Generative artificial intelligence (AI), particularly large language models (LLMs), is increasingly embedded in health economics and outcomes research (HEOR) workflows. Researchers are now using these tools to support activities such as systematic literature reviews, health economic modeling, and real-world evidence generation.

As adoption grows, so does a fundamental question for the HEOR community:

How should the use of generative AI be transparently and consistently reported within HEOR workflows?

To address this question, the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Working Group on AI has developed ELEVATE-GenAI — a reporting guideline specifically designed to document and communicate how generative AI is used in HEOR research.

Why a dedicated reporting guideline is needed

HEOR has a strong tradition of structured reporting, supported by well-established standards for systematic reviews, economic evaluations, and real-world evidence. However, the rapid integration of LLMs into HEOR workflows has outpaced the development of HEOR-specific guidance on how their use should be reported.

LLMs are now being applied to:

• Screening and classifying abstracts in systematic literature reviews
• Extracting data and assessing bias
• Building or replicating health economic models
• Transforming unstructured real-world data into analyzable formats

While these applications offer efficiency and scalability, they also introduce new challenges related to transparency, reproducibility, factual accuracy, bias, uncertainty, and data governance. Existing AI reporting guidelines do not fully address these challenges in the context of HEOR decision-making, regulatory review, or health technology assessment (HTA).

ELEVATE-GenAI was developed to fill this gap by providing clear, HEOR-specific guidance for reporting the use of generative AI within research workflows.

What is ELEVATE-GenAI?

ELEVATE-GenAI is a reporting framework and checklist intended for HEOR studies in which generative AI plays a substantive role in evidence generation, synthesis, or analysis. Its goal is not to evaluate the performance of specific AI tools or to prescribe how AI should be used, but rather to ensure that AI-assisted workflows are clearly described, interpretable, and reproducible.

The guideline is designed to support:

• Authors, by clarifying what information should be reported
• Reviewers and editors, by enabling consistent evaluation
• HTA bodies and regulators, by improving transparency and trust

Importantly, ELEVATE-GenAI is not intended for studies that use AI only for minor tasks such as editing or formatting text. Instead, it applies when generative AI meaningfully influences HEOR outputs.

Reporting generative AI across HEOR workflows: The 10 ELEVATE domains

At the center of ELEVATE-GenAI is a set of 10 reporting domains that together describe how generative AI is integrated into HEOR workflows and how its outputs are assessed.

1. Model characteristics

This domain ensures clarity about what AI system was used. Authors are encouraged to report the model name and version, developer, access method, license type, architecture, and — where available — training and fine-tuning data sources.

2. Accuracy assessment

Accuracy reporting focuses on how closely AI-generated outputs align with expected or correct results, using task-appropriate benchmarks such as expert review, gold-standard datasets, or quantitative performance measures.

3. Comprehensiveness assessment

Comprehensiveness addresses whether AI outputs fully cover all relevant elements of a task — for example, whether all key studies were captured in a literature review or all required components were included in an economic model.

4. Factuality verification

This domain emphasizes verification of factual correctness, including identifying and correcting hallucinated citations, incorrect data, or unsupported claims generated by the model.

5. Reproducibility and generalizability

Authors are encouraged to document prompts, parameters, workflows, and model versions to support reproducibility, and to discuss whether the AI-assisted approach can be applied to similar HEOR questions or settings.

6. Robustness checks

Robustness reporting addresses how sensitive AI outputs are to changes in inputs, such as minor prompt variations, ambiguous wording, or typographical errors.

7. Fairness and bias monitoring

Where applicable, studies should assess whether AI outputs introduce or reinforce biases related to demographic or population characteristics relevant to HEOR analyses.

8. Deployment context and efficiency

This domain captures practical aspects of AI deployment, including hardware and software configurations, processing time, scalability, and resource requirements — factors that influence real-world feasibility.

9. Calibration and uncertainty

Calibration focuses on whether AI confidence aligns with actual performance and how uncertainty is handled, such as defining thresholds for human review in hybrid AI–human workflows.

10. Security and privacy measures

Authors should describe how sensitive data, intellectual property, and regulatory requirements (e.g., GDPR or HIPAA) are addressed when generative AI is used in HEOR workflows.

Each domain is accompanied by reporting guidance and an assessment of metric maturity, recognizing that some areas — such as fairness and uncertainty — are still evolving.

From framework to practice: The ELEVATE checklist

To facilitate adoption, ELEVATE-GenAI includes a practical checklist that translates the 10 domains into concrete reporting questions. An optional scoring system allows authors and reviewers to summarize reporting completeness, while emphasizing that this score is not a measure of methodological quality or study validity.

The authors demonstrate the applicability of the guideline by retrospectively applying it to two published HEOR studies — one focused on systematic literature review automation and another on health economic modeling. These examples show how ELEVATE-GenAI can be used to consistently describe AI-assisted workflows across different HEOR applications and to identify areas where reporting can be strengthened.

Why ELEVATE-GenAI matters for HEOR

As generative AI becomes more deeply integrated into HEOR workflows, transparent reporting is essential to maintain scientific credibility and stakeholder trust. ELEVATE-GenAI provides a shared structure for documenting how AI is used, how outputs are evaluated, and what limitations may affect interpretation.

By establishing common expectations for reporting generative AI in HEOR, ELEVATE-GenAI supports responsible innovation while aligning with the needs of journals, HTA bodies, and regulators.

Final takeaways

ELEVATE-GenAI positions itself as a foundational guideline for reporting the use of generative AI in HEOR workflows. By focusing on transparency, reproducibility, and interpretability, it helps ensure that AI-augmented research can be critically assessed and confidently used in healthcare decision-making.

As a living guideline, ELEVATE-GenAI will continue to evolve alongside advances in generative AI — providing the HEOR community with a practical framework for integrating new technologies without compromising rigor or trust.

Interested in learning more?

Read the full paper: “ELEVATE-GenAI: Reporting Guidelines for the Use of Large Language Models in Health Economics and Outcomes Research: An ISPOR Working Group Report.”

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