healthcare quality reporting overview

Healthcare Quality Reporting with Semantic Technologies

Christopher Pierce, Ph.D.

Cleveland ClinicMedical Informatics Grand Rounds

20 August 2010

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Healthcare Quality ReportingOverview

• Demand for quality reporting is growing rapidly and requirements are increasing in complexity and institutional impact

• Traditional process of reporting is labor intensive, scales poorly and yields inconsistent results

• To address these deficiencies the Cleveland Clinic has developed a semantic approach for producing quality reports

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Health Care Quality Reporting Agencies and Databases

• Government and Industry Groups– CMS– Leapfrog– National Quality Forum (NQF)

• National Databases– ACC National Cardiovascular Data

Registries– ACS National Surgical Quality

Improvement Program

• 3rd Party Payors– Blue Cross Blue Shield– United Health– Anthem

• Private Quality Tracking Groups– US News and World Report– Health Grades

National Cardiovascular Reporting Databases

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1985-89 1990-04 1995-99 2000-04 2005-09

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Quality Reporting ComplexitiesSmoking/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 - present

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 - present

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

Current or recent cigarette smoker < 1 year of surgery

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Typical Reporting Process

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Typical Reporting Process

• 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

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The Semantic Reporting Process

Utilize semantic technology to link concepts and translate core data Utilize semantic technology to link concepts and translate core data into answers to reporting questionsinto answers to reporting questions

Utilize semantic technology to link concepts and translate core data Utilize semantic technology to link concepts and translate core data into answers to reporting questionsinto answers to reporting questions

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• Data Federation– Relevant data obtained from multiple source systems

as electronic feeds whenever possible

• Core Data Elements– Source data mapped to core data elements in

federated repository

• Computer Reasoning– Use inference to deduce answers to questions in

specific reports from core data elements

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The Semantic Reporting ProcessFederation with SemanticDB

• Virtual or actual aggregation of source system data into semantic repository through feeds and manual abstraction

• Data mapped to common RDF model with well-documented meanings that supports computer reasoning

• RDF model linked to expressive ontologies of medical terms to contextualize term meanings

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The Semantic Reporting Process Core Data Elements

• Critical concepts mentioned in queries or variable definitions

e.g. “Indicate if the patient developed a hematoma at the percutaneous entry site.”

• Support unified data meanings for multiple purposes

Internal and external reporting, research, and ad hoc queries

• Provide targets for aligning with standard medical terminologies and taxonomies

• Critical concepts mentioned in queries or variable definitions

e.g. “Indicate if the patient developed a hematoma at the percutaneous entry site.”

• Support unified data meanings for multiple purposes

Internal and external reporting, research, and ad hoc queries

• Provide targets for aligning with standard medical terminologies and taxonomies

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The Semantic Reporting Process Computer Reasoning

• Reasoning: Use of ontologies and rules to derive logical entailments from existing data– Kind of, part of, temporal sequence (pre-

procedure, post-procedure), etc.

• Forward Reasoning: derive entailments before query to create targets for simplified queries

• Backward Reasoning: derive specific entailments at query time

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Definition Pathway for Specific Variable1. Define Core Data Element (CDE) set for variable

2. Expand CDE set to include all critical concepts

3. Provide formal definition of all CDEs

4. Map CDEs to standard taxonomies and ontologies (SNOMED-CT, FMA, LOINC, Cyc, etc.)

5. Identify primary source systems where all data pertinent to CDEs are collected

6. Produce formal logical methods for deducing variable values based on CDE definitions (ontologies and rules)

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Source DataCore Data ElementsIn SemanticDB

Source Term a

Source Term b

Source Term c

Source Term d

Source Term e

Source Term f

Question A

CDE 1

CDE 2

CDE 3

CDE 4

CDE 5

CDE 6

Answer toQuestion


ReasoningReasoningMapping andFederation

Mapping andFederation

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Example:ACC CathPCI National Registry (version 4.3)

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CathPCI v 4.3 Report Flow

Common Data Model

Common Data Model InferInfer CathPCI

ReportsCathPCIReports

CathPCIReportsCathPCIReports

CathPCIv 4.3

Reports

CathPCIv 4.3

Reports

Acute MI DBAcute MI DB

Clarity DBClarity DB

Misys (Labs) DBMisys (Labs) DB

Interventional DBInterventional DB

Sensis DBSensis DB

Diag. Cath DBDiag. Cath DB

General demographic, prior history and billing datafor all Cleveland Clinic patients

