The agentic MLR reviewer Veeva bought rather than built

What it is: A tool — Veeva Falcon MLR, the agentic review platform Veeva announced on 23 June after acquiring Copli, the company that pioneered it (veeva.com).

Why it’s worth your time: This is a step beyond the pre-checks we’ve covered before. Where the earlier Quick Check Agent flagged issues for a human to weigh, Falcon is pitched as running the review itself — checking promotional and medical materials against the approved label and local regulations — and Veeva is putting a number on it: the potential to remove 70 per cent or more of manual MLR labour within five years. Whether or not that figure lands, the direction is the thing to register. If your review cycle is your bottleneck — and for most teams it is — the vendor that already owns your content stack now intends to automate the slowest part of it. Better understood before it turns up in a release note.

How to use it:

• If you’re on PromoMats, get on the 9 July webinar Veeva is running to show it, and treat it as competitive intelligence whether or not you ever buy.

• Pressure-test the 70 per cent claim against your own mix: an agent will do most for high-volume, low-variation assets and least for the nuanced, claim-heavy pieces where the judgement actually lives.

• Decide now, on paper, what a human reviewer still signs even when the agent comes back clean — before the efficiency case decides it for you.

Watch out for: “Agentic” review automates the checking, not the accountability. A clean Falcon pass is not regulatory clearance, and the named reviewer is still the named reviewer. Treat the labour saving as real and the sign-off as non-negotiable.

A fair-balance pre-check you can run before the file leaves your desk

What it is: A prompt routine — a self-audit you run on your own draft that sets benefit against risk before it goes anywhere near review.

Why it’s worth your time: With the FDA now using AI to screen ads for exactly this failure (see From the Floor), the cheapest insurance is to read your own copy the way the regulator’s tool will. Generative drafts skew efficacy-heavy and quietly under-weight safety language; a structured pass catches the imbalance while it’s still yours to fix. Run against a near-final piece this week, it surfaced an ISI that had drifted below the fold and a benefit claim with no risk in the same eyeline — both things a reviewer would have bounced.

How to use it:

• Paste the draft and ask, in one instruction: “List every efficacy or benefit claim and every risk or safety statement in two columns. Flag any benefit without an adjacent risk, and any place the safety information is less prominent than the claim it qualifies.”

• Ask it to rank the three weakest fair-balance moments and explain why — not to rewrite them. You want the diagnosis; the rewrite stays yours.

• Keep the rule in front of it: the current FDA/FTC line is that risk must carry equal prominence to benefit, in the piece itself, not behind a “see more” or a linked page (improvado.io).

Watch out for: This finds imbalance; it does not certify balance. A model that helped write the copy is not a neutral judge of it, and a clean self-check is a reason to submit with confidence — never a reason to skip MLR.

One to watch your vendor build; one to run yourself before Friday.

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🥜🥜🥜🥜🥜 CRACKING

What it is: A copy-paste prompt template, posted on X by @MedCommsAI.

Why it’s worth your time: It forces the output into three sections — Clinical Context, Decision Support, Educational Points — which is how clinicians actually consult reference material, so you get something scannable and decision-oriented rather than an essay. Tested on turning a dense treatment guideline into a one-page HCP reference, it produced a usable first draft with the right architecture and tone in seconds. The structure is therapeutic-area-agnostic, so the same prompt works for any condition.

How to use it:

• Paste the template and swap [THERAPEUTIC AREA/CONDITION] for your topic.

• Keep the three section headers exactly — Clinical Context, Decision Support, Educational Points. They are what make the output usable.

• Close with the format instruction: “Concise, scannable layout. Professional health communications tone.”

Watch out for: It generates structure, not evidence — every clinical claim it produces needs a writer and a source check before it goes anywhere near a physician or MLR.

