Query Health Concept-to-Codes (C2C) SWG Meeting #2

1. Query Health Concept-to-Codes (C2C) SWGMeeting #2 December 13, 2011

2. Today’s Agenda

3. Proposed Timeline TODAY • Tasks • Introductions • Scope • Proposed Approach • Identify SME and presentation timeline for next few meetings • Tasks • Presentation from 3-4 SMEs on Concept Mapping in their organizations • Tasks • Presentation from 3-4 SMEs on Concept Mapping in their organizations • Tasks • Presentation from 3-4 additional SMEs on Concept Mapping in their organizations (if needed) • Review of presented concept mapping frameworks to develop a proposed approach • Tasks • Presentation and review of Draft Deliverable with the SWG Coordinate offline activities to summarize approaches and develop draft deliverable from presenters

4. Proposed Presentation Schedule on Wiki Link - http://wiki.siframework.org/C2C+SME+Presentation+Schedule

5. Questions for Considerations

6. Proposed Layout of Deliverable • The deliverable will be a Word document divided into the following sections. • Abstract – Short summary of recommended concept mapping approach • Background • Scope and Objectives • Discovery • SMEs / Organization Scan • Distributed query systems • Concept mapping experts on standards/frameworks • Other relevant concept mapping tools • Development and discussion of targeted questions • Review of short presentations by SME • Discuss Summary of gathered information on concept mapping • General summary of the organization • Summary of the tool, framework, or standard presented • Key points from the presentation • Conclusion • Recommendation on proposed framework for Concept Mapping • Next Steps

7. Andy Gregorowicz Distributed Query Framework - hQuery

8. Concept Mapping December 13th, 2011

9. Overview • hQuery is a distributed query system • Queries are expressed in JavaScript • JavaScript program is passed a clinical record object • Based on greenCDA for C32 • The program can interact with the clinical record object to extract information • e.g. patient.medications() • The program returns information in its own data structure • hQuery aggregates the results • Approach originated from popHealth

10. Finding Relevant Information Codes/code sets are placed in the JavaScript The program iterates through items in the clinical record object matching codes/code sets

11. Full Example: NQF 43 - Pneumonia Vaccination Status for Older Adults function map(patient) { varoutpatientEncounterCodes = { "CPT": ["99201", …], "ICD-9-CM": ["V70.0", …] }; varpneumococcalMedicationCodes = { "RxNorm": ["854931", …] }; varpneumococcalProcedureCodes = { "CVX": ["33", …], "CPT": ["90669", …] }; varstart = newDate(2010, 1, 1); varend = newDate(2010, 12, 31);

12. Full Example continued function population(patient) { return (patient.age(start)>=64); } functiondenominator(patient) { varencounters = patient.encounters().match( outpatientEncounterCodes, start, end); return (encounters>0); } function numerator(patient) { varmedication = patient.medications().match( pneumococcalMedicationCodes, null, end); varprocedure = patient.procedures().match( pneumococcalProcedureCodes, null, end); return medication || procedure; } function exclusion(patient) { returnfalse; }

13. Full Example continued if(population(patient)) { emit("population", 1); if (denominator(patient)) { if (numerator(patient)) { emit("denominator", 1); emit("numerator", 1); } else if (exclusion(patient)) { emit("exclusion", 1); } else { emit("denominator", 1); } } } }

14. Concept Mapping • No special facilities in hQuery • popHealth used spreadsheets released with Stage 1 MU Measures to generate concepts • Possible IPR issues with reuse

15. Shawn Murphy Distributed Query Framework - i2B2

16. Ontology services for the i2b2 query platform Shawn Murphy MD, Ph.D.

17. Introduction to i2b2 Ontology • I2b2 Data model • Concept representation • Value representation • Modifier representation • I2b2 Metadata • Defining concepts • Defining values • Building queries with i2b2 metadata • Building advanced queries with i2b2 metadata • Look into National Center for Biomedical Ontology (NCBO) Web Services from which we derive new (standard) ontologies. • Run through an example of creating an i2b2 ontology mapping.

