Utility of automated data transfer for cancer clinical trials and considerations for implementation

Background: The burden of data collection on site staff for cancer clinical trials is steadily increasing, and inefficiencies in data entry into electronic data capture (EDC) systems lead to poor data quality and delays in reporting. Software that facilitates automated transfer of mapped structured...

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Bibliographic Details
Main Authors: M. Pfeffer, M. Deneris, A. Shelley, P. Salcuni, I. Altomare
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:ESMO Real World Data and Digital Oncology
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Online Access:http://www.sciencedirect.com/science/article/pii/S2949820125000013
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Summary:Background: The burden of data collection on site staff for cancer clinical trials is steadily increasing, and inefficiencies in data entry into electronic data capture (EDC) systems lead to poor data quality and delays in reporting. Software that facilitates automated transfer of mapped structured data from the electronic health record (EHR) to EDC can help to address these challenges. Materials and methods: We examined the impact of multi-site usage of an embedded point and click EHR-to-EDC tool on study data capture across multiple phase I cancer clinical trials by conducting a retrospective analysis of volume of and time for data transfer across protocols. Results: During a 15-month observation period, the EHR-to-EDC tool was used to transfer 11 342 individual data points (89% laboratory values, 8% vitals and 3% concomitant medications) representing 955 unique case report form (CRF) submissions. Use was consistent across protocols. The average time for a user to launch, complete and submit a CRF was 37 s (range 15-59 s). Conclusions: This study demonstrates efficiencies in clinical trial conduct provided by EHR-to-EDC technology and supports growing adoption among sites and sponsors, while highlighting how variability in data standards and interoperability across EHR systems pose practical challenges to widespread implementation.
ISSN:2949-8201