Query Health Concept-to-Codes (C2C) SWG Meeting #2 December 13,

Today’s Agenda TopicTime Allotted Welcome new SWG participants Review of Timeline Presentation Schedule posted on Wiki 3:00 – 3:05 Questions for Consideration3:05 - 3:10 Proposed Layout for deliverable3:05 – 3:10 Presentation by Subject Matter Experts3:10-3:55 Andy Gregorowicz - hQuery3:10 - 3:25 Shawn Murphy – i2B23:25 -3:40 Dan Vreeman / Zeshan Rajput – RELMA (LOINC)3:40 - 4:55 Discussion/ Questions3:55 - 4:00 2

Proposed Timeline Meeting 1 – Dec 6Meeting 2 – Dec 13Meeting 3 – Dec 20Meeting 4 – Dec 27Meeting 5 – Jan 03 Tasks Presentation and review of Draft Deliverable with the SWG 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 from 3-4 SMEs on Concept Mapping in their organizations Tasks Presentation from 3-4 SMEs on Concept Mapping in their organizations Tasks Introductions Scope Proposed Approach Identify SME and presentation timeline for next few meetings TODAY Coordinate offline activities to summarize approaches and develop draft deliverable from presenters 3

Proposed Presentation Schedule on Wiki Link - 4

Questions for Considerations Frameworks (Ex. - i2B2, PMN, hQuery) Tools (Ex. RxNav, RELMA, LexEVS) Standards Overview and Current Status How do you define concept mapping within your system (e.g. are you mapping in between standards, or are you mapping from standards to your local data dictionary)? Are there any internal mechanism? Do you use any external tools? Are you able to maintain the integrity of the original data in its native form (i.e. data as collected and not modified)? How does your tool function? Are you able to maintain the integrity of the original data in its native form (i.e. data as collected and not modified)? How do your standards relate to concept mapping? Are you able to maintain the integrity of the original data in its native form (i.e. data as collected and not modified)? Integration and Infrastructure How can you integrate with external tools for mapping? JavaScript library? Java? Web Services API? How can your tool be leveraged? Are there any external APIs or other interfaces? What infrastructure is necessary to implement / utilize your standard? Alignment to Query Health Is your framework geared towards the Data Source? The Information Requestor? Or Both? Is your tool geared towards the Data Source? The Information Requestor? Or Both? Are the standards developed around concept mapping at the data source level? The Information Requestor level? Or Both? Maintenance Who maintains your concept mapping tool? Who maintains the mappings and how often are they released? What is the associated cost with maintenance? Who maintains your concept mapping tool? Who maintains the mappings and how often are they released? What is the associated cost with maintenance ? Who maintains the development of standards? Who maintains the mappings and how often are they released? What is the associated cost with maintenance and periodic releases? 5

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 6

Andy Gregorowicz Distributed Query Framework - hQuery 7

Approved for Public Release © 2011 The MITRE Corporation. All Rights Reserved. Concept Mapping December 13th, 2011

Approved for Public Release © 2011 The MITRE Corporation. All Rights Reserved 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 9

Approved for Public Release © 2011 The MITRE Corporation. All Rights Reserved 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 10

Approved for Public Release © 2011 The MITRE Corporation. All Rights Reserved Full Example: NQF 43 - Pneumonia Vaccination Status for Older Adults function map(patient) { var outpatientEncounterCodes = { "CPT": ["99201", …], "ICD-9-CM": ["V70.0", …] }; var pneumococcalMedicationCodes = { "RxNorm": ["854931", …] }; var pneumococcalProcedureCodes = { "CVX": ["33", …], "CPT": ["90669", …] }; var start = new Date(2010, 1, 1); var end = new Date(2010, 12, 31); 11

Approved for Public Release © 2011 The MITRE Corporation. All Rights Reserved Full Example continued function population(patient) { return (patient.age(start)>=64); } function denominator(patient) { var encounters = patient.encounters().match( outpatientEncounterCodes, start, end); return (encounters>0); } function numerator(patient) { var medication = patient.medications().match( pneumococcalMedicationCodes, null, end); var procedure = patient.procedures().match( pneumococcalProcedureCodes, null, end); return medication || procedure; } function exclusion(patient) { return false; } 12

Approved for Public Release © 2011 The MITRE Corporation. All Rights Reserved 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); } 13

Approved for Public Release © 2011 The MITRE Corporation. All Rights Reserved Concept Mapping  No special facilities in hQuery  popHealth used spreadsheets released with Stage 1 MU Measures to generate concepts –Possible IPR issues with reuse 14

Shawn Murphy Distributed Query Framework - i2B2 15

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

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.

