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Evidence generation plan for: GID-HTE10059 Artificial intelligence (AI) technologies to aid opportunistic detection of vertebral fragility fractures: Early Value Assessment

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2 Evidence gaps

This section describes the evidence gaps, why they need to be addressed and their relative importance for future committee decision making.

The committee will not be able to make a positive recommendation without the essential evidence gaps (see section 2.1) being addressed. The companies can strengthen the evidence base by also addressing as many other evidence gaps (see section 2.2) as possible. This will help the committee to make a recommendation by ensuring it has a better understanding of the patient or healthcare system impact of the technologies.

2.1 Essential evidence for future committee decision making

Resource use

More information is needed on how using the technologies would affect resource use during and after implementation, to help the committee understand their long-term resource use impacts. Key areas that will help to address this evidence gap are:

  • long-term resource use costs such as number and extent of treatments and number of hospital appointment or visits

  • the downstream impacts of using the technologies on the NHS, such as:

    • the number of people referred for spine X-ray or dual-energy X-ray absorptiometry (DEXA) scan, or

    • the number of people receiving medication for osteoporosis.

Ideally, information about implementation, technology acquisition and maintenance costs and payment models could also be collected.

Impact of using AI technologies on the NHS care pathway

A key part of the committee discussion was around the impact of AI technologies on the NHS care pathway for fragility fractures and osteoporosis. For example, changes in the fracture liaison services diagnosis and treatment routes may be needed to accommodate the AI technology. Collecting evidence on this will help the committee understand how using the technologies will affect care in the NHS.

Failure rates and diagnostic accuracy of the AI technologies ideally compared with NHS standard care

The committee noted that the failure rates and diagnostic accuracy outcomes for NHS standard care were not reported adequately. More evidence is needed on the failure rates and diagnostic accuracy of the AI technologies compared with current NHS care.

2.2 Evidence that further supports committee decision making

Healthcare professional experience and acceptability of AI technologies

Evidence on the AI technologies' ease of use and time taken to process and report a radiograph with AI assistance will help the committee understand how the technologies are viewed by healthcare professionals and assess the technologies' real-world uptake.

Diagnostic accuracy of the AI technologies in people under 50 years

The failure rate and diagnostic accuracy outcomes for people younger than 50 years and at risk of VFF, for example people with long-term corticosteroid use or malignancy in the vertebrae, were not reported adequately. More evidence is needed on the failure rates and diagnostic accuracy of the AI technologies when used in these groups.