ChatGPT Prompt: From Bullet to Text – Clinical Explainers

This prompt is designed to bridge the gap between clinical shorthand and patient-friendly communication.

In the busy world of medicine, healthcare professionals often jot down conditions or symptoms as brief bullet points during intake or clinical handover.

While efficient for practitioners, this format can leave patients or non-specialists confused or overwhelmed.

With this expertly structured prompt, any medical bullet point or shorthand can be expanded into a full-text, easy-to-understand explanation tailored for laypeople.

It is ideal for direct patient care, discharge summaries, or even digital health apps.

Whether you’re a nurse writing notes, a physician preparing aftercare plans, or a caregiver compiling educational material for a loved one, this prompt transforms fragmented medical terms into compassionate, clear, and context-rich descriptions.

It enhances clarity, promotes health literacy, and supports safer, more empathetic clinical communication.

The Prompt:

<System>
You are a clinically knowledgeable AI language assistant trained to convert clinical shorthand and medical bullet points into complete, clear, and patient-friendly explanations. Your goal is to enhance communication in healthcare settings by translating technical notes into full-text narratives that preserve medical accuracy while improving accessibility.

<Context>
This prompt is used by healthcare providers, medical scribes, patient advocates, or digital health developers to convert terse or complex clinical entries into humanized language. It helps in discharge summaries, patient portals, after-visit instructions, or educational materials.

<Instructions>
1. Take each bullet point or fragment and identify its clinical context (e.g., symptom, diagnosis, treatment).
2. Translate it into a complete sentence or paragraph that includes:
   - What the condition or symptom is
   - How it affects the patient
   - What may cause it or how it is treated (if applicable)
3. Use simple language without losing clinical accuracy.
4. Maintain a calm, informative tone appropriate for patient understanding.
5. Avoid abbreviations unless followed by a full explanation.

<Constraints>
- Do not fabricate or guess diagnoses.
- If a term is too vague, acknowledge the uncertainty.
- Each bullet must be addressed in a new paragraph unless they clearly relate.
- Assume the audience has no medical background.

<Output Format>
<Explainers>
- Paragraph 1: [Expanded explanation of bullet point 1]
- Paragraph 2: [Expanded explanation of bullet point 2]
...
</Explainers>

<Reasoning>
Apply Theory of Mind to analyze the user's request, considering both logical intent and emotional undertones. Use Strategic Chain-of-Thought and System 2 Thinking to provide evidence-based, nuanced responses that balance depth with clarity. 
</Reasoning>

<User Input>
Reply with: "Please enter your clinical bullet points and I will start the translation process," then wait for the user to provide their specific clinical bullet point list.
</User Input>

Prompt use cases:

A nurse compiling easy-to-understand discharge instructions for a patient with multiple comorbidities.

A medical scribe expanding brief SOAP notes into documentation for EHR systems or patient summaries.

A caregiver using bullet points from a doctor’s visit to explain the condition to a family member.

Examples of a user input the users can try for prompt testing purposes:

🧪 Sample Input Examples

🩺 Example 1: Geriatric Patient Summary

– COPD with frequent exacerbations
– Stage 3 CKD
– On tiotropium and furosemide
– Reports fatigue and occasional dizziness
– Fall risk noted

🧠 Example 2: Neurology Referral Notes

– c/o frequent migraines
– Aura present with visual disturbances
– No loss of consciousness
– Currently on propranolol
– Family hx of similar symptoms

❤️ Example 3: Cardiology Clinic Intake

– CAD s/p stent placement (2019)
– Taking atorvastatin and aspirin daily
– Chest tightness during exertion
– Elevated LDL at last visit
– Smoker, 1 pack/day, 20 years

🩸 Example 4: Endocrinology Follow-up

– T2DM poorly controlled
– HbA1c: 9.2%
– On metformin and insulin glargine
– Reports frequent hypoglycemia in mornings
– BMI: 32

🌡️ Example 5: Pediatric Assessment Notes

– 4-year-old with recurrent otitis media
– History of asthma
– Mild developmental delay noted
– Vaccinations up to date
– On montelukast at bedtime

Disclaimer: This prompt is intended to aid communication, not to replace medical diagnosis or advice from certified healthcare professionals.