preparing electronic health records for multi-site cer studies michael g. kahn 1,3,4, lisa schilling...

Preparing Electronic Health Recordsfor Multi-Site CER Studies

Michael G. Kahn1,3,4, Lisa Schilling2

1Department of Pediatrics, University of Colorado, Denver2Department of Medicine, University of Colorado, Denver

3Colorado Clinical and Translational Sciences Institute 4Department of Clinical Informatics, Children’s Hospital Colorado

AcademyHealth Annual Research MeetingBuilding a Data Infrastructure for Multi-stakeholder Comparative Effectiveness Research

26 June 2012Michael.Kahn@ucdenver.edu

Funding provided by AHRQ 1R01HS019908 (Scalable Architecture for Federated Translational Inquiries Network)

Setting the context:AHRQ Distributed Research Networks

• AHRQ ARRA OS: Recovery Act 2009: Scalable Distributed Research Networks for Comparative Effectiveness Research (R01)

• Goal: enhance the capability and capacity of electronic health networks designed for distributed research to conduct prospective, comparative effectiveness research on outcomes of clinical interventions.

Funding provided by AHRQ 1R01HS019908 (Scalable Architecture for Federated Translational Inquiries Network)

AHRQ Distributed Research Networks Funded Projects

• SAFTINet: Scalable Architecture for Federated Therapeutic Inquiries Network– Lisa M. Schilling, University of Colorado Denver

(R01 HS19908-01)

• SCANNER: Scalable National Network for Effectiveness Research– Lucila Ohno-Machado, University of California San Diego

(R01 HS19913-01)

• SPAN: Scalable PArtnering Network for CER: Across Lifespan, Conditions, and Settings– John F. Steiner, Kaiser Foundation Research Institute

(R01 HS19912-01)

Funding provided by AHRQ 1R01HS019908 (Scalable Architecture for Federated Translational Inquiries Network)

SAFTINet Partners• Clinical partners

– Colorado Community Managed Care Network and the Colorado Associated Community Health Information Enterprise

• Colorado Federally Qualified Health Centers– Denver Health and Hospital Authority– Cherokee Health Systems, Tennessee

• Technology partners– University of Utah, Center for High Performance Computing– QED Clinical, Inc., d/b/a CINA

• Medicaid partners– Colorado Health Care Policy & Financing– Utah Department of Public Health (partnership in development)– TennCare and Tennessee managed care organizations (partnership in

development)

• Leadership– University of Colorado Denver– American Academy of Family Physicians, National Research Network

Key Differences between EHR and CER data

EHR Data CER Data EHR->CER task

Fully identified LDS or de-identified Strip identifiers; keep mappings?

Local codes and values Standardized codes and values

Terminology and value set mapping (manual!)

Broad data domains Focused data domains Filtering by patient, encounter, date, facility

Variable data quality; high level of missingness

Substantial data quality processes applied

Data profiling; iterative investigations

Lots of free text Fully coded data only NLP or ignore free text

Local access only Shared access Distributed or centralized data access

Single data source Multiple data sources Record linkage

Key Differences between EHR and CER data

EHR Data CER Data EHR->CER task

Fully identified LDS or de-identified Strip identifiers; keep mappings?

Local codes and values Standardized codes and values

Terminology and value set mapping (manual!)

Broad data domains Focused data domains Filtering by patient, encounter, date, facility

Variable data quality; high level of missingness

Substantial data quality processes applied

Data profiling; iterative investigations

Lots of free text Fully coded data only NLP or ignore free text

Local access only Shared access Distributed or centralized data access

Single data source Multiple data sources Record linkage

Key Differences between EHR and CER data

EHR Data CER Data EHR->CER task

Fully identified LDS or de-identified Strip identifiers; keep mappings?

Local codes and values Standardized codes and values

Terminology and value set mapping (manual!)

Broad data domains Focused data domains Filtering by patient, encounter, date, facility

Variable data quality; high level of missingness

Substantial data quality processes applied

Data profiling; iterative investigations

Lots of free text Fully coded data only NLP or ignore free text

Local access only Shared access Distributed or centralized data access

Single data source Multiple data sources Record linkage

Key Differences between EHR and CER data

EHR Data CER Data EHR->CER task

Fully identified LDS or de-identified Strip identifiers; keep mappings?

