Enterprise AI Analysis
Using Generative Artificial Intelligence in Health Economics and Outcomes Research: A Primer on Techniques and Breakthroughs
This primer introduces health economists to the essentials of using Generative AI (GenAI) tools, particularly Large Language Models (LLMs), in HEOR projects, streamlining tasks like literature reviews, data extraction, and economic modelling.
Transforming HEOR with AI: Key Impact Metrics
GenAI promises significant efficiency gains and improved analytical depth in Health Economics and Outcomes Research.
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Efficiency Gain in Literature Reviews
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Reduction in Data Extraction Time
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Improvement in Model Accuracy
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Explore the foundational understanding of AI, ML, DL, GenAI, and LLMs, as outlined in the article's core definitions and hierarchical relationships.
Discover how LLMs can be applied to HEOR tasks such as summarising, data extraction, report drafting, code generation, question answering, and quality review, with practical examples.
Understand the critical considerations of security, bias, transparency, and reproducibility when integrating LLMs into HEOR workflows, including best practices for responsible AI adoption.
Enterprise Process Flow
Identify HEOR Task
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Select LLM & Deployment
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Prompt Engineering
→
Data Integration (RAG)
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Generate & Validate Output
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Human-in-the-Loop Review
30%
Potential Efficiency Increase in HEOR Tasks
| Feature | Traditional AI | GenAI |
|---|---|---|
| Purpose | Classification, Prediction | Content Generation, Complex Reasoning |
| Data Requirements | Specific, Labelled Data | Massive, Diverse Datasets |
| Adaptability | Limited to specific tasks | Flexible, adapts to new tasks (few-shot, zero-shot) |
| Key Risks | Bias, Security | Hallucinations, Bias, IP, Energy Use |
RAG for Enhanced Accuracy
A study by Liu et al. demonstrated that incorporating Retrieval-Augmented Generation (RAG) significantly improved accuracy in systematic literature reviews and meta-analyses, achieving a 1.35 odds ratio increase compared to using LLMs alone. This highlights RAG's potential to mitigate LLM 'hallucinations' by grounding responses in factual external sources like policy documents or clinical guidelines.
Estimate Your AI Transformation ROI
Calculate potential savings and efficiency gains by integrating AI into your HEOR operations.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your Journey to AI-Powered HEOR
A phased approach to integrate GenAI responsibly and effectively into your health economics workflows.
Phase 1: Foundation & Training
Build foundational understanding of GenAI/LLMs, establish ethical guidelines, and train teams on responsible use and prompt engineering.
Phase 2: Pilot & Integration
Identify pilot projects for LLM integration (e.g., literature review summarization), set up secure API access, and validate initial outputs against benchmarks.
Phase 3: Scaling & Optimization
Expand LLM use to more complex tasks (e.g., economic modeling support), optimize workflows with advanced techniques like RAG, and continuously monitor performance and bias.
Phase 4: Advanced Innovation
Explore multimodal AI, 'living' HEOR materials, and patient empowerment initiatives, ensuring ongoing ethical scrutiny and regulatory compliance.
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