AI in HEOR: The Road Ahead to 2030

Introduction

As we move further into the decade, artificial intelligence (AI) is poised to become an indispensable part of Health Economics and Outcomes Research (HEOR). Building on the early innovations and current applications detailed in my previous posts, it is clear that by 2030 AI will be embedded across HEOR activities – from economic modeling to evidence generation. In this third installment, I focus on how the next wave of AI, including large language models (LLMs), advanced predictive analytics, and automation, will shape the future of HEOR deliverables, project timelines, and analytic quality. The horizon through 2030 promises unprecedented speed, reproducibility, and real-time adaptability in our work, fundamentally changing how we design studies, analyze data, and communicate results.

AI-Accelerated Economic Modeling and Simulations

One of the most immediate impacts of AI will be on budget impact models (BIM) and other economic simulations. By 2030, constructing a BIM or a benefit design model is likely to be faster and more dynamic. AI-driven tools will automatically gather relevant cost and epidemiological data, suggest model structures, and even populate inputs with minimal manual effort. This means health economists can rapidly produce robust models to project the financial impact of new therapies or insurance benefit changes. Crucially, simulation and scenario generation will become core features of these models. Rather than manually running one scenario at a time, analysts will let AI simulate hundreds of potential scenarios in minutes – varying patient populations, uptake rates, or healthcare utilization assumptions – to stress-test outcomes. For example, an AI-augmented BIM could quickly model best-case, worst-case, and base-case budget impacts for a new drug launch, giving decision-makers a real-time range of potential costs. These enhancements not only compress development timelines for models but also improve quality: every assumption can be tested, and the risk of oversight drops as the AI flags anomalies or extreme results for further review. In short, economic modeling is set to become faster, more iterative, and more thorough. The increased reproducibility of AI-generated analyses (with automated logs of data and code) will make results more credible to payers and policymakers who scrutinize these models.

Comparative Effectiveness and Outcome Evaluations

AI will also transform how we conduct comparative effectiveness research (CER) and other outcomes evaluations like cost-effectiveness studies. Today's labor-intensive evidence reviews and data analyses will be assisted by generative AI and smart automation by 2030. Imagine an HEOR analyst exploring a new therapy's value: an LLM could instantly sift through publications and real-world evidence to synthesize findings on the therapy versus standard of care. Instead of weeks of literature review, an AI assistant might produce a draft comparative effectiveness report in a day – complete with references to key studies and summary of outcome differences. Likewise, AI will enable on-the-fly data synthesis for CER. When head-to-head trial data are lacking, we may use AI to generate credible synthetic data or virtual control arms by drawing on vast patient databases, helping to fill evidence gaps in comparisons. By 2030, evaluating incremental cost-effectiveness (the ICER per QALY, or cost per quality-adjusted life year) will be faster and more responsive. HEOR teams will feed updated clinical data into AI-driven cost-effectiveness models that continuously recalculate ICERs as new evidence emerges. This real-time adaptability means that as soon as a new study or real-world outcome data becomes available, the comparative analysis and QALY evaluations can be refreshed. The result is that payers and providers will always have the most up-to-date evidence on value – a crucial advantage when making coverage decisions. Overall, AI will make comparative effectiveness research more comprehensive and timely, while preserving (or even improving) analytic rigor through automated cross-checks and validation routines built into the tools.

Intelligent Pricing Strategy and Market Access

In the realm of market access, AI techniques will guide pricing strategy and stakeholder negotiations with far more analytical firepower than today. By leveraging predictive analytics on large datasets (e.g. prior launches, market trends, and outcomes data), AI can help forecast how different price points or discount schemes might play out in the real world. For instance, by 2030 a pharmaceutical company could use an AI model to simulate how a new drug's price affects its formulary placement, uptake, and downstream medical cost offsets. These offsets – the savings from avoided hospitalizations or disease complications due to an effective new therapy – can be tough to estimate. AI will improve accuracy here by quickly analyzing massive claims and electronic health record datasets to identify patterns (e.g. reductions in emergency visits when patients are on the new drug) and quantifying those savings. This means pricing teams can more confidently set a price that reflects not just the drug's cost, but the net economic impact on the healthcare system. Additionally, AI will support scenario planning for pricing: teams can ask, "What if we price 10% lower to drive wider adoption? Will the increased volume and cost offsets compensate?" and get evidence-based answers from an AI-driven simulation. These capabilities enable a more data-driven and responsive pricing strategy. By reducing the time needed to analyze each scenario from weeks to hours, AI lets market access professionals iterate quickly and refine their approach before finalizing a launch price or a value-based contract. The end result by 2030 is likely to be pricing decisions that are reached faster, with greater insight into long-term outcomes – aligning prices more closely with value delivered.

Streamlined Payer Submissions and Communications

Preparing value communication materials and payer submissions is another area where AI will dramatically improve efficiency. Crafting a comprehensive dossier for health plans or health technology assessment agencies (such as an AMCP dossier or NICE submission) typically requires pulling together clinical evidence, economic models, and narrative justifications into a coherent package. In the near future, large language models will act as intelligent writing assistants, automatically drafting substantial portions of these submissions. By 2030, an HEOR specialist might prompt an AI: "Generate the budget impact section using our model results and include a comparison to current standard of care," and within seconds have a well-structured draft to refine. This kind of automation not only saves time in writing but also helps maintain consistency and accuracy – the AI will faithfully incorporate the latest data from the models and references from the evidence library without accidentally omitting key points. Reproducibility across documents improves as well; for example, the economic results described in a slide deck for payers will exactly match those in the written dossier, because both were generated from the same up-to-date data source by the AI. We can also expect more interactive and adaptive communications. Rather than static one-size-fits-all dossiers, by 2030 companies might deploy AI-driven platforms where payers can explore the data themselves. An interactive model interface could allow a payer to adjust a few assumptions (like their specific member population size or adherence rate) and immediately see the impact on outcomes and budget – all powered behind the scenes by automated HEOR simulations. While human expertise will still be required to interpret and endorse the results, the overall process of creating and updating payer-facing materials will be much faster and more tailored. In turn, this means HEOR findings can be communicated in near real-time as new evidence comes out or as payer needs change, fostering more trust and transparency in the HEOR results.

Key Changes by 2030

Reflecting on these developments, several key changes stand out as we look toward 2030 in HEOR:

• ●Significantly shorter project timelines – AI-driven automation will compress the time needed to conduct analyses and produce HEOR deliverables, allowing weeks-long tasks to be completed in days without sacrificing quality.
• ●Greater reproducibility and consistency – Workflows powered by AI will reduce human error and variability. Analyses will be easily repeated and updated, ensuring that results are reliable and methods transparent for all stakeholders.
• ●Real-time adaptability of models – HEOR models and evaluations will not be one-off static exercises. They will evolve continuously with incoming data, using AI to instantly recalibrate predictions and outcomes so that decisions can always be based on the latest evidence.

Conclusion

Ultimately, the next few years will see AI become a standard partner in producing faster, more rigorous, and more responsive HEOR insights. By 2030, HEOR professionals will not be replaced by AI, but those who skillfully leverage AI tools will deliver results with a speed and precision that once seemed out of reach. Embracing these technologies will enable us to focus more on strategy and interpretation, confident that the groundwork – from modeling to report generation – is handled at "AI speed" with uncompromising quality. The future of AI in HEOR is bright, and it's right around the corner.