Healthcare Quality Reporting with Semantic Technologies Christopher Pierce, Ph.D. Cleveland Clinic Medical Informatics Grand Rounds 20 August 2010

2 Healthcare Quality Reporting Overview 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

3 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

4 Quality Reporting Complexities Smoking/Tobacco Use History STS Adult Cardiac Surgery Database Present Any tobacco use history Used < 1 mo. of surgery Current or recent cigarette smoker < 1 year of surgery STS General Thoracic Surgery Database 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 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

5 Typical Reporting Process

6 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

7 The Semantic Reporting Process Utilize semantic technology to link concepts and translate core data into answers to reporting questions

8 The Semantic Reporting Process 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

9 The Semantic Reporting Process Federation 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

10 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

11 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

12 Definition Pathway for Specific Variable 1.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)

13 Source DataCore Data Elements In 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 to Question The Semantic Reporting Process Reasoning Mapping and Federation

14 Example: ACC CathPCI National Registry (version 4.3)

15 CathPCI v 4.3 Report Flow Common Data Model Infer CathPCI Reports CathPCI Reports CathPCI Reports CathPCI Reports CathPCI v 4.3 Reports CathPCI v 4.3 Reports Acute MI DB Clarity DB Misys (Labs) DB Interventional DB Sensis DB Diag. Cath DB General demographic, prior history and billing data for 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)

16 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 Acute MI DB Clarity DB Misys (Labs) DB Interventional DB Sensis DB Dx Cath DB 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)

17 CathPCI v 4.3 Report Flow Acute MI DB Clarity DB Misys (Labs) DB Common Data Model Infer Interventional DB Sensis DB Reasoning : Access data in common data model store Use rule encodings of CathPCI v4.3 variable definitions to deduce values CathPCI Reports CathPCI Reports CathPCI Reports CathPCI Reports CathPCI v 4.3 Reports CathPCI v 4.3 Reports Acute MI DB Clarity DB Misys (Labs) DB Interventional DB Sensis DB Dx 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)))

18 Source Data Core 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”

19 Source Data Core Data ElementsQuestion to Answer Anti-anginal medication Date/time Medication prescribed Medication taken “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”

20 Source Data Core Data ElementsQuestion to Answer Anti-anginal medication Medication type: Anti-anginal medication Date/time Medication prescribed Medication taken Medication prescribed or taken “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”

21 Source Data Core Data ElementsQuestion to Answer Anti-anginal medication Medication type: Anti-anginal medication Date/time Medication prescribed Medication taken Medication prescribed or taken “Indicate if the patient has taken or has been prescribed anti-anginal medication within the past two weeks.” “Indicate the date of the patient’s most recent anti-anginal prescription.” Example Variable: CathPCI v4.3 #5025 “Anti-Anginal Meds”

22 Source Data Core Data ElementsQuestion to Answer Anti-anginal medication Medication type: Anti-anginal medication Date/time Medication prescribed Medication taken Medication prescribed or taken Beta Blocker Ca Channel Blocker Long-acting Nitrate Ranolazine “Indicate if the patient has taken or has been prescribed anti-anginal medication within the past two weeks.” “Indicate the date of the patient’s most recent anti-anginal prescription.” Example Variable: CathPCI v4.3 #5025 “Anti-Anginal Meds”

23 Source Data Core Data ElementsQuestion to Answer Anti-anginal medication Medication type: Anti-anginal medication Date/time Medication prescribed Medication taken Medication prescribed or 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 anti-anginal medication within the past two weeks.” “Indicate the date of the patient’s most recent anti-anginal prescription.” Example Variable: CathPCI v4.3 #5025 “Anti-Anginal Meds”

24 Source Data Core Data ElementsQuestion to Answer Anti-anginal medication Medication type: Anti-anginal medication Date/time Medication prescribed Medication taken Medication prescribed or 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.” “Indicate if the patient has taken or has been prescribed anti-anginal medication within the past two weeks.” “Indicate the date of the patient’s most recent anti-anginal prescription.” Example Variable: CathPCI v4.3 #5025 “Anti-Anginal Meds”

25 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.”

