Amelia Jones | 18/12/2025
This poster was presented at ISPOR 2025 in Glasgow (9-12 November).
Author Cassandra Springate explains the motivation behind the research and provides further insight into the findings.
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AI has been a hot topic in health economics and outcomes research (HEOR), promising efficiencies in evidence generation, modelling, and decision-making. While its potential is clear, consolidated guidance on responsible adoption has remained elusive. Our research set out to review existing guidelines and explore how HEOR professionals can engage with AI in a way that is both effective and risk aware.
By conducting a targeted literature review and applying thematic analysis, we examined a broad sample of guidelines covering diverse activities and perspectives. This approach allowed us to identify recurring themes and core principles designed to mitigate risks such as bias, reproducibility issues, and inconsistent practices.
Our findings reveal strong alignment across guidelines on key principles, including accountability, transparency, and risk-based assessment. However, few guidelines addressed the rationale for using AI – a step we believe is critical. Incorporating this decision into the research design stage ensures AI is deployed where it adds genuine value rather than by default.
For HEOR professionals, understanding these principles can inform the development of bespoke frameworks tailored to specific use cases. For guideline developers, the findings underscore the need for clarity and consistency. As AI continues to shape healthcare decision-making, embedding responsible practices will be essential to harness its benefits while safeguarding rigor and trust.
If you have any questions or would like to hear more about our research please contact Cassandra Springate, Principal Systematic Reviewer at HEOR: cassandra.springate@heor.co.uk.