Workflow:

TRIGGER → New treatment guideline or label update lands from medical affairs

STEP 1 — Source gather (medical writer): pull the guideline, label and pivotal data into one place

STEP 2 — Draft scaffold (featured prompt): generate the three-section reference structure in Claude or ChatGPT

STEP 3 — Evidence fill (medical writer): replace each placeholder with sourced, referenced claims

STEP 4 — MLR review (human / Veeva Vault): claims, fair balance and references checked and signed off

OUTPUT → Approved one-page physician reference tool in the house template

An MLR pre-check that lives inside Veeva Vault

🥜🥜🥜🥜 GOOD NUT

What it is: A tool — two AI agents, Quick Check and Content, now built into Veeva PromoMats (announced December 2025).

Why it’s worth your time: The Quick Check Agent screens content against editorial, brand, market, channel and compliance rules before it reaches MLR, so reviewers stop burning hours on the errors a checklist could have caught; the Content Agent summarises documents and reads the visuals to orient reviewers fast. The part that matters in a regulated environment is that it runs inside Vault — your promotional copy never leaves the system, which is the usual reason teams won’t put it near a chatbot. Moderna is among the first users and is openly aiming at “nearly touch-free” routine review.

How to use it:

• If you’re already on Veeva PromoMats, ask your admin about early access to Veeva AI.

• Pilot it on one high-volume, low-risk asset type first — HCP emails or banners — before trusting it anywhere near a launch.

Watch out for: It’s Veeva-Vault-only with no free tier, still early access (single-digit customers), and the real test is whether its flags are consistent and auditable enough for a regulator — treat it as a first pass, never the reviewer of record.

A free library of 90 medical-writing prompts worth raiding

🥜🥜🥜🥜 GOOD NUT

What it is: A resource — an ungated library of 90 copy-paste prompt templates from aingens, organised by research and writing task.

Why it’s worth your time: Unlike most “prompt pack” lead magnets, these are free, fully visible and well-built, with placeholders for population, condition, endpoint, audience and reading level, and sensible defaults baked in (PICO, GRADE, effect sizes, safety language). The abstract, medical affairs brief, patient handout and FAQ templates are strong scaffolds you can drop straight into any model. They earn their keep as structure even if you never touch the vendor’s own tool.

How to use it:

• Grab the template that matches your task — abstract, brief, patient handout, FAQ.

• Fill every bracketed placeholder before you run it; don’t leave a single one in.

• Add your own constraints on top: word count, region, reading level, output format.

Watch out for: Several prompts ask the model to “extract” study statistics and citations, which a generic model will cheerfully fabricate — use them for structure, and verify every number and reference before anything goes near MLR.

Also worth a look

Two more from the queue — worth a glance, but they didn’t clear the bar for a full slot.

Indegene’s NEXT MLR Review Automation

🥜🥜🥜 DECENT

Another agentic pre-MLR platform, this one promising claims and label alignment, local ABPI and PAAB checks, and integration with Veeva and Workfront. Squarely aimed at regulated content, but it’s a demo-gated enterprise product with no free tier and nothing you can test from the blog. File it under “proof the category is maturing,” not “try this tonight.”

Copilot Researcher Agent for Medical Affairs

🥜🥜🥜 DECENT

A set of sensible workflows — KOL prep, coaching reports, SWOT drafts — for the Researcher agent most teams already have in Microsoft Copilot and never use. The catch: the actual prompts are gated behind an email signup, and this is more Medical Affairs operations than content production — useful adjacent reading rather than a core comms tool.

That clears the strong end of the queue — back next week once the latest crop has been put through its paces.

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🥜🥜🥜🥜🥜 CRACKING

What it is: A set of four copy-paste prompt templates, shared in a LinkedIn article by Feisia Dam.

Why it’s worth your time: The four prompts cover the congress workflow medical affairs teams actually run every season — prioritising which sessions to attend, pulling structured data out of abstracts, tracking competitor activity across a programme, and generating an .ics calendar file from the agenda. Tested against a real oncology congress agenda, the abstract-extraction prompt returned a clean table of objective, population, endpoint and result without inventing numbers, and the session-triage prompt is the one that saves you an afternoon. Nothing here needs adapting before you run it.

