Remote Monitoring and Data Collection for Decentralized Clinical Trials
Open accessAbstract
Importance: Less than 5% of patients with cancer enroll in a clinical trial, partly due to financial and logistic burdens, especially among underserved populations. The COVID-19 pandemic marked a substantial shift in the adoption of decentralized trial operations by pharmaceutical companies. Objective: To assess the current global state of adoption of decentralized trial technologies, understand factors that may be driving or preventing adoption, and highlight aspirations and direction for industry to enable more patient-centric trials. Design, Setting, and Participants: The Bloomberg New Economy International Cancer Coalition, composed of patient advocacy, industry, government regulator, and academic medical center representatives, developed a survey directed to global biopharmaceutical companies of the coalition from October 1 through December 31, 2022, with a focus on registrational clinical trials. The data for this survey study were analyzed between January 1 and 31, 2023. Exposure: Adoption of decentralized clinical trial technologies. Main Outcomes and Measures: The survey measured (1) outcomes of different remote monitoring and data collection technologies on patient centricity, (2) adoption of these technologies in oncology and all therapeutic areas, and (3) barriers and facilitators to adoption using descriptive statistics. Results: All 8 invited coalition companies completed the survey, representing 33% of the oncology market by revenues in 2021. Across nearly all technologies, adoption in oncology trials lags that of all trials. In the current state, electronic diaries and electronic clinical outcome assessments are the most used technology, with a mean (SD) of 56% (19%) and 51% (29%) adoption for all trials and oncology trials, respectively, whereas visits within local physician networks is the least adopted at a mean (SD) of 12% (18%) and 7% (9%), respectively. Looking forward, the difference between the current and aspired adoption rate in 5 years for oncology is large, with respondents expecting a 40% or greater absolute adoption increase in 8 of the 11 technologies surveyed. Furthermore, digitally enabled recruitment, local imaging capabilities, and local physician networks were identified as technologies that could be most effective for improving patient centricity in the long term. Conclusions and Relevance: These findings may help to galvanize momentum toward greater adoption of enabling technologies to support a new paradigm of trials that are more accessible, less burdensome, and more inclusive.
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Some types of monitoring have stronger evidence for their benefits and efficiency than others.
“Some types of monitoring have stronger evidence for their benefits and efficiency than others.”
From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [468]
New, decentralised monitoring options are becoming more widely available.
“New, decentralised monitoring options are also becoming more widely available.468”
From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [468]
Central statistical monitoring has been shown to improve multiple indicators of data quality.
“For example, central statistical monitoring has been shown to improve multiple indicators of data quality.468 469”
From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [468]
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Cites
Participation in Cancer Clinical Trials: Race-, Sex-, and Age-Based Disparities
Murthy, V. H., Krumholz, H. M., & Gross, C. P. (2004) · JAMA · doi:10.1001/jama.291.22.2720 · Cited by 2,078 · see citing works
Patient Income Level and Cancer Clinical Trial Participation
Unger, J. M., Gralow, J. R., Albain, K. S., Ramsey, S. D., & Hershman, D. L. (2015) · JAMA Oncology · doi:10.1001/jamaoncol.2015.3924 · Cited by 206 · see citing works
Switzer, J. A., Demaerschalk, B. M., Xie, J., Fan, L., Villa, K. F., & Wu, E. Q. (2012) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.112.967125 · Cited by 170 · see citing works
Pfister, D. G., Rubin, D. M., Elkin, E. B., Neill, U. S., Duck, E., Radzyner, M. H., & Bach, P. B. (2015) · JAMA Oncology · doi:10.1001/jamaoncol.2015.3151 · Cited by 145 · see citing works
Geographic Accessibility to Clinical Trials for Advanced Cancer in the United States
