Digitizing clinical trials
Digitizing clinical trials
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DOI：
10.1038/s41746-020-0302-y
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发表时间：
2020-07-31
影响因子：
15.2
通讯作者：
Califf, R. M.
中科院分区：
文献类型：
--
作者：
Inan, O. T.;Tenaerts, P.;Califf, R. M.
临床试验是用于评估新药和医疗器械以及其他卫生系统干预措施的有效性和安全性的基本工具。传统的临床试验系统是开发和实施新药、器械和卫生系统干预措施的质量漏斗。“数字临床试验”的概念涉及利用数字技术来改善参与者的访问，参与，试验相关的测量和/或干预，实现隐藏的随机干预分配，并有可能改变临床试验并降低其成本。2019年4月，美国国立卫生研究院（NIH）和美国国家科学基金会（NSF）举办了一次研讨会，汇集了临床试验、数字技术和数字分析方面的专家，讨论在临床试验中使用数字技术的策略，同时考虑潜在的挑战。本立场文件以此次研讨会为基础，描述了数字化临床试验的最新技术水平，包括（1）定义和概述数字化试验的组成和要素;（2）描述使用数字化技术的招募和保留;（3）概述数据收集要素，包括移动的健康、可穿戴技术、应用程序编程接口（API）、数据的数字传输，考虑对数据安全、隐私和远程提供知情同意的监管监督和指导;（4）阐明利用人工智能和机器学习算法的数字分析和数据科学方法;以及（5）制定未来的优先事项和战略，以成功地利用数字方法和这些技术的无数好处，临床研究
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.