Abstract: 

Health economic evaluations are comparative analyses of alternative courses of action in terms of their costs and consequences. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement, published in 2013, was created to ensure health economic evaluations are identifiable, interpretable, and useful for decision making. It was intended as guidance to help authors report accurately which health interventions were being compared and in what context, how the evaluation was undertaken, what the findings were, and other details that may aid readers and reviewers in interpretation and use of the study. The new CHEERS 2022 statement replaces previous CHEERS reporting guidance. It reflects the need for guidance that can be more easily applied to all types of health economic evaluation, new methods and developments in the field, as well as the increased role of stakeholder involvement including patients and the public. It is also broadly applicable to any form of intervention intended to improve the health of individuals or the population, whether simple or complex, and without regard to context (such as health care, public health, education, social care, etc). This summary article presents the new CHEERS 2022 28-item checklist and recommendations for each item. The CHEERS 2022 statement is primarily intended for researchers reporting economic evaluations for peer reviewed journals as well as the peer reviewers and editors assessing them for publication. However, we anticipate familiarity with reporting requirements will be useful for analysts when planning studies. It may also be useful for health technology assessment bodies seeking guidance on reporting, as there is an increasing emphasis on transparency in decision making.

Abstract: 

The Payback Framework, originally developed to examine the ‘impact’ or ‘payback’ of health services research, is explained. The Payback Framework is a research tool used to facilitate data collection and cross-case analysis by providing a common structure and so ensuring cognate information is recorded. It consists of a logic model representation of the complete research process, and a series of categories to classify the individual paybacks from research. Its multi-dimensional categorisation of benefits from research starts with more traditional academic benefits of knowledge production and research capacity-building, and then extends to wider benefits to society. 

Abstract:

Background: Research translation, particularly in the biomedical area, is often discussed but there are few methods that are routinely used to measure it or its impact. Of the impact measurement methods that are used, most aim to provide accountability – to measure and explain what was generated as a consequence of funding research. This case study reports on the development of a novel, conceptual framework that goes beyond measurement. The Framework To Assess the Impact from Translational health research, or FAIT, is a platform designed to prospectively measure and encourage research translation and research impact. A key assumption underpinning FAIT is that research translation is a prerequisite for research impact.

Methods: The research impact literature was mined to understand the range of existing frameworks and techniques employed to measure and encourage research translation and research impact. This review provided insights for the development of a FAIT prototype. A Steering Committee oversaw the project and provided the feedback that was used to refine FAIT.

Results: The outcome of the case study was the conceptual framework, FAIT, which is based on a modified program logic model and a hybrid of three proven methodologies for measuring research impact, namely a modified Payback method, social return on investment, and case studies or narratives of the process by which research translates and generates impact.

Conclusion: As funders increasingly seek to understand the return on their research investments, the routine measurement of research translation and research impact is likely to become mandatory rather than optional. Measurement of research impact on its own is insufficient. There should also be a mechanism attached to measurement that encourages research translation and impact – FAIT was designed for this task.

Abstract:

Background: Health Technology Assessment (HTA) is a crucial tool for evaluating the worth and roles of health technologies, and providing evidence-based guidance for their adoption and use. Artificial intelligence (AI) can enhance HTA processes by improving data collection, analysis, and decision-making. This study aims to explore the opportunities and challenges of utilizing artificial intelligence (AI) in health technology assessment (HTA), with a specific focus on economic dimensions. By leveraging AI’s capabilities, this research examines how innovative tools and methods can optimize economic evaluation frameworks and enhance decision-making processes within the HTA context.

Methods: This study adopted Arksey and O’Malley’s scoping review framework and conducted a systematic search in PubMed, Scopus, and Web of Science databases. It examined the benefits and challenges of AI integration into HTA, with a focus on economic dimensions.

Findings: AI significantly enhances HTA outcomes by driving methodological advancements, improving utility, and fostering healthcare innovation. It enables comprehensive assessments through robust data systems and databases. However, ethical considerations such as biases, transparency, and accountability emphasize the need for deliberate planning and policymaking to ensure responsible integration within the HTA framework.

Conclusion: AI applications in HTA have significant potential to enhance health outcomes and decision-making processes. However, the development of robust data management strategies and regulatory frameworks is essential to ensure effective and ethical implementation. Future research should prioritize the establishment of comprehensive frameworks for AI integration, fostering collaboration among stakeholders, and improving data quality and accessibility on an ongoing basis.