Corporate document
Section A: Technologies suitable for evaluation using the evidence standards framework
Section A: Technologies suitable for evaluation using the evidence standards framework
The NICE evidence standards framework (ESF) has been designed to be suitable for the evaluation of most digital health technologies (DHTs) that are likely to be commissioned in the UK health and social care system.
DHTs are digital products intended to benefit people or the wider health and social care system. This may include:
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smartphone apps
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standalone software
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online tools for treating or diagnosing conditions, preventing ill health, or for improving system efficiencies
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programmes that can be used to analyse data from medical devices such as scanners, sensors or monitors.
The ESF is not intended to be used for evaluating the following types of DHT:
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software that is integral to, or embedded in, a medical device or in vitro diagnostic (IVD), also called software in a medical device (SiMD)
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DHTs designed for providing training to health or care professionals (such as virtual reality [VR] or augmented reality [AR] surgical training)
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DHTs that facilitate data collection in research studies.
The ESF can be used to evaluate all DHTs commissioned in the health and care system for medical, health or wellness, or system efficiency purposes. It is applicable to medical therapeutic and diagnostic technologies including IVDs and screening technologies. The ESF can be used for technologies where the intended benefit is at the population level as well as those that benefit the individual service user or the health and care system.
The ESF is intended to be used alongside requirements for regulation and does not constitute or replace any regulatory process. The accompanying user guide describes how the ESF fits alongside other regulatory and quality assurance measures for digital healthcare in the NHS and care system. It can be used to evaluate DHTs that are regulated as medical devices or IVDs in the UK.
The ESF has been updated in 2022 to include standards relevant to DHTs whose performance is expected to change over time (such as those with machine-learning algorithms that are expected to retrain over time).