ECG timing and result data for acute MI patients

Point-of-care database for cath lab visits

Official registry for PCI procedure data (some of which is automatically pulled from Sensis)

Lab test data for all Cleveland Clinic patients

Official registry for Diagnostic Cath data (some of which is automatically pulled from Sensis)

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CathPCI v 4.3 Report Flow• Specify mappings from 405 distinct DB fields to structures in common data model

• Use integration software to import

raw data values from source databases into store that accommodates common data model, for each patient record in cohort





Sensis DBSensis DB

Dx Cath DBDx Cath DB

Common Data Model

Common Data Model

Example 1:

CATHUSER.SPECTSTRESSTEST = 1=>(?TEST a Event_evaluation_cardiac_stress_test)(?TEST hasCardiacStressTestType CardiacStressTestType_SPECT_MPI)(?TEST contains ?DATE)(?DATE a EventStartDate)(?DATE hasDateTimeMax ?MAX)

Example 2:

PROCEDURE_MASTER.SUPPORT_DEVICE_CD = 1=>(?INDEX a Event_management_percutaneous_intervention)(INDEX startsNoEarlierThan ?ESTART)(INDEX startsNoLaterThan ?LSTART)(?INDEX contains ?DATA)(?DEV a CardiacAssistDevice) (?DEV hasCardiacAssistDeviceType CardiacAssistDeviceType_intra-aortic_balloon_pump)

Example 3:

CATHPCI_V4_LAB_VISIT.ANGINALCLASS_5020 = 1=>(?EVT a Event_evaluation_history_and_physical)(?EVT startsNoEarlierThan ?ESTART)(?EVT startsNoLaterThan ?LSTART)(?EVT hasCanadianHeartClass CanadianHeartClass_0)

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CathPCI v 4.3 Report Flow




Common Data Model

Common Data Model InferInfer


Sensis DBSensis DB

Reasoning:• Access data in common data model store• Use rule encodings of CathPCI v4.3 variable definitions to deduce values



CathPCIv 4.3

Reports

CathPCIv 4.3

Reports





Sensis DBSensis DB

Dx Cath DBDx Cath DB

Example 1:

“Indicate if stress testing with SPECT imaging was performed within 6 months prior to current procedure.”

(eventOfTypePriorToWithinIntervalWithValueForOf ?TEST ?INDEX CardiacStressTest (MonthsDuration 6) hasCardiacStressTestType StressTestWithSPECTMPI)))

Example 2:

“Indicate if the patient required the use of an Intra-Aortic Balloon Pump between start of procedure and end of procedure.”

(and (hasDetail ?INDEX ?DEV) (isa ?DEV CardiacAssistDeviceData) (hasCardiacAssistDeviceType ?DEV IntraAorticBalloonPump)))

Example 3:

“Indicate if the patient required the use of an Intra-Aortic Balloon Pump between start of procedure and end of procedure.”

((eventOfTypePriorToWithinIntervalWithValueForOf ?TEST ?INDEX ClinicalExam-HAndP (MonthsDuration 6) hasCanadianHeartClass ?CLASS-VALUE)))

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Source DataCore Data ElementsQuestion to Answer

“Indicate if the patient has taken or has been prescribed anti-anginal medication within the

past two weeks.”

Example Variable:

CathPCI v4.3 #5025 “Anti-Anginal Meds”

Example Variable:


1919


Anti-anginal medication

Date/time

Medication prescribed

Medication taken


past two weeks.”

Example Variable:


Example Variable:


2020


Anti-anginal medicationMedication type:


Date/time Date/time


Medication taken

Medication prescribed or taken


past two weeks.”

Example Variable:


Example Variable:


2121




Date/time Date/time


Medication taken



past two weeks.”

“Indicate the date of the patient’s most recent anti-anginal

prescription.”

Example Variable:


Example Variable:


2222




Date/time Date/time


Medication taken


Beta Blocker

Ca Channel Blocker

Long-acting Nitrate

Ranolazine


past two weeks.”


prescription.”

Example Variable:


Example Variable:


2323




Date/time Date/time


Medication taken


Beta Blocker

Ca Channel Blocker

Long-acting Nitrate

Ranolazine

Beta Blocker

Ca Channel Blocker

Long-acting Nitrate

Ranolazine


past two weeks.”


prescription.”

Example Variable:


Example Variable:


2424




Date/time Date/time


Medication taken


Beta Blocker

Ca Channel Blocker

Long-acting Nitrate

Ranolazine

Beta Blocker

Ca Channel Blocker

Long-acting Nitrate

Ranolazine

“Indicate if the patient has taken or has been prescribed ranolazine

in the past six months.”


past two weeks.”


prescription.”