Galsky, M. D., Stensland, K. D., McBride, R. B., Latif, A., Moshier, E. L., Oh, W. K., & Wisnivesky, J. P. (2014) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2014.6300 · Cited by 74 · see citing works
Adams, D. V., Long, S. S., & Fleury, M. E. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.20053 · Cited by 59 · see citing works
Assessing the Financial Value of Decentralized Clinical Trials
DiMasi, J. A., Smith, Z., Oakley-Girvan, I. A., Mackinnon, A. J., Costello, M., Tenaerts, P., & Getz, K. A. (2022) · Therapeutic Innovation & Regulatory Science · doi:10.1007/s43441-022-00454-5 · Cited by 58 · see citing works
Reimagining patient-centric cancer clinical trials: a multi-stakeholder international coalition
Li, B. T., Daly, B., Gospodarowicz, M. K., Bertagnolli, M. M., Brawley, O. W., Chabner, B. A., Fashoyin‐Aje, L. A., de Claro, R. A., Franklin, E. F., Mills, J., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01775-6 · Cited by 51 · see citing works
Self-citationOncology Patients' Perspectives on Remote Patient Monitoring for COVID-19
Daly, B., Lauria, T. S., Holland, J. C., Garcia, J., Majeed, J., Walters, C. B., Zablocki, M., Chow, K. Y., Strachna, O., Giles, C. E., et al. (2021) · JCO Oncology Practice · doi:10.1200/op.21.00269 · Cited by 33 · see citing works
Self-citationDoshi, S. D., Bange, E. M., Daly, B., Kuperman, G. J., Panageas, K. S., & Morris, M. J. (2024) · The Cancer Journal · doi:10.1097/ppo.0000000000000691 · Cited by 28 · see citing works
Self-citationWilliams, E. L., Pierre, D. L., Martin, M. E., Beg, M. S., & Gerber, D. E. (2021) · JCO Oncology Practice · doi:10.1200/op.21.00524 · Cited by 14 · see citing works
Self-citationLeyens, L., Simkins, T., & Horst, N. K. (2022) · Trials · doi:10.1186/s13063-022-06707-w · Cited by 9 · see citing works
Flagged first, then by citations
Cited by
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SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials
Hróbjartsson, A., Boutron, I., Hopewell, S., Moher, D., Schulz, K. F., Collins, G. S., Tunn, R., Aggarwal, R., Berkwits, M., Berlin, J. A., et al. (2025) · BMJ · doi:10.1136/bmj-2024-081660 · Cited by 171 · see citing works
Artificial intelligence to revolutionize IBD clinical trials: a comprehensive review
Sedaño, R., Solitano, V., Vuyyuru, S. K., Yuan, Y., Hanžel, J., Ma, C., Nardone, O. M., & Jairath, V. (2025) · Therapeutic Advances in Gastroenterology · doi:10.1177/17562848251321915 · Cited by 57 · see citing works
Leptomeningeal metastatic disease: new frontiers and future directions
Ozair, A., Wilding, H. E., Bhanja, D., Mikolajewicz, N., Glantz, M., Grossman, S. A., Sahgal, A., Rhun, É. L., Weller, M., Weiß, T., et al. (2024) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-024-00970-3 · Cited by 35 · see citing works
Cancer in rural America: Improving access to clinical trials and quality of oncologic care
Unger, J. M., McAneny, B. L., & Osarogiagbon, R. U. (2025) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.70006 · Cited by 28 · see citing works
From reactive to proactive: Continuous protein monitoring for preventive health care
Donnelly, J. M., Neff, R. A., Sedlack, A. J. H., Juska, V. B., Ayala‐Cardona, L. F., Bass, J. T., McNally, E. M., Shah, S. J., Alshurafa, N. I., Kimchi, E. Y., et al. (2025) · Science · doi:10.1126/science.ady6497 · Cited by 25 · see citing works
Advancing Global Health Equity in Oncology Clinical Trial Access
Coalition, B. N. E. I. C., Adewole, T., Albrecht, B., Beg, S., Brawley, O. W., Chang, I., Daly, B., Claro, A. D., Dent, J., Dickler, M. N., et al. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-24-1288 · Cited by 14 · see citing works
Self-citationPatel, T. H., Corneli, A. L., Eusebio-Balcazar, P., Lipset, C. H., Calvert, S. B., Mervin-Blake, S., Nalawade, V. V., & Kluetz, P. G. (2025) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-24-3357 · Cited by 8 · see citing works
Unger, J. M., Andrews, H. S., Levit, L. A., McKelvey, B. A., Stewart, M. D., Canin, B. E., Flaherty, K. T., Kimball, D., Miller, T. M., Onitilo, A. A., et al. (2025) · JCO oncology advances. · doi:10.1200/oa-25-00021 · Cited by 7 · see citing works
“28,29 Although decentralized clinical trial elements have been considered over decades, the COVID-19 pandemic enabled their rapid adoption, creating the scenario for a natural experiment in which the impact on trial conduct could be feasibly evaluated.”
