1. Clinical Observations Interoperability (COI): How can Semantic Web Technologies Help?
Vipul Kashyap
CSHALS 2008
February 25, 2009
Cambridge, MA
Acknowledgments: Helen Chen, Eric P and Holger Stenzhorn for COI Demo!
Parsa Mirhaji for providing the real world clinical data!

2. Outline W3C Task Force on Clinical Observations Interoperability
Healthcare and Life Sciences (HCLS): A Taxonomy
HCLS Ecosystem: Current and Goal State
Use Cases and Functional Requirements
Use Case Demo Step Through
Advantages of Semantic Web Technologies
Next Steps

3. W3C Task Force on Clinical Observations Interoperability
Goals and Objectives
Establish a collaboration between Providers, Pharma and other HCLS stakeholders for re-use of EMR data in Clinical Research
Establish the key stakeholders and respective value proposition
Create consensus on a common use case, needs statements and functional requirements
Develop Proofs of Concept by implementing key use cases
Participants
Healthcare Providers
Partners, Cleveland Clinic, Intermountain Healthcare, Mayo Clinic, VA/Regenstrief
Pharmaceutical Companies
Eli Lilly, Astra Zeneca, Novartis, Pfizer, Bristol Myers Squibb
Consortia
W3C, CDISC, HL7

4. What is Translational Medicine (TM)?
Outcomes and Utilization
Research
Biological
Translational
Research
Risk and Cost
Assessment
Clinical
Research
Practice

5. HCLS Ecosystem: Current State
Characterized by silos with uncoordinated
supply chains leading to inefficiencies in the system
Patients,
Public
Patients
FDA
National Institutes
Of Health
Center for
Disease
Control
Pharmaceutical
Companies
Hospitals
Payors
Universities,
Academic Medical
Centers (AMCs)
Clinical Research
Organizations (CROs)
Hospitals
Doctors
Biomedical Research
Clinical Practice
Patients
Patients
Clinical Trials/Research
Clinical Practice

6. Some interesting developments …
Payors are performing analyses to enable
Employers to better identify health issues and optimize offerings
Employees/members to make better medical decisions
For cost/utilization optimization and claim adjudication.
Providers are performing clinical studies and reviews:
To evaluate the quality and consistency of clinical care
To perform clinical research and evaluate clinical protocols
Pharmaceuticals are performing:
Clinical Trials
Evaluating secondary uses of healthcare data, e.g., use of EMRs for clinical research

7. HCLS Ecosystem: Goal State
NIH
(Research)
FDA
CDC
Pharmaceutical
Companies
Universities,
AMCs
Patients, Public
CROs
Hospitals
Doctors
Payors
From FDA, CDC
Clinical Observations Interoperability will be a
Critical Enabler to realize this Vision!

8. Functional Requirements
X identifies the Use Cases, Systems and Functional Requirement under consideration of the
COI Task Force
Based on the Functional Requirements Specification developed by EHRVA/HIMSS

9. Need for a bi-directional EMR – CTMS Link: Shareable Open Source Models of Clinical Data
DCM
SDTM
BRIDG
Snomed
MedDRA
NCIT
…..
Clinical Trial 1
Healthcare Provider 1
Clinical Trial 2
Healthcare Provider 2
Clinical
Observations
Clinical
Observations … …
Clinical Trial M
Healthcare Provider N

10. Use Case: Patient Screening
Clinical
Research
Protocol
Eligibility Criteria: -
Inclusion
Exclusion
EMR DATA
Meds
Procedures
Diagnoses
Demographics …
Fail
Pass
5/8 criteria
met
Yes
Criteria #3
(Pass/Fail/
Researcher
Needs to
Evaluate)
3/8 criteria No
No Criteria #2
6/8 criteria
Criteria #1
# Criteria
Met / Total
Criteria in
Potentially
Eligible for
Patient MR #
Research Coordinator
selects protocol for patient
screening:
Coordinator
views list of
patients and
selects which
ones to approach
in person for
evaluation and
recruitment.
Evaluation and
Recruitment
* Thanks to Rachel Richesson

11. COI Demo – Clinical Trial Eligibility Criteria

12. Use Case Step-Through
(Textual) specification of the eligibility criteria for a given clinical trial
Ontology-based translation of the eligibility criteria into SPARQL queries
Translation of the SPARQL queries into database-specific queries
Execution of the queries at the databases – results contain all eligible patients
Return of a list of eligible patients to clinical trial administrator

13. COI Demo – Selecting Inclusion Criteria
Inclusion in SDTM based ontology
SDTM based clinical trial ontology

14. COI Demo – Drug Ontology Inference
Subclasses of “anticoagulant”
Drug ontology
Exclusion in Drug ontology

15. COI Demo – Selecting Mapping Rules
#check all drugs that "may_treat obese"
{?A rdfs:subClassOf ?B; rdfs:label ?D.
?B a owl:Restriction;
owl:onProperty :may_treat;
owl:someValuesFrom :C }
=>
{?D a :WeightLoseDrug}.

