1. From the Bench All the Way to Bedside Clinical Decision Support: The Role of Semantic Technologies in a Knowledge Management Infrastructure for Translational Medicine
Tonya Hongsermeier, MD, MBA
Corporate Manager, Clinical Knowledge Management and Decision Support
Clinical Informatics R&D
Partners Healthcare System

2. Current State of Translational Medicine
17 year innovation adoption curve from discovery into accepted standards of practice
Even if a standard is accepted, patients have a 50:50 chance of receiving appropriate care, a 5-10% probability of incurring a preventable, anticipatable adverse event
The market is balking at healthcare inflation, new diagnostics and therapeutics will find increasing resistance for reimbursement

3. The Volume and Velocity of Knowledge Processing Required for Care Delivery
Medical literature doubling every 19 years
Doubles every 22 months for AIDS care
2 Million facts needed to practice
Genomics, Personalized Medicine will increase the problem exponentially
Typical drug order today with decision support accounts for, at best, Age, Weight, Height, Labs, Other Active Meds, Allergies, Diagnoses
Today, there are molecular diagnostic tests on the market, typical HIT systems cannot support complex, multi-hierarchical chaining clinical decision support
Covell DG, Uman GC, Manning PR.
Ann Intern Med Oct;103(4):596-9

4. Today’s Health IS Vendor Knowledge Management Capabilities:
Knowledge “hardwired” or structured in proprietary modes into applications, not easily updated or shared
Little or no standardization of HIT vendors on SNOMED, no shared interface terminologies for observation capture, no standard order catalogues
Most EMRs have a task-interfering approach to decision support, sub-optimal usability
Knowledge-engineering tools typically edit into transaction, no support for provenance, versioning, life-cycle, propagation, discovery or maintenance
Consequently, clinical systems implementations are under-resourced with adequate knowledge to meet current workflow and quality needs
Labor of converting knowledge into Clinical Decision Support is vastly underestimated
Doesn’t bode well for personalized medicine

5. Personalized Medicine
Knowledge Management and Decision Support Intersection Points in Translational Medicine
Diagnostic Test ordering
and documentation
guidance
Patient Encounter
(direct care or clinical trial)
Clinical
Trials Referral
Personalized Medicine
Decision Support
Knowledge Repository
Structured Test
Result Interpretations
Tissue-bank
Therapeutic Intervention
Ordering and documentation
guidance
Knowledge
Discovery,
Acquisition &
Management
Structured Research
Annotations
Bench R&D
Integrated Genotypic
Phenotypic Databases
Clinical Trials 1- 4
Pharmacovigilance

6. Composite Decision Support Application: Diabetes Management
Guided Data Interpretation
Guided Observation Capture
Guided Ordering

7. What healthcare needs from Semantic Web Technologies…
Reduce the cost, duration, risk of drug discovery
Data integration, Knowledge integration, Visualization
Knowledge representation  New Knowledge Discovery
Reduce the cost/duration/risk of clinical trial management
Patient identification and referral
Trial design (ie to capture better safety surveillance)
Data quality and clinical outcomes measurement
Post-market surveillance
Reduce the cost/duration/liability of knowledge acquisition and maintenance for clinical decision support and clinical performance measurement
Knowledge provenance and representation
Conversion of “discovery algorithms” into “clinical practice algorithms”
Event-driven change management and propagation of change