Clinical trial screening in gynecologic oncology: Defining the need and identifying best practices
Castellano, T., Lara, O. D., McCormick, C. B., Chase, D., Bae-Jump, V. L., Jackson, A. L., Pepin, J. T., Ghamande, S., Moore, K. N., Pothuri, B., et al. (2024) · Gynecologic Oncology · doi:10.1016/j.ygyno.2024.11.009 · Cited by 5 · see citing works
Quality Indicators for Renal Cancer Care: A Systematic Review
Pecoraro, A., Costagli, S., Marandino, L., Varma, R., Albersen, M., Umberto Capitanio, A., Jewett, M. A. S., Kutikov, A., MacLennan, S., Stewart, G. D., et al. (2025) · European Urology Oncology · doi:10.1016/j.euo.2025.05.012 · Cited by 5 · see citing works
Integrating mHealth Innovations into Decentralized Oncology Trials
ÇİÇİN, F. N., & Çiçin, İ. (2025) · Journal of Medical Systems · doi:10.1007/s10916-025-02233-9 · Cited by 4 · see citing works
“These tools provide clinicians with granular, time-stamped data that supports timely patient mobility issues, and logistical inefficiencies, making clinical trials more inclusive and accessible (24).”
Baizer, L., Bures, R. M., Nadkarni, G. N., Reyes-Guzman, C. M., Ladwa, S. A., Cade, B. E., Westover, M. B., Durmer, J. S., de Zambotti, M., Desai, M., et al. (2025) · SLEEP · doi:10.1093/sleep/zsaf035 · Cited by 4 · see citing works
Gutiérrez-Sandoval, R., Gutiérrez-Castro, F., Muñoz-Godoy, N., Rivadeneira, I., Lagos, A., Muñoz, Í., Iturra, J., Krakowiak, F., Peña-Vargas, C., Vidal, M., et al. (2025) · Biomedicines · doi:10.3390/biomedicines13092101 · Cited by 3 · see citing works
“The format can also complement real-world evidence (RWE) platforms, retrospective audits, or expanded-access reviews [88].”
A Multiregional Decentralized Clinical Trial Program to Improve Access
Haddad, T. C., Mercado, L. A., Le‐Rademacher, J. G., Enarson, D. A., Sener, U. T., Kumar, A., Price, K. A., Juhn, Y. J., Cartin‐Ceba, R., Colón‐Otero, G., et al. (2026) · JAMA Network Open · doi:10.1001/jamanetworkopen.2026.17204 · Cited by 3 · see citing works
Ensuring Virtual Vigilance in Decentralized Clinical Trials
Hernandez, A. F., & Lindsell, C. J. (2024) · JAMA · doi:10.1001/jama.2024.22640 · Cited by 3 · see citing works
Huey, R. W., Pritchett, J. C., Vokinger, K. N., Swenson, W. T., Haddad, T. C., & Adelson, K. B. (2025) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk-25-473822 · Cited by 2 · see citing works
Efficient data consensus algorithm integrating FL and blockchain dynamic partition protocol PBFT
Huang, G. (2025) · Scientific Reports · doi:10.1038/s41598-025-30487-3 · Cited by 2 · see citing works
Fischer, S. K. (2025) · Bioanalysis · doi:10.1080/17576180.2025.2458453 · Cited by 1 · see citing works
Reıd, I. O. A., Hussain, M. A., Osman, S. M., & Bakheet, S. M. (2026) · Results in Chemistry · doi:10.1016/j.rechem.2026.103120 · Cited by 1 · see citing works
Bentley, C., McTaggart-Cowan, H. M., Kato, D., Sundquist, S., Dancey, J. E., & Peacock, S. J. (2025) · Health Policy and Technology · doi:10.1016/j.hlpt.2025.101031 · Cited by 1 · see citing works
Shi, Y., Bao, Y., Pu, Y., Jiang, J., Yan, B., & Zheng, H. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1513975
“The results of the practical investigation showed that the respondents’ participation in DCT activities was relatively low, and the application of digital health technologies was largely consistent with Daly’s global survey findings (21).”
