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 Title slide is intended to be the first slide of your presentation. You can edit the Title and subtitle on this slide to reflect your own information. The title should be the same as your Abstract title. A title slide will be provided for Research Day which may include your photograph.

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

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 The "text slide" contains the standard text format for the presentation. By default new slides will match this format. The standard text slide includes bulleted subtext as well as the standard text format. To create bulleted points, first type you point text, then highlight the point and indent it with the tab key.

Increasing Reporting Obligations

Reporting Complexities Smoking/Tobacco Use History STS Adult Cardiac Surgery Database 2002 - 2007 2008 - Present Any tobacco use history Used < 1 mo. of surgery Current or recent cigarette smoker < 1 year of surgery STS General Thoracic Surgery Database 2004 - 2009 2009 - Chew user Cigarette user Pipe user Other tobacco user Days quit before surgery History of cigarette smoking Never Quit > 1 mo. of surgery Smoked < 1 mo. of surgery ACC NCDR CathPCI Registry History of tobacco use The “divider” may be used to divide portions of your presentation: Question or Objectives Population Results Summary Conclusions

Reporting Complexities Surgical Site Infection STS Adult Cardiac Surgery Database All of: Wound reoperation (I&D) Positive culture Treated with antibiotics STS General Thoracic Surgery Database Two of: Wound reoperation (I&D) ACS National Surgical Improvement Program One of: Purulent drainage Wound reop or dehisces Abscess/other sign of infect Diagnosis by physician The “divider” may be used to divide portions of your presentation: Question or Objectives Population Results Summary Conclusions

Typical Reporting Process

Problems with Typical Approach 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

A Semantic Approach

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

Overview of Semantic Approach

Smoking/Tobacco use History 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

Surgical Site Infection 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

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

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

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:unionOf rdf:parseType="Collection"> <rdf:Description rdf:about="&sts;VSDRepair"/> <rdf:Description rdf:about="&sts;ASDRepair"/> </owl:unionOf> </owl:Class> </owl:complementOf> <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>

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 } .

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 } .

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

“Triple Store” Reporting

“In Memory” Reporting

Evaluating the Approaches “Triple Store” “In Memory” Scalability ? + Speed Automation Declarative Reusable Current Data - Current Logic

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.

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

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