16. Medication
:M0271 a sdtm:Medication; spl:classCode 6809 ; #metformin sdtm:subject :P0006; sdtm:dosePerAdministration [ sdtm:hasValue 500; sdtm:hasUnit "mg„ ]; sdtm:startDateTime " T00:00:00"^^xsd:dateTime ; sdtm:endDateTime " T00:00:00"^^xsd:dateTime .

17. Criteria in SPARQL metformin anticoagulant Exclusion Criteria
?medication1 sdtm:subject ?patient ; spl:activeIngredient ?ingredient1 .
?ingredient1 spl:classCode
OPTIONAL {
?medication2 sdtm:subject ?patient ; spl:activeIngredient ?ingredient2 . ?ingredient2 spl:classCode
} FILTER (!BOUND(?medication2))
metformin
anticoagulant
Exclusion Criteria

18. SDTM to HL7 Transformation
Clinical Trial Ontology
sdtm:Medication
sdtm:dosePer- Administration
{ ?x a sdtm:Medication ; sdtm:dosePer Administration ?y } => { ?x hl7:Substance Administration ;
hl7:doseQuantity ?y }
hl7:Substance- Administration
hl7:doseQuantity
Clinical Practice Ontology

19. HL7 to EMR Database Transformation
SPARQL in Clinical Practice Ontology
{ hl7:substanceAdministration [
a hl7:SubstanceAdministration ;
hl7:consumable [
hl7:displayName ?takes ;
spl:activeIngredient [
spl:classCode ?ingred ]
] ;} => { {
?indicItem Item_Medication:PatientID ?person;
Item_Medication:PerformedDTTM ?indicDate ;
Item_Medication:EntryName ?takes . .}
hl7:Substance- Administration
hl7:doseQuantity
Item_Medication:EntryName ?takes .
Medication:ItemID ?indicItem;
SQL to EMR Database

20. Pushing Query to Database
SPARQL in SDTM ontology to SPARQL in HL7 ontology
SPARQL in HL7 ontology to SQL in EMR database
List of eligible patients
EMR
HL7 DCM/RIM
CT Eligibility
SPARQL
SPARQL
SQL

21. SPARQL in SDTM PREFIX sdtm: <http://www.sdtm.org/vocabulary#>
PREFIX spl: <
SELECT ?patient ?dob ?sex ?takes ?indicDate?contra
WHERE {
?patient a sdtm:Patient ;
sdtm:middleName ?middleName ;
sdtm:dateTimeOfBirth ?dob ;
sdtm:sex ?sex .
[ sdtm:subject ?patient ;
sdtm:standardizedMedicationName ?takes ;
spl:activeIngredient [ spl:classCode ?code ] ;
sdtm:startDateTimeOfMedication ?indicDate
] .
OPTIONAL {
sdtm:standardizedMedicationName ?contra ;
spl:activeIngredient [ spl:classCode ] ;
sdtm:effectiveTime [
sdtm:startDateTimeOfMedication ?contraDate
} FILTER (!BOUND(?contra) && ?code = 6809) }

22. SDTM-HL7 Mapping Rules CONSTRUCT { ?patient a sdtm:Patient ;
sdtm:middleName ?middleName ;
sdtm:dateTimeOfBirth ?dob ;
sdtm:sex ?sex .
[ a sdtm:ConcomitantMedication ;
sdtm:subject ?patient ;
sdtm:standardizedMedicationName ?takes ;
spl:activeIngredient [ spl:classCode ?ingred ] ;
sdtm:startDateTimeOfMedication ?start
] .} WHERE {
?patient a hl7:Person ;
hl7:entityName ?middleName ;
hl7:livingSubjectBirthTime ?dob ;
hl7:administrativeGenderCodePrintName ?sex ;
hl7:substanceAdministration [
a hl7:SubstanceAdministration ;
hl7:consumable [
hl7:displayName ?takes ;
spl:activeIngredient [ spl:classCode ?ingred ]
] ;
hl7:effectiveTime [ hl7:start ?start ]
] . }

23. SPARQL in HL7 Via SWtranformer
PREFIX hl7: <
SELECT ?patient ?dob ?sex ?takes ?indicDate
WHERE {
?patient hl7:entityName ?middleName .
?patient hl7:livingSubjectBirthTime ?dob .
?patient hl7:administrativeGenderCodePrintName ?sex .
?patient a hl7:Person .
?patient hl7:substanceAdministration ?b0035D918_gen0 .
?b0035D918_gen0 hl7:consumable ?b0035C798_gen1 .
?b0035D918_gen0 a hl7:SubstanceAdministration> .
?b0035D918_gen0 hl7:effectiveTime ?b0035C5E8_gen3 .
?b0035C798_gen1 hl7:displayName ?takes .
?b0035C798_gen1 hl7:activeIngredient ?b0035C848_gen2 .
?b0035C848_gen2 hl7:classCode ?code .
?b0035C5E8_gen3 hl7:start ?indicDate .
FILTER ( ?code = 6809 ) }

