Digitizing clinical trials
Open accessAbstract
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.
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Digital clinical trials could leverage multiple sources of participants' data to enable automatic phenotyping and subgrouping, which could be useful for adaptive clinical trial designs that use ongoing results to modify the trial in real time.
“Digital clinical trials could leverage multiple sources of participants' data to enable automatic phenotyping and subgrouping 34 , which could be useful for adaptive clinical trial designs that use ongoing results to modify the trial in real time 39,40 .”
From Multimodal biomedical AI (2022) · cites this paper as [34]
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Experimental — accuracy not yet measuredData from wearable technology and smartphone-enabled self-reported questionnaires can be useful for monitoring clinical trial patients, identifying adverse events or ascertaining trial outcomes.
“Data from wearable technology (including heart rate, sleep, physical activity, electrocardiography, oxygen saturation and glucose monitoring) and smartphone-enabled self-reported questionnaires can be useful for monitoring clinical trial patients, identifying adverse events or ascertaining trial outcomes 34 .”
From Multimodal biomedical AI (2022) · cites this paper as [34]
The Digital health data collection and Digital monitoring and biomarkers sections describe smartphone-collected patient-reported outcomes, wearable measurements, and their potential use as trial endpoints. Safety monitoring in digital clinical trials says continuous digital data might improve detection of infrequent events and reporting of adverse events. The paper supports the overall claim, but the cited passages do not identify oxygen-saturation monitoring, one of the examples in the citing sentence. (LLM, conf=0.84)
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Clinical Trials Without Clinical Sites
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The State of the Art of Pediatric Multiple Sclerosis
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Digital Clinical Trials for Substance Use Disorders in the Age of Covid-19
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Unlocking digitally enabled research in oncology: the time is now
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Social Media and Clinical Research in Dermatology
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- Authors
- 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.
- Affiliations
- Georgia Institute of Technology (US)
- Duke University (US)
- National Institutes of Health (US)
- New York University (US)
- Brown University (US)
- VA Palo Alto Health Care System (US)
- University of California, San Francisco (US)
- Yale New Haven Hospital (US)
- University of Massachusetts Amherst (US)
- Boston Children's Hospital (US)
- University of Michigan (US)
- Northwestern University (US)
- Scripps Research Institute (US)
- Corresponding author
- Inan, O. T.
- ORCID
- Venue
- npj Digital Medicine
- Published
- 2020-07-31
- Volume
- 3
- Issue
- 1
- Pages
- 101-101
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC7395804
- OpenAlex
- W3046739387
- Cited by
- 329
- References
- 44
- Citations by year
- 2026: 29 · 2025: 55 · 2024: 67 · 2023: 56 · 2022: 70 · 2021: 49 · 2020: 3
- FWCI
- 8.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- National Science Foundation (1922762)
- American Heart Association
- Bristol-Myers Squibb
- Pfizer
- AstraZeneca
- Sanofi
- Yale University
- Cytokinetics
- Shenzhen Center for Health Information
- Verily Life Sciences
- National Institutes of Health (UL1TR001114)
- U.S. Food and Drug Administration
- Abbott Vascular
- National Heart, Lung, and Blood Institute
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- digital health technologies, participant recruitment and retention, mobile health, wearable devices, application programming interfaces, data security and privacy, artificial intelligence in clinical trials, machine learning, data analytics, regulatory oversight
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