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A Semantic Approach to Health Care Quality Reporting

A Semantic Approach to Health Care Quality Reporting. Chris Pierce (CCF) Chris Deaton (Cycorp) Brian Beck (EmCee Partners) Chimezie Ogbuji (CCF) Semantic Technology Conference 15 June 2009. Outline. Demands and complexity health care quality reporting Current approaches to reporting

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A Semantic Approach to Health Care Quality Reporting

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  1. A Semantic Approach to Health Care Quality Reporting Chris Pierce (CCF) Chris Deaton (Cycorp) Brian Beck (EmCee Partners) Chimezie Ogbuji (CCF) Semantic Technology Conference 15 June 2009

  2. Outline • Demands and complexity health care quality reporting • Current approaches to reporting • A semantic approach • Two different methods of semantic reporting • Evaluations

  3. Health Care Quality Reporting • Government and Industry Groups • CMS • Leapfrog • National Quality Forum (NQF) • National Databases • STS Cardiac & Thoracic Surgery Databases • ACC National Cardiovascular Data Registries • ACS National Surgical Quality Improvement Program • 3rd Party Payors (Insurance Companies) • Blue Cross Blue Shield • United Health • Anthem • Private Quality Tracking Groups • US News and World Report • Health Grades

  4. Increasing Reporting Obligations

  5. Reporting ComplexitiesSmoking/Tobacco Use History

  6. Reporting ComplexitiesSurgical Site Infection

  7. Typical Reporting Process

  8. Redundant and costly Same data collected multiple times Managing multiple databases with overlapping content plus separate databases for research Inconsistent Same measures may be collected differently in separate databases Potential for reporting different results for same measures Low data reusability for research Changing definitions Different definitions Problems with Typical Approach

  9. A Semantic Approach

  10. Semantic Reporting Requirements • Performance • Scalable • Fast • Automatable • Maintainability • Declarative • Reusable • Currency • Responsive to data changes • Responsive to logic changes

  11. Overview of Semantic Approach

  12. Core Clinical Facts • Smoking/Tobacco use History • Any history of tobacco use • Date-time of data source • If tobacco used • What was used (cigs, cigar, chew, etc.) • Date quit • Date-time of procedure • Date-time of hospital admit

  13. Core Clinical Facts • Surgical Site Infection • Surgical wound I&D procedure performed • Date-time of procedure • Positive culture • Culture results; Date-time of culture sample taken • Treatment with antibiotics • Antibiotic taken; Date antibiotic started and stopped • Purulent drainage, abscess or other sign • Sign; Date-time sign began • Diagnosis of a surgical site infection • Date-time of diagnosis • Fever >38 degrees C • Date-time of fever onset

  14. Federation with SemanticDB™ • A Semi-structured content management system • Supports: • Extensible RDF data model and OWL ontology • Automated, model-driven dual data representation in XML and RDF • Manual data entry via dynamically generated user interfaces • Electronic data import using a variety of protocols • Rich XML and RDF processing

  15. Inferential Report Derivation • Ontological and Rule-based derivation of report variables and values from core clinical facts • Forward reasoning of selected entailments into expanded RDF graphs • Backward reasoning of additional entailments, if necessary, through queries at run time

  16. Ontological Forward Reasoning • STS Adult Cardiac Surgery Variable 2410 OCarCong – Congenital Defect Repair • <owl:Class rdf:about="&sts;CongenitalDefectRepair"> • <rdfs:subClassOf rdf:resource="&sts;MajorProcedure"/> • <owl:intersectionOf rdf:parseType="Collection"> • <owl:Class> • <owl:complementOf> • <owl:Class> • <owl:unionOf rdf:parseType="Collection"> • <rdf:Description rdf:about="&sts;VSDRepair"/> • <rdf:Description rdf:about="&sts;ASDRepair"/> • </owl:unionOf> • </owl:Class> • </owl:complementOf> • </owl:Class> • <rdf:Description rdf:about="&ptrec;SurgicalProcedure_congenital_heart_procedure"/> • </owl:intersectionOf> • <skos:definition>Indicate whether the patient had a congenital defect repair either in • conjunction with, or as the primary surgical procedure.</skos:definition> • <skos:prefLabel>OCarCong</skos:prefLabel> • </owl:Class>

  17. Rule-Based Forward Reasoning • Derivation of hasHospitalization and PostOpInHospitalEvent in Notation 3 rules • { ?HOSP a ptrec:Event_encounter_hospitalization; • dnode:contains ?HOSP_START_DATE, ?HOSP_STOP_DATE. • ?HOSP_START_DATE a ptrec:EventStartDate; ptrec:hasDateTimeMin ?ENCOUNTER_START. • ?HOSP_STOP_DATE a ptrec:EventStopDate; ptrec:hasDateTimeMax ?ENCOUNTER_STOP. • ?EVT_DATE a ptrec:EventStartDate; ptrec:hasDateTimeMin ?EVT_START_MIN . • ?EVT dnode:contains ?EVT_DATE ; a ?EVT_KIND . • ?EVT_KIND log:notEqualTo ptrec:Event_encounter_hospitalization . • ?EVT log:notEqualTo ?HOSP . • ?EVT_START_MIN str:lessThanOrEqualTo ?ENCOUNTER_STOP. • ?EVT_START_MIN str:greaterThanOrEqualTo ?ENCOUNTER_START } => { ?EVT csqr:hasHospitalization ?HOSP } . • { ?IDX_OP a csqr:QualifyingOperation; • csqr:hasHospitalization ?HOSP. • ?EVENT csqr:hasHospitalization ?HOSP; • cyc:startsAfterStartingOf ?IDX_OP } => { ?EVENT a csqr:PostOpInHospitalEvent } .

  18. Rule-Based Forward Reasoning • Derivation of STS-ACS variable 2740 COpReGft – Reop for graft occlusion • { ?OPERATION a csqr:PostOpInHospitalEvent; • cyc:startsAfterStartingOf ?MORBIDITY; • dnode:contains ?CABG. • ?CABG a ptrec:SurgicalProcedure_vascular_coronary_artery_bypass . • ?MORBIDITY a csqr:PostOpInHospitalEvent; • a ptrec:Event_morbidity_coronary_artery_bypass_graft_occlusion } => { ?OPERATION a sts:ReopForGraftOcclusion } .

  19. Approaches to Semantic Reporting • Two methods being developed and evaluated • “Triple Store” approach • Stores expanded RDF graphs in relational triple store • Uses Cyc to query store and generate reports variable by variable • “In Memory” approach • Expands and queries individual graphs in memory to generate reports record by record on the fly

  20. “Triple Store” Reporting

  21. “In Memory” Reporting

  22. Evaluating the Approaches

  23. Benefits of Semantic Reporting • Cost savings • Eliminate redundant data collection • Reduce data management costs • Reporting consistency • Guarantee reporting of same values for same measures • Data reusability • Same core data usable for reporting, research, marketing, etc.

  24. Challenges of Semantic Reporting • Availability of structured data • EMRs often store data as narrative • Requires manual abstraction or text mining • Impact of temporal fuzziness on reasoning • Timing of medical events can be fuzzy or ambiguous • Requires careful rule construction and checks for missed cases • Agency requirements at odds • Requirements implement quality control through specific data collection UI requirements • Need to allow quality control with derivation logic

  25. Acknowledgements • Funding: • CCF Growth Board • CCF Heart and Vascular Institute • Sponsorship: • Dr. Eugene Blackstone

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