Transcript 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.

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
Current State of
Translational Medicine
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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
The Volume and Velocity of Knowledge
Processing Required for Care Delivery
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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 3000+ 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. 1985 Oct;103(4):596-9
Today’s Health IS Vendor
Knowledge Management
Capabilities:
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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 underresourced 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
Knowledge Management and Decision Support
Intersection Points in Translational Medicine
Patient Encounter
(direct care or clinical trial)
Personalized Medicine
Decision Support
Knowledge Repository
Knowledge
Discovery,
Acquisition &
Management
Diagnostic Test ordering
and documentation
guidance
Clinical
Trials Referral
Structured Test
Result Interpretations
Tissue-bank
Therapeutic Intervention
Ordering and documentation
guidance
Structured Research
Annotations
Bench R&D
Integrated Genotypic
Phenotypic Databases
Clinical Trials 1- 4
Pharmacovigilance
Composite Decision Support Application: Diabetes Management
Guided Data Interpretation
Guided Observation Capture
Guided Ordering
What healthcare needs from
Semantic Web Technologies…
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Reduce the cost, duration, risk of drug discovery
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Reduce the cost/duration/risk of clinical trial management
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Data integration, Knowledge integration, Visualization
Knowledge representation  New Knowledge Discovery
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
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Knowledge provenance and representation
Conversion of “discovery algorithms” into “clinical practice algorithms”
Event-driven change management and propagation of change
KM for Translational Medicine:
Functional/Business Architecture
R&D
DIAGNOSTIC Svs LABs
CLINICAL TRIALS
LIMS
ASSAYS
ANNOTATIONS
DATA REPOSITORIES
AND SERVICES
NCI Metathesaurus, UMLS,
SNOMED CT, DxPlain, ETC
other Knowledge Sources
EHR
DIAGNOSTIC TEST RESULTS
ASSAY INTERPRETATIONS
Genotypic
Semantic Inferencing
KNOWLEDGE
And Agent-based
ACQUISITION
Discovery Services
AND DISCOVERY
SERVICES
Data Analysts and
Collaborative Knowledge
Engineers
CLINICAL CARE
PORTALS
APPLICATIONS
ORDERS AND OBSERVATIONS
KNOWLEDGE and
WORKFLOW DELIVERY
SERVICES FOR ALL
PORTAL ROLES
Phenotypic
State
Management
Services
Knowledge Asset Management
and Repository Services
Logic/Policy Domains
Knowledge Domains
Data Domains
MetaKnowledge
INFERENCING AND VOCABULARY
ENGINES
Workflow
Support Services
Collaboration
Support Services
Decision
Support Services
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
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
Composite Decision Support Application: Diabetes Management
Guided Data Interpretation
Guided Observation Capture
Guided Ordering
The Maintenance and
Propagation Challenge…
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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
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The rate of change for contraindication definition today is slow,
yet clinical decision support systems are not keeping up…
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When molecular diagnostics take off, this rate of change could
be “daily” or “hourly”
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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…
Role of Semantic Technologies…
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Data/Knowledge Integration and Visualization
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Clinical Decision Support
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Ontology based approaches
Integration across multiple data sources:
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Genotypic/Phenotypic data from LIMS/HER
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Knowledge Repositories for data interpretation
Inference engines - SWRL
Description logics for “recognition” - OWL
Knowledge representation
Etc.
Knowledge Acquisition, Maintenance and Evolution
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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)
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