Blogs

At ISPOR 2025 in Montreal, our team at Value Analytics Labs—in collaboration with Takeda Pharmaceuticals—presented groundbreaking research on the use of generative AI in health economics. Our study explored a frontier application: using large language models to replicate published cost-effectiveness models.

Value Analytics Labs made a remarkable impact at ISPOR Europe 2024, the premier event in Health Economics and Outcomes Research (HEOR). Our team showcased a series of groundbreaking presentations, highlighting our expertise and innovative approaches in leveraging Generative AI to advance HEOR methodologies.

In the first part of this series, we explored the transformative power of generative AI in Health Economics and Outcomes Research (HEOR) and Market Access (MA), highlighting practical applications and demonstrations from our recent workshop at Harvard. Now, let's dive deeper into the advanced AI skills discussed, the challenges identified, and the key takeaways that point toward the future of AI in our field.

The Health Economics and Outcomes Research (HEOR) and Market Access (MA) communities are increasingly under pressure to generate quick insights by assimilating vast amounts of information from multiple data sources, identifying evidence gaps, and developing strategic solutions. Time constraints are a significant challenge in this environment. Generative AI is already revolutionizing industries such as the computer software sector and has immense potential to drastically transform how we work in the HEOR and MA spaces.

The advent of General Artificial Intelligence (Gen AI) promises a seismic shift in the pharmaceutical industry. With its capability to automate tasks, enhance decision-making, and improve customer experiences, Gen AI stands at the forefront of a technological revolution.

When it comes to health economic modeling, the way we estimate patient survival is crucial for determining the cost-effectiveness of healthcare interventions. Traditionally, this has been done using general population mortality statistics to predict future survival rates. But is this approach still valid in a world where healthcare is rapidly advancing?