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      Digitizing clinical trials

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          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.

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          Most cited references32

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          A literature review on the representativeness of randomized controlled trial samples and implications for the external validity of trial results

          Randomized controlled trials (RCTs) are conducted under idealized and rigorously controlled conditions that may compromise their external validity. A literature review was conducted of published English language articles that reported the findings of studies assessing external validity by a comparison of the patient sample included in RCTs reporting on pharmaceutical interventions with patients from everyday clinical practice. The review focused on publications in the fields of cardiology, mental health, and oncology. A range of databases were interrogated (MEDLINE; EMBASE; Science Citation Index; Cochrane Methodology Register). Double-abstract review and data extraction were performed as per protocol specifications. Out of 5,456 de-duplicated abstracts, 52 studies met the inclusion criteria (cardiology, n = 20; mental health, n = 17; oncology, n = 15). Studies either performed an analysis of the baseline characteristics (demographic, socioeconomic, and clinical parameters) of RCT-enrolled patients compared with a real-world population, or assessed the proportion of real-world patients who would have been eligible for RCT inclusion following the application of RCT inclusion/exclusion criteria. Many of the included studies concluded that RCT samples are highly selected and have a lower risk profile than real-world populations, with the frequent exclusion of elderly patients and patients with co-morbidities. Calculation of ineligibility rates in individual studies showed that a high proportion of the general disease population was often excluded from trials. The majority of studies (n = 37 [71.2 %]) explicitly concluded that RCT samples were not broadly representative of real-world patients and that this may limit the external validity of the RCT. Authors made a number of recommendations to improve external validity. Findings from this review indicate that there is a need to improve the external validity of RCTs such that physicians treating patients in real-world settings have the appropriate evidence on which to base their clinical decisions. This goal could be achieved by trial design modification to include a more representative patient sample and by supplementing RCT evidence with data generated from observational studies. In general, a thoughtful approach to clinical evidence generation is required in which the trade-offs between internal and external validity are considered in a holistic and balanced manner. Electronic supplementary material The online version of this article (doi:10.1186/s13063-015-1023-4) contains supplementary material, which is available to authorized users.
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            Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review

            Clinical trials are time consuming, expensive, and often burdensome on patients. Clinical trials can fail for many reasons. This survey reviews many of these reasons and offers insights on opportunities for improving the likelihood of creating and executing successful clinical trials. Literature from the past 30 years was reviewed for relevant data. Common patterns in reported successful trials are identified, including factors regarding the study site, study coordinator/investigator, and the effects on participating patients. Specific instances where artificial intelligence can help improve clinical trials are identified.
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              SMART on FHIR: a standards-based, interoperable apps platform for electronic health records

              Objective In early 2010, Harvard Medical School and Boston Children’s Hospital began an interoperability project with the distinctive goal of developing a platform to enable medical applications to be written once and run unmodified across different healthcare IT systems. The project was called Substitutable Medical Applications and Reusable Technologies (SMART). Methods We adopted contemporary web standards for application programming interface transport, authorization, and user interface, and standard medical terminologies for coded data. In our initial design, we created our own openly licensed clinical data models to enforce consistency and simplicity. During the second half of 2013, we updated SMART to take advantage of the clinical data models and the application-programming interface described in a new, openly licensed Health Level Seven draft standard called Fast Health Interoperability Resources (FHIR). Signaling our adoption of the emerging FHIR standard, we called the new platform SMART on FHIR. Results We introduced the SMART on FHIR platform with a demonstration that included several commercial healthcare IT vendors and app developers showcasing prototypes at the Health Information Management Systems Society conference in February 2014. This established the feasibility of SMART on FHIR, while highlighting the need for commonly accepted pragmatic constraints on the base FHIR specification. Conclusion In this paper, we describe the creation of SMART on FHIR, relate the experience of the vendors and developers who built SMART on FHIR prototypes, and discuss some challenges in going from early industry prototyping to industry-wide production use.
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                Author and article information

