By Niyati Parikh, VP, Product Readiness, Panalgo, and Mike Munsell, Director, RWD and HEOR, Panalgo

Evidence generation in health economics and outcomes research (HEOR) has long been a rigorous, but time-intensive process. From literature reviews to cohort design, researchers have traditionally spent weeks or even months on foundational tasks before analysis could even begin.

Today, AI is beginning to reshape that workflow.

While it doesn’t replace the scientific rigor at the core of HEOR, AI is acting as a powerful accelerator, helping teams move faster from research question to insight. Essentially, AI is becoming the “sous chef” of evidence generation, handling the prep work so researchers can focus on higher-value analysis and decision making.

From Manual to Accelerated: How AI Is Transforming HEOR

Historically, HEOR evidence generation has relied heavily on manual effort. AI is now streamlining many of these early-stage activities.

Large language models (LLMs) and other AI tools can assist with protocol interpretation, quickly scan literature, and generate initial code lists or cohort definitions. This “first pass” significantly reduces the time required to get studies off the ground.

However, AI is not replacing researchers; it’s augmenting them. Much like a research assistant or intern, AI handles the initial groundwork, while experienced analysts validate outputs, refine methodologies, and ensure scientific integrity.

Three Ways AI Is Being Used in Evidence Generation

1. AI for Systematic Literature Review: AI can quickly scan and synthesize vast amounts of published research, identifying relevant studies and summarizing key findings in a fraction of the time it would take manually. While it doesn’t replace formal systematic reviews, it provides a strong starting point, helping researchers understand the study-specific landscape early and focus their efforts more strategically.
2. AI for Cohort Building: Translating a clinical question into a structured cohort definition is often one of the most complex steps in HEOR. AI can assist by generating candidate code lists, identifying relevant clinical parameters, and even suggesting appropriate datasets. This reduces the friction between study design and execution, enabling teams to move more quickly into analysis. Tools purpose-built for this stage are required to ensure traceability, retaining a clear record of how cohorts were defined and which data sources were used, which is critical for reproducibility and regulatory scrutiny.
3. AI for Evidence Synthesis: Once analyses are complete, AI can support the synthesis of findings: summarizing results, organizing outputs, and identifying gaps relative to the original research question. Researchers can use AI as a thought partner to refine narratives, improve clarity, and find blind spots.

Balancing Speed with Rigor: Managing the Risks

Despite its potential, AI introduces new considerations that HEOR teams must navigate carefully, including:‍

- Bias: AI outputs are only as good as the design of the system, and unchecked outputs can introduce unintended bias into the research process.‍

- Validation and reproducibility: Any AI-assisted workflow must allow researchers to verify results and reproduce findings, especially when evidence is used to inform regulatory or health technology assessment decisions.

-Transparency: Evidence generation requires clear documentation and auditability, and AI-generated outputs must be traceable back to their sources and assumptions.

Perhaps most importantly, risks compound across the workflow. Each step in evidence generation builds on the last, making human oversight at every stage essential. Incorporating checkpoints throughout the process helps ensure that errors or biases do not propagate downstream.

Using AI Effectively in HEOR

AI delivers the most value when applied to early-stage research workflows: accelerating literature review, study setup, and initial synthesis. But success depends on thoughtful implementation.

Human involvement is critical for maintaining scientific rigor, ensuring appropriate study design, and interpreting results in context. At the same time, organizations need to stay aligned with evolving regulatory expectations, as standards for AI use in evidence generation continue to take shape in real time.

When used responsibly, AI can significantly increase the speed and efficiency of HEOR studies without compromising the quality, transparency, and reproducibility that the field demands.

In the end, AI isn’t replacing expertise; it’s amplifying it.

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Ella AI, Panalgo’s GenAI assistant, is fit-for-purpose and transparent, making it easier for life sciences teams to understand patient journeys, refine study designs, and accelerate evidence generation safely. Contact us to learn more.