Digital Health Technologies in Clinical Trials: An Ontology-Driven Analysis to Inform Digital Sustainability Policies.

Clinical trials Decentralized trials Digital health Environmental sustainability Research policy

Journal

Therapeutic innovation & regulatory science
ISSN: 2168-4804
Titre abrégé: Ther Innov Regul Sci
Pays: Switzerland
ID NLM: 101597411

Informations de publication

Date de publication:
Nov 2023
Historique:
received: 09 03 2023
accepted: 24 07 2023
medline: 7 8 2023
pubmed: 7 8 2023
entrez: 6 8 2023
Statut: ppublish

Résumé

Digital health technologies (DHTs) can facilitate the execution of de-centralized trials that can offer opportunities to reduce the burden on participants, collect outcome data in a real-world setting, and potentially make trial populations more diverse and inclusive. However, DHTs can also be a significant source of electronic waste (e-waste). In recognition of the potential health and environmental impact from DHT use in trials, private and public institutions have recently launched initiatives to help measure and manage this e-waste. But in order to develop sound e-waste management policies, it will be necessary to first estimate the current volume of e-waste that results from the use of DHTs in trials. A Web Ontology Language (OWL)-compliant ontology of DHTs was created using a list of 500 DHT device names derived from a mixture of public and private sources. The U.S. clinical trials registry, ClinicalTrials.gov, was then queried to identify and classify trials using any of the devices in the ontology. The ClinicalTrials.gov records from this search were then analyzed to characterize the volume and properties of trials using DHTs, as well as estimating the total volume of individual DHT units that have been provisioned (or are planned to be provisioned) for clinical research. Our ontology-driven search identified 2326 unique clinical trials with a reported "actual" enrollment of 200,947 participants and a "planned" enrollment of an additional 4,094,748 participants. The most-used class of DHTs in our ontology was "wearables," (1852 trials), largely driven by the use of smart watches and other wrist-worn sensors (estimated to involve 149,391 provisioned devices). The most-used subtype of DHTs in trials was "subcutaneous" devices (367 trials), driven by the prevalent use and testing of glucose monitors (estimated to involve 17,666 provisioned devices). Thousands of trials, involving hundreds of thousands of devices, have already been completed, and many more trials (potentially involving millions more devices) are planned. Despite the great opportunities that are afforded by DHTs to the clinical trial enterprise, if the industry lacks the ability to track DHT use with sufficient resolution, the result is likely to be a great deal of e-waste. A new ontology of DHTs, combined with rigorous data science methods like those described in this paper, can be used to provide better information across the industry, and in turn, help create a more sustainable and equitable clinical trials enterprise.

Sections du résumé

BACKGROUND BACKGROUND
Digital health technologies (DHTs) can facilitate the execution of de-centralized trials that can offer opportunities to reduce the burden on participants, collect outcome data in a real-world setting, and potentially make trial populations more diverse and inclusive. However, DHTs can also be a significant source of electronic waste (e-waste). In recognition of the potential health and environmental impact from DHT use in trials, private and public institutions have recently launched initiatives to help measure and manage this e-waste. But in order to develop sound e-waste management policies, it will be necessary to first estimate the current volume of e-waste that results from the use of DHTs in trials.
MATERIALS AND METHODS METHODS
A Web Ontology Language (OWL)-compliant ontology of DHTs was created using a list of 500 DHT device names derived from a mixture of public and private sources. The U.S. clinical trials registry, ClinicalTrials.gov, was then queried to identify and classify trials using any of the devices in the ontology. The ClinicalTrials.gov records from this search were then analyzed to characterize the volume and properties of trials using DHTs, as well as estimating the total volume of individual DHT units that have been provisioned (or are planned to be provisioned) for clinical research.
RESULTS RESULTS
Our ontology-driven search identified 2326 unique clinical trials with a reported "actual" enrollment of 200,947 participants and a "planned" enrollment of an additional 4,094,748 participants. The most-used class of DHTs in our ontology was "wearables," (1852 trials), largely driven by the use of smart watches and other wrist-worn sensors (estimated to involve 149,391 provisioned devices). The most-used subtype of DHTs in trials was "subcutaneous" devices (367 trials), driven by the prevalent use and testing of glucose monitors (estimated to involve 17,666 provisioned devices).
CONCLUSION CONCLUSIONS
Thousands of trials, involving hundreds of thousands of devices, have already been completed, and many more trials (potentially involving millions more devices) are planned. Despite the great opportunities that are afforded by DHTs to the clinical trial enterprise, if the industry lacks the ability to track DHT use with sufficient resolution, the result is likely to be a great deal of e-waste. A new ontology of DHTs, combined with rigorous data science methods like those described in this paper, can be used to provide better information across the industry, and in turn, help create a more sustainable and equitable clinical trials enterprise.

Identifiants

pubmed: 37544966
doi: 10.1007/s43441-023-00560-y
pii: 10.1007/s43441-023-00560-y
pmc: PMC10579130
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1269-1278

Informations de copyright

© 2023. The Author(s).

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Auteurs

Spencer Phillips Hey (SP)

Prism Analytic Technologies, 245 Main St., Cambridge, MA, 02142, USA. spencer@prism.bio.

Maria Dellapina (M)

Prism Analytic Technologies, 245 Main St., Cambridge, MA, 02142, USA.

Kristin Lindquist (K)

Prism Analytic Technologies, 245 Main St., Cambridge, MA, 02142, USA.

Bert Hartog (B)

Janssen-Cilag B.V., 4837 DS, Breda, The Netherlands.

Jason LaRoche (J)

Janssen Research & Development, LLC, Raritan, NJ, 08869, USA.

Classifications MeSH