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Vipul Kashyap CSHALS 2008 February 25, 2009 Cambridge, MA

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Presentation on theme: "Vipul Kashyap CSHALS 2008 February 25, 2009 Cambridge, MA"— Presentation transcript:

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


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