A Semantic Approach to A Semantic Approach to Health Care Quality Health Care Quality

ReportingReporting

Chris Pierce (CCF)Chris Deaton (Cycorp)

Brian Beck (EmCee Partners)Chimezie Ogbuji (CCF)

Semantic Technology Conference15 June 2009

OutlineOutline

1. Demands and complexity health care quality reporting

2. Current approaches to reporting

3. A semantic approach

4. Two different methods of semantic reporting

5. Evaluations

Health Care Quality ReportingHealth Care Quality ReportingGovernment 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

Increasing Reporting Increasing Reporting ObligationsObligations

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Reporting ComplexitiesReporting ComplexitiesSmoking/Tobacco Use HistorySmoking/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

2004 - 2009 2009 -

History of tobacco use Never Quit > 1 mo. of surgery Used < 1 mo. of surgery

Current or recent cigarette smoker < 1 year of surgery

Reporting ComplexitiesReporting ComplexitiesSurgical Site InfectionSurgical 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) Positive culture Treated with antibiotics

ACS National Surgical Improvement Program

One of: Purulent drainage Wound reop or dehisces Abscess/other sign of infect Diagnosis by physician

Typical Reporting ProcessTypical Reporting Process

Problems with Typical ApproachProblems 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 ApproachA Semantic Approach

Semantic Reporting Semantic Reporting RequirementsRequirements

Performance• Scalable• Fast• Automatable

Maintainability• Declarative• Reusable

Currency• Responsive to data changes• Responsive to logic changes

Overview of Semantic ApproachOverview of Semantic Approach

Core Clinical FactsCore Clinical FactsSmoking/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

Core Clinical FactsCore Clinical FactsSurgical 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 SemanticDBFederation 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 DerivationInferential Report DerivationOntological 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 ReasoningOntological Forward ReasoningSTS 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>

Rule-Based Forward ReasoningRule-Based Forward ReasoningDerivation 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 ReasoningRule-Based Forward ReasoningDerivation 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 Approaches to Semantic ReportingReporting

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” ReportingTriple Store” Reporting

““In Memory” ReportingIn Memory” Reporting

Evaluating the ApproachesEvaluating the Approaches

“Triple Store” “In Memory”

Scalability ? +Speed ? ?Automation + +Declarative + +Reusable + +Current Data - +Current Logic + -

Benefits of Semantic ReportingBenefits 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 Challenges of Semantic ReportingReporting

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

AcknowledgementsAcknowledgements

Funding:

• CCF Growth Board

• CCF Heart and Vascular Institute

Sponsorship:

• Dr. Eugene Blackstone