Enhancing Clinical Data Management through Barcode Integration and REDCap: Innovations in Scalability and Adaptability (Preprint)
Authors: Zhang Rendong , Chiron Sophie , Tyree Regina , Carson Kate , Raber Larry , Ramadass Karthik , Gao Chenyu , Kim Michael E. , Zuo Lianrui , Li Yike , Wan Zhiyu , Harris Paul A. , Liu Qi , Lau Ken S. , Coburn Lori A. , Wilson Keith T. , Landman Bennett A. , Huo Yuankai , Bao Shunxing 
Published: January 2025

BACKGROUND

Effective data management is crucial in clinical studies for precise tracking, secure storage, and reliable analysis of samples. Traditional systems often encounter challenges like barcode recognition errors, inadequate data detail, and diminished performance under heavy workloads.

OBJECTIVE

This paper aims to enhance clinical data management by improving barcode robustness, increasing data granularity, and boosting system throughput. These improvements address key challenges in barcode informatics systems, as highlighted in prior studies, to better support real clinical applications. Additionally, we aim to validate the design criteria on various gastrointestinal (GI) related studies, ensuring it can be easily integrated into other clinical data management workflows.

METHODS

We evaluated the robustness of various barcode technologies under significant blurring conditions, implemented a dynamic organ-specific archive in the REDCap database for various clinical study data collection criteria, and utilized Docker to containerize the informatics software for different studies. Additionally, we proposed a local cache system to reduce interaction times with REDCap for large-scale data records. Experimental setups include assessing barcode recognition accuracy under various levels of image blurring, showcasing different study types managed with the organ-specific archive, and measuring system throughput and response times with and without the proposed local cache system.

RESULTS

Our findings demonstrate that the DataMatrix barcode exhibits superior resilience, maintaining high recognition accuracy under blurred conditions. The dynamic organ-specific archive in REDCap enabled precise tracking of sample origins, improving data granularity. Docker containerization streamlines software deployment and ensures consistency across studies. The local cache system significantly reduces interaction times with REDCap, decreasing operating time by nearly eightfold compared to the naïve strategy when handling large patient datasets.

CONCLUSIONS

The proposed enhancements significantly improve barcode robustness, data granularity, and system throughput in the informatics system, addressing key limitations identified in previous studies. These optimizations ensure efficient data management and robust support for diverse clinical research needs.

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