AI in HEOR Radar · Jigar Rajpura

HTA & JCA: AI predicted the first JCA scope without seeing it

Thu, 29 Oct 2026 12:00:00 +0000

The team ran an AI-enabled PICO scoping workflow for tovorafenib in paediatric low-grade glioma while the published Joint Clinical Assessment report was kept out of its reach. They then scored the prediction against that report, which defined 3 populations and 8 PICOs, and report high concordance with the official scope.

Why it matters: Most published PICO simulators have been graded against simulation exercises or the predictor's own reasoning. This is among the first tests against a real, published JCA scope.

Watch out: It is a single case. Read the full abstract for how concordance was scored before generalising.

Source: Prospective AI-enabled PICO prediction for EU Joint Clinical Assessment: blinded validation using the first published JCA report on tovorafenib (Conference abstract, ISPOR Europe 2026, Vienna · Value in Health 29(12S)).

From the Autumn 2026 edition of the AI in HEOR Radar.

Economic modelling: An AI agent that runs TECH-VER checks on health economic models

Thu, 29 Oct 2026 12:00:00 +0000

The agent pairs a retrieval-augmented language model with deterministic parsers for Excel workbooks and R or Python code. It maps each model component to TECH-VER and AdViSHE checks and returns a graded error log. On 25 models seeded with 120 known errors, it caught 88% of them with 96% specificity against three experienced modellers (Cohen's κ 0.82). Verification time fell from about 6.2 hours to 9 minutes.

Why it matters: Technical verification is the quality step most often squeezed under deadline. A checklist-anchored agent makes it routine and documented rather than optional.

Watch out: The errors were planted, not found in real projects, and the agent missed about one in eight. It did worse on script-based models than on Excel (84% vs 91%). Treat it as a first pass, not a sign-off.

Source: Automated technical verification of health economic models using a generative-AI agent: concordance with expert review across spreadsheet and programmatic implementations (Conference abstract, ISPOR Europe 2026, Vienna · Value in Health 29(12S)).

From the Autumn 2026 edition of the AI in HEOR Radar.

Real-world evidence: LLM chart abstraction in a real RWE pilot, with honest numbers

Thu, 29 Oct 2026 12:00:00 +0000

The pilot used de-identified notes from 24 patients with a haematological malignancy, with several hundred to more than 1,000 notes each. A structured prompt extracted treatment response, ECOG, regimens and dates, transplant status and discontinuation reasons, and attached source snippets to each answer. First-pass outputs matched the adjudicated study values in 70.8% of 96 non-date fields, dates fell within 7 days in 86.3%, and treatment response agreed in 66.7%.

Why it matters: Most published LLM-abstraction results come from vendors' tuned pipelines. This shows what an enterprise LLM with a well-built prompt delivers, and why source-referenced output matters for traceability in HTA submissions.

Watch out: The sample was small, and agreement was lowest for co-mutations and transplant status. Human adjudication remained essential.

Source: A practical framework for LLM-assisted abstraction and validation of RWE variables from unstructured physician notes (Conference abstract, ISPOR Europe 2026, Vienna · Value in Health 29(12S)).

From the Autumn 2026 edition of the AI in HEOR Radar.