How to use it:

• Start with the session-prioritisation prompt: paste the agenda plus your therapy area and KOL list, and it ranks sessions by relevance.

• Feed individual abstracts into the extraction prompt for a consistent summary table across the whole programme.

• Run the .ics prompt last to turn your final shortlist into a calendar you can import.

Watch out for: Anything the extraction prompt pulls still needs a human check against the source abstract before it reaches a slide or a field team — fast table-building is not the same as a citable figure.

Workflow:

TRIGGER → Congress agenda published; medical affairs team planning coverage

STEP 1 — Agenda intake (SharePoint): save the agenda and competitor list to the congress folder

STEP 2 — Session triage (Claude): run the prioritisation prompt to rank sessions by therapy-area relevance

STEP 3 — Abstract extraction (featured prompt): feed shortlisted abstracts through the extraction prompt for a structured data table

STEP 4 — Medical review (human / MLR): reviewer checks every extracted figure against the source abstract

OUTPUT → Verified coverage plan and abstract summary table in the approved template

A prompt that turns field metrics into a 200-word leadership brief

🥜🥜🥜🥜🥜 CRACKING

What it is: A single copy-paste prompt template, posted on X by @MedCommsAI.

Why it’s worth your time: It takes raw Medical Affairs field intelligence — KOL interactions, insights, activity metrics — and drafts a strategic narrative pitched at MA leadership, capped at 200 words. The value is in the constraints: it fixes the audience and the length, which is exactly where most AI-drafted internal comms fall apart. Run against a quarter of field insights, it produced a brief that led with the so-what rather than a data dump.

How to use it:

• Paste the prompt, then drop your field metrics and insights in as plain text underneath.

• Keep the ≤200-word and “for MA leadership” instructions intact — they do the heavy lifting.

• Ask for two or three variants and pick the framing that matches your leadership’s priorities.

Watch out for: The model will smooth over gaps in the underlying data, so confirm every claimed trend is actually supported by the metrics you pasted before it lands in a leadership deck.

Workflow:

TRIGGER → Quarterly field intelligence collected from the MSL team

STEP 1 — Data pull (Veeva CRM): export the quarter’s KOL interactions and insights to a working doc

STEP 2 — Clean-up (Excel / Word): strip identifiers and tidy the metrics into plain text

STEP 3 — Narrative drafting (featured prompt): run the prompt to generate the ≤200-word leadership brief

STEP 4 — Medical/compliance review (human): medical lead checks claims against source data and signs off

OUTPUT → Approved 200-word strategic brief ready for the leadership readout

One held back for next time: a three-section physician-reference prompt from the same feed, evaluated and lined up for its own slot.

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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

Pseudonymise before you paste

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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Ned’s Nuts: rated 1–5 🥜 for immediate utility to health communications professionals.

1. The peer-reviewed guide to prompt engineering that actually covers clinical contexts

🥜🥜🥜🥜🥜 5/5 — ESSENTIAL

Journal of Medical Internet Research | Prompt Engineering in Clinical Practice: Tutorial for Clinicians

Most prompt engineering guides are written for software developers or general-purpose AI use. This one — peer-reviewed, published in JMIR — was written for clinical environments, and it shows. It covers the four techniques that matter most: zero-shot prompting (just ask), few-shot prompting (give examples), chain-of-thought (ask the model to show its reasoning), and meta-prompting (ask the model to help you write the prompt). It also examines four dimensions specific to healthcare AI: accuracy, bias mitigation, privacy, and workflow integration.

If you only read one piece on prompting this year, make it this one. It is written for people who care about whether AI outputs can actually be trusted — which is precisely the concern that defines our work.