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

27 CathPCI v4.3 seq. #6130: “Mid/Distal LAD, Diag Branches Stenosis” Expand relevant CDE set to all critical concepts: “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.” Expands into additional critical concepts: Diagonal 1 Diagonal 2 Diagonal 3 Lateral First Diagonal Lateral Second Diagonal Lateral Third Diagonal Left Anterior Descending Major Septal Perforator

28 CathPCI v4.3 seq. #6130: “Mid/Distal LAD, Diag Branches Stenosis” Expand relevant CDE set to all critical concepts: “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.” Expands into additional critical concepts: Operation CABG procedure

29 CathPCI v4.3 seq. #6130: “Mid/Distal LAD, Diag Branches Stenosis” Expand relevant CDE set to all critical concepts: “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.” Expands into additional critical concepts: Diagnostic catheterization Percutaneous coronary intervention Cardiac angiogram

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

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

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

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

34 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)) CathPCI v4.3 seq. #6130: “Mid/Distal LAD, Diag Branches Stenosis” There is a dx cath within 1 month prior to the current procedure that records a stenosis value for a particular artery. There is no CABG affecting that artery between the aforementioned dx cath and the current procedure.

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

36 Source Data Core Data Elements “Indicate if the patient has taken or has been prescribed anti-anginal medication within the past two weeks.” 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

37 Source Data Core Data Elements “Indicate if the patient has taken or has been prescribed anti-anginal medication within the 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 Reasoning Distinct qualitative source data can be brought into alignment quantitatively:

38 Source Data Core Data Elements “Indicate if the patient has taken or has been prescribed anti-anginal medication within the 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 Reasoning Hematoma Size in centimeters Distinct qualitative source data can be brought into alignment quantitatively:

39 Source Data Core Data Elements “Indicate if the patient has taken or has been prescribed anti-anginal medication within the 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 Reasoning Hematoma Size in centimeters Small hematoma Medium hematoma Medium to large hematoma Large Hematoma Distinct qualitative source data can be brought into alignment quantitatively:

40 Source Data Core Data Elements “Indicate if the patient has taken or has been prescribed anti-anginal medication within the 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 Reasoning Hematoma Size in centimeters Small hematoma Medium hematoma Medium to large hematoma Large Hematoma < 5 cm hematoma 5-7 cm hematoma >7-10 cm hematoma > 10 cm hematoma Distinct qualitative source data can be brought into alignment quantitatively:

41 Source Data Core Data Elements “Indicate if the patient has taken or has been prescribed anti-anginal medication within the 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 Reasoning 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 Medium to large hematoma Large Hematoma Distinct qualitative source data can be brought into alignment quantitatively:

42 Source Data Core Data Elements “Indicate if the patient has taken or has been prescribed anti-anginal medication within the past two weeks.” 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.” Qualitative/Quantitative Reasoning

43 Source Data Core Data Elements “Indicate if the patient has taken or has been prescribed anti-anginal medication within the past two weeks.” Question to Answer “Indicate whether the patient has an evaluation that indicates left atrial enlargement.” Qualitative/Quantitative Reasoning Left atrium Diameter of object Rule indicating atrial enlargement Distinct quantitative source data can treated in a qualitatively uniform way:

44 Source Data Core Data Elements “Indicate if the patient has taken or has been prescribed anti-anginal medication within the past two weeks.” Question to Answer “Indicate whether the patient has an evaluation that indicates left atrial enlargement.” Qualitative/Quantitative Reasoning Left atrium Diameter of object Rule indicating atrial enlargement Left atrium Diameter in centimeters Evaluation Distinct quantitative source data can treated in a qualitatively uniform way:

45 Source Data Core Data Elements “Indicate if the patient has taken or has been prescribed anti-anginal medication within the past two weeks.” Question to Answer “Indicate whether the patient has an evaluation that indicates left atrial enlargement.” Qualitative/Quantitative Reasoning Left atrium Diameter of object Rule indicating atrial enlargement Left atrium Diameter in centimeters Evaluation Male patient Female patient Rule indicating male atrial enlargement Rule indicating female atrial enlargement Distinct quantitative source data can treated in a qualitatively uniform way:

46 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

47 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