How Common Data Models in Real-World Evidence Research Can Improve Operational Healthcare
Real-world evidence (RWE) research has grown rapidly alongside the widespread adoption of electronic health record (EHR) systems. As healthcare organizations digitized clinical workflows, they began generating vast amounts of patient data that could be analyzed to improve treatments, evaluate medical products, and better understand real-world outcomes.
But the same infrastructure used for research can also unlock significant value for operational care—especially when data models evolve from periodic research datasets to near-real-time operational platforms.
This article explores how Common Data Models (CDMs)—originally designed to support research collaboration—can also enable more advanced operational analytics and cross-institutional clinical decision support.
What Are Real-World Data and Real-World Evidence?
The U.S. Food and Drug Administration defines these concepts as follows:
- Real-World Data (RWD): Data related to patient health status and healthcare delivery that is routinely collected from a variety of sources.
- Real-World Evidence (RWE): Clinical evidence about the usage and potential benefits or risks of medical products derived from analyzing RWD.
Sources of RWD include:
- Electronic health records (EHRs)
- Insurance claims
- Patient registries
- Clinical systems
- Digital health technologies
While EHRs are not the only source of RWD, they remain one of the most important contributors to large-scale healthcare datasets.
Why Research Consortia Use Common Data Models
Most research studies require access to large populations of eligible patients. A single healthcare institution rarely has enough data to support many types of clinical research or large-scale studies.
To overcome this limitation, healthcare organizations often collaborate through research alliances and consortiums. These collaborations aggregate real-world data from multiple institutions into unified or federated repositories.
However, because participating organizations use different systems and data structures, the data must be standardized. This is where Common Data Models (CDMs) come in.
A CDM defines a shared data structure that allows organizations to:
- Harmonize patient data from multiple sources
- Enable consistent analytics across institutions
- Support federated research queries
- Facilitate multi-center clinical studies
Several CDMs are widely used in healthcare research, including:
- OMOP
- i2b2
- CDISC
- Other specialized models used by research networks
The Role of ETL in CDM Data Harmonization
To populate CDM repositories, data from various EHRs and healthcare systems must be extracted and transformed into the standardized model.
This process is known as ETL (Extract, Transform, Load).
ETL pipelines typically involve:
- Extracting data from source systems such as EHRs
- Mapping concepts and terminology to standardized vocabularies
- Transforming data structures into the target CDM schema
- Loading harmonized data into the shared repository
This process is complex and labor-intensive because it requires detailed mapping between source systems and the CDM structure.
Traditionally, CDM repositories are refreshed infrequently—often quarterly. During these refresh cycles, the repository is updated with:
- New patient records
- Updates to existing records
- CDM version upgrades
- Vocabulary updates
For research use cases, this schedule is usually sufficient.
Why Operational Care Needs Real-Time CDMs
While periodic updates work for research workflows, they limit the usefulness of CDM repositories for operational healthcare scenarios.
Operational use cases often require near-real-time access to harmonized data, such as:
- Cross-institutional clinical decision support
- Operational analytics
- Population health management
- Care coordination across providers
Instead of integrating separately with each organization’s EHR system for every operational use case, organizations could use CDM repositories as shared operational data platforms.
This approach offers several advantages:
- A unified data model for analytics and decision support
- Consistent clinical terminology across institutions
- Access to richer and higher-quality harmonized data
- Reduced integration complexity
However, enabling this requires a major shift in how CDMs are updated.
Enabling Incremental CDM Updates
Traditional CDM refresh cycles rely on batch updates. Real-time operational use cases require incremental synchronization.
Instead of refreshing the entire dataset quarterly, systems must update:
- Individual patient records
- Specific clinical events
- Portions of patient histories
These updates must occur incrementally and continuously as data changes in the source systems.
Achieving this level of synchronization requires different tools and technologies than those typically used in research ETL workflows.
Bridging Research and Operations with Incremental CDM Pipelines
To support incremental CDM updates, Clinovera has partnered with InterSystems to implement new integration approaches using healthcare interoperability technologies.
The solution uses:
- InterSystems IRIS for Health
- InterSystems HealthConnect
These technologies have long been used in operational healthcare environments for system integration.
Their capabilities are now being extended to support incremental ETL pipelines into Common Data Models, enabling CDM repositories to be synchronized with operational systems in near real time.
This approach allows organizations to:
- Support both RWE research and operational care
- Maintain continuously updated CDM repositories
- Enable real-time analytics and decision support
Expanding the Role of CDMs in Healthcare
Several contract research organizations (CROs) and research institutions are already implementing incremental CDM pipelines in collaboration with Clinovera and InterSystems.
These initiatives demonstrate how CDM infrastructure can evolve beyond research datasets to become foundational platforms for data-driven healthcare operations.
By enabling near-real-time harmonization of clinical data, healthcare organizations can simultaneously:
- Accelerate clinical research
- Improve care delivery
- Enable more advanced analytics and decision support
- Unlock greater value from real-world data
As healthcare data ecosystems continue to expand, the convergence of RWE research platforms and operational data infrastructure may play a critical role in improving both patient outcomes and research efficiency.
Updated March 2026
FAQ
What is a common data model in healthcare?
A common data model is a standardized framework used to organize healthcare data from multiple sources into a consistent structure. It allows different datasets—such as EHRs, claims records, and registries—to be analyzed using the same format and terminology. This standardization supports large-scale analytics and improves the comparability of clinical data.
Why are common data models used in real-world evidence research?
Real-world evidence research analyzes healthcare data generated during routine clinical care. Because these datasets come from different systems and formats, they must be standardized before meaningful analysis is possible. Common data models provide a consistent structure that allows researchers to analyze data across organizations and studies.
How can common data models support operational healthcare?
When healthcare data is standardized, organizations can perform more reliable analytics on clinical operations and patient outcomes. Providers can analyze care patterns, evaluate treatment effectiveness, and identify trends across patient populations. This helps healthcare teams make more informed operational and clinical decisions.
What types of healthcare data are included in common data models?
Common data models typically include patient demographics, diagnoses, procedures, medications, laboratory results, and clinical encounters. These datasets are mapped to standardized vocabularies so that information from different healthcare systems can be analyzed together in a consistent way.
What challenges are involved in implementing common data models?
Implementing a CDM requires mapping data from multiple systems into a shared structure, which can be technically complex. Organizations must ensure data quality, consistent terminology, and accurate data mappings. Maintaining governance and updating the model as healthcare data evolves are also important parts of successful CDM implementation.