Example Variable:


Example Variable:


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CathPCI v4.3 seq. #6130:“Mid/Distal LAD, Diag Branches Stenosis”

CathPCI v4.3 Data Dictionary Coding Instructions:

“Indicate the best estimate of most severe percent stenosis in mid/distal left anterior descending (LAD), including all diagonal coronary artery branches as determined by angiography.

Note: It is acceptable to use prior cath lab visit information as long as there have been no changes in coronary anatomy.

Target value: The highest value between one month prior to current procedure and current procedure.”

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Isolate Core Data Elements:

“Indicate the best estimate of most severe percent stenosis in mid/distal left anterior descending (LAD), including all

diagonal coronary artery branches as determined by angiography.



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Expand relevant CDE set to all critical concepts:





Expands into additional critical concepts:

Diagonal 1 Diagonal 2Diagonal 3Lateral First DiagonalLateral Second DiagonalLateral Third DiagonalLeft Anterior Descending Major Septal Perforator

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OperationCABG procedure

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Diagnostic catheterizationPercutaneous coronary interventionCardiac angiogram

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Indentify pertinent source data:

From Interventional DB:• Cohort who had PCI performed in relevant timeframe• Dates of those PCIs• Dates of the cath lab visits that subsume those PCIs• Coronary artery stenosis values for LAD and relevant diagonals as

determined during PCI

From Diagnostic Cath DB:• Dx Cath procedures, with stenoses determined by angiography• Dates of the cath lab visits that subsume those PCIs

From SemanticDB:• CABG operations, with affected coronary regions

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Store as Common Data Model:

Import the following relevant types of structure:

1. PCIs and associated cath lab visits

2. Diagnostic caths and associate cath lab visits

3. Stenosis findings from all PCIs and Diagnostic caths

4. CABG procedures and associated operations

5. Coronary artery graft data

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Example Semantic Query:

(ist CCF-CAE-QueryMt (and (elementOf ?ARTERY-TYPE (TheSet MiddleLeftAnteriorDescendingArtery-Coronary LeftAnteriorDescendingDistalArtery-Coronary CoronaryArtery-Diagonal1 LateralFirstDiagonalCoronaryArtery CoronaryArtery-Diagonal2 LateralSecondDiagonalCoronaryArtery CoronaryArtery-Diagonal3 LateralThirdDiagonalCoronaryArtery LeftAnteriorDescendingMajorSeptalPerforator)) (cathOrPCIHasStenosisForCoronaryRegion ?INDEX ?ARTERY-TYPE ?DEGREE))

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Example Reasoning Rule:

(implies (and (isa ?INDEX InterventionalCatheterization) (hasFinding ?INDEX ?STENOSIS) (isa ?STENOSIS CoronaryArteryStenosis-Finding) (hasCoronaryArtery ?STENOSIS ?REGION-TYPE) (hasVesselStenosisDegree ?STENOSIS ?DEGREE)) (cathOrPCIHasStenosisForCoronaryRegion ?INDEX ?REGION-TYPE ?DEGREE))

“If the current procedure records a stenosis value for a particular artery, then that stenosis value is a stenosis value for that region for the current procedure.”


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Example Rule:(implies (and (isa ?INDEX InterventionalCatheterization) (startsNoEarlierThan ?INDEX ?INDEX-MIN) (contains ?PTREC ?INDEX) (isa ?STENOSIS CoronaryArteryStenosis-Finding) (hasCoronaryArtery ?STENOSIS ?REGION-TYPE) (hasVesselStenosisDegree ?STENOSIS ?DEGREE) (hasFinding ?EARLIER-DIAG ?STENOSIS) (closestEventOfTypeAtOrPriorToWithValueFor ?EARLIER-DIAG ?INDEX CardiacCatheterization-Diagnostic hasFinding) (startsNoEarlierThan ?EARLIER-DIAG ?DIAG-MIN) (greaterThanOrEqualTo (MonthsDuration 1) ?DURATION) (timeElapsedBetween-MinMin-CCF ?INDEX ?EARLIER-DIAG ?DURATION) (unknownSentence (thereExists ?OP (thereExists ?OP-MIN (thereExists ?CABG (thereExists ?CAG (thereExists ?CAGS (thereExists ?CAGDA (and (isa ?CABG CoronaryArteryBypassGraft-SurgicalProcedure) (isa ?OP Operation) (contains ?PTREC ?OP) (sksiLaterThan ?INDEX-MIN ?OP-MIN) (sksiLaterThan ?OP-MIN ?DIAG-MIN) (hsaCoronaryArtery ?CABG ?REGION-TYPE) (startsNoEarlierThan ?OP ?OP-MIN) (time:intervalContains ?OP ?CABG)))))))))) (cathOrPCIHasStenosisForCoronaryRegion ?INDEX ?REGION-TYPE ?DEGREE))


There is a dx cath within 1 month prior to the current procedure that records astenosis value for a particular artery.

