The Physician's Prompt Engineering Playbook: 8 Patterns That Work

The difference between a physician who gets unreliable output from AI and one who gets consistently useful results is not the model — it's the prompt. After testing hundreds of prompt patterns across clinical workflows, we've identified 8 that produce reliable, auditable results every time.

Why Prompts Matter More Than Models

A well-structured prompt on GPT-4o will outperform a lazy prompt on the most expensive model available. The model is the engine; the prompt is the steering wheel. Without structure, you get generic, sometimes dangerous output. With structure, you get a reliable assistant.

Pattern 1: The Structured Extract

Use case: Summarizing a long clinical document into a structured format.

• •Template:
• •```
• •Extract the following from this clinical note:
• •Chief complaint (1 sentence)
• •Key findings (bulleted list)
• •Assessment (numbered diagnoses)
• •Plan (numbered items with responsible party)

Source note: [paste note] ```

Why it works: The template constrains output format, preventing the model from wandering or fabricating sections that don't exist in the source.

Pattern 2: The Differential Filter

Use case: Generating a ranked differential diagnosis from clinical findings.

Template: ``` Given these findings: [findings] Generate a differential diagnosis ranked by likelihood. For each diagnosis, include: 1. Probability estimate (high/moderate/low) 2. Key supporting evidence from the findings 3. One test that would most change the probability Limit to top 5 diagnoses. ```

Pattern 3: The Prior Auth Builder

Use case: Building payer-ready prior authorization justifications.

This is one of the highest-ROI patterns we've found. Instead of manually writing justifications, feed the model the diagnosis, proposed procedure, and relevant clinical history, then ask it to generate a justification using the payer's specific medical necessity criteria.

Pattern 4: The Medication Reconciler

Use case: Comparing two medication lists and identifying discrepancies.

Feed the model the hospital medication list and the patient's home medication list, and ask it to identify additions, removals, dose changes, and potential interactions — with clinical significance ratings.

Patterns 5-8

The remaining four patterns — Policy Checker, Patient Summary, Follow-Up Generator, and Audit Narrator — follow the same principle: constrain the output format, provide explicit source material, and require citations.

The 3 Anti-Patterns

Every prompt should have three things: a specific task, source material, and an output format. Miss any one and you've created a liability, not a tool.