> ## Documentation Index
> Fetch the complete documentation index at: https://playbook.pharmatools.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Extract Key Messages from Evidence

> Pull evidence-supported key messages from clinical sources, structured for content development and stakeholder communications.

export const RiskBadge = ({level = "low"}) => {
  const styles = {
    "low": {
      bg: "#D1FAE5",
      fg: "#065F46",
      label: "Low"
    },
    "low-medium": {
      bg: "#ECFCCB",
      fg: "#365314",
      label: "Low–Medium"
    },
    "medium": {
      bg: "#FEF3C7",
      fg: "#854D0E",
      label: "Medium"
    },
    "medium-high": {
      bg: "#FFEDD5",
      fg: "#9A3412",
      label: "Medium–High"
    },
    "high": {
      bg: "#FECACA",
      fg: "#991B1B",
      label: "High"
    },
    "critical": {
      bg: "#DC2626",
      fg: "#FFFFFF",
      label: "Critical"
    }
  };
  const s = styles[level] || styles.low;
  return <span style={{
    display: "inline-block",
    backgroundColor: s.bg,
    color: s.fg,
    padding: "2px 12px",
    borderRadius: "999px",
    fontSize: "0.75rem",
    fontWeight: 700,
    letterSpacing: "0.03em",
    textTransform: "uppercase",
    verticalAlign: "middle",
    whiteSpace: "nowrap"
  }}>
      Risk tier · {s.label}
    </span>;
};

<Info>
  <RiskBadge level="medium" />

  \~15 min with AI, \~60 min without
  Enhanced review with source cross-check required.

  Source evidence → AI candidate messages → Source cross-check → Verified message set
</Info>

## Best for

* Developing a messaging framework or key message platform for a product or therapeutic area
* Preparing for content planning meetings where you need to articulate what the evidence supports
* Building briefing documents that connect evidence to communication objectives
* Reviewing new data to identify what it adds to the existing evidence story

## Inputs

* Full text of the source paper, CSR summary, or data package
* Context on intended use of the key messages (e.g., HCP communications, internal briefing, payer value story)
* Any existing messaging framework or approved claims for reference
* Therapeutic area context and competitive landscape (if relevant to message framing)

## Steps

<Steps>
  <Step title="Review the source evidence">
    Read the paper or data source yourself. Understand the study design, its strengths, and its limitations before asking AI to extract messages.
  </Step>

  <Step title="Provide full source text and context">
    Give the AI the complete source material together with the intended audience and purpose for the messages. Partial inputs produce incomplete or misframed messages.
  </Step>

  <Step title="Generate candidate key messages">
    Use the prompt pattern below to produce a first set of evidence-based messages, organised by category (efficacy, safety, PROs, etc.).
  </Step>

  <Step title="Review each message against the source">
    Verify that every key message is directly supported by the evidence. Check for overstatement, selective emphasis, missing qualifiers, and conflated endpoints.
  </Step>

  <Step title="Refine and prioritise">
    Edit for accuracy, clarity, and relevance to project objectives. Remove or flag any messages that go beyond what the evidence supports.
  </Step>

  <Step title="Cross-reference with existing messaging">
    If an approved messaging framework exists, check alignment and identify genuinely new messages versus restatements of existing ones.
  </Step>

  <Step title="Verify with RefCheckr">
    Run final messages through RefCheckr — it verifies each message against the cited source, rewrites any that don't match, and re-checks the rewrites for ABPI compliance, looping until each message passes.
  </Step>
</Steps>

## Output

A set of 5–15 key messages organised by category (efficacy, safety, PROs, practical considerations), each paired with the specific data point that supports it, the evidence strength (primary endpoint, secondary, subgroup, post-hoc), and any required qualifiers or limitations. Messages use professional, evidence-based language — not promotional superlatives.

<Accordion title="Worked example: key messages from a cardiovascular outcomes trial">
  **Source data (from paper):**

  > MACE occurred in 8.7% of Drug Y patients vs 11.2% of placebo patients (HR 0.76, 95% CI: 0.63–0.92; p=0.005). CV death occurred in 3.1% vs 4.4% (HR 0.71, p=0.02). Hospitalisation for heart failure occurred in 2.8% vs 4.1% (HR 0.67, p=0.008). Hypotension requiring treatment discontinuation occurred in 2.3% of Drug Y patients vs 0.8% of placebo patients. Renal impairment (eGFR decline ≥40%) was reported in 5.1% vs 3.8%.

  **AI-generated messages (before review):**

  1. Drug Y significantly reduced cardiovascular events by 24%
  2. Drug Y demonstrated a major benefit in reducing heart failure hospitalisations
  3. Drug Y showed a strong safety profile in cardiovascular patients

  **Issues caught in review:**

  * ❌ Message 1: "reduced cardiovascular events by 24%" — should specify MACE, state it's a relative risk reduction, and include the absolute rates and CI
  * ❌ Message 2: "major benefit" — promotional language not supported by the source; the source presents the data without this characterisation
  * ❌ Message 3: "strong safety profile" — contradicted by the source data showing higher rates of hypotension requiring discontinuation and renal impairment vs placebo
  * ❌ No safety messages at all — the set is unbalanced