Inequalities in access to industry‐funded interventional phase I– IV drug trials in epilepsy
Hoepner, R., Betancourt, A. X. L., Marti, S., & Seiler, A. (2025) · Epilepsia Open · doi:10.1002/epi4.70076
“Potential solutions to improve equity in trial distribution include the use of centralized or remote patient screening processes, 23 targeted support for training and mentoring early-career neurologists in low-resource settings, 24 and commitments by trial sponsors to ensure post-trial…”
Agile by adaptive design: An algorithm for decentralized trials
Bakar, K. S. (2025) · Contemporary Clinical Trials · doi:10.1016/j.cct.2025.108169
Wang, J., Li, S., Chen, Z., Wang, J., Xiong, W., Hu, L., & Zhang, X. (2025) · Systematic Reviews · doi:10.1186/s13643-025-03045-1
Artificial Intelligence in Clinical Trials of Drugs
Yingngam, B. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5258-9.ch008
Remote data acquisition and monitoring
Kalia, H., & George, T. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-13750-1.00026-4
The role of the contract research organization
Povieng, B., & Lubenow, T. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-13750-1.00030-6
Alkurdi, D., Shqair, L., Tariq, S., Mehrzad, S., Bear, X., Alani, O., Patel, D., Alkurdi, E., Richmond, J. M., & Schwager, Z. A. (2026) · Discover Public Health · doi:10.1186/s12982-026-01720-9
Focused ultrasound for brain metastases
Ozair, A., Moiz, B., Anastasiadis, P., Gandhi, D., Amatya, B., McDermott, M. W., Woodworth, G. F., & Ahluwalia, M. S. (2025) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdaf131
“…trials with digitally enabled recruitment and digital-first monitoring/follow-up approach 194,195 Enabled by remote patient monitoring (RPM) 196 When designing protocols, investigators carefully reconsider each criterion (many are copied from older trials) Use of modern causal inference…”
Navigating Clinician-Researchers’ Duties in Decentralized Clinical Trials
Patrinos, D., & Zawati, M. H. (2026) · Canadian Journal of Bioethics · doi:10.7202/1124205ar
Decentralised clinical trials of investigational medicinal products
Douiri, A., Alhnan, M. A., Asghar, M. R., Balaskas, K., Benaouda, F., Bolodeoku, J., Campbell-Cooper, C., Carroll, C., Chen, T., Dorricott, K., et al. (2026) · Drug Discovery Today · doi:10.1016/j.drudis.2026.104676
Precision Cancer Medicine in Clinical Trial Design
Bhowmik, A., Ghosh, S. D. S., & Basu, B. (2026) · Springer eBooks · doi:10.1007/978-981-95-6845-1_6
Factors to consider when designing clinical trials for repurposed drugs
Gordon, S., Klaś, K., & Waligóra, M. (2026) · Expert Review of Clinical Pharmacology · doi:10.1080/17512433.2026.2671276
“Examples of some specific remote assessments used in decentralized models include collecting electronic patient-reported outcomes (ePROs) via secure digital platforms, performing home phlebotomy for laboratory testing, and conducting telemedicine visits for safety monitoring and follow-up [59].”
