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A tailored maturity model for REDCap-based patient-facing technology: revealing development stages and guiding future
Zhengcan Xie1, Yuheng Shi1, Eric Yang1
1D. Bradley McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States.
Objectives:
This integrative review aims to analyze the REDCap-based patient-facing technologies (PFTs) and propose a tailored maturity model to characterize their development stages, challenges, and future evolution toward intelligent, proactive care.
Materials And Methods:
Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a focused search of PubMed, Web of Science, and Embase identified relevant studies. Data were analyzed using aggregate and thematic synthesis. Combining literature evidence with pilot case insights, a tailored maturity model was developed to define the distinct stages of REDCap-based PFT development.
Results:
The proposed model progresses from Data Collection (Tier 1) and System Integration (Tier 2) to Personalized Insights (Tier 3), culminating in a conceptual Intelligence Hub (Tier 4). Analysis of 14 studies (2016-2026) reveals a rapidly evolving field, with a surge in publications since 2021 (n = 12, 86%). Most included studies were US-based (n = 11, 79%), primarily pilot tests (n = 6) and system design studies (n = 5). Most implementations (n = 10, 71%) remain at early maturity stages (Tiers 1 and 2), with only a minority (n = 4, 29%) achieving Personalized Insights (Tier 3).
Conclusion:
These findings underscore the promise of REDCap-based PFTs but reveal a critical gap between current capabilities and the advanced Intelligence Hub envisioned in the maturity model. Applying the tailored maturity model highlights priorities for advancing research, including rigorous, longitudinal evaluations and strategies to achieve higher levels of integration and optimization.
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