Evolving digital health technologies: aligning with and enhancing the National Institute for Health and Care Excellence Evidence Standards Framework
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Published version
OA Location
Author(s)
Bahadori, Shayan
Buckle, Peter
Soukup Ascensao, Tayana
Ghafur, Saira
Kierkegaard, Patrick
Type
Journal Article
Abstract
The rapid advancement of artificial intelligence (AI)–driven diagnostics and wearable health technologies is transforming health care delivery by enabling real-time health monitoring and early disease detection. These innovations are catalyzing a shift toward personalized medicine, with interventions tailored to individual patient profiles with unprecedented precision. This paper examines the current National Institute for Health and Care Excellence (NICE) evidence standards framework (ESF) for digital health technologies (DHTs) and evaluates the challenges associated with integrating DHTs into existing health and care systems. A comprehensive review of the NICE ESF guidelines was conducted, alongside an evaluation of their applicability to emerging AI and wearable technologies. Key limitations and barriers were identified, with particular focus on the framework’s responsiveness to technologies that evolve through machine learning and real-world data integration. Our findings indicate that while the NICE ESF provides a structured approach for evaluating DHTs, it lacks the adaptability required for rapidly evolving innovations. The framework does not sufficiently incorporate real-world evidence or support continuous learning models, which are critical for the safe and effective deployment of AI-based diagnostics and wearables. To remain effective and relevant, the NICE ESF should transition to a dynamic, adaptive model co-designed with industry stakeholders. By embedding real-world evidence-based strategies and promoting transparency, efficiency, and collaborative innovation, the updated framework would better facilitate the integration of AI-driven diagnostics and wearables into health care systems, ultimately enhancing patient outcomes and optimizing health care delivery.
Date Issued
2025-01-01
Date Acceptance
2025-07-21
Citation
JMIR mHealth and uHealth, 2025, 13
ISSN
2291-5222
Publisher
JMIR Publications
Journal / Book Title
JMIR mHealth and uHealth
Volume
13
Copyright Statement
© Shayan Bahadori, Peter Buckle, Tayana Soukup Ascensao, Saira Ghafur, Patrick Kierkegaard. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 22.08.2025. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license information must be included.
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Publication Status
Published
Article Number
67435
Date Publish Online
2025-08-22