Two things from the queue that held up under testing.
The closed-loop claims checker that fixes its own mistakes
What it is: A workflow tool — refcheckr.pharmatools.ai — built by Nick Lamb PhD, CMPP, that runs a full medical claims verification cycle without breaking into separate manual steps.
Why it’s worth your time: Most AI-assisted claims checking stops at flagging. This one completes the loop: it verifies the claim, identifies what’s wrong, rewrites to fix it, re-verifies the rewrite, and checks ABPI compliance — all in sequence, powered by Perplexity for source retrieval and Anthropic for reasoning. For a health communications professional sitting with a 40-claim annotation grid, the difference between flagging and closing is significant. The ABPI compliance check in particular is doing work that currently lives entirely in the reviewer’s head.
How to use it:
Navigate to refcheckr.pharmatools.ai and paste the claim text alongside the source reference. The tool runs the verify → detect → rewrite → re-verify → ABPI check chain automatically. Review the output — the rewritten claim and the compliance reasoning — before passing to MLR.
Watch out for: The tool reasons against ABPI standards as coded into its prompts; treat its compliance output as a first-pass screen, not a definitive ruling — MLR remains the authority.
Workflow
TRIGGER: Promotional materials draft received from agency for client review
STEP 1: Extract claims — copy individual claims from Word or PowerPoint into a working list
STEP 2: Retrieve source documents — locate references in Veeva Vault or SharePoint; have PDFs ready
STEP 3: Refcheckr (the featured tool) — paste claim + reference text; tool verifies, rewrites if needed, flags ABPI issues
STEP 4: Human review — medical writer reviews rewritten claims and compliance flags before MLR submission
OUTPUT: Annotated claims list with verified wording, ready for MLR cycle
What it is: An open-source tool — Redacta (github.com/nhsengland/redacta) — that strips patient-identifiable information from medical documents before they go anywhere near an AI system.
Why it’s worth your time: The gap between “we use AI responsibly” and “we actually do” often comes down to whether someone remembered to remove the patient details before pasting into Claude. Redacta closes that gap systematically: it uses pattern-matching for NHS numbers, National Insurance numbers, and postcodes, plus agent-level reasoning for names and addresses that don’t follow predictable formats. With 800+ installs and an MIT-0 licence, it is ready to use without procurement conversations. For agencies working on patient case studies, chart reviews, or any real-world evidence material, this removes a category of risk that currently depends on individual vigilance.
How to use it:
Install via the instructions in the GitHub repo (no sign-up, no vendor relationship). Run any document through Redacta before pasting content into any AI tool. Review the pseudonymised output — check that names and indirect identifiers have been caught before proceeding.
Watch out for: Redacta reduces identifiability risk; it does not guarantee full anonymisation under UK GDPR — a data protection review remains appropriate for sensitive materials.
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