2. Ninety prompts built specifically for life sciences researchers and medical writers

🥜🥜🥜🥜 4/5 — STRONG

aingens.com | 90 AI Prompts For Researchers And Medical Writers

A well-organised prompt library covering the tasks that actually come up in life sciences writing: PubMed searches, abstract drafting, literature summaries, FAQs, blogs, and data visualisation descriptions. These are not generic templates with "health" bolted on — they have been built for the regulated, evidence-heavy context we work in. Worth bookmarking and working through methodically rather than cherry-picking.

The prompts are free to access. The site also offers a tool called MACg that integrates real-time PubMed access and automated citation support — worth a look if you spend significant time in the literature.

3. The hallucination problem, with real numbers attached

🥜🥜🥜🥜 4/5 — STRONG

pharmaphorum | Controlling AI hallucinations: Building evidence-based trust in clinical and scientific workflows

This April 2026 piece does something most hallucination articles do not: it puts a cost on the problem. In a 2025 cross-industry survey, 44% of organisations reported negative consequences from generative AI use, with average losses of $4.4 million per incident. In clinical and pharmaceutical environments the risks compound — AI errors in documentation do not stay in one place; they propagate.

The practical advice is clear: shift from open-ended AI use towards document-grounded approaches, where the model is anchored to your verified source materials and surfaces a citation for every claim it makes. That is how you move from checking everything to confirming what you already expect to be right.

Written by the CEO of AINGENS (yes, same company as item 2 — make of that what you will), but the argument stands independently of the commercial context.

4. The ten-chapter academic guide — for when you want to go deep

🥜🥜🥜 3/5 — DECENT

Frontiers in Artificial Intelligence | A structured framework for effective and responsible GenAI chatbot prompt engineering throughout the scientific process

A comprehensive guide — ten chapters — covering how generative AI applies across the full health and medical research workflow, from literature review to knowledge translation. It is peer-reviewed and evidence-informed, and takes the methodological complexity of health research seriously rather than assuming the reader is writing marketing copy.

The reason it sits at 3/5 rather than higher is that it is long, academic, and not written for someone who wants one thing to do differently on Monday morning. But if you want to build a proper framework for how your team uses AI, this is the foundational document to work from.

Sources found via automated web search. All four are free to access. Next run: June 2026.

— Ned

What it is: A LinkedIn post by Dheeraj Shinde Ph.D — a prompt engineering framework for clinical regulatory writing, structured around four specific constraints that address the failure modes that actually matter in regulated content.

Why it’s worth your time: Most prompting advice tells you to “be more specific.” Shinde’s framework goes further: define the regulatory scope upfront (e.g. ICH E3), enforce MedDRA-controlled vocabulary for adverse event terms, specify output architecture before any content instruction, and — the standout idea — instruct the model to output [DATA MISSING] wherever a data point is absent rather than infer it silently. That last constraint is the gap most health communications professionals leave open. Tested against a CSR adverse event narrative, the ICH E3 constraint instruction works cleanly, and the DATA MISSING marker caught missing p-values that a hallucinating model would otherwise have estimated.

How to use it:

- Open your next AI-assisted regulatory task with a framework declaration: “You are writing to ICH E3 guidelines. Address Section [X] only.”

- Add a vocabulary constraint: “Use only MedDRA-controlled terminology for adverse event descriptions. Do not paraphrase.”

- Add the hallucination guard: “If any data point is missing or uncertain, output [DATA MISSING]. Do not infer or estimate.”

- Specify output architecture — headings, section order, character or word limits — before any content instruction.

Watch out for: The steps are illustrative rather than copy-paste templates — you will need to adapt them to your document type and regulatory jurisdiction, but that takes minutes not hours.

—

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Ned’s Nuts: GOOD NUT — 4/5

Ned’s Nuts is The Irreplaceables’ rating for practical AI content in health communications. Items are scored 1–5: 5 (CRACKING) — immediately actionable, no adaptation needed; 4 (GOOD NUT) — clearly practical, minor adaptation needed for a regulated context; 3 (DECENT) — useful, with a genuine health comms angle but not immediately actionable. Items scoring below 3 do not appear in the Practical section.