Integrating digital health technologies into complex clinical systems

The shortcomings of this technology-centric focus can be seen, for example, when reviewing the lack of successful clinical deployment of the multitude of ML algorithms developed during the pandemic to support the diagnosis and management of COVID-19.3 More broadly, the apparent success of ML algorit...

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Bibliographic Details
Published in:BMJ health & care informatics 2023-10, Vol.30 (1), p.e100885
Main Author: Sujan, Mark
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
BMJ Health Informatics
COVID-19
Editorial
Health care
Health Information Systems
Information Technology
Machine learning
Medical equipment
Pandemics
Patient safety
Technology
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Summary:The shortcomings of this technology-centric focus can be seen, for example, when reviewing the lack of successful clinical deployment of the multitude of ML algorithms developed during the pandemic to support the diagnosis and management of COVID-19.3 More broadly, the apparent success of ML algorithms found in retrospective evaluation studies is frequently not replicated in subsequent prospective studies.4 5 The difficulty of translating successful retrospective evaluation of algorithms into useful clinical practice has been referred to as the challenge of the last mile.6 Arguably, consideration of the challenge of the last mile, that is, of the realities of complex clinical systems, cannot be left to the end, but needs to inform the design of AI and, more generally, digital health technologies from the outset. The recent British Standard BS 30440, which outlines an auditable validation framework for healthcare AI, is another example of this.13 Finally, we need to continue efforts to build capacity and capability within health and care organisations to enhance their readiness to deploy such technologies meaningfully, for example, in the case of the National Health Service in England by extending training opportunities with NHS England (the former NHS Digital team) on digital clinical safety and AI safety or with the Health Services Safety Investigation Body on system-based investigation methods. Nat Mach Intell 2021; 3: 199–217. doi:10.1038/s42256-021-00307-0 4 Nagendran M, Chen Y, Lovejoy CA, et al. n.d. Artificial intelligence versus Clinicians: systematic review of design, reporting standards, and claims of deep learning studies.
ISSN:2632-1009
2632-1009
DOI:10.1136/bmjhci-2023-100885