1. Metadata Construction in Collaborative Research Networks
Hanieh Razzaghi, MPH

2. PEDSnet A National Pediatric Learning Health System (Forrest et al
What is PEDSnet?
Clinical Data Research Network (CDRN)
Facilitate large-scale research
Employs a Common Data Model
Aggregates pediatric EHR data from multiple institutions
Challenges
Opportunities
lack of EHR data’s orientation toward research analytics
diversity in EHR configurations
semantic heterogeneity
data peculiarities in pediatrics
large-scale
Building partnerships between researchers, clinicians, data-oriented scientists
Common Data Model (OMOP) – unified vocabulary
Building a framework for metadata to facilitate research

3. PEDSnet Data Flow and Opportunities for Metadata
PEDSnet sites
(at various locations)
PEDSnet Common Data Model
Extract-transform-load (ETL) data quarterly
Regular online meetings
GitHub version control
Data Quality and Characterization
Query Fulfillment
Data Science
PCORnet Common Data Model
Data Modeling
ETL specifications
Data Quality
Study Participation

4. Metadata Construction Opportunities
Data Life Cycle Step
Process Metadata
Content Metadata
Provenance
analyst approach in identifying appropriate source tables/fields
description of source tables (e.g., dates of use, purpose, use in site operations)
source gaps
ETL Process
analyst approach of resolving sources of ontology mismappings
ontology conflicts between source (local EHR) and PEDSnet data model
semantic and structural differences between source and PEDSnet data
Data Operations Management
frequency of updates
process of data integration
ID management
domains populated
storage and transfer information
size of database
permissions and access control
Data Quality
applying data quality checks
frequency of application
determination of domains, fields that checks apply to
integrating data from DQ catalog with data in GH repository (e.g., response time, resolution status, ranking, causes)
Scientific Data Request
documents needed to complete request
process documenting decisions made by investigator for dataset
exploring key concepts in research proposal to specific terms of interest
mapping of data variables to data model
integrating information about data quality, provenance, and ETL process
interface for researchers to understand how research proposal may look in a dataset