Decentralized clinical trials in hematology: the promise and the peril
Petrone, G. E. M., Uy, G. L., & Bolton, K. L. (2026) · Blood · doi:10.1182/blood.2025031439
Park, Y. C., Kim, S.-Y., Hong, G., Hong, J. H., Park, D., & Sunwoo, J. (2026) · Healthcare · doi:10.3390/healthcare14182953
“…limitations of conventional site-based trials and accelerated interest in decentralized clinical trials [DCTs], which incorporate telehealth visits, electronic consent (eConsent), electronic clinical outcome assessment (eCOA), digital health technologies (DHTs), and home-based monitoring [6–12].”
Tankişi, H., Bostock, H., Hobson‐Webb, L. D., Holder, D. S., Howells, J., Olausson, H., Rutkove, S. B., Shahrizaila, N., Z'Graggen, W. J., Valls-Solé, J., et al. (2026) · Clinical Neurophysiology Practice · doi:10.1016/j.cnp.2026.09.003
“In parallel with these developments, home measurements in addition to in-person, in-clinic visits have become of interest (Daly et al., 2024).”
Tripathi, R. K., Kapadia, U. R., Kajrolkar, A. V., & Parmar, U. I. (2026) · Cureus · doi:10.7759/cureus.117881
Digital transformation of clinical trials with remote patient monitoring solutions
Piault, E., Palacholla, R. S., PULMAN‐JONES, S., Sartor, E. A., Simön, S., & Bell, J. (2026) · npj Health Systems · doi:10.1038/s44401-026-00161-5
- Authors
- Daly, B., Brawley, O. W., Gospodarowicz, M. K., Olopade, O. I., Fashoyin-Aje, L., Smart, V. W., Chang, I.-F., Tendler, C. L., Kim, G., Fuchs, C. S., Beg, M. S., Zhang, L., Legos, J. J., Duran, C. O., Kalidas, C., Qian, J., Finnegan, J., Pilarski, P., Keane, H., Shen, J., Silverstein, A., Wu, Y.-L., Pazdur, R., & Li, B. T.
- Affiliations
- Memorial Sloan Kettering Cancer Center (US)
- Johns Hopkins University (US)
- University of Toronto (CA)
- University of Chicago Medical Center (US)
- Oncology Center of Excellence, US Food and Drug Administration, Silver Spring, Maryland
- Susan G. Komen Breast Cancer Foundation (US)
- Amgen (United States) (US)
- Johnson & Johnson (United States) (US)
- BeiGene, Cambridge, Massachusetts
- Genentech (US)
- Harold C. Simmons Comprehensive Cancer Center (US)
- Jiangsu Hengrui Medicine (China) (CN)
- Novartis (Switzerland) (CH)
- AstraZeneca (United Kingdom) (GB)
- Bayer (Germany) (DE)
- Asia Society (US)
- Bloomberg (United States) (US)
- McKinsey & Company (United States) (US)
- Guangdong Academy of Medical Sciences (CN)
- United States Food and Drug Administration (US)
- Corresponding author
- Daly, Bobby
- Li, Bob T.
- ORCID
- Venue
- JAMA Network Open
- Publisher
- American Medical Association (AMA)
- Published
- 2024-04-12
- Volume
- 7
- Issue
- 4
- Pages
- e246228
- ISSN
- 2574-3805 (ISSN-L)
- OpenAlex
- W4394748253
- Cited by
- 38
- References
- 12
- Citations by year
- 2026: 15 · 2025: 19 · 2024: 4
- FWCI
- 15.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JAMA Network Open (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- Knowledge@UChicago (University of Chicago) (submittedVersion, cc-by-nc-nd)
- University of Chicago (cc-by)
- Funders
- Emerson Collective
- National Institutes of Health
- National Cancer Institute (P30CA008748)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics in Clinical Research
- Keywords
- decentralized clinical trials, remote monitoring, electronic diary, patient-centricity, cancer clinical trials, technology adoption, barriers and facilitators, online recruitment, biopharmaceutical industry, underserved populations, COVID-19 pandemic
- MeSH terms
- Clinical Trials as Topic, Data Collection, Humans, Medical Oncology, Neoplasms
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