A new standard for secure, evidence-governed LLM-assisted audits of HEOR models in Excel.
HEOR FAME™ is the Framework for LLM-Assisted Audits of HEOR Models in Excel—a fixed, governed, capability-aware methodology for rapid first-pass review of complex HEOR workbooks. Findings are anchored in documented workbook, behavior and evidence, creating a reproducible audit trail for human review.
PERFORMANCE SUMMARY
Mixture-Cure
Partitioned-Survival Model
100.0%
Markov
Cohort Model
96.9%
Pooled sensitivity for seeded-error detection.
No false positives for seeded decoys.
WHY HEOR FAME
Strategic value.
HEOR FAME strengthens model quality, governance, and review efficiency by making LLM-assisted auditing more standardized, traceable, and scalable.
Standardized,
Reproducible QA
Common workflow, evidence rules, and capability boundaries make audits more consistent, repeatable, and governable across teams.
Traceability &
Documentation
Findings are anchored in workbook evidence and preserved in a structured audit trail, supporting human review and follow-up.
Better Model
Governance
Explicit scope, human confirmation, and documented limitations support responsible use of LLMs in review of high-stakes HEOR models.
Earlier
Identification
Broader first-pass coverage can surface consequential defects, inconsistencies, and review priorities earlier in model review process.
Scalable Quality
Assurance
Enables consistent first-pass review across large, complex Excel models, based on consolidated institutional knowledge.
Focused Use of
Expert Time
Automated first-pass inspection frees HEOR experts to concentrate on higher value activities involving scientific judgement and issue resolution.
A structured workflow from capability testing to audit report.
Phase
0
1
2
Capability Gate
Workbook Inventory
Output Trace Map
3
Structural Audit
4
HEOR Logic Audit
5
Targeted Tests