18. i2b2 Fact Table In i2b2, the database is organized as a star schema and most data is in the observation_fact table, where every row is an observation on a patient. Examples of FACTS: Diagnoses Procedures Health History Genetic Data Lab Data Provider Data Demographics Data An observation is not necessarily the same thing as an event

19. i2b2 Dimension Tables Dimension tables contain descriptive information about facts. In i2b2 there are four dimension tables concept_dimension provider_dimension visit/event_dimension patient_dimension modifier_dimension

20. Values in i2b2 data model

21. Relationship of Metadata to Star Schema Star Schema contains one fact and many dimension tables. Concepts in these tables are defined in a separate metadata table or tables. The structure of the metadata is integral to the visualization of concepts as well as for querying the data. All metadata tables have the same basic structure.

22. Performing Queries

23. Typical i2b2 Metadata Categories Diagnoses Procedures Demographics Lab Tests Encounters (visits or observations) Providers (observers) Health History (physical findings and vital signs) Transfusion Microbiology

24. i2b2 Hierarchies Diseases and injuries \ Diseases of the circulatory system \ Cerebrovascular disease \ Intracerebral hemorrhage

25. c_fullname and c_name c_fullnameis the hierarchical path that leads to the term and is the key for the itme in the ontology \RPDR \Diagnoses \Musculoskeletal and connective tissue (710-739) \Arthropathies (710-719) \(714) Rheumatoid arthritis and other arthropathies \(714-0) Rheumatoid arthritis c_name is the actual term Rheumatoid arthritis Atrophic arthritis RA [Rheumatoid arthritis] Chronic rheumatic arthritis

26. Value queries are directed by i2b2 XML metadata for each element [ Also includes ETL directives ] <ValueMetadata>  <Version>3.02</Version> <CreationDateTime> <TestID/> <TestName/> <DataType></DataType> <Flagstouse/> <Oktousevalues/> <UnitValues> <NormalUnits/> <EqualUnits/> <ConvertingUnits/> <Units/> <MultiplyingFactor/> <ConvertingUnits/> <ExcludingUnits/> </UnitValues> <EnumValues> <Val description=”"/> <ExcludingVal description=”"/> </EnumValues> <MaxStringLength/> <CommentsDeterminingExclusion> <Com></Com> </ CommentsDeterminingExclusion > <LowofLowReference/> <HighofLowReference/> <LowofHighReference/> <HighofHighReference/> <LowofToxicReference/> <HighofToxicReference/> <Analysis> <Enums/> <Counts/> <New/> </Analysis> </ValueMetadata> <ValueMetadata>             <DataType/>             <Flagstouse/>             <Oktousevalues/>             <UnitValues>             <NormalUnits/>             <EqualUnits/>             <ConvertingUnits/> <Units/> <MultiplyingFactor/>             <ConvertingUnits/>             <ExcludingUnits/>             </UnitValues>             <EnumValues>                         <Val description=”"/>             </EnumValues>             <MaxStringLength/>             <LowofLowReference/>             <HighofLowReference/>             <LowofHighReference/>             <HighofHighReference/> </ValueMetadata>

27. Query by values

28. Fields used to construct queries Select * from observation_fact where c_facttablecolumn in (select concept_cd from c_tablename where c_columname c_operator ‘c_dimcode%’) Select * from observation_fact where concept_cd in (select concept_cd from concept_dimension where concept_path like ’\RPDR\Demographics\Gender\Female\%’)

