Building the Next Generation of HEOR: Loon's Multi-Agent AI Architecture

The Current State of HEOR: Why We Built Cognitive Ensemble AI Systems

At Loon, we've spent years working within the health economics and outcomes research ecosystem, witnessing firsthand how resource constraints and methodological limitations impact patient access to innovative therapies. Our experience at Canada's Drug Agency and in clinical research organizations revealed a fundamental problem: the tools and processes used for HEOR haven't scaled with the complexity of modern healthcare decision-making.

This observation led us to develop our patent-pending Cognitive Ensemble AI Systems™ - a multi-agent architecture specifically designed for the unique challenges of health economic analysis. Rather than attempting to replace human expertise, we've built a system that amplifies the capabilities of health economists by deploying hundreds of specialized AI agents to handle the computational and analytical tasks that currently consume months of manual effort.

The Bottlenecks We're Addressing

The 2,500 Hour Problem

Based on our analysis of typical HEOR projects, a comprehensive systematic review and economic evaluation requires approximately 2,500 person-hours. This translates to 12-18 months of elapsed time, during which clinical landscapes evolve, new evidence emerges, and patient access remains delayed. For pharmaceutical companies, this represents not just operational cost but significant revenue loss as products await reimbursement decisions.

The Scope-Quality Trade-off

Current methodologies force organizations to choose between analytical depth and breadth. A thorough cost-effectiveness analysis examining multiple comparators, patient subgroups, and scenario analyses requires proportionally more resources. This constraint often results in analyses that, while methodologically sound, may not capture the full value proposition of innovative therapies or address all stakeholder questions.

"Having worked on hundreds of HTA submissions, I've seen how resource constraints force teams to limit their analyses. We built Loon's AI systems to eliminate these artificial boundaries while maintaining the methodological rigor that regulators require." - Dr. Ghayath Janoudi, CEO of Loon

Our Approach: Validated Multi-Agent AI Systems

The Cognitive Ensemble Architecture

Loon's Cognitive Ensemble AI Systems deploy approximately 500 specialized agents, each trained on specific aspects of HEOR methodology. This isn't a monolithic AI model attempting to handle all tasks - it's a carefully orchestrated system where each agent has deep expertise in its domain, whether that's identifying relevant literature, extracting specific data types, or constructing economic models.

Scientific Validation at the Core

What distinguishes our approach is our commitment to scientific validation. We've published peer-reviewed studies demonstrating that our AI agents achieve 99% sensitivity and 95.5% accuracy in systematic review screening tasks. This validation isn't just about performance metrics - it's about building trust with regulatory bodies and ensuring that AI-generated evidence meets the standards required for healthcare decision-making.

Loon's Validated Performance Metrics

Systematic Review Performance

• 99% sensitivity (peer-reviewed)

• 95.5% accuracy in screening

• 95% timeline reduction

• Continuous evidence updating

Quality Assurance Features

• Confidence score calibration

• Human-in-the-loop validation

• HTA-compliant (NICE, CDA, ISPOR)

• Full audit trail documentation

Real-World Implementation: What Our Systems Enable

Comprehensive HTA Evidence at Scale

Our clients use the Loon agentic AI systems for HEOR and HTA to conduct and gather extensive evidence-based inputs to inform comrehensive analyses that would be impractical with traditional methods. For instance, a recent project examined 24 scenarios for health economic modelling requiring input across 5 patient populations and various time horizons - work that would typically require 6 months was completed in 5 weeks. This removes the artificial constraints that limit analytical scope while maintaining scientific rigour.

Dynamic Evidence Integration

Our work with various industry and stakeholders, research groups, and academic institutions demonstrated how AI agents can continuously monitor and integrate new evidence into existing analyses. When new clinical trials are published or real-world evidence becomes available, our systems automatically identify, extract, and incorporate this information into economic models. This ensures that decision-makers always have access to current evidence without requiring complete re-analysis.

The Impact on Market Access and Reimbursement

Accelerating Time to Reimbursement

By reducing HEOR project timelines from 12-18 months to 2-4 weeks, we're directly addressing one of the most significant barriers to patient access. Our work with pharmaceutical companies has shown that faster evidence generation translates to earlier reimbursement submissions and, ultimately, faster patient access to innovative therapies. In dollar terms, this acceleration can represent $1-5 million in recovered revenue per drug per jurisdiction.

Enhancing Submission Quality

Speed without quality would be meaningless in HEOR. Our systems enable more comprehensive analyses within compressed timelines, allowing submission teams to explore additional scenarios, address potential HTA concerns proactively, and provide more robust evidence packages. This thoroughness has contributed to more favorable reimbursement recommendations for our clients' products.

Where We're Taking HEOR Next

Predictive Reimbursement Analytics

We're expanding our AI capabilities to include predictive analytics for reimbursement outcomes. By analyzing patterns across thousands of HTA decisions, our systems can forecast likely reimbursement conditions and help companies optimize their evidence generation strategies before clinical trials begin. This proactive approach represents a fundamental shift from reactive to predictive market access planning.

Global Evidence Harmonization

Different HTA bodies have varying methodological requirements and evidence preferences. Our next generation of AI agents will automatically adapt analyses to meet jurisdiction-specific requirements while maintaining a core evidence base. This capability will significantly reduce the duplication of effort currently required for global market access strategies.

Getting Started with AI-Assisted HEOR

• Begin with pilot projects to demonstrate value within your organization

• Ensure alignment between AI capabilities and your HEOR objectives

• Establish clear quality assurance protocols for AI-generated analyses

• Train your team on interpreting and validating AI outputs

• Develop workflows that integrate AI assistance with human expertise

• Monitor regulatory guidance on AI use in HTA submissions

Our Published Performance Data

99%

Sensitivity (recall)

96%

Accuracy

90%

Precision (post-routing)

These metrics come from our published validation studies, available on medRxiv. We believe in transparency and scientific rigor - every performance claim we make is backed by peer-reviewed research. This commitment to validation has been crucial in building trust with regulatory bodies and pharmaceutical companies.