1. Deploying ONC terminology standards in SNOW SHRINE i2b2 data warehouse
James R. Campbell MD
Scott Campbell PhD
Jay Pedersen MS
Bret Gardner MS
James McClay MD
Nebraska Medicine
University of Nebraska Medical Center

2. UNMC Operational Expectations of i2b2 load
Query by instance or aggregate data sets across collaborator sites MUST occur with little or no mapping
Queries MUST run in collaborating datamarts without mapping or revision
Store facts so that query by value MUST be supported by datatype: Numeric, Code lists, structured text
Narrative reflects content of many EHRs but is not interoperable and relevant elements for research use cases SHOULD be structured when extracted
Managing and querying the datamart SHOULD not require SQL features not common to all industry operating systems
Satisfying these expectations would assure ease of data
consolidation across GPC for GROUSE 7 data set creation

3. Threats to network interoperation of i2b2 data
Structure and organization of observation facts(OF) are idiosyncratic between data sites
Metadata is dependent upon OF structure and must be identical across datamarts for interoperation of queries, especially aggregation (folder searches)
ONC ontologies are variably deployed across i2b2 community and largely not current in deployment
Large ontologies are costly and time consuming to install as i2b2 metadata
Change management of site data coded with ONC ontologies is spotty, difficult to do and inconsistently applied at metadata install

4. Desiderata for interoperability of OBSERVATION_FACTs
In the ONC standard information model, ontologies
have differing uses:
Diagnoses and findings are fully coordinated assertions of fact (ICD*, SNOMED CT clinical findings)
Procedures and regimes may be coordinated within the vendor information model to populate orders, billable events or results (CPT, ICD*, SNOMED CT)
Clinical and laboratory Observables are meant to be the ‘question’ and are coordinated with an ‘answer’ or result to coordinate an evaluation finding (LOINC, SNOMED CT)
Medications and treatments (RXNORM) may be coordinated within the vendor information model to populate orders and prescriptions. NDC are inventory codes used by pharmacists and nursing to coordinate and record dispense and med administration events.
When possible for observables, CONCEPT_CD should align with interoperability reference standards as the question and valuesets of answers should be placed in NVAL_NUM or TVAL_CHAR
Pre-coordinate question-answer into CONCEPT_CD only if valueset is small and there is no standard observable code
When coded ontologies are the answer , use…MODIFIER_CD for imposing context of the question
Complex data records (allergy list, medication orders, complex tests) should be organized into set of facts that are organized for context by MODIFIER_CD and linked by INSTANCE_NUM
Choice of TVAL_CHAR, NVAL_NUM or BLOB for results should reflect datatypes; use published standard valuesets always for interoperability

5. Desiderata for interoperability of OBSERVATION_FACTs
When possible for observables, CONCEPT_CD should align with interoperability reference standards as the question and valuesets of answers should be placed in NVAL_NUM or TVAL_CHAR; metadataxml should support query by value and units conversion
Choice of TVAL_CHAR, NVAL_NUM or BLOB for results should reflect reference datatypes; use published standard valuesets whenever possible for interoperability
Pre-coordinate question-answer into CONCEPT_CD only if valueset is small and there is no standard observable code
When coded ontologies are the finding , use…MODIFIER_CD for imposing context from the vendor information model
Complex data records (allergy list, medication orders, complex tests) should be organized into set of facts that are organized for context by MODIFIER_CD and linked by INSTANCE_NUM

6. Nebraska Medicine i2b2 Data Architecture
I2b2 Information Class
Standard Metadata Ontology
ADT history
Epic Facility
Cancer registry
ICD-O
Clinical measurements
Social history
LOINC; SNOMED CT
Demographics
Diagnoses (Encounter dx; Problems; Past Med History)
(ICD-9-CM); ICD-10-CM;
SNOMED CT
Encounters
Epic Facility; Encounter classes
Laboratory results
LOINC
Medications (Orders and Rx;
Dispense records)
RXNORM;
NDC
Procedures (Professional services; Hospital procedures;
Procedure history )
CPT;
(ICD-9-CM); ICD-10-PCS; HCPCS;

