HTA Lab projects

The HTA Lab enables NICE to develop creative solutions to complex problems in health technology assessment (HTA).

It offers a ‘safe space’ for creating solutions in collaboration with system partners and stakeholders.

Current projects

AI in HTA

AI is expected to have transformative applications in many areas of healthcare. We have already produced guidance on AI-enabled health technologies. There are also ways that AI might be used to inform evidence about health technologies.

In collaboration with teams across NICE, we’ve led on the development of a NICE position statement. This sets out our view on the use of AI methods in the generation and reporting of evidence considered by our evaluation programmes. The position statement seeks to ensure their use is justified, proportionate and transparent.

We’ve also started an HTA Lab project to explore how generative AI techniques might be used to inform all aspects of economic modelling, from development to validation.

Comparing the environmental impacts of health technologies

We’re looking into how feasible it is to assess the environmental impacts of competing medicines and health technologies where there’s little or no expected difference in health or cost outcomes. In such cases, it would make sense to choose the option that’s better for the environment. We’re hoping to find a proportionate and pragmatic way to do this, bearing in mind that data and methods in this can area change quite quickly.

If we can identify a suitable approach, we’ll look into conducting a pilot evaluation.

This kind of evaluation would be consistent with the findings of our NICE Listens deliberative public engagement dialogue. This explored views on our role in making healthcare more environmentally sustainable.

Disease modifying dementia treatments

New disease modifying dementia treatments (DMDTs) are anticipated to alter disease progression and change the management of Alzheimer’s disease.

To help NICE medicines evaluation team prepare, the HTA Lab is conducting a project to identify the key issues that are likely to emerge during the evaluation of these treatments. This is with the input of patient organisation groups, our partners and international HTA agencies.   

Further detail of this work is included in this first report from the project (PDF).

• Appendix A: HTA literature review summary (PDF)
• Appendix B: Review of economic models (PDF)
• Appendix C: Alzheimer’s disease and mild cognitive impairment in UK general practice (PDF)
• Appendix D: DSU report (PDF).

Metabolic dysfunction-associated steatohepatitis (MASH)

The emerging pipeline of treatments for metabolic dysfunction-associated steatohepatitis (MASH) offers promising therapeutic prospects. However, it also presents significant challenges for health technology assessment and implementation.

MASH affects a diverse and possibly very large group of patients. And with no approved treatments available for it right now, it represents a new therapeutic area for NICE’s technology evaluation programmes.

This project aims to identify and examine the potential challenges in evaluating MASH treatments. It also seeks to increase the consistency of future economic models in this area. To achieve this, the team will:

• conduct systematic reviews
• analyse the alignment between clinical trials and current NHS practice
• engage with stakeholders to co-develop a consensus-based economic model concept for MASH technology evaluations.

These efforts will improve our readiness to appraise future MASH treatments, supporting robust, consistent and timely assessments.

Methods for the evaluation of diagnostics used in multiple care pathways

The number of AI and genomics-based technologies that can be used in multiple disease areas or at multiple positions in the disease pathway ('multi-indication' technologies) is increasing.

Evaluating multi-indication technologies is likely to pose a challenge to the current NICE Diagnostics Assessment Programme (DAP) methods and processes.

The first stage of this project is now complete. The team has produced a report that outines a potential approach for evaluating multi-indication technologies (PDF), ensuring their value is appropriately captured.

If you are the developer of a multi-indication technology, and are interested in working with us on a pilot prioritisation exercise to identify use cases for modelling, please contact htalab@nice.org.uk.

Platform technologies that enable highly personalised therapy

Personalised genomic therapy represents a potentially transformative field in medicine, offering tailored treatments based on individual patient characteristics and genetics. The anticipated pipeline of therapies derived from platform technologies may pose significant implementation challenges for the healthcare system, and it is important for NICE to consider its place in evaluating such therapies.

This project aims to get a clearer understanding of such treatments, the development pipeline and associated challenges, and ensure that NICE's evaluation processes remain efficient and effective, supporting timely patient access to innovative treatments.

To achieve this, we will:

• engage with system partners and stakeholders
• examine the clinical evidence underpinning personalised therapeutics
• identify issues around assessing their clinical and cost-effectiveness in previous HTAs and published literature
• review identified issues and consider their implications for NICE’s standard medicines evaluation processes.

Ultimately, this work will improve NICE's capacity to appraise emerging personalised treatments, and identify the challenges involved in evaluating technologies that enable multiple highly targeted treatments.

Using real-world evidence from managed access: a review of cancer appraisals

The Systemic Anti-Cancer Therapy (SACT) dataset has the potential to provide valuable information to NICE committees, addressing uncertainties in decision-making during re-appraisal at the exit point of a managed access agreement (MAA). However, inconsistencies in data access and quality currently limit its effective use.

Working with the Decision Support Unit (DSU), this project aims to review how data from SACT were used in a sample of appraisals that have exited the Cancer Drugs Fund. This may lead to generalisable learnings about when SACT data are more likely to be informative for decision making at re-appraisal, and recommendations for improved data collection and analysis.

We also hope to identify alternative applications of SACT data at NICE beyond re-appraisals and identify generalisable learnings for MAA data collected in non-cancer contexts.

Past projects