NICE (National Institute for Health and Care Excellence) introduces CHEERS-AI! It’s a new reporting standard optimized for health economic evaluations of AI technologies in healthcare. The EU funded CHEERS-AI as part of the Next Generation Health Technology Assessment (HTx) project. Its primary aim is to enhance the transparency and quality of cost-effectiveness studies specifically tailored for AI-enabled healthcare interventions.
CHEERS-AI seeks to systematically address the challenges facing health economic evaluations of AI interventions. A systematic review has identified several issues, including poor quality input data, author conflicts of interest, lack of transparent reporting, and vague information regarding AI functionality.
Building on the established CHEERS 2022 standards, CHEERS-AI incorporates the original 28 standards while adding 8 new details for AI-related nuances and 10 AI-specific standards. The extra standards tackle key aspects such as user autonomy, continuous AI learning, and the methodology behind developing the AI component.
The primary goal of CHEERS-AI is to ensure transparent and reproducible reporting of AI-specific details in health economic evaluations. This standard will aid healthcare decision-makers in understanding the value of AI-enabled treatments, ultimately facilitating faster access to promising AI technologies for patients.
ISPOR endorsed CHEERS-AI and is now included in the EQUATOR Network checklist of reporting guidelines. This endorsement ensures integration into best practices for health economic evaluation reporting.
Refining the quality and transparency of health economic evaluations involving AI technologies is an important objective. In doing so, CHEERS-AI aims to elevate the standards of economic evaluation reporting. This improvement is vital for reimbursement decisions and the broader adoption of AI in healthcare.
In summary, CHEERS-AI represents an advancement in standardizing and enhancing health economic evaluations of AI technologies, thereby supporting improved decision-making in healthcare.
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