Details

Autor(en) / Beteiligte

• Inan, O. T.
• Tenaerts, P.
• Prindiville, S. A.
• Reynolds, H. R.
• Dizon, D. S.
• Cooper-Arnold, K.
• Turakhia, M.
• Pletcher, M. J.
• Preston, K. L.
• Krumholz, H. M.
• Marlin, B. M.
• Mandl, K. D.
• Klasnja, P.
• Spring, B.
• Iturriaga, E.
• Campo, R.
• Desvigne-Nickens, P.
• Rosenberg, Y.
• Steinhubl, S. R.
• Califf, R. M.

Titel

Digitizing clinical trials

Ist Teil von

• NPJ digital medicine, 2020-07, Vol.3

Ort / Verlag

London: Nature Publishing Group UK

Erscheinungsjahr

2020

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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.