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RWD/RWE Fragmentation Hinders Progress

  //food-wine.news-articles.net/content/2026/02/07/rwd-rwe-fragmentation-hinders-progress.html
  Published in Food and Wine on Saturday, February 7th 2026 at 16:50 GMT by Clinical Trials Arena
      Locales: UNITED STATES, UNITED KINGDOM

Saturday, February 7th, 2026 - The promise of real-world data (RWD) and real-world evidence (RWE) continues to swell, heralded as a transformative force in healthcare, drug development, and patient care. However, that promise remains partially unrealized, shackled by a persistent issue: fragmentation. While the volume of RWD is exploding - stemming from electronic health records (EHRs), insurance claims, patient registries, wearable sensors, and increasingly, social determinants of health data - its diverse and often incompatible formats are hindering its effective use and, critically, its regulatory acceptance. The call for a common data foundation isn't merely a technical plea; it's a necessary step towards unlocking the full potential of RWD/RWE and ensuring its responsible integration into the healthcare ecosystem.

The Patchwork Quilt of Data Today

Currently, the RWD landscape resembles a patchwork quilt, stitched together from disparate fabrics. Each data source operates with its own nuances - differing data formats, coding systems (ICD-10, SNOMED CT, LOINC), and varying levels of quality control. An EHR system in a rural hospital might capture data in a fundamentally different way than a large, integrated health network's system. Claims data, while comprehensive in terms of utilization, often lacks the clinical detail present in EHRs. Patient registries, valuable for specific disease areas, represent a limited snapshot. And the influx of data from wearable devices, while exciting, introduces challenges around data validity and standardization. This lack of interoperability isn't just an inconvenience; it's a significant barrier to generating robust, reliable, and comparable RWE.

The Pillars of a Common Data Foundation

The solution isn't to mandate a single data source, but to establish a standardized foundation upon which all sources can converge. This foundation rests on four key pillars:

1. Standardized Data Formats: The industry is increasingly converging around HL7 FHIR (Fast Healthcare Interoperability Resources) as a preferred standard. FHIR's modular approach and RESTful API make it well-suited for exchanging data across different systems. Adoption needs to be widespread and consistently implemented.

2. Common Data Models (CDMs): CDMs, such as OMOP (Observational Medical Outcomes Partnership) and PCORnet, provide a consistent way to represent data, regardless of its original source. Transforming data into a CDM allows for standardized analysis and pooling of information across multiple databases. The challenge lies in selecting and consistently applying a CDM, balancing granularity with computational feasibility.

3. Robust Data Quality Standards: "Garbage in, garbage out" remains a critical concern. Data quality standards must address completeness, accuracy, consistency, and timeliness. This requires investment in data cleaning, validation, and ongoing monitoring. Machine learning algorithms are increasingly being utilized to identify and correct data anomalies.

4. Comprehensive Metadata Standards: Metadata - data about the data - is crucial for transparency and reproducibility. This includes information about data provenance (origin), collection methods, quality control procedures, and any transformations applied. Detailed metadata allows researchers and regulators to assess the reliability of the RWE generated.

The Ripple Effects of Standardization

The benefits of a common data foundation extend far beyond simply making data easier to analyze.

• Regulatory Confidence: Regulatory agencies like the FDA and EMA are increasingly receptive to RWE, but they demand rigorous evidence of data quality and reliability. A standardized foundation builds trust and streamlines the approval process for new drugs and therapies. We're already seeing a shift towards incorporating RWE into post-market surveillance and label expansion applications.

• Accelerated Research: By reducing the time and cost associated with data harmonization, a common foundation accelerates research timelines. Researchers can focus on generating insights rather than wrestling with data formats. This is particularly crucial in areas like rare diseases, where patient populations are small and data sharing is paramount.

• Improved Patient Outcomes: A more holistic view of patient data enables personalized medicine and more effective treatment strategies. RWD/RWE can identify patterns and predictors of treatment response, allowing clinicians to tailor therapies to individual patients.

• Innovation Ecosystem: A standardized data environment fosters innovation by lowering the barriers to entry for developers of new RWD/RWE applications. This could lead to breakthroughs in areas like predictive analytics, disease management, and clinical trial design.

Challenges and the Path Forward

Despite the clear benefits, establishing a common data foundation is not without its challenges. Data governance, data privacy (especially concerning sensitive health information), and the cost of implementation are significant hurdles. Furthermore, maintaining a dynamic and evolving foundation that can accommodate new data sources and technologies requires ongoing commitment and collaboration.

The path forward requires a multi-stakeholder approach involving regulators, data providers, technology developers, and patient advocates. Investment in infrastructure, standardized training programs, and open-source tools are essential. Ultimately, a unified approach to RWD/RWE isn't just about technology; it's about building a more connected, efficient, and patient-centered healthcare system.