1. Enhancing interoperation: an i2b2 ETL schema for Epic EHRs
James R. Campbell MD
James McClay MD
Departments of Internal Medicine & Emergency Medicine
University of Nebraska Medical Center

2. Outline Organizing i2b2 for interoperation
I2b2 Extract, Transfer and Load architecture for Epic EHRs
Data warehouse extracts vs CCDA vs FHIR interface for transportability of code

3. Operational Expectations of i2b2 load
Query / aggregate data across collaborators with little or no mapping
(Move towards US standard data model)
Store facts so that query by value is supported: Numeric, Code lists, structured text
Narrative reflects content of many EHRs but is not interoperable and should be structured when extracted

4. ONC Top Level Model for Semantic Interoperability
Information model:
Social and medical history:
Problem list/encounter diagnoses:
Lab results
Radiology and other test results:
Physical findings:
Medication orders:
Laboratory Orders:
Immunizations:
Procedures:
Documents:
Demographics: LOINC, HL7/OMB code set
Social and medical history: SNOMED CT
Problem list/encounter diagnoses: SNOMED CT / ICD-10-CM
Lab results (observables): Lab LOINC
Physical findings: LOINC, SNOMED CT observables
Medication orders: RxNORM, SNOMED CT
Laboratory Orders: LOINC
Immunizations: CVX, MVX
Procedures: CPT, HCPCS
Documents: LOINC
(Clinical) Observables
Findings and Situations
Findings, Events and Situations
(Laboratory) Observables
(Clinical) Observables
(Laboratory) Observables

5. ONC Terminology Model for Semantic Interoperability
Demographics: LOINC, HL7/OMB code set
Social and medical history: SNOMED CT
Problem list/encounter diagnoses: SNOMED CT / ICD-10-CM
Lab results (observables): Lab LOINC
Physical findings: LOINC, SNOMED CT observables
Medication orders: RxNORM, SNOMED CT
Laboratory Orders: LOINC
Immunizations: CVX, MVX
Procedures: CPT, HCPCS
Documents: LOINC
(Clinical) Observables
Findings and Situations
Findings, Events and Situations
(Laboratory) Observables
(Clinical) Observables
(Laboratory) Observables

6. I2b2 Star schema: One fact per record (= One question + answer)
i2b2 Observation fact
Encounter
Patient
Observation Fact
Instance_num
Concept_CD
Modifier_CD
Provider
Start_date
End_date
VALTYPE_CD
UNITS_CD
TVAL_CHAR
NVAL_NUM OBSERVATION_BLOB

7. Desiderata for interoperability of OBSERVATION_FACTs (OFs)
When possible for observables, CONCEPT_CD should align with interoperability reference standards
When coded ontologies are the answer, use…MODIFIER_CD for imposing information model context
Complex data records (allergy list, medication orders) should be organized into set of facts that are organized by content with MODIFIER_CD linked by INSTANCE_NUM
Precoordinate results into CONCEPT_CD only if valueset is small and there is no reference observable code
Choice of TVAL_CHAR, NVAL_NUM or BLOB for results should reflect datatypes; use published valuesets always for interoperability

8. What is the patient hemoglobin? “13.2 mg/dl”
i2b2 Observation fact
Encounter
Patient
Modifier_CD
Concept_CD
LOINC:2951-2
Observation Fact
Provider
11/1/2016 7:30AM
End_date
13.2
Mg/dl

9. Epic Laboratory in i2b2 LRRCLARITY_COMPONENT Laboratory results
LABS_TRANSFORM.sql
LRRCLARITY_COMPONENT
|“Hemoglobin”|”HGB”|LOINC:718-7|
Maps
ORD ORDER_RESULTS
Record Data

10. What is the patient problem? “Breast cancer”
i2b2 Observation fact
Encounter
Patient
Concept_CD
SNOMEDCT: (Malignant tumor of breast)
Observation Fact
Modifier_CD
DX|PROB\ACTIVE
Provider
Start_date
End_date

11. I2b2 Metadata
I2b2 client employs reference ontologies such as ONC terminologies in the user interface:
displays the hierarchical structure of the terminology which may be useful for concept navigation
supports queries of sets of concepts (hierarchical sub-trees) supported by boolean logic

12. “Severe allergic rx to aspirin with anaphylaxis”
i2b2 Observation fact
Encounter
Patient
Concept_CD
SNOMEDCT: (Anaphylaxis)
Observation Fact
Instance:10030
Modifier_CD
DX|ALGRX
Provider
Start_date
End_date

13. 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;

14. Loading an i2b2 warehouse from Epic with ONC terminology
Install and maintain terminology maps in Epic and i2b2
Refresh research Clarity (Epic includes mapping data)
Run ETLs and load (identified) staging tables
Obfuscate dates, anonymize patients and encounters and populate (identified and de-identified) OBSERVATION_FACT tables
Install and maintain i2b2 standards metadata & metadataxml for browsing and query
Extract CDMV3 tables (SAS) from OBSERVATION_FACT

15. I2b2 Load Documentation on the Epic 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

16. FHIR datatypes supported by Epic
*Adverse reaction
*Allergy/Intolerance
*Conditions
Devices
Document Reference
*Family History
Goals
Immunizations
Lab results
*Medications
*Prescription
*Patient
*Practitioner
Procedures
*Substance
*Social history; smoking status
*Vital signs
*2015 release

17. Questions?
Comments?