An Overview of Technology Availability to Support Remote Decentralized Clinical Trials

Authors: S. Huber, B. Schnalzer, B. Alcalde, S. Hanke, L. Mpaltadoros, T. G. Stavropoulos, S. Nikolopoulos, I. Kompatsiaris, L. Pérez-Breva, V. Rodrigo-Casares, J. Fons-Martínez, J. de Bruin

Abstract:

Developing new medicine and health solutions and improving patient health currently rely on the successful execution of clinical trials, which generate relevant safety and efficacy data. For their success, recruitment and retention of participants are some of the most challenging aspects of protocol adherence. Main barriers include: i) lack of awareness of clinical trials; ii) long distance from the clinical site; iii) the burden on participants, including the duration and number of clinical visits, and iv) high dropout rate. Most of these aspects could be addressed with a new paradigm, namely the Remote Decentralized Clinical Trials (RDCTs). Furthermore, the COVID-19 pandemic has highlighted additional advantages and challenges for RDCTs in practice, allowing participants to join trials from home and not depending on site visits, etc. Nevertheless, RDCTs should follow the process and the quality assurance of conventional clinical trials, which involve several processes. For each part of the trial, the Building Blocks, existing software and technologies were assessed through a systematic search. The technology needed to perform RDCTs is widely available and validated but is yet segmented and developed in silos, as different software solutions address different parts of the trial and at various levels. The current paper is analyzing the availability of technology to perform RDCTs, identifying gaps and providing an overview of Basic Building Blocks and functionalities that need to be covered to support the described processes.

Keywords: architectures and frameworks for health informatics systems, clinical trials, information and communications technology, remote decentralized clinical trials, technology availability

References:

[1] Greg Alexander and Nancy Staggers. 2009. A Systematic Review of the Designs of Clinical Technology. Adv. Nurs. Sci. 32, 3 (July 2009), 252–279. DOI:https://doi.org/10.1097/ANS.0b013e3181b0d737
[2] Maria Apostolaros, David Babaian, Amy Corneli, Annemarie Forrest, Gerrit Hamre, Jan Hewett, Laura Podolsky, Vaishali Popat, and Penny Randall. 2020. Legal, Regulatory, and Practical Issues to Consider When Adopting Decentralized Clinical Trials: Recommendations from the Clinical Trials Transformation Initiative. Ther. Innov. Regul. Sci. 54, 4 (July 2020), 779–787. DOI:https://doi.org/10.1007/s43441-019-00006-4
[3] Olivia Choudhury, Noor Fairoza, Issa Sylla, and Amar Das. 2019. A Blockchain Framework for Managing and Monitoring Data in Multi-Site Clinical Trials. (February 2019). Retrieved from http://arxiv.org/abs/1902.03975
[4] Philip Coran, Jennifer C. Goldsack, Cheryl A. Grandinetti, Jessie P. Bakker, Marisa Bolognese, E. Ray Dorsey, Kaveeta Vasisht, Adam Amdur, Christopher Dell, Jonathan Helfgott, Matthew Kirchoff, Christopher J. Miller, Ashish Narayan, Dharmesh Patel, Barry Peterson, Ernesto Ramirez, Drew Schiller, Thomas Switzer, Liz Wing, Annemarie Forrest, and Aiden Doherty. 2019. Advancing the use of mobile technologies in clinical trials: recommendations from the Clinical Trials Transformation Initiative. Digit. Biomarkers 3, 3 (November 2019), 145–154. DOI:https://doi.org/10.1159/000503957
[5] Felix Köpcke and Hans-Ulrich Prokosch. 2014. Employing computers for the recruitment into Clinical Trials: a comprehensive systematic review. J. Med. Internet Res. 16, 7 (July 2014), e161. DOI:https://doi.org/10.2196/jmir.3446
[6] Guillaume Marquis-Gravel, Matthew T. Roe, Mintu P. Turakhia, William Boden, Robert Temple, Abhinav Sharma, Boaz Hirshberg, Paul Slater, Noah Craft, Norman Stockbridge, Bryan McDowell, Joanne Waldstreicher, Ariel Bourla, Sameer Bansilal, Jennifer L. Wong, Claire Meunier, Helina Kassahun, Philip Coran, Lauren Bataille, Bray Patrick-Lake, Brad Hirsch, John Reites, Rajesh Mehta, Evan D. Muse, Karen J. Chandross, Jonathan C. Silverstein, Christina Silcox, J. Marc Overhage, Robert M. Califf, and Eric D. Peterson. 2019. technology-enabled clinical trials. circulation 140, 17 (October 2019), 1426–1436. DOI:https://doi.org/10.1161/CIRCULATIONAHA.119.040798
[7] Briggs W. Morrison, Chrissy J. Cochran, Jennifer Giangrande White, Joan Harley, Cynthia F Kleppinger, An Liu, Jules T Mitchel, David F Nickerson, Cynthia R Zacharias, Judith M Kramer, and James D Neaton. 2011. Monitoring the quality of conduct of clinical trials: a survey of current practices. Clin. Trials J. Soc. Clin. Trials 8, 3 (June 2011), 342–349. DOI:https://doi.org/10.1177/1740774511402703
[8] Esther Nanzayi Ngayua, Jianjia He, and Kwabena Agyei-Boahene. 2020. Applying advanced technologies to improve clinical trials: a systematic mapping study. Scientometrics (November 2020). DOI:https://doi.org/10.1007/s11192-020-03774-1
[9] Aynaz Nourani, Haleh Ayatollahi, and Masoud Solaymani Dodaran. 2019. A review of clinical data management systems used in clinical trials. Rev. Recent Clin. Trials 14, 1 (January 2019), 10–23. DOI:https://doi.org/10.2174/1574887113666180924165230
[10] Yu Zhuang, Lincoln Sheets, Zonyin Shae, Jeffrey J P Tsai, and Chi-Ren Shyu. 2018. Applying blockchain technology for health information exchange and persistent monitoring for clinical trials. AMIA ... Annu. Symp. proceedings. AMIA Symp. 2018, (2018), 1167–1175. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/30815159
[11] R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
[12] Chao Qian, Yang Yu, Ke Tang, 2018, Approximation guarantees of stochastic greedy algorithms for subset selection, Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18), Retrieved from https://www.ijcai.org/Proceedings/2018/0205.pdf