OneMedNet and Navidence to integrate computable study definitions into real-world data workflows

OneMedNet and Navidence have announced a collaboration designed to support more consistent identification and use of real-world data (RWD), helping research organizations generate reliable real-world evidence (RWE) for regulatory, clinical, and commercial decision-making.

The collaboration will see the integration of Navidence’s Computable Operational Definitions (CODefs) with the OneMedNet iRWD™ platform, powered by Palantir Foundry. By providing reusable and standardized reference for defining diseases, treatments, and clinical measures, CODefs translate study criteria into computable formats, enabling more consistent definitions of cohorts, endpoints, and eligibility across multimodal RWD datasets. Commenting on the announcement, the second strategic development from the company this month, Navidence’s Aaron Kamauu highlighted the importance of these standardized definitions in supporting reproducible research. He noted that:

“Computable Operational Definitions (CODefs) provide the foundation for consistent and reproducible research independent of the data source. By integrating our content with the OneMedNet iRWD™ platform, researchers can discover precisely defined cohorts and datasets that accelerate study development and improve consistency across clinical research.”

The collaboration reflects increasing demand across life sciences for tools that can convert large volumes of healthcare data into structured, research-ready assets. As datasets continue to grow in scale and complexity, organizations are prioritizing approaches that support precise study definitions and reproducible data selection. The global RWD market is projected to expand from approximately $1.9 billion in 2025 to $6.4 billion by 2034.

By aligning computable study definitions with available datasets in near real time, the integrated approach aims to support more efficient study design while reducing the risk of pursuing infeasible studies or acquiring unnecessary data.

The integration is also expected to deliver operational efficiencies by accelerating the progression from study ideation to execution and enabling more precise matching between study requirements and available datasets, which may help reduce costs. Furthermore, by combining OneMedNet’s data de-identification capabilities with Navidence’s standardized definitions, the approach may also support compliance with regulatory and payer expectations related to transparency, reproducibility, and data governance.

These capabilities are expected to be particularly relevant in complex research settings, including precision medicine. In such contexts, combining imaging-based RWD, such as MRI and CT scans, with CODefs may support more detailed evaluation of disease progression and treatment outcomes. The consistent use of standardized definitions across datasets may also improve comparability between studies and strengthen the reliability of downstream analyses.

Aaron Green, CEO and President of OneMedNet, emphasized the importance of structured data methods in managing growing volumes of healthcare information, stating:

“Healthcare organizations today have access to more data than ever before and turning that data into reliable evidence requires both scale and precision. By combining OneMedNet’s multimodal RWD platform with Navidence’s computable definitions, we can help researchers identify the right data faster and design studies that deliver high-impact evidence.”