The Advent of Patient-Centric Technologies to Combat the High Dropout Rates: Revolutionizing Clinical Trials
Combating Attrition in Clinical Trials: Embracing a Patient-Centric Approach Through Innovative Technologies
DOI:
https://doi.org/10.30953/thmt.v9.474Keywords:
clinical trials, decentralized clinical trials, dropouts, dropout rates, patient-centric approachAbstract
The article addresses a critical challenge in medical research: the high dropout rates in clinical trials, which often exceed 30%, posing a threat to therapeutic development. It identifies financial strain, time constraints, and the rigid structure of clinical trial protocols as major contributing factors to participant attrition. The article advocates for a paradigm shift towards patient-centric clinical trials that prioritize the participant experience to enhance retention and ensure diverse and representative study populations. It underscores the role of patient-centric technologies, such as digital platforms and artificial intelligence, in revolutionizing clinical trials. These technologies improve remote accessibility, mitigate financial barriers, streamline patient recruitment, and improve data capture. The integration of AI, in particular, is lauded for its ability to efficiently identify and engage potential participants. The ultimate goal of employing such technologies is to make clinical trials more accessible and inclusive, fostering a more diverse participant base, and yielding results that are representative of the broader population. The article concludes by emphasizing the urgent need for patient-centric methodologies and technologies to overcome the dropout dilemma and advance medical research towards better global health outcomes.
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References
Alexander W. The uphill path to successful clinical trials: keeping patients enrolled. P T. 2013;38(4):225–7.
Hwang TJ, Carpenter D, Lauffenburger JC, Wang B, Franklin JM, Kesselheim AS. Failure of investigational drugs in late-stage clinical development and publication of trial results. JAMA Intern Med. 2016;176(12):1826–33. https://doi.org/10.1001/jamainternmed.2016.6008
Fogel DB. Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: a review. Contemp Clin Trials Commun. 2018;11:156–64. https://doi.org/10.1016/j.conctc.2018.08.001
Saeed H, El Naqa I. Artificial intelligence in clinical trials. In: El Naqa I, Murphy MJ (eds) Machine and Deep Learning in Oncology, Medical Physics and Radiology. Springer, 2022. https://doi.org/10.1007/978-3-030-83047-2_19
Dockendorf MF, Hansen BJ, Bateman KP, Moyer M, Shah KJ, Shipley LA. Digitally enabled, patient-centric clinical trials: shifting the drug development paradigm. Clin Transl Sci. 2021;14(2):445–459. https://doi.org/10.1111/cts.12910
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Copyright (c) 2024 Harsha K. Rajasimha, PhD
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