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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

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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
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
the volume and velocity of knowledge processing required for care delivery
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 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
Today’s Health IS VendorKnowledge 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
knowledge management and decision support intersection points in translational 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

slide6

Composite Decision Support Application: Diabetes Management

Guided Data Interpretation

Guided Observation Capture

Guided Ordering

what healthcare needs from semantic web technologies
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
km for translational medicine functional business architecture
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

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

example diabetes
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
slide10
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
slide11

Composite Decision Support Application: Diabetes Management

Guided Data Interpretation

Guided Observation Capture

Guided Ordering

the maintenance and propagation challenge
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…
role of semantic technologies
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)
market drivers will make semantic web technologies an imperative for translational medicine
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