24. HL – Database Mapping Rules: Tables
PREFIX xsd: <
PREFIX Person: <
PREFIX Sex_DE: <
PREFIX Item_Medication: <
PREFIX Medication: <
PREFIX Medication_DE: <
PREFIX NDCcodes: <

25. HL – Database Mapping Rules: Schema
CONSTRUCT {
?person a hl7:Person ;
hl7:entityName ?middleName ;
hl7:livingSubjectBirthTime ?dob ;
hl7:administrativeGenderCodePrintName ?sex ;
hl7:substanceAdministration [ a hl7:SubstanceAdministration ;
hl7:consumable [
hl7:displayName ?takes ;
spl:activeIngredient [ spl:classCode ?ingred]
] ;
hl7:effectiveTime [ hl7:start ?indicDate ]
] .
} WHERE {
?person Person:MiddleName ?middleName ;
Person:DateOfBirth ?dob ;
Person:SexDE ?sexEntry .
OPTIONAL { ?indicItem Item_Medication:PatientID ?person ;
Item_Medication:PerformedDTTM ?indicDate ;
Item_Medication:EntryName ?takes .
?indicMed Medication:ItemID ?indicItem ;
Medication:DaysToTake ?indicDuration ;
Medication:MedDictDE ?indicDE .
?indicDE Medication_DE:NDC ?indicNDC . }

26. Drug Class Information in CT #8
monotherapy with metformin, insulin secretagogue, or alpha-glucosidase inhibitors and a low dose combination of all
Long term insulin therapy
Therapy with rosiglitazone (Avandia) or pioglitazone (Actos), or extendin-4 (Byetta), alone or in combination
corticosteroids
weightloss drugs e.g., Xenical (orlistat), Meridia (sibutramine), Acutrim (phenylpropanol-amine), or similar medications
nonsteroidal anti-inflammatory drugs
Use of warfarin (Coumadin), clopidogrel (Plavix) or other anticoagulants
Use of probenecid (Benemid, Probalan), sulfinpyrazone (Anturane) or other uricosuric agents

27. Prescription Information in Patient Database
"132139","131933"," ","GlipiZIDE-Metformin HCl MG Tablet"," ",98630," ","TABS","","MG "," ","15","GlipiZIDE-Metformin HCl ","","GlipiZIDE-Metformin HCl MG Tablet“
"132152","131946"," ","GlipiZIDE-Metformin HCl MG Tablet"," ",98629," ","TABS","","MG "," ","15","GlipiZIDE-Metformin HCl ","","GlipiZIDE-Metformin HCl MG Tablet“
"132407","132201"," ","GlipiZIDE-Metformin HCl MG Tablet"," ",98628," ","TABS","","MG "," ","15","GlipiZIDE-Metformin HCl ","","GlipiZIDE-Metformin HCl MG Tablet“
"132642","132436","C ","GlipiZIDE-Metformin HCl TABS"," ",98630,"","TABS",""," "," ","15","GlipiZIDE-Metformin HCl ","","GlipiZIDE-Metformin HCl TABS"
NDC Code

28. Drug Ontology By Stanford
from drug ontology documentation

29. Mapping Between CT and Patient Record
Drug Ontology
MechanismOfAction
GeneralDrugType CT
metformin,
insulin secretagogue
alpha-glucosidase inhibitors
anticoagulants
uricosuric agents
nonsteroidal anti-inflammatory C C
drugBank: DB00331
RxNORM: 6809 C C
NDC: : GlipiZIDE-Metformin HCl MG Tablet
NDC: : GlipiZIDE-Metformin HCl MG Tablet
NDC: :GlipiZIDE-Metformin HCl TABS

30. Advantages of Semantic Web Technologies
Plug and play use of multiple ontologies and information models based on industry standards (e.g., CDISC, HL7).
Ability to access multiple points of view through declarative specification of mappings.
Mappings across CDISC/SDTM and HL7 based information models
Mappings across terminologies such as NDC, RxNorm and Stanford’s Drug Ontology
Ability to map across terminologies via compositional definition of concepts, e.g., Obesity drugs
Late binding of coding systems and database schema
Transform SPARQL to SQL in real time, reflecting real time discovery and integration needs

31. Next Steps
Solicit Feedback and Participation from the broader Biomedical Informatics communities
Develop proof of concepts for a wider variety of use cases in collaboration with various participants in the HCLS Ecosystem
Adverse Drug Event Reporting and Resolution
Clinical Trials Data Collection
Pharmaco-vigilance