Local codes and values Standardized codes and values

Terminology and value set mapping (manual!)

Broad data domains Focused data domains Filtering by patient, encounter, date, facility

Variable data quality; high level of missingness

Substantial data quality processes applied

Data profiling; iterative investigations

Lots of free text Fully coded data only NLP or ignore free text

Local access only Shared access Distributed or centralized data access

Single data source Multiple data sources Record linkage

Key Differences between EHR and CER data

EHR Data CER Data EHR->CER task

Fully identified LDS or de-identified Strip identifiers; keep mappings?

Local codes and values Standardized codes and values

Terminology and value set mapping (manual!)

Broad data domains Focused data domains Filtering by patient, encounter, date, facility

Variable data quality; high level of missingness

Substantial data quality processes applied

Data profiling; iterative investigations

Lots of free text Fully coded data only NLP or ignore free text

Local access only Shared access Distributed or centralized data access

Single data source Multiple data sources Record linkage

Key Differences between EHR and CER data

EHR Data CER Data EHR->CER task

Fully identified LDS or de-identified Strip identifiers; keep mappings?

Local codes and values Standardized codes and values

Terminology and value set mapping (manual!)

Broad data domains Focused data domains Filtering by patient, encounter, date, facility

Variable data quality; high level of missingness

Substantial data quality processes applied

Data profiling; iterative investigations

Lots of free text Fully coded data only NLP or ignore free text

Local access only Shared access Distributed or centralized data access

Single data source Multiple data sources Record linkage

Key Differences between EHR and CER data

EHR Data CER Data EHR->CER task

Fully identified LDS or de-identified Strip identifiers; keep mappings?

Local codes and values Standardized codes and values

Terminology and value set mapping (manual!)

Broad data domains Focused data domains Filtering by patient, encounter, date, facility

Variable data quality; high level of missingness

Substantial data quality processes applied

Data profiling; iterative investigations

Lots of free text Fully coded data only NLP or ignore free text

Local access only Shared access Distributed or centralized data access

Single data source Multiple data sources Record linkage

A common data model is critical!

CINACDR

Other EHR

Local Data

Warehouse

Other EHR

ExistingClinical

Registries

Other EHR

Limited Data SetCommon Data Model

Common Terminology

Common Query Interface

Limited Data SetCommon Data Model

Common Terminology

Limited Data SetCommon Data Model

Common Terminology

Crossing the CER chasm !!CER

ROSITA-GRID-PORTAL

Grid Portal

Why ROSITA?

• ROSITA: Reusable OMOP and SAFTINet Interface Adaptor

• ROSITA: The only bilingual Muppet

• Converts EHR data into research limited data set1. Replaces local codes with standardized codes2. Replaces direct identifiers with random identifiers3. Supports clear-text and encrypted record linkage4. Provides data quality metrics5. Pushes data sets to grid node for distributed queries

ROSITA: transforming EHR data for comparative effectiveness research

ETLXML

ETLXMK

ROSITA

JDBC

JDBC

OMOP CDM V3Grid Data Service

SAFTINet Data QualityData Service

Client CDW

Medicaid

SAFTINet ETL specifications

SAFTINet ETL Specifications

SAFTINet ETL Specifications

Transforming EHR Data:What does ROSITA do?

What does ROSITA do?

What does ROSITA do?

Why ROSITA?

• Converts EHR data into research limited data set1. Replaces local codes with standardized codes2. Replaces direct identifiers with random identifiers3. Supports clear-text and encrypted record linkage4. Provides data quality metrics5. Pushes data sets to grid node for distributed

queries

Do not have Medicaid figured out

ROSITA Security Discussion Framework

ROSITA: Current Status

• Software development underway– In Phase 1: 16 week development

• clinical data only; no Medicaid– Phase 2: Medicaid + record linkage

• OMOP data model V4 finalized!– Clinical & financial extensions

• All SAFTINet partners have begun ETL activities– Two sites have provided full ETL extracts for

development and testing• Everything is/will be available

Questions?

Michael.Kahn@ucdenver.edu

Funding provided by AHRQ 1R01HS019908 (Scalable Architecture for Federated Translational Inquiries Network)