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Clinical Observations Interoperability (COI): How can Semantic Web Technologies Help?

Clinical Observations Interoperability (COI): How can Semantic Web Technologies Help?. Vipul Kashyap vipul.kashyap@cigna.com http://esw.w3.org/topic/HCLS/ClinicalObservationsInteroperability CSHALS 2008 February 25, 2009 Cambridge, MA

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Clinical Observations Interoperability (COI): How can Semantic Web Technologies Help?

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  1. Clinical Observations Interoperability (COI):How can Semantic Web Technologies Help? Vipul Kashyap vipul.kashyap@cigna.com http://esw.w3.org/topic/HCLS/ClinicalObservationsInteroperability 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)? Risk and Cost Assessment Outcomes and Utilization Research Biological Translational Research 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 Clinical Research Organizations (CROs) Universities, Academic Medical Centers (AMCs) 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 From FDA, CDC Payors 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 Clinical Observations Clinical Observations • Open • Source • Clinical • Models • DCM • SDTM • BRIDG • Snomed • MedDRA • NCIT • ….. Clinical Trial 1 Healthcare Provider 1 Clinical Trial 2 Healthcare Provider 2 … … Clinical Trial M Healthcare Provider N

  10. Use Case: Patient Screening - - Research Coordinator selects protocol for patient Clinical Research Protocol screening: Eligibility Criteria: - Inclusion - Exclusion EMR DATA Meds Procedures Research Diagnoses Demographics Coordinator views list of patients and selects which ones to approach in person for … Patient MR # Potentially # Criteria Criteria #1 No Criteria #2 Criteria #3 Eligible for Met / Total (Pass/Fail/ (Pass/Fail/ (Pass/Fail/ evaluation and Protocol Criteria in Researcher Researcher Researcher recruitment. Protocol Needs to Needs to Needs to Evaluate) Evaluate) Evaluate) … 0011111 Yes 6/8 criteria Pass Pass Pass met … 0022222 No 3/8 criteria Pass Fail Pass met Clinical … 0033333 Yes 5/8 criteria Pass Pass Fail Evaluation and met 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 :C0028754} => {?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 "20070101T00:00:00"^^xsd:dateTime ; sdtm:endDateTime "2008-0101T00:00:00"^^xsd:dateTime .

  17. Criteria in SPARQL metformin anticoagulant Exclusion Criteria ?medication1 sdtm:subject ?patient ;spl:activeIngredient ?ingredient1 . ?ingredient1 spl:classCode 6809 . OPTIONAL { ?medication2 sdtm:subject ?patient ; spl:activeIngredient ?ingredient2 .?ingredient2 spl:classCode 11289 . } FILTER (!BOUND(?medication2))

  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 SQL SPARQL

  21. SPARQL in SDTM PREFIX sdtm: <http://www.sdtm.org/vocabulary#> PREFIX spl: <http://www.hl7.org/v3ballot/xml/infrastructure/vocabulary/vocabulary#> 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:subject ?patient ; sdtm:standardizedMedicationName ?contra ; spl:activeIngredient [ spl:classCode 11289 ] ; 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: <http://www.hl7.org/v3ballot/xml/infrastructure/vocabulary/vocabulary#> 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: <http://www.w3.org/2001/XMLSchema#> PREFIX Person: <http://hospital.example/DB/Person#> PREFIX Sex_DE: <http://hospital.example/DB/Sex_DE#> PREFIX Item_Medication: <http://hospital.example/DB/Item_Medication#> PREFIX Medication: <http://hospital.example/DB/Medication#> PREFIX Medication_DE: <http://hospital.example/DB/Medication_DE#> PREFIX NDCcodes: <http://hospital.example/DB/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 NDC Code • "132139","131933","98630 ","GlipiZIDE-Metformin HCl 2.5-250 MG Tablet","54868079500 ",98630,"2.5-250 ","TABS","","MG "," ","15","GlipiZIDE-Metformin HCl ","","GlipiZIDE-Metformin HCl 2.5-250 MG Tablet“ • "132152","131946","98629 ","GlipiZIDE-Metformin HCl 2.5-500 MG Tablet","54868518802 ",98629,"2.5-500 ","TABS","","MG "," ","15","GlipiZIDE-Metformin HCl ","","GlipiZIDE-Metformin HCl 2.5-500 MG Tablet“ • "132407","132201","98628 ","GlipiZIDE-Metformin HCl 5-500 MG Tablet","54868546702 ",98628,"5-500 ","TABS","","MG "," ","15","GlipiZIDE-Metformin HCl ","","GlipiZIDE-Metformin HCl 5-500 MG Tablet“ • "132642","132436","C98630 ","GlipiZIDE-Metformin HCl TABS","54868079500 ",98630,"","TABS",""," "," ","15","GlipiZIDE-Metformin HCl ","","GlipiZIDE-Metformin HCl TABS"

  28. Drug Ontology By Stanford from drug ontology documentation

  29. Mapping Between CT and Patient Record Drug Ontology CT MechanismOfAction metformin, insulin secretagogue GeneralDrugType nonsteroidal anti-inflammatory drugBank: DB00331 RxNORM: 6809 alpha-glucosidase inhibitors anticoagulants uricosuricagents NDC:54868079500:GlipiZIDE-Metformin HCl 2.5-250 MG Tablet NDC: 54868518802: GlipiZIDE-Metformin HCl 5-500 MG Tablet NDC:54868079500:GlipiZIDE-Metformin HCl TABS C1299007 C0050393 C0066535 C0025598

  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 http://esw.w3.org/topic/HCLS/ClinicalObservationsInteroperability http://hcls.deri.org/coi/demo • 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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