Survey
* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project
Download for observables - i2b2 Community Wiki
Document related concepts
no text concepts found
Transcript
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 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 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 ONC Top Level Model for Semantic Interoperability Information model:(Clinical) Demographics: LOINC,Observables HL7/OMB code set Social and medical history: Findings SNOMED and CT Situations Problem list/encounter diagnoses: SNOMED CT Findings, Events and Situations ICD-10-CM /Lab results Lab resultsand (observables): Lab LOINC Radiology other test results: (Laboratory) Observables LOINC,Observables SNOMED CT Physical findings: (Clinical) observables Medication orders: orders: RxNORM, SNOMED CT Medication Laboratory Orders: Observables Orders: (Laboratory) LOINC Laboratory Immunizations: CVX, MVX Immunizations: Procedures: Procedures: Documents: CPT, HCPCS Documents: LOINC ONC Terminology Model for Semantic Interoperability Demographics: (Clinical) LOINC,Observables HL7/OMB code set Social and medical history:Findings SNOMED and CT Situations Problem list/encounter diagnoses: SNOMED CT Findings, Events and Situations / ICD-10-CM Lab results (observables):(Laboratory) Lab LOINC Observables Physical findings: (Clinical) LOINC,Observables SNOMED CT observables Medication orders: RxNORM, SNOMED CT Observables Laboratory Orders: (Laboratory) LOINC Immunizations: CVX, MVX Procedures: CPT, HCPCS Documents: LOINC I2b2 Star schema: One fact per record (= One question + answer) i2b2 Observation fact Patient Encounter Concept_CD Observation Fact Modifier_CD Instance_num Provider VALTYPE_CD UNITS_CD TVAL_CHAR NVAL_NUM OBSERVATION_BLOB Start_date End_date 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 What is the patient hemoglobin? “13.2 mg/dl” i2b2 Observation fact Patient Encounter Concept_CD LOINC:2951-2 Provider Observation Fact 13.2 Mg/dl Modifier_CD 11/1/2016 7:30AM End_date Epic Laboratory in i2b2 Maps LRRCLARITY_COMPONENT 1534435|“Hemoglobin”|”HGB”|LOINC:718-7| Record Data ORD ORDER_RESULTS Laboratory results LABS_TRANSFORM.sql What is the patient problem? “Breast cancer” i2b2 Observation fact Patient Encounter Concept_CD SNOMEDCT:254837009 (Malignant tumor of breast) Provider Observation Fact Modifier_CD DX|PROB\ACTIVE Start_date End_date 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 “Severe allergic rx to aspirin with anaphylaxis” i2b2 Observation fact Patient Encounter Concept_CD Observation Fact Modifier_CD SNOMEDCT:39579001 (Anaphylaxis) Instance:10030 DX|ALGRX Start_date Provider End_date 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 LOINC; SNOMED CT 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; SNOMED CT 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 deidentified) OBSERVATION_FACT tables Install and maintain i2b2 standards metadata & metadataxml for browsing and query Extract CDMV3 tables (SAS) from OBSERVATION_FACT I2b2 Load Documentation on the Epic UserWeb https://datahandbook.epic.com/Reports/Details/9000400 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 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 Questions? Comments?
Related documents