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

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

Values in i2b2 data model

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.

Performing Queries

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

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

c_fullname and c_name c_fullname is the hierarchical path that leads to the term and is the key for the itme in the ontology \RPDR \Diagnoses \Musculoskeletal and connective tissue ( ) \Arthropathies ( ) \(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

Value queries are directed by i2b2 XML metadata for each element 3.02 [ Also includes ETL directives ]

Query by values

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\%’)

Each query panel usually made up of multiple paths select distinct(patient_num) from observation_fact where concept_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\( ) Surgery\( ) Hemic and Lymphatic Systems\( ) Bone Marrow or Stem Cell\(38242) Bone marrow or blood-deri~\%' or concept_path LIKE 'PRC\CPT\( ) Surgery\( ) Hemic and Lymphatic Systems\( ) Bone Marrow or Stem Cell\(38240) Bone marrow or blood-deri~\%' or concept_path LIKE 'PRC\CPT\( ) Surgery\( ) Hemic and Lymphatic Systems\( ) 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 ( )\Complications of medical care ( )\(996) Complications peculiar to c~\(996-8) Complications of transpla~\(996-85) Complications of bone ma~\%')

Ontology terms can define more types of queries Patient_Dimension

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)

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

NCBO supports large ontologies in i2b2 Key activities Create and maintain a library of biomedical ontologies. Build tools and Web services to enable the use of ontologies and their derivatives. Collaborate with communities that develop and use ontologies.

Ontology Services Download Traverse Search Comment Download Traverse Search Comment Widgets Tree-view Auto-complete Graph-view Tree-view Auto-complete Graph-view Annotation Services Data Services Mapping Services Create Download Upload Create Download Upload Views Concept recognition Fetch “data” annotated with a given concept

ONTOLOGY SERVICES Accessing, browsing, searching and traversing ontologies in Your application

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VIEWS Custom subsets of large ontologies

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 38

Views in BioPortal 39

MAPPINGS Using NCBO technology to integrate terminologies and ontologies

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

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

WIDGETS Using NCBO technology on your web pages

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

Use-cases for users of i2b2

Example – Using BioPortal SNOMED_CT to populate i2b2 ontology

Bioportal Clinical Finding view

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

Extracted SNOMED terms

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. Mapper Cell Request to map local code ICD9  SNOMED_CT NCBO REST XML NCBO REST XML Locally Mapped data

Integrating two i2b2 ontologies with NCBO services Integrate ICD-9 into SNOMED For each SNOMED term, find a mapped ICD9 term: Request to integrate Integration Workflow ICD9 into SNOMED_CT SNOMED with ICD9 Data Mapper Cell Add new terms with SNOMED hierarchy, ICD9 name, basecode NCBO REST XML NCBO REST XML

SNOMED folder with ICD9 terms

ICD10 folder with ICD9 terms

THANK YOU

Dan Vreeman / Zeshan Rajput Concept Mapping Tool – RELMA (LOINC) 55

Introduction to RELMA Objectives –Overview of LOINC and RELMA - Very Brief! –Demonstrate the concept mapping functionality in RELMA Disclaimer –Contractor to ONC to support S&I Framework (Standards Development support team Informatics Lead) –Any opinions are my own and not those of ONC, Accenture, etc. For More Information LOINC.org The Current Lab LOINC TutorialLOINC Tutorial

Background LOINC - Logical Observation Identifiers Names and Codes Use to code questions in healthcare –Lab Questions (e.g., "What is my patient's hemoglobin?" ) –Clinical Questions (e.g., "How far can my patient walk in 6 minutes?" ) Answers are represented using other standards –Value + UCUM –SNOMED –etc.

RELMA Overview Tool distributed with LOINC –Same free use (see license) Provides manual and automated mapping functions –User creates file of local term/names and codes –RELMA tools help assign a LOINC term to local test/battery code Features –"Common Tests" subset –"Google-Like" search functions

Four Ways to Present Local Codes Direct entry into LMOF from within RELMA (painful) Create an access table that mimics LMOF structure (less painful but tedious) Create a delimited ASCII file from your local test catalog (good) Load directly from HL7 v2.x messages (best) –Pulls data from OBR and OBX segments –Stores NTE segment data

Demonstration Run LOINC Load LMOF One or two manual searches Help file Auto mapper and conflict reconciliation

Tips for Improving Mapping Success Expand Abbreviations Standardize Colloquial Terms ("ABG" for arterial blood gas) Ignore "Administrative" Terms Standardize Time References RELMA has built in tools for these

Discussion 62