Executive summary
The quality of an answer from a clinical AI is often a direct reflection of the quality of the question asked. As generative AI tools become more common in UK healthcare, prompt engineering for medicine—the skill of crafting effective queries—is emerging as a crucial professional competency. Multiple studies now show that a clinical large language model's accuracy is highly sensitive to the quality of the prompt; structured, technical prompts consistently outperform casual, lay-language inputs, with even minor typos shown to sway recommendations (Nature, PMC, MIT News).
This guide provides a practical, evidence-based framework for writing clinical prompts that get safer, more accurate, and more useful answers. We will outline a four-part structure that improves fidelity and auditability, and stress the importance of specifying trusted UK sources. While powerful new tools like EBSCO's Dyna AI exemplify the move towards citation-first assistants, the principles of good prompting remain essential. Crucially, all use of these tools must be governed by a "clinician-in-the-loop" model, in line with guidance from the WHO, ICO, and NHS England.
The evidence: why the way you ask changes the answer
- Prompt structure drives accuracy: Research in npj Digital Medicine has shown that using appropriate, structured prompting techniques can significantly improve the correctness of answers from medical AI models (Nature).
- Structured reasoning helps the AI "think": Prompts that guide the AI through a logical sequence (e.g., "start with a problem representation, then list differentials, then suggest tests") have been shown to boost diagnostic accuracy on complex clinical vignette tasks (PMC).
- Input quality matters: An MIT study found that minor, non-clinical "noise" in prompts—such as typos or slang—measurably reduced the clinical quality of the AI's output and even altered its care-seeking advice. Precision matters (MIT News).
- It can improve your own reasoning: When used correctly, LLM assistance can measurably improve a clinician's own diagnostic reasoning, as demonstrated in randomised vignette studies. The key is using the AI as a tool to augment your process, not replace it (JAMA Network, PMC).
Governance & safe use in the UK
Before you even type your first prompt, it's vital to understand the professional guardrails.
- WHO LMM guidance: Emphasises the principles of ethics, transparency, and mandatory human oversight for any clinical use of large multimodal models.
- ICO guidance: Sets out the UK rules for fairness, data minimisation, and auditability for any AI system that handles personal data.
- NHS England: Provides live guidance for the deployment of AI and ambient tools. The principles—such as clinical oversight and clear documentation—are directly adaptable to clinical prompting.
- Reporting prompts: New academic reporting guidelines like CHART are urging researchers to explicitly disclose the exact prompts used in their studies. This is a best-practice principle that should be adopted for audit trails in clinical settings (bmjmedicine.bmj.com).
What “good” looks like: principles for clinical prompting
- Be specific, technical, and UK-contextual: Frame your query clearly (e.g., "UK GP setting; adult patient; follow NICE/SIGN guidance").
- Provide structured, de-identified data: Use a "problem representation" format: age/sex, salient positive/negative history, key medications/allergies, and relevant exam or lab findings.
- Define the task and its boundaries: Tell the AI exactly what you want it to do (e.g., "rank the top 3 differentials," "list red-flag symptoms," "flag any uncertainty").
- Force an output format: Instruct the AI on how to present the answer (e.g., "return as a bulleted list," "create a table," "provide references with live links").
- Avoid ambiguity: Correct typos and avoid slang or speculative phrasing, as this has been shown to degrade the quality of the output (MIT News).
The 4-part framework (copy-paste templates)
To put these principles into practice, use this four-part structure for your clinical prompts, particularly with generalist models like ChatGPT.
A. Context: "You are a UK consultant [specialty] advising a GP. Follow NICE and SIGN guidelines." B. Data: "[Provide a concise, structured, and fully de-identified case summary here]." C. Task: "Provide a ranked list of the top 3 differential diagnoses. For each, list the initial investigations required and highlight any 'don’t miss' red flags." D. Output: "Return the answer as a bulleted list. Provide a one-line rationale for each differential. You must cite any UK guidelines used."
This structured approach mirrors the reasoning prompts that have been proven to improve the quality of diagnostic outputs from AI models (PMC).
Before/after exemplars
- Weak prompt: "What could be causing chest pain in a 60-year-old?"
- Result: A vague, generic list of possibilities with no specific context or prioritisation.
- Strong prompt (using the 4-part framework):
- Result: A ranked, UK-cited, and audit-ready answer with specific red flags highlighted, providing genuine clinical value.
Patterns & mini-prompts by task
- Differential diagnosis: Use the structured reasoning prompt from section 4.
- Guideline lookup: "Summarise the current NICE guidance for managing acute asthma in adults. Cite the specific sections and list the key decision thresholds for hospital admission."
- Drug questions: "Check for interactions between apixaban, amiodarone, and clarithromycin according to the BNF. Output a table with three columns: Interaction | Mechanism | Recommended Action."
- Patient education: "Write a plain-English after-visit summary for a patient diagnosed with gout. Use UK spelling and aim for a reading age of 11. List key safety-netting advice and cite the relevant NHS.uk page."
- Dyna AI specifics: As Dyna AI is a retrieval-augmented system built on DynaMedex, your prompts can be more direct. You can ask it to show you the source panels and evidence levels for its recommendations.
What to Avoid (and Why)
- Underspecified context: The model will fill in the gaps itself, which is a major hallucination risk.
- Casual, typo-ridden input: This has been shown to measurably reduce the quality of care-seeking recommendations.
- Asking for a definitive diagnosis without verification: This breaches governance norms. Always verify answers against the cited primary sources and your own clinical judgement.
Documentation & audit
For professional and medico-legal safety, a clear audit trail is essential.
- Save the full prompt and output, including the date, time, and model version used.
- Link the AI-assisted output to your final clinical decision note.
- Record the sources (e.g., the NICE guideline number) that the AI cited and that you verified.
FAQs
- Does prompt engineering really change accuracy?
- Yes. Multiple peer-reviewed studies have now shown that using structured, technical prompts significantly improves the performance of AI on medical Q&A and diagnostic reasoning tasks.
- Is using informal language dangerous?
- It can be. A key MIT study found that introducing typos and slang into prompts reduced the overall quality of the output and altered the AI's care-seeking advice.
- Where does a tool like Dyna AI fit in?
- Dyna AI is a modern, retrieval-augmented, citation-first assistant from EBSCO. Good prompting still matters for getting the best results, but its "walled garden" approach provides an inherent layer of safety.
- What governance applies in the NHS?
- You must follow the principles of the WHO, the ICO's data-protection guidance, and any specific implementation guidance from NHS England. Non-negotiable human oversight is the central principle.