Challenges and opportunities of combining linked primary and secondary care data from EHR into i2b2
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1 Challenges and opportunities of combining linked primary and secondary care data from EHR into i2b2 Harry Boutselakis October 2015
2 Outline Farr London linked EHR dataset CALIBER data warehouse and research portal Why and how i2b2 experience Next steps and challenges
3 Linked Data Sources Clinical Practice research database (CPRD) Myocardial Infraction National Audit Project(MINAP) Demographics, Diagnoses, Symptoms, Referrals, Investigations, prescriptions and health behaviours (READ codes) For Acute Coronary Syndromes : EGG findings, troponins (MINAP), Quantitative measurements CALIBER Discharge Diagnoses, procedures, (ICD, OPCS codes), admission and discharge dates Cause- specific mortality and social deprivation data Hospital Episode Statistics (HES) Office For National Statistics(ONS)
4 CALIBER Data Warehouse Unified relational data model Structured and unstructured data > 5m patients > 700 expert curated variables 350m diagnoses 400m prescriptions 250m tests 28m hospitalizations 12m procedures
5 CALIBER Portal Denaxas S. et al (UCL, Farr London)
6 Patient journey through the EHR landscape Healthy, GP registration Stable angina Pneumonia hospitalization Myocardial infarction hospitalization See GP for follow- up Death Patient s experience Primary care New patient check: blood pressure, smoking status, alcohol use etc. Diagnosis of stable angina. Blood tests (e.g. cholesterol). Prescription of aspirin, nitrates etc. Diagnosis of myocardial infarction Blood tests, blood pressure. Prescriptions of beta blocker, statin, etc. Time Sudden death Hospitalization (HES) Admit / discharge dates. Primary diagnosis: Viral pneumonia, not elsewhere classified Admit / discharge dates. Primary diagnosis: Acute myocardial infarction Procedure: Percutaneous coronary intervention Disease Registry (MINAP) ECG, cardiac markers. Diagnosis: STEMI Death Census (ONS) Date of death. Cause: 1) Rupture of abdominal aortic aneurysm 2) Old myocardial infarction Denaxas et al, CALIBER, Intl J Epidemiology, 2012;41(6):
7 Why i2b2? Overcome data silos but maintain data security Shorter translational journey and lower costs Rapid identification of large patient cohorts by researchers Elicit new research questions as the diversity of linked data sources and types increases Acceleration of analytical proposal development Large community adoption - transmart
