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

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

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

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

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

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;

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

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

SNOW SHRINE+ SCILHS 2.02

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

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

Questions? Comments?

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

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

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)

I2b2 Load Documentation on the UserWeb https://datahandbook. epic UNMC i2b2 ETL Procedures 20160803.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