  **Reviewed messages (final):**

  1. **Efficacy — primary endpoint:** Drug Y reduced the risk of MACE compared to placebo (8.7% vs 11.2%; HR 0.76, 95% CI: 0.63–0.92; p=0.005)
  2. **Efficacy — heart failure:** Hospitalisation for heart failure occurred in 2.8% of Drug Y patients vs 4.1% of placebo patients (HR 0.67, p=0.008)
  3. **Safety — hypotension:** Hypotension requiring treatment discontinuation was more frequent with Drug Y (2.3% vs 0.8%)
  4. **Safety — renal:** eGFR decline ≥40% was reported in 5.1% of Drug Y patients vs 3.8% of placebo patients
</Accordion>

## Prompt pattern

```
You are a medical writing assistant specialising in evidence-based messaging. Your task is to extract key messages from the following source document.

For each key message:
1. State the message clearly in one sentence
2. Cite the specific data point or finding that supports it (include numbers, endpoints, and statistical results)
3. Note the strength of the supporting evidence (e.g., primary endpoint, secondary endpoint, subgroup analysis, post-hoc)
4. Flag any qualifiers or limitations that should accompany the message

Organise messages into these categories where applicable:
- Efficacy
- Safety and tolerability
- Patient-reported outcomes / quality of life
- Mechanism / pharmacology
- Practical considerations (dosing, administration, etc.)

Rules:
- Base messages only on the provided source. Do not include information from outside the source material.
- Do not overstate findings. If the data shows non-inferiority, do not frame it as superiority. If a result is from a subgroup, say so.
- Include relevant safety messages, not just efficacy highlights.
- Flag any message where the supporting evidence is exploratory or hypothesis-generating.

Source document:
[INSERT FULL TEXT]

Intended use for these messages: [SPECIFY — e.g., HCP slide deck, internal briefing, messaging framework development]
```

<Tip>
  **Customisation:** Add a "Comparator context:" section to the prompt when you need messages framed against a specific competitor. For payer audiences, add an instruction to include any health economic or resource-use data points.
</Tip>

## Why this works

AI pulls candidate messages from a dense 15-page paper in minutes, drafting each in a consistent format (message + data point + evidence strength + qualifiers) and organising them by theme. This gives the writer and strategy team a structured starting set to evaluate, rather than starting from scratch, freeing human effort for the judgement calls: which messages matter, how strong the evidence is, and how to frame findings for the specific audience.

## Common mistakes

<AccordionGroup>
  <Accordion title="Overstated messages">
    AI states "Treatment X demonstrated superior efficacy" when the trial was designed and powered for non-inferiority. If this enters a messaging framework, it contaminates every downstream deliverable. Review every message against the specific data point cited and ask: does the evidence actually say this?
  </Accordion>

  <Accordion title="Cherry-picked findings">
    AI foregrounds a striking subgroup result (e.g., 40% improvement in patients \<65) while omitting that the overall population result was modest. Require at least one safety/tolerability message and one limitations message for every set of efficacy messages.
  </Accordion>

  <Accordion title="Conflated endpoints">
    AI merges a primary and secondary endpoint into a single message, making both sound like primary results. Verify each message is attributed to the correct endpoint, analysis type, and population.
  </Accordion>

  <Accordion title="Missing qualifiers">
    A message about response rates omits that this was in treatment-experienced patients, making it sound like a first-line result. Check every message for population, subgroup, comparator, and analysis-type qualifiers.
  </Accordion>

  <Accordion title="Promotional framing">
    AI uses language like "best-in-class" or "transformative" that would immediately be flagged in MLR review. Review language for promotional signals before messages are shared with brand or strategy teams.
  </Accordion>
</AccordionGroup>

## Tool stack

| Tool                          | Role                                                                                       |
| ----------------------------- | ------------------------------------------------------------------------------------------ |
| [PubCrawl](/tools/pubcrawl)   | Identify related publications and build the evidence base before extracting messages       |
| [RefCheckr](/tools/refcheckr) | Closed-loop: verify, rewrite, and re-check extracted key messages against the cited source |

**Alternatives:** [Claude Cowork](https://claude.ai) for synthesising messages across multiple source documents in a structured workspace. [NotebookLM](https://notebooklm.google.com) for identifying key themes across uploaded papers. [Claude](https://claude.ai) or [ChatGPT](https://chatgpt.com) for initial message brainstorming. [Elicit](https://elicit.com) for cross-paper evidence synthesis. [Otter.ai](https://otter.ai) or [Fireflies.ai](https://fireflies.ai) for transcribing advisory boards, KOL interviews, and focus groups when messages need to come from spoken insight.

## Review checklist

<Accordion title="Human review checklist">
  * Every key message is directly supported by a specific, cited finding in the source
  * No message overstates the evidence (e.g., non-inferiority framed as superiority)
  * Subgroup and post-hoc findings are clearly identified as such
  * Safety and tolerability messages are included and fairly represent the data
  * Limitations and qualifiers are noted for each message
  * Messages are appropriate for the stated intended use
  * No messages introduce information not present in the source
  * Messages do not use promotional language unless intended for a promotional context and subject to MLR review
  * If an existing messaging framework exists, new messages are consistent or flagged as additions
</Accordion>

***

**Next steps:** Use your key messages to [Build a Content Outline](/workflows/build-content-outline) for a manuscript, slide deck, or other deliverable. Run messages through [Verify Claims Against References](/workflows/verify-claims-against-references) to confirm source support.

***

*Last reviewed: 15 April 2026*