EDEN: Towards a Computational Framework to Align Incentives in Healthy Aging

Incentive misalignment among healthcare stakeholders poses significant barriers to promoting healthy aging, hindering efforts to mitigate the burden of long-term care. Despite extensive research in public health, incentive gaps persist, as static implementation guidelines often fail to accommodate dynamic and conflicting incentives. This study introduces and evaluates EDEN (eden.ethz.ch), a computational framework designed to dynamically map stakeholder incentives using a Retrieval-Augmented Generation pipeline. A comparative study using a health insurer use case evaluates alternative incentive analyses; qualitative content analysis, large language models, and EDEN. The evaluation assesses their ability to identify and address incentive gaps. Preliminary findings demonstrate the EDEN's ability to map incentives and highlight misalignment compared to alternative approaches. These findings demonstrate how EDEN can offer evidence-based strategies for key healthcare stakeholders, such as health insurers, based on retrieval features to align incentives in healthy aging.