-centric clinical data supporting
research, outcomes, marketing, reporting …
Population-centric
Clinical Data
Demographic Data 29