Jamie Elvidge

Objectives: New technologies are being developed in a context of scarcity. Health technology assessment (HTA) aims to support decision makers in providing equitable and affordable access to effective innovations. This study aims to summarize the policy-related findings of a Horizon2020 project on innovating HTA methods and discuss their implications for the governance of HTA in Europe. Methods: A thematic analysis of policy-oriented papers ( n = 18) from the Next Generation Health Technology Assessment (HTx) project was carried out to summarize challenges and solutions. Subsequently, via an online survey and in a 2-day meeting, European and global stakeholders ( n = 21) were invited to comment on these solutions and to prioritize future strategies. Results: Reported challenges included a lack of access to standardized data, differences in evidentiary needs, existing policy structures, and a lack of capacity and knowledge. Suggested solutions were capacity building, national and international dialogues, standardization, and increased European collaboration. Stakeholders had different expectations with respect to the likely success of these solutions. Conclusion: Innovation of HTA requires alignment of evidentiary needs through dialogues, standardization through increased European collaboration, and capacity building. However, without additional investments in personnel capacity, HTA agencies must still prioritize some activities at the expense of others. Furthermore, although European collaboration is important, global alignment might be required to enforce standardization.

Objectives: Generative AI (GenAI) has emerged in the current decade as a paradigm-shifting technology with potential to transform the process of health economic evaluation (HEE), a resource-intensive element of health technology assessment. This systematic literature review aims to identify the current applications of GenAI in HEE and its potential advantages, challenges and limitations. Methods: We searched Medline, Embase, EconLit, Cochrane Library, International HTA database and Epistemonikos for English-language, publicly available literature without date restrictions, and hand-searched the ISPOR presentations database (2023 to 2025) for articles describing or investigating the use of GenAI in HEE. Quantitative data on performance outcomes were collected along with qualitative data on stakeholder opinions and experience. Results: We identified 25 eligible studies: 18 primary studies, 6 narrative reviews and 1 expert opinion piece. The primary studies comprised 16 case studies and 2 qualitative studies. Over 90% of studies were conference abstracts published in 2024 from commercial authors. The emphasis across studies was on early exploratory research, particularly model replication. Where reported, execution time (3 studies), accuracy and error rate (7 studies), and user experience (4 studies) showed promising results across multiple use cases but there is a high risk of bias inherent in relying on conference abstracts with limited reporting, which warrants cautious interpretation. Conclusion: The current evidence landscape has revealed potential benefits of GenAI across multiple applications to health economics, but only sparse dissemination of early case study findings via conference submissions. Further research is needed to validate all use cases and to address perceived barriers to implementation.

Introduction: The COVID-19 pandemic put substantial strain on healthcare systems globally. Early decision-making about diagnostic tests and treatments was driven by the need for rapid responses with a focus on reducing clinical burden. As COVID-19 continues its transition into an endemic state, health technology assessment (HTA) agencies will need to consider the clinical- and cost-effectiveness of tests and treatments, as with other conditions.Methods: We first conducted a systematic literature review in July 2021 and updated the search in July 2023. The review aimed to identify economic evaluations of diagnostics for SARS-CoV-2 and treatments for COVID-19 using predefined search strategy across journal databases and sources of grey literature. In the update, an additional targeted search was completed with terms relating to novel treatments. Search results were screened by title and abstract, and full texts of potentially relevant studies were reviewed against selection criteria. Studies with very serious methodological limitations were excluded. Findings from studies were synthesized narratively due to high levels of heterogeneity.Results: The database search identified 8,287 unique records, of which 54 full texts were reviewed, 28 were quality assessed, and 15 were included. Three further studies were included through HTA sources and citation checking. Of the 18 studies ultimately included, 16 evaluated pharmacological treatments including corticosteroids, antivirals, and immunotherapies. Two studies in lower-income settings evaluated the cost-effectiveness of rapid antigen tests and critical care provision. In most studies, a healthcare or payer perspective was used, and the comparator was standard care. There were 17 modeling analyses and one trial-based evaluation. Cost–utility analyses using QALYs were the most common analysis type.Conclusions: This update indicates that there are cost-effective treatments for COVID-19, with repurposed pharmacological treatments like dexamethasone presenting best value. There also appear to be promising options for people with severe disease alongside standard care. Future economic evaluations would benefit from reflecting the changing context around COVID-19 with parameters that reflect current circumstances, and fully incremental analyses comparing different treatment options.

Introduction: Progress and innovation in artificial intelligence (AI)-based healthcare interventions continue to develop rapidly. However, there are limitations in the published health economic evaluations (HEEs) of AI interventions, including limited reporting on characteristics and development of algorithms. We developed an extension to the existing Consolidated Health Economic Evaluation Reporting Standards (CHEERS) to improve consistency, transparency, and reliability of the reporting of HEEs of AI interventions.Methods: The Delphi method was used, following a prespecified study protocol. A steering group with expert oversight was formed to guide the development process. A long list of potential items was defined based on two recent systematic reviews of HEEs of AI-based interventions. The steering group identified and invited 119 experts to the three-stage survey. Participants were asked to score each item on a nine-point Likert scale, and they were also able to provide free-text comments. The final checklist was piloted on a random sample of nine HEEs of AI-based interventions.Results: Three stages of the Delphi survey were completed by 58, 42, and 31 multidisciplinary respondents, respectively, including HTA specialists, health economists, AI experts, and patient representatives. The CHEERS-AI extension includes 18 AI-specific reporting items. Ten are entirely new items, including considerations about user autonomy, validation of the AI component, and AI-specific uncertainty. In addition, elaborations on eight existing CHEERS items were added to emphasize important AI-specific nuances. Some participants highlighted that CHEERS-AI can provide key benefits; for example, it could clarify the misconception that the predictive algorithms supporting AI-driven healthcare interventions are available for use without cost.Conclusions: CHEERS-AI can aid in improved reporting quality for researchers, editors, and reviewers conducting or assessing HEEs of AI interventions.