8 Data Source and Destination Source schema > 300 tables i2b2 schema 5 tables SQL I2B2DEMODATA.MODIFIER_DIMENSION * P MODIFIER_PATH VARCHAR2 (700 BYTE) * MODIFIER_CD VARCHAR2 (50 BYTE) NAME_CHAR VARCHAR2 (2000 BYTE) MODIFIER_BLOB CLOB UPDATE_DATE DATE DOWNLOAD_DATE DATE IMPORT_DATE DATE SOURCESYSTEM_CD VARCHAR2 (50 BYTE) UPLOAD_ID NUMBER (38) MODIFIER_DIMENSION_PK (MODIFIER_PATH) MODIFIER_DIMENSION_PK (MODIFIER_PATH) MD_IDX_UPLOADID (UPLOAD_ID) I2B2DEMODATA.CONCEPT_DIMENSION * CONCEPT_PATH VARCHAR2 (700 BYTE) * CONCEPT_CD VARCHAR2 (50 BYTE) * NAME_CHAR VARCHAR2 (2000 BYTE) CONCEPT_BLOB CLOB UPDATE_DATE DATE DOWNLOAD_DATE DATE IMPORT_DATE DATE SOURCESYSTEM_CD VARCHAR2 (50 BYTE) UPLOAD_ID NUMBER (38) CD_UPLOADID_IDX (UPLOAD_ID) I2B2DEMODATA.VISIT_DIMENSION * P ENCOUNTER_NUM NUMBER (38) * P PATIENT_NUM NUMBER (38) ACTIVE_STATUS_CD VARCHAR2 (50 BYTE) START_DATE DATE END_DATE DATE INOUT_CD VARCHAR2 (50 BYTE) LOCATION_CD VARCHAR2 (50 BYTE) LOCATION_PATH VARCHAR2 (900 BYTE) LENGTH_OF_STAY NUMBER (38) VISIT_BLOB CLOB UPDATE_DATE DATE DOWNLOAD_DATE DATE IMPORT_DATE DATE SOURCESYSTEM_CD VARCHAR2 (50 BYTE) UPLOAD_ID NUMBER (38) VISIT_DIMENSION_PK (ENCOUNTER_NUM, PATIENT_NUM) I2B2DEMODATA.OBSERVATION_FACT * P ENCOUNTER_NUM NUMBER (38) * PATIENT_NUM NUMBER (38) P * CONCEPT_CD VARCHAR2 (50 BYTE) P * PROVIDER_ID VARCHAR2 (50 BYTE) P * START_DATE DATE P * MODIFIER_CD VARCHAR2 (100 BYTE) P * INSTANCE_NUM NUMBER (18) VALTYPE_CD VARCHAR2 (50 BYTE) TVAL_CHAR VARCHAR2 (255 BYTE) NVAL_NUM NUMBER (18,5) VALUEFLAG_CD VARCHAR2 (50 BYTE) QUANTITY_NUM NUMBER (18,5) UNITS_CD VARCHAR2 (50 BYTE) END_DATE DATE LOCATION_CD VARCHAR2 (50 BYTE) OBSERVATION_BLOB CLOB CONFIDENCE_NUM NUMBER (18,5) UPDATE_DATE DATE DOWNLOAD_DATE DATE IMPORT_DATE DATE SOURCESYSTEM_CD VARCHAR2 (50 BYTE) UPLOAD_ID NUMBER (38) I2B2DEMODATA.PROVIDER_DIMENSION * P PROVIDER_ID VARCHAR2 (50 BYTE) * PROVIDER_PATH VARCHAR2 (700 BYTE) NAME_CHAR VARCHAR2 (850 BYTE) PROVIDER_BLOB CLOB UPDATE_DATE DATE DOWNLOAD_DATE DATE IMPORT_DATE DATE SOURCESYSTEM_CD VARCHAR2 (50 BYTE) UPLOAD_ID NUMBER (38) PROVIDER_DIMENSION_PK (PROVIDER_ID) PD_IDX_NAME_CHAR (PROVIDER_ID, NAME_CHAR) PROD_UPLOADID_IDX (UPLOAD_ID) VISIT_DIMENSION_PK (ENCOUNTER_NUM, PATIENT_NUM) VD_UPLOADID_IDX (UPLOAD_ID) VISITDIM_EN_PN_LP_IO_SD_IDX (ENCOUNTER_NUM, PATIENT_NUM, LOCATION_PATH, INOUT_CD, START_DATE, END_DATE, LENGTH_OF_STAY) VISITDIM_STD_EDD_IDX (START_DATE, END_DATE) I2B2DEMODATA.PATIENT_DIMENSION * P PATIENT_NUM NUMBER (38) VITAL_STATUS_CD VARCHAR2 (50 BYTE) BIRTH_DATE DATE DEATH_DATE DATE SEX_CD VARCHAR2 (50 BYTE) AGE_IN_YEARS_NUM NUMBER (38) LANGUAGE_CD VARCHAR2 (50 BYTE) RACE_CD VARCHAR2 (50 BYTE) MARITAL_STATUS_CD VARCHAR2 (50 BYTE) RELIGION_CD VARCHAR2 (50 BYTE) ZIP_CD VARCHAR2 (10 BYTE) STATECITYZIP_PATH VARCHAR2 (700 BYTE) INCOME_CD VARCHAR2 (50 BYTE) PATIENT_BLOB CLOB UPDATE_DATE DATE DOWNLOAD_DATE DATE IMPORT_DATE DATE SOURCESYSTEM_CD VARCHAR2 (50 BYTE) UPLOAD_ID NUMBER (38) PATIENT_DIMENSION_PK (PATIENT_NUM) PD_IDX_DATES (PATIENT_NUM, VITAL_STATUS_CD, BIRTH_DATE, DEATH_DA PD_IDX_ALLPATIENTDIM (PATIENT_NUM, VITAL_STATUS_CD, BIRTH_DATE, D PD_IDX_STATECITYZIP (STATECITYZIP_PATH, PATIENT_NUM) PATD_UPLOADID_IDX (UPLOAD_ID) PATIENT_DIMENSION_PK (PATIENT_NUM) OBSERVATION_FACT_PK (ENCOUNTER_NUM, CONCEPT_CD, PROVIDER_ID, START_DATE, MODIFIER_CD, INSTANCE_NUM) OF_CTX_BLOB (OBSERVATION_BLOB) OBSERVATION_FACT_PK (ENCOUNTER_NUM, CONCEPT_CD, PROVIDER_ID, START_DATE, MODIFIER_CD, INSTANCE_NUM) FACT_NOLOB (PATIENT_NUM, START_DATE, CONCEPT_CD, ENCOUNTER_NUM, INSTANCE_NUM, NVAL_NUM, TVAL_CHAR, VALTYPE_CD, MODIF FACT_CNPT_PAT_ENCT_IDX (CONCEPT_CD, INSTANCE_NUM, PATIENT_NUM, ENCOUNTER_NUM) FACT_PATCON_DATE_PRVD_IDX (PATIENT_NUM, CONCEPT_CD, START_DATE, END_DATE, ENCOUNTER_NUM, INSTANCE_NUM, PROVIDER_ID, N
9 Schema and application re- deployment workflow Add farr project in i2b2hive schema Run the DDL scripts for each farr schema Configure workbench for new project Add farr user in i2b2pm And set the roles for the farr user Edit the configuration files for the application to include the new project and re- deploy Test everything works even though there is no data yet Create schemas farr_data farr_meta Farr_work Add the farr service to the webclient index file Ready to load the farr training data
10 Farr i2b2 schemas Load lookup tables and ontologies DICTIONARY_DRUG DICTIONARY_READ DEATH_CODES (ICD10) PRACTICE_CODES MINAP_CODES PROVIDERS (HES, GP, MINAP) DICTIONARY ICD10 (FROM I2B2) farr_meta Load the star schema Patients to patient_dimension Ontology columns to concept_dimension Ontology to modifier_dimension Providers (HES, GP, MINAP) to provider_dimension Each event of prescription, diagnosis, visit, therapy, death, spell, episode to visit_dimension Finally the the observation fact is loaded from the facts of the source tables and joins of the dimension tables farr_data Existing DDL from i2b2workdata Provides functionality for the drag and drop saving of cohorts for future use farr_work
11 Loading Workflow patient_dimension from patients Create provider (source) Ontology farr_meta provider_dimension custom DML visit_dimension Readcodes menu (ontology) farr_meta ont - > concept_dimension observation_fact joining dimension tables + raw facts from farr_fake
12 System Overview Data Architect/Managers/Developers Researchers/Clinicians/Managers emedlab and/or UCL Safe Haven Staging TransMart Genomics I2b2 ETL Standardise Clean Complete Link Annotate webserver app server analytic nodes analytic nodes analytic nodes Sources Primary Care Hospital Episodes NHIC Genomic Bespoke Studies UK Biobank transmart ONS
13 Next steps and challenges SDLC in a safe haven environment. Continuous integration of ever increasing heterogeneous data sources. Omic data integration Standardising ontology and look up entities. standardised phenotypic metamodel to exchange, validate and improve research quality computable phenotypes Standardised EHR data model
14 Acknowledgements Data Lab Team Farr London- UCL IHI Dr. Spiros Denaxas Dr. Kenan Direk Natalie Fitzpatrick Dr. Arturo González- Izquierdo Christiana McMahon Harry Boutselakis Data Architect Farr London / UCL
15 Thank you Questions?
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