7. BD2K Grant Summary
SA1: Develop and support ONC compliant metadata tooling for procurement, installation and temporal management of i2b2 datamarts
SA2: Develop, test and deploy terminology extensions for structured reporting, clinical care and research in cancer including anatomic pathology and genomic/molecular data
SA3: Employ structured AP and MP data in managing a research tissue biobank
SA4: Install the ONC compliant metadata (SA1) and terminology extensions (SA2) in a research data network and demonstrate interoperability of relevant research queries across multiple cooperating datamarts

8. UNMC Goals for SNOW SHRINE + SCILHS
Support interoperable i2b2 client queries between GPC and collaborating sites
Support exposure of full spectrum of site data, NOT just CDMV3 dataset
Support library of ETL s of data compliant with ONC reference terminologies for all EHRs
For major datasets, distribute and install comprehensive ONC compliant SCILHS metadata developed in collaboration with Harvard
Develop distribution and maintenance architecture for collaborators supporting six month refresh cycle of ontology metadata which can be easily and quickly installed and will support site management of historicity
Develop tooling for interoperation of SCILHS metadata and recruit SNOW SHRINE datamarts as collaborators

9. SNOW SHRINE+ SCILHS 2.02

10. Observations: SNOW SHRINE+ SCILHS 2.02(2.20)
Demographics: CDMV3 only; have not deployed pre-coordinated age folders
Diagnosis: ICD-9-CM and ICD-10-CM good; SNOMED CT (conditions) not deployed
Lab results: LOINC coded; 141M/202.4M of UNMC facts; has not deployed many common tests
Medication: RXNORM and NDC; 40.6M/61M UNMC facts; have not evaluated modifier structure
Vital: CDMV3 only; no other physical data

11. BD2K Grant proposals
SA1: Collaborate with Harvard and enhance SCILHS metadata and tooling; UNMC will develop distribution and maintenance infrastructure under federal funding and will support standard i2b2 metadata and temporal management tools for GPC and SCILHS
SA2: Identify one or more sites to collaborate on deploying structured pathology data into i2b2
SA3: Identify collaborators for expanded tissue biobank indexing and management
SA4: Identify SNOW SHRINE collaborators to deploy ONC-SCILHS metadata and tooling; agree on research questions for one or more demonstrations and papers on research data interoperability

12. Questions?
Comments?

14. UNMC Research Data Development Model
Meaningful Use
Standards(Epic &UNMC)
Shared SQL ETLs
UNMC Standards mapping C L A R I T Y
(SQL)
I2b2 Ontology
Metadata C H R O N I L E S
(Cache) P C O R I D M
(SQL) i 2 b
(SQL
Star
Schema) C A B O D L E
(SQL)
SAS/SQL
Popmednet Standards

15. ONC Terminology Model for Semantic Interoperability
Demographics: LOINC, HL7/OMB code set
Social and medical history: SNOMED CT
Problem list: SNOMED CT
Encounter and billing diagnoses: ICD-10-CM
Lab results (observables): Lab LOINC
Physical findings: LOINC, SNOMED CT observables
Medication orders: RxNORM, SNOMED CT
Medication dispense & administration records: NDC
Laboratory orders: LOINC
Immunizations: CVX, MVX
Procedures: ICD-10-PCS, CPT, HCPCS
Documents: LOINC

16. EPIC ETLs of Standards Data
Problem list – US ed SNOMED CT
Past medical history – US ed SNOMED CT
Encounter, billing diagnoses – ICD-9-CM, ICD-10-CM
Procedures – CPT, ICD-9-CM, ICD-10-PCS
Surgical history – US ed SNOMED CT
Laboratory results – LOINC
Pathology and genomics – LOINC and SNOMED CT
Clinical findings/Vital signs/Social history – LOINC, SNOMED CT
Medication orders and prescriptions – RXNORM
Medication administration/dispense events – NDC
(Immunizations – CVX, MVX)
(Allergies – SNOMED CT and RXNORM)

17. I2b2 Load Documentation on the UserWeb https://datahandbook. epic
UNMC i2b2 ETL Procedures docx
Identified (idwk) dataset procedures:
Heronloader data extracts and table builds(ETLs)
Blueheronmetadata metadata build
De-identified (deid) dataset procedures:
Heronloader extracts and table builds
Blueheronmetadata
CDMV3 extracts from deid
Python scripts:
Fact counter code