Abstract
Clinical trials are a fundamental tool used to evaluate the efficacy and safety of new drugs and medical devices and other health system interventions. The traditional clinical trials system acts as a quality funnel for the development and implementation of new drugs, devices and health system interventions. The concept of a “digital clinical trial” involves leveraging digital technology to improve participant access, engagement, trial-related measurements, and/or interventions, enable concealed randomized intervention allocation, and has the potential to transform clinical trials and to lower their cost. In April 2019, the US National Institutes of Health (NIH) and the National Science Foundation (NSF) held a workshop bringing together experts in clinical trials, digital technology, and digital analytics to discuss strategies to implement the use of digital technologies in clinical trials while considering potential challenges. This position paper builds on this workshop to describe the current state of the art for digital clinical trials including (1) defining and outlining the composition and elements of digital trials; (2) describing recruitment and retention using digital technology; (3) outlining data collection elements including mobile health, wearable technologies, application programming interfaces (APIs), digital transmission of data, and consideration of regulatory oversight and guidance for data security, privacy, and remotely provided informed consent; (4) elucidating digital analytics and data science approaches leveraging artificial intelligence and machine learning algorithms; and (5) setting future priorities and strategies that should be addressed to successfully harness digital methods and the myriad benefits of such technologies for clinical research.
| Original language | English (US) |
|---|---|
| Article number | 101 |
| Journal | npj Digital Medicine |
| Volume | 3 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 1 2020 |
Funding
The workshop that resulted in this work was funded through the National Institutes of Health (NIH), National Heart, Lung and Blood Institute (NHLBI), and the National Science Foundation (NSF) under Grant Number 1922762. The paper was also supported in part by both the Extramural and Intramural research programs of the NIH (NHLBI and NIDA). S.R.S. is supported in part by a grant from the National Center for Advancing Translational Sciences/NIH (grant UL1TR001114). Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the NHLBI, the NIH, or the NSF. We thank W. Nilsen (NSF) for her contributions to this effort; her input was invaluable. We also acknowledge the following people for their contributions to and/ or participation in the NIH/NSF Digital Clinical Trials Workshop: D. Goff, W. Cleaveland, A. Abernathy, N. Pereira, C. Lipset, J. Wilbanks, G. Alterovitz, S. Shore, B. Mynatt, R. Harrington, A. Hernandez, S. Grinspoon, L. Belen, K. Velarde, S. Czaja, H. Taylor, B. Rao, T. Zayas-Caban, B. Patel, I. Prichard, A. Kravets, D. Wolff-Hughes, J. Bamdad, B. Newsome, R. Roper, C. Sasiela, H. Nicastro, A. Lee, N. Redmond, V. Sachdev, P. Schum, S. Czajkowski, A. Oh, T. Lash, R. Conroy, C. Aklin, B. Jean-Francois, V. Florance, N. Langhals, C. Lungu, D. Whittingslow, and N. Gurel. All authors participated in the NIH/NSF Digital Clinical Trials Workshop held in Bethesda, MD in April 2019 where the concepts described in this manuscript were discussed and formed, and participated in the writing, editing, and review of the manuscript. O.T.I. acquired funding from the NSF for the workshop.
ASJC Scopus subject areas
- Medicine (miscellaneous)
- Health Informatics
- Computer Science Applications
- Health Information Management