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Clinical trial data collection still relies on a manual entry from information available in the medical record. This process introduces delay and error risk. Automating data transfer from Electronic Health Record (EHR) to Electronic Data Capture (EDC) system, under investigators’ supervision, would gracefully solve these issues. The present paper describes the design of the evaluation of a technology allowing EHR to act as eSource for clinical trials. As part of the EHR2EDC project, for 6 ongoing clinical trials, running at 3 hospitals, a parallel semi-automated data collection using such technology will be conducted focusing on a limited scope of data (demographic data, local laboratory results, concomitant medication and vital signs). The evaluation protocol consists in an individual participant data prospective meta-analysis comparing regular clinical trial data collection to the semi-automated one. The main outcome is the proportion of data correctly entered. Data quality and associated workload for hospital staff will be compared as secondary outcomes. Results should be available in 2020. [ABSTRACT FROM AUTHOR]

The complexity and heterogeneity of cancers leads to variable responses of patients to treatments and interventions. Developing models that accurately predict patient's care pathways using prognostic and predictive biomarkers is increasingly important in both clinical practice and scientific research. The main objective of the ATHENA project is to: (1) accelerate data driven precision medicine for two use cases - bladder cancer and multiple myeloma, (2) apply distributed and privacy-preserving analytical methods/ algorithms to stratify patients (decision support), (3) help healthcare professionals deliver earlier and better targeted treatments, and (4) explore care pathway automations and improve outcomes for each patient. Challenges associated with data sharing and integration will be addressed and an appropriate federated data ecosystem will be created, enabling an interoperable foundation for data exchange, analysis and interpretation. By combining multidisciplinary expertise and tackling knowledge gaps in ATHENA, we propose a novel federated privacy preserving platform for oncology research.

Clinical trial data collection still relies on a manual entry from information available in the medical record. This process introduces delay and error risk. Automating data transfer from Electronic Health Record (EHR) to Electronic Data Capture (EDC) system, under investigators' supervision, would gracefully solve these issues. The present paper describes the design of the evaluation of a technology allowing EHR to act as eSource for clinical trials. As part of the EHR2EDC project, for 6 ongoing clinical trials, running at 3 hospitals, a parallel semi-automated data collection using such technology will be conducted focusing on a limited scope of data (demographic data, local laboratory results, concomitant medication and vital signs). The evaluation protocol consists in an individual participant data prospective meta-analysis comparing regular clinical trial data collection to the semi-automated one. The main outcome is the proportion of data correctly entered. Data quality and associated workload for hospital staff will be compared as secondary outcomes. Results should be available in 2020.