Data Innovation Methods
CPCRC supports a range of data innovation methods designed to help researchers work with real-world primary care data.
These methods enable the transformation, integration, and analysis of complex datasets, supporting research that reflects how care is delivered across diverse clinical settings.
What This Includes
CPCRC supports methodological approaches across the full data lifecycle, from preparation to advanced analysis.
Data Transformation & Standardization
Preparing primary care data for research use.
Includes:
- EMR data cleaning and normalization
- Application of standard coding frameworks
- Variable harmonization across sites and networks
- Data quality assessment and validation
Data Integration & Linkage
Connecting datasets to support broader analysis.
Includes:
- Linking EMR data with administrative or external datasets
- Integration of patient-reported measures (PREMs and PROMs)
- Multi-source dataset development
- Cross-jurisdictional data alignment
Statistical & Analytical Methods
Supporting core analytical approaches used in primary care research.
Includes:
- Descriptive and comparative analysis
- Regression modeling and stratification
- Cohort creation and longitudinal analysis
- Multi-site and cross-network analysis
Data Science & Advanced Methods
Supporting computational and predictive approaches.
Includes:
- Predictive modeling and risk stratification
- Machine learning classification and clustering
- Natural language processing (NLP) for clinical text
- Simulation and scenario modeling
- Feature engineering and data preparation for modeling
Real-World Research Methods
Applying methods within clinical and practice-based environments.
Includes:
- Pragmatic and embedded trial designs
- Implementation science approaches
- Rapid-cycle evaluation
- Mixed-methods designs combining quantitative and qualitative data
Federated & Distributed Methods
Supporting analysis across decentralized data environments.
Includes:
- Federated analytics across participating nodes
- Distributed data querying
- Privacy-preserving data collaboration
- Secure multi-site analysis approaches
How We Support Researchers
CPCRC provides access to data innovation methods through its national network of partner organizations and specialists.
Support includes:
- Methodological consultation during study design
- Guidance on selecting appropriate methods based on data and research questions
- Access to analytics and data science expertise
- Alignment with data access, privacy, and feasibility requirements
- Connections to collaborators with specialized methodological expertise