Introduction: The National Health Service (NHS) in England aims to be carbon neutral by 2045, acknowledging the link between planetary and human health. By 2028, NHS suppliers will need to report carbon footprint data for their products, including the medicines and technologies that the National Institute for Health and Care Excellence (NICE) appraises. Additionally, company level data are already being reported. This type of data may provide NICE with an opportunity to support system environmental sustainability goals.Methods: The NICE Science Policy and Research team conducted an options appraisal to consider the feasibility and acceptability of different ways NICE might engage with environmental impact data (EID). We held discussions with NICE teams that could be affected by the collection and use of EID. Discussions examined the array of potential options for NICE—from being a simple conduit for EID to incorporating EID into its decision-making—and sought to identify those that were practical for further consideration. We then discussed these options with key external stakeholders (NHS England, industry, and commissioners) to understand their expected usefulness and practicality.Results: Several options for NICE to use EID were identified as suitable for further consideration. Using company level data, NICE could encourage companies to engage with NHS sustainability goals by citing their carbon reduction plans alongside guidance or using them to prioritize its activities. Using product level data, NICE could pilot an evaluation comparing the environmental outcomes of competing health technologies that have no differential direct health outcomes. NICE could also publish environmental impact assessment tools to help commissioners consider EID in procurement decisions. Better data and methodological standards are needed before NICE might consider embedding product-level EID in its usual decision-making frameworks.Conclusions: Our options appraisal has identified several ways that NICE might start to engage with EID to have a positive impact on NHS sustainability goals and wider planetary health. Future work will scope out how these activities should proceed. The identified options are not necessarily mutually exclusive and may evolve as the data and methods around sustainable health care continue to advance.

Objectives: The sandbox approach, developed in the financial technologies sector, creates an environment to collaboratively develop and test innovative new products, methods and regulatory approaches, separated from business as usual. It has been used in health care to encourage innovation in response to emerging challenges, but, until recently, has not been used in health technology assessment (HTA). This article summarizes our learnings from using the sandbox approach to address three challenges facing HTA organizations and to identify implications for the use of this approach in HTA. Methods: We identified three challenging contemporary HTA-related topics to explore in a sandbox environment, away from the pressures and interests of “live” assessments. We convened a pool of 120 stakeholders and experts to participate in various sandbox activities and ultimately co-develop solutions to help HTA organizations respond to the identified challenges. Results: Important general learnings about the potential benefits and implementation of a sandbox approach in HTA were identified. Consequently, we developed recommendations to guide its use, including how to implement an HTA sandbox in an effective way and the types of challenges for which it may be best suited. Conclusions: For many HTA organizations, it is difficult to carefully consider emerging challenges and innovate their processes due to risks associated with decision errors and resource limitations. The sandbox approach could reduce these barriers. The potential benefits of addressing HTA challenges in a collaborative “safe space” are considerable.

Objectives: As the initial crisis of the COVID-19 pandemic recedes, healthcare decision makers are likely to want to make rational evidence-guided choices between the many interventions now available. We sought to update a systematic review to provide an up-to-date summary of the cost-effectiveness evidence regarding tests for SARS-CoV-2 and treatments for COVID-19.Methods: Key databases, including MEDLINE, EconLit and Embase, were searched on 3 July 2023, 2 years on from the first iteration of this review in July 2021. We also examined health technology assessment (HTA) reports and the citations of included studies and reviews. Peer-reviewed studies reporting full health economic evaluations of tests or treatments in English were included. Studies were quality assessed using an established checklist, and those with very serious limitations were excluded. Data from included studies were extracted into predefined tables.Results: The database search identified 8,287 unique records, of which 54 full texts were reviewed, 28 proceeded for quality assessment, and 15 were included. Three further studies were included through HTA sources and citation checking. Of the 18 studies ultimately included, 17 evaluated treatments including corticosteroids, antivirals and immunotherapies. In most studies, the comparator was standard care. Two studies in lower-income settings evaluated the cost effectiveness of rapid antigen tests and critical care provision. There were 17 modelling analyses and 1 trial-based evaluation.Conclusion: A large number of economic evaluations of interventions for COVID-19 have been published since July 2021. Their findings can help decision makers to prioritise between competing interventions, such as the repurposed antivirals and immunotherapies now available to treat COVID-19. However, some evidence gaps remain present, including head-to-head analyses, disease-specific utility values, and consideration of different disease variants.Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021272219], identifier [PROSPERO 2021 CRD42021272219].