                Contributors
                inan@gatech.edu
                Journal
                NPJ Digit Med
                NPJ Digit Med
                NPJ Digital Medicine
                Nature Publishing Group UK (London )
                2398-6352
                31 July 2020
                31 July 2020
                2020
                : 3
                : 101
                Affiliations
                [1 ]GRID grid.213917.f, ISNI 0000 0001 2097 4943, School of Electrical and Computer Engineering, , Georgia Institute of Technology, ; Atlanta, GA 30332 USA
                [2 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Clinical Trials Transformation Initiative, , Duke University, ; Durham, NC 27708 USA
                [3 ]GRID grid.48336.3a, ISNI 0000 0004 1936 8075, Coordinating Center for Clinical Trials, Office of the Director, , National Cancer Institute at the National Institutes of Health, ; Bethesda, MD 20892 USA
                [4 ]GRID grid.137628.9, ISNI 0000 0004 1936 8753, School of Medicine, , New York University, ; New York, NY 10003 USA
                [5 ]GRID grid.40263.33, ISNI 0000 0004 1936 9094, The Lifespan Cancer Institute, , Brown University, ; Providence, RI 02912 USA
                [6 ]GRID grid.279885.9, ISNI 0000 0001 2293 4638, National, Heart, Lung and Blood Institute at the National Institutes of Health, ; Bethesda, MD 20892 USA
                [7 ]GRID grid.168010.e, ISNI 0000000419368956, VA Palo Alto Health Care System and the Center for Digital Health, , Stanford University, ; Stanford, CA 94305 USA
                [8 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Department of Epidemiology and Biostatistics, , University of California, ; San Francisco, CA 94143 USA
                [9 ]GRID grid.420090.f, ISNI 0000 0004 0533 7147, Intramural Research Program of the National Institute on Drug Abuse at the National Institutes of Health, ; Baltimore, MD 21224 USA
                [10 ]GRID grid.47100.32, ISNI 0000000419368710, The Center for Outcomes Research, Yale New Haven Hospital, , Yale University, ; New Haven, CT 06510 USA
                [11 ]GRID grid.47100.32, ISNI 0000000419368710, Section of Cardiovascular Medicine, Department of Internal Medicine, , Yale School of Medicine, ; New Haven, CT 06510 USA
                [12 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Health Policy and Management, , Yale School of Public Health, ; New Haven, Connecticut 06510 USA
                [13 ]GRID grid.266683.f, ISNI 0000 0001 2184 9220, College of Information and Computer Sciences, , University of Massachusetts at Amherst, ; Amherst, MA 01003 USA
                [14 ]GRID grid.38142.3c, ISNI 000000041936754X, Computational Health Informatics Program at Boston Children’s Hospital, Departments of Biomedical Informatics and Pediatrics, , Harvard Medical School, ; Boston, MA 02115 USA
                [15 ]GRID grid.214458.e, ISNI 0000000086837370, School of Information, , University of Michigan, ; Ann Arbor, MI 48109 USA
                [16 ]GRID grid.16753.36, ISNI 0000 0001 2299 3507, Northwestern University Feinberg School of Medicine, ; Chicago, IL 60611 USA
                [17 ]GRID grid.279885.9, ISNI 0000 0001 2293 4638, National Heart, Lung, and Blood Institute at the National Institutes of Health, ; Bethesda, MD 20892 USA
                [18 ]Scripps Research Translational Institute, La Jolla, CA 92037 USA
                [19 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, School of Medicine, , Duke University, ; Durham, NC 27710 USA
                [20 ]Verily Life Sciences and Google Health, South San Francisco, CA 94080 USA
                [21 ]Present Address: Fortira at AstraZeneca, Gaithersburg, MD 20877 USA
                Author information
                http://orcid.org/0000-0003-2046-127X
                http://orcid.org/0000-0002-2626-3410
                http://orcid.org/0000-0002-9781-0477
                http://orcid.org/0000-0002-9256-7914
                Article
                302
                10.1038/s41746-020-0302-y
                7395804
                32821856
                e7337b7f-3f0b-4c90-bc66-8f44afe8e6ec
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 15 April 2020
                : 19 June 2020
                Categories
                Perspective
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                © The Author(s) 2020

                clinical trials,translational research
                clinical trials, translational research

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