HEOR

Health Economics & Outcomes Research

AI-powered workflows for evidence generation, economic modeling, literature intelligence, and outcomes analysis — accelerating insights while preserving methodological rigor.

Economic Modeling Evidence Synthesis Landscape Reports RWE Analysis Evidence Gaps
Economic Modeling & Analysis

From Markov models to multi-country adaptations

ValueGen.AI builds and adapts health-economic models across cost-effectiveness, budget impact, and scenario analysis — with country-specific threshold logic embedded.

Cost-Effectiveness Analysis (CEA)

Builds Markov models, partition survival models, and decision trees for incremental cost per QALY/LYG. Supports probabilistic sensitivity analysis (PSA), one-way tornado diagrams, and country-specific threshold analyses.

Budget Impact Modeling (BIM)

Estimates net budget impact for payers adopting a new therapy — accounting for market uptake, patient share, cost offsets, and real-world adherence patterns.

Model Verification & Validation

Ensure existing models are technically sound, transparent, and fit for purpose through verification and validation aligned with ISPOR-SMDM guidance.

Modeling Capabilities

ValueGen.AI's modeling engine is purpose-built to mirror the structure of submission-grade economic evaluations across jurisdictions.

  • Markov cohort & microsimulation
  • Monte Carlo probabilistic sensitivity
  • Multi-country model adaptations
  • Patient-level simulation models
  • Lifetime horizon projections
  • HEOR-specific reliability scoring
  • Early economic modeling & scoping
Evidence Generation

Literature Intelligence Using Deep Research Agents

An agentic framework that conducts multi-source retrieval across published literature, HTA databases, and regulatory documents — with citations and confidence scores.

Automated Landscape Assessment

Generates comprehensive disease-area landscape reports in 48 hours — synthesizing HTA reports, guidelines, and published literature across 7+ markets in original language, summarized in English.

Evidence Gap Identification

Algorithmically pinpoints gaps in the evidence base that may affect market access. Ranks gaps by strategic priority to guide clinical study design and real-world evidence collection.

Real-World Evidence (RWE) Analysis

Analyzes payer database data, RWE studies, and observational research to build supplementary evidence packages. Supports comparative effectiveness and adherence analyses.

HTA Analytics & Regulatory Intelligence

HTA Body-Specific Analyses

Tailored analytical outputs aligned with the evidentiary expectations of each HTA body and regulator.

NICE — UK

Technology Appraisal Alignment

QALY thresholds, SMC adaptations, and reference case framing.

G-BA / AMNOG — Germany

Added-Benefit Dossiers

Subgroup analyses and IQWiG methodology alignment.

HAS — France

ASMR / SMR Evidence

Evidence requirements and medico-economic dossier support.

ICER — US

Value Framework

Value-based price benchmarks and scoping analysis.

CADTH — Canada

CDEC Deliberation Patterns

CDR submission requirements and reimbursement context.

AIFA / AEM — Italy & Spain

Negotiation Context

Regional reimbursement and price negotiation insight.

Multi-country Rollups

Cross-Market Evidence Summaries

Synthesized HTA comparison reports across markets — surfacing differences in comparator choice, modeling assumptions, and decision outcomes.

Regulatory Submission Support

FDA / EMA-Ready Narratives

Drafts clinical sections of regulatory dossiers, value-of-information analyses, and evidence summaries for advisory committee meetings.

Key Value Drivers

Mapped to Payer Decision Criteria

Identifies the top clinical, humanistic, and economic value drivers for a therapy — mapped to HTA body priorities and payer decision criteria.

Methodology

Aligned with ISPOR Working Group Guidance on Generative AI in HTA

Designed in line with the ISPOR Working Group Report on Generative AI for Health Technology Assessment (Chhatwal et al., 2025), including standards for transparency, traceability, and human oversight (Fleurence et al., 2025).

References

  1. Chhatwal, J., Fleurence, R. L., Bian, J., Wang, X., Xu, H., Dawoud, D., Higashi, M. K., et al. (2025) 'Generative AI for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations — An ISPOR Working Group Report', Value in Health. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC11786987/.
  2. Fleurence, R. L., Dawoud, D., Bian, J., Higashi, M. K., Wang, X., Xu, H., Chhatwal, J. and Ayer, T. (2025) 'ELEVATE-GenAI: Reporting Guidelines for the Use of Large Language Models in Health Economics and Outcomes Research — An ISPOR Working Group Report', Value in Health, 28(11), pp. 1611–1625. Available at: https://pubmed.ncbi.nlm.nih.gov/40653157/.

Accelerate Your Next HEOR Deliverable

From early economic scoping to submission-ready dossiers — ValueGen.AI compresses timelines without compromising methodological rigor.

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