> ## 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.

# Adapt Content for Different Audiences

> Transform reviewed medical content from one audience level to another while preserving factual accuracy.

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="low-medium" />

  \~15 min with AI, \~60 min without
  Enhanced review with source cross-check; higher scrutiny for patient or regulatory content.

  Reviewed source → AI audience adaptation → Accuracy cross-check → Adapted version
</Info>

## Best for

* Repurposing approved specialist content for GPs, nurses, payers, or patients
* Preparing multi-audience materials from a single evidence base
* Adapting a technical manuscript summary into a client-facing or internal briefing
* Creating tiered stakeholder content from the same core data
* Shifting emphasis (e.g., efficacy-led to practical-considerations-led) for a different reader

## Inputs

* Source content: reviewed, accurate, with clear references
* Target audience specification: be specific (e.g., "community pharmacists in primary care," not just "HCPs")
* Context on the target audience's knowledge level, priorities, and information needs
* Format or length requirements for the adapted version
* Regulatory context for the adapted version (promotional, non-promotional, educational, patient-facing)

## Steps

<Steps>
  <Step title="Confirm source content is verified">
    Only adapt content that has already been reviewed for accuracy. This workflow transforms — it does not create. If the source has not been through QC, do that first.
  </Step>

  <Step title="Define the target audience precisely">
    Generic audience labels produce generic adaptations. Specify who will read this, what they already know, and what they need from it.
  </Step>

  <Step title="Provide source content and audience specification to AI">
    Paste the source content along with clear instructions about the target audience, format, and any regulatory context. Use the prompt pattern below.
  </Step>

  <Step title="Generate the adapted version">
    Run the prompt in LLMentor (or Patiently AI for patient audiences). Request multiple options if you are unsure of the right framing.
  </Step>

  <Step title="Review for meaning preservation">
    The highest-priority check. Read each clinical claim in the adapted version and confirm it says the same thing as the source — not just something that sounds similar.
  </Step>

  <Step title="Cross-check qualifiers, safety data, and data points">
    List every qualifier in the source and verify each is preserved or appropriately rephrased. Confirm safety information has not been compressed out. Check all numbers.
  </Step>

  <Step title="Compliance review">
    If the adapted version has different regulatory requirements from the original (e.g., scientific to promotional, HCP to patient), ensure it meets those requirements before release.
  </Step>
</Steps>

## Output

A well-adapted document reads naturally for its target audience, not like a mechanical word substitution of the original. It preserves all essential factual content including safety information, adjusts emphasis to match what the target audience needs most, and contains no claims that cannot be traced to the source content.

## Prompt pattern

```
You are a medical writing assistant specialising in audience adaptation. Your task is to adapt the following content for a different audience.

Source content:
[INSERT REVIEWED SOURCE CONTENT]

Original audience: [SPECIFY — e.g., oncologists, clinical researchers]
Target audience: [SPECIFY — e.g., general practitioners, primary care nurses, patients with moderate health literacy]

Adaptation requirements:
- Adjust language complexity and terminology for the target audience
- Maintain all factual accuracy — do not change what the content says, only how it says it
- Preserve key data points and findings
- Adjust emphasis to reflect the target audience's priorities and information needs
- [SPECIFY any format or length requirements]

Rules:
- Do not add information that is not in the source content
- Do not remove safety information or important limitations
- If a medical term is simplified, ensure the simplified version is medically accurate
- If the source includes specific data (numbers, statistics), retain them unless the adaptation format explicitly calls for a non-data summary
- Flag any areas where simplification may have changed the meaning with [REVIEW]
```

<Tip>
  **Customisation:** Swap the original and target audience fields to adapt in any direction. For patient-facing adaptations, add explicit instructions about reading level and tone sensitivity.
</Tip>

## Why this works

AI handles the mechanical work of adjusting vocabulary, restructuring paragraphs, and shifting emphasis — tasks that are time-consuming but low-risk when done from verified source content. The human writer retains the high-judgement decisions: what the audience needs to know, whether simplified claims still mean the same thing, and whether the adapted version meets its regulatory requirements.

## Common mistakes

<AccordionGroup>
  <Accordion title="Meaning drift during simplification">
    Source says "Treatment X demonstrated non-inferior efficacy to Treatment Y." The GP-facing adaptation reads "Treatment X works as well as Treatment Y." These are not the same claim. Cross-check every clinical claim line-by-line against the source, reading for meaning rather than surface similarity.
  </Accordion>

  <Accordion title="Dropped qualifiers">
    Source states efficacy "in patients with moderate-to-severe disease (PASI ≥12)." The adapted version drops the qualifier, making the claim appear to cover all patients. List every qualifier in the source and confirm each one survives the adaptation.
  </Accordion>

  <Accordion title="Hedging language removed">
    Source uses "may provide benefit" or "showed a trend toward improvement." The adaptation writes "provides benefit" or "improved outcomes." Compare certainty levels claim by claim between source and output.
  </Accordion>

  <Accordion title="Safety information omitted for brevity">
    A 2-page GP summary drops the safety section to save space, leaving a one-sided efficacy narrative. Safety information must appear in every adapted version. If space is limited, compress — do not remove.
  </Accordion>

  <Accordion title="AI introduces unsourced claims">
    AI adds a sentence about mechanism of action or treatment guidelines drawn from its training data rather than the source. Verify that every statement in the output traces to the source content. Flag any sentence that sounds like background context; this is where training data leaks in.
  </Accordion>
</AccordionGroup>

## Tool stack

| Tool                                | Role                                           |
| ----------------------------------- | ---------------------------------------------- |
| [LLMentor](/tools/llmentor)         | Primary tool for audience-level adaptation     |
| [Patiently AI](/tools/patiently-ai) | When the target audience is patients or carers |

**Alternatives:** [Claude](https://claude.ai) or [ChatGPT](https://chatgpt.com) for general-purpose audience rewriting. LLMentor differs in that it is shaped specifically for medical communications audience adaptation, not general text simplification.

## Review checklist

<Accordion title="Human review checklist">
  * All factual claims in the adapted version match the source content
  * No new information has been introduced that is not in the source
  * Safety information is preserved and appropriately represented
  * Qualifiers and limitations are retained
  * Language is genuinely appropriate for the target audience (not just slightly simplified)
  * Medical terms that have been simplified remain accurate
  * Data points are correctly reproduced
  * The adapted version meets any regulatory or compliance requirements for the target audience/channel
  * Emphasis and framing are appropriate for the target audience's priorities
  * The adapted version would make sense to a reader from the target audience without access to the original
</Accordion>

***

**Next steps:** If your target audience is patients or carers, use [Create a Plain Language Summary](/workflows/create-plain-language-summary). Once adapted, run [Final Human Review](/workflows/final-human-review) before release, or [Repurpose Across Channels](/workflows/repurpose-content-across-channels) to adapt for different channel formats.

***

*Last reviewed: 15 April 2026*