8. KM for Translational Medicine: Functional/Business Architecture
R&D
DIAGNOSTIC Svs LABs
CLINICAL TRIALS
CLINICAL CARE
PORTALS
LIMS
EHR
APPLICATIONS
ASSAYS
ANNOTATIONS
DIAGNOSTIC TEST RESULTS
ASSAY INTERPRETATIONS
ORDERS AND OBSERVATIONS
KNOWLEDGE and WORKFLOW DELIVERY SERVICES FOR ALL PORTAL ROLES
Genotypic
Phenotypic
DATA REPOSITORIES
AND SERVICES
State
Management
Services
Semantic Inferencing
And Agent-based
Discovery Services
KNOWLEDGE ACQUISITION
AND DISCOVERY SERVICES
Data Integration
Semantics and ontology based approaches
Across multiple data sources:
Genotypic: LIMS
Phenotypic: EHR
Actionable Decision Support
Semantic Inferences
Management of Patient State
Knowledge Acquisition, Maintenance and Evolution
Ontology-based Definitions Management
Ontology Change and Versioning
Impact on Knowledge Assets
Knowledge Asset Management and Repository Services
Workflow
Support Services
Data Analysts and
Collaborative Knowledge
Engineers
Collaboration
Support Services
Logic/Policy Domains
Meta-
Knowledge
Knowledge Domains
Data Domains
NCI Metathesaurus, UMLS,
SNOMED CT, DxPlain, ETC
other Knowledge Sources
Decision
Support Services
INFERENCING AND VOCABULARY
ENGINES

9. Example: Diabetes Epidemic, associated with obesity
Quality measures drive reimbursement of hospitals and physicians
Maintain HbA1c <7 (diet, oral agents and/or insulin)
If Renal Disease and no contraindication, should be on ACE inhibitor or ARB
If lipid disorder and no contraindication, should be on a Statin
National problem of non-compliance with these standards of care  compromised patient longevity, quality of life, ability to maintain employment  CMS and employer financial risk

10. Imagine this CDS Rule: If Renal Disease and DM and no contraindication, should be on ACE inhibitor or ARB
Renal disease =
Chronic Renal Failure
Nephropathy, chronic renal failure, end-stage renal disease, renal insufficiency, hemodialysis, peritoneal dialysis on Problem List (SNOMED)
Creatinine > 2
Calculated GFR < 50
Malb/creat ratio test > 30
Diabetes
Many variants on the problem list
On Insulin or oral hypoglycemic drug
Contraindication to ACE inhibitor
Allergy, Cough on ACE on adverse reaction list, or Hyperkalemia on problem list, Pregnant (20 sub rules to define this state)
K test result > 5

11. Composite Decision Support Application: Diabetes Management
Guided Data Interpretation
Guided Observation Capture
Guided Ordering

12. The Maintenance and Propagation Challenge…
These “complex” definitions must be identical in rules (if that is how “recognition” is handled), documentation templates for structured data capture, and in reporting systems that drive payor reimbursement
The rate of change for contraindication definition today is slow, yet clinical decision support systems are not keeping up…
When molecular diagnostics take off, this rate of change could be “daily” or “hourly”
Further, when a patient has a molecular diagnostic test result in the EMR that is currently of “unknown significance” and later, with new knowledge, the interpretation of the former result is “contraindication” to a drug, then this “interpretation” must be updated to ensure proper CDS functioning…

13. Role of Semantic Technologies…
Data/Knowledge Integration and Visualization
Ontology based approaches
Integration across multiple data sources:
Genotypic/Phenotypic data from LIMS/HER
Knowledge Repositories for data interpretation
Clinical Decision Support
Inference engines - SWRL
Description logics for “recognition” - OWL
Knowledge representation
Etc.
Knowledge Acquisition, Maintenance and Evolution
Ontology-based Definitions Management
Versioning, life-cycle, propagation into “dependent” objects such as rules, templates orders/documentation, reporting systems
Knowledge Provenance
Reconciling knowledge representation among different stakeholders (care givers, payors, performance measurement, clinical trials, R&D)

14. Market Drivers Will Make Semantic Web Technologies an Imperative for Translational Medicine
Genomics: personalized medicine will require decision support architectures that can proactively support complex decision making – answering 1,000,000 of questions before run-time
These systems will require self-adaptive, machine learning modes of knowledge acquisition, purely human dependent knowledge acquisition will not scale
Pharma will need cooperative relationships with HIT vendors to make speed the translational medicine life-cycle