29. Each query panel usually made up of multiple paths select distinct(patient_num) from observation_fact whereconcept_cd in (Select concept_cd From concept_dimension Where concept_path LIKE 'PRC\ICD9 (Inpatient)\(40-41) Operations on hemic and lymphatic system\(p41) Operations on bone marrow a~\(p41-0) Bone marrow or hematopoie~\%' or concept_path LIKE 'PRC\CPT\(10021-69990) Surgery\(38100-38999) Hemic and Lymphatic Systems\(38204-38242) Bone Marrow or Stem Cell\(38242) Bone marrow or blood-deri~\%' or concept_path LIKE 'PRC\CPT\(10021-69990) Surgery\(38100-38999) Hemic and Lymphatic Systems\(38204-38242) Bone Marrow or Stem Cell\(38240) Bone marrow or blood-deri~\%' or concept_path LIKE 'PRC\CPT\(10021-69990) Surgery\(38100-38999) Hemic and Lymphatic Systems\(38204-38242) Bone Marrow or Stem Cell\(38241) Bone marrow or blood-deri~\%' or concept_path LIKE '(Pre) Transplants and Tracheostomy\Surgical\(481) Bone Marrow Transplant\%' or concept_path LIKE 'zz V-codes\Conditions influencing health status (V40-V49)\(V42) Organ or tissue replaced by~\(V42-8) Other specified organ or ~\(V42-81) Bone marrow replaced by ~\%' or concept_path LIKE 'PRC\LMR\(LPA547) bone marrow transplant\%' or concept_path LIKE 'Injury and poisoning (800-999)\Complications of medical care (996-999)\(996) Complications peculiar to c~\(996-8) Complications of transpla~\(996-85) Complications of bone ma~\%')

30. Ontology terms can define more types of queries Patient_Dimension

31. Ontology terms can define comples types of queries select patient_num from [c_facttablename] where [c_facttablecolumnname] IN (select [c_facttablecolumnname] from [c_tablename] where [c_columnname] [c_operator] [c_dimcode]) select patient_num from patient_dimension where birth_date BETWEEN sysdate – (365.25*18) AND sysdate – (365.25*10)

32. Ontology Creation support in i2b2 Client - Small, hand created terminologies

33. NCBO supports large ontologies in i2b2Key activities • Create and maintain a library of biomedical ontologies. • Buildtools and Web services to enable the use of ontologies and their derivatives. • Collaborate with communities that develop and use ontologies.

34. Download • Traverse • Search • Comment Ontology Services Views • Create • Download • Upload Mapping Services • Tree-view • Auto-complete • Graph-view http://rest.bioontology.org Widgets Annotation Services Concept recognition Fetch “data” annotated with a given concept Data Services http://bioportal.bioontology.org

35. Ontology services Accessing, browsing, searching and traversing ontologies in Your application

36. www.bioontology.org/wiki/index.php/NCBO_REST_services

37. Views Custom subsets of large ontologies

38. Views and Value Sets • Users can contribute their derivatives of BioPortal ontologies, • which become first-class objects in BioPortal and can be used as all other ontologies are (e.g., as value sets) • Recently added: a view-extractor service • Enables users to extract a subtree of an ontology in OWL

39. Views in BioPortal

40. Mappings Using NCBO technology to integrate terminologies and ontologies

41. Mappings R Root Term-1 Term-2 t1 t2 Term-2 t1 Term-3 Term-4 t3 Term-5 t5 t4 t5 t7 Term-5 t6 Ontology B Ontology A Upload or Download mapping subsets

42. Using Mappings for query federation TO (site #2) FROM (site #1) Convulsion disorder Seizure Seizure NOS Direct Mappings Single Seizure Single Seizure Epilepsy Partial Seizure Partial Epilepsy Complex Seizure Temporal Epilepsy

43. Widgets Using NCBO technology on your web pages

44. Ontology Widgets UI components with “BioPortal inside”: • term-selection widget for a specific ontology • form fields with auto-complete from a specific BioPortal ontology • RSS feed for an ontology • Visualization widget • Tree widget

45. Use-cases for users of i2b2

46. Example – Using BioPortal SNOMED_CT to populate i2b2 ontology

47. Bioportal Clinical Finding view

48. NCBO Extraction workflow Extraction Workflow Request to extract ontology NCBOREST XML SNOMED view Process Extracted Data i2b2 Metadata

49. Extracted SNOMED terms

50. Providing mappings using NCBO services • Mapping cell • Need for mapping different site-specific ontologies in cross-institutional settings. (SNOMED_CT ICD-9, RxNORM NDC) • First look for locally mapped data • Then seek mappings through NCBO services. http://bioportal.bioontology.org/mappings/service/1101/ NCBOREST XML MapperCell Request to map local code Locally Mapped data ICD9  SNOMED_CT