There is no CABG affecting that artery between the aforementioned dx cath and the current procedure.

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Query could find multiple stenosis values for a single region:

Diagonal 1 Stenosis = 50%

Diagonal 2 Stenosis = 60%

Query post-processing (backward reasoning) selects the highest stenosis value returned by the query:

(FirstInListFn (SortSetViaBinPredFn (SetOfValuesOfFn ?RESULT) greaterThan))))


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Source DataCore Data Elements


past two weeks.”

Distinct qualitative source data can be brought into alignment quantitatively:Distinct qualitative source data can be brought into alignment quantitatively:

Question to Answer

“Indicate the maximum dimension, in

centimeters, of the hematoma:

< 5 cm,5-10 cm,>10 cm.”

Qualitative/Quantitative Reasoning

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past two weeks.”

Question to Answer

Hematoma

Size in centimeters“Indicate the maximum

dimension, in centimeters, of the

hematoma: < 5 cm,

5-10 cm,>10 cm.”

Qualitative/Quantitative ReasoningDistinct qualitative source data can be brought into alignment quantitatively:Distinct qualitative source data can be brought into alignment quantitatively:

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past two weeks.”

Question to Answer

Hematoma



hematoma: < 5 cm,

5-10 cm,>10 cm.”


Hematoma

Size in centimeters


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past two weeks.”

Question to Answer

Hematoma



hematoma: < 5 cm,

5-10 cm,>10 cm.”


Hematoma

Size in centimeters

Small hematoma

Medium hematoma

Medium to largehematoma

Large Hematoma


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past two weeks.”

Question to Answer

Hematoma



hematoma: < 5 cm,

5-10 cm,>10 cm.”


Hematoma

Size in centimeters

Small hematoma

Medium hematoma


Large Hematoma

< 5 cm hematoma

5-7 cm hematoma

>7-10 cm hematoma

> 10 cm hematoma


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past two weeks.”

Question to Answer

Hematoma



hematoma: < 5 cm,

5-10 cm,>10 cm.”


Hematoma

Size in centimeters

< 5 cm hematoma

5-7 cm hematoma

>7-10 cm hematoma

> 10 cm hematoma

Small hematoma

< 3 cm hematoma

Small hematoma

Medium hematoma


Large Hematoma


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past two weeks.”

Distinct quantitative source data can treated in a qualitatively uniform way:Distinct quantitative source data can treated in a qualitatively uniform way:

Question to Answer

“Indicate whether the patient has an evaluation that

indicates left atrial enlargement.”


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past two weeks.”

Question to Answer




Left atrium

Diameter of object

Rule indicating atrial enlargement


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past two weeks.”

Question to Answer




Left atrium

Diameter of object


Left atrium

Diameter in centimeters

Evaluation


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past two weeks.”

Question to Answer




Left atrium

Diameter of object


Left atrium

Diameter in centimeters

Evaluation

Male patient

Female patient

Rule indicating male atrial enlargement

Rule indicating female atrial enlargement


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Benefits of Semantic Reporting• Consistent

– Guarantees reporting of same values for same measures across different reports

– Data corrections can be made in one location, the source database

– Guides clinical documentation towards well-defined core data elements

• Reusable– Same core data and reasoning usable for reporting,

research, marketing, etc.• Responsive

– Able to rapidly change core data elements and reasoning logic to respond to new requirements

• Cheaper– Eliminates redundant data collection and reduces

data management costs

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Challenges of Semantic Approach

• Source Data– Fields in sources systems often poorly defined– Much medical information is still narrative requiring

later abstraction– Access to many source systems remains difficult

• Core Data Elements– No universal set of core medical data– Pragmatic definitions based on existing requirements

• Reasoning– Few good medical ontologies– Need to create ontologies and rules as needed

healthcare quality reporting overview

Documents

external reporting

costlysame data

data pertinent

unified data meanings

measureslow data reusability

semantic repository

semantic approach

multiple source systems