Clinical Prompting Flowchart
Step-by-step guide for building effective LLM prompts in clinical practice — Eduardo Mayorga, MD, 2026
You Have a Patient to Work Up Using an LLM
Follow this sequence: De-identify → Frame Your Prompt (RTF or Domain-Specific) → Context → Differential → Workup → Treatment → Verify
1
De-identify All Patient Data
Before typing anything, strip: names, DOB (use age), MRN/SSN, facility names, room numbers, admission dates (use “Day 1, Day 3”).
Instead of
"John Smith, DOB 03/15/1974, MRN 123456, admitted to Bascom Palmer on 3/28/2026…"
Write
"A 52-year-old male, admitted Day 1 to an academic eye center…"
Frame Your Prompt
Decision: How complex is your clinical question?
Quick, focused question → RTF | Complex case with many variables → Domain-Specific Framework
Simpler RTF Framework
Use when you have a focused clinical question: a single differential, a quick drug check, a guideline lookup. Three elements are all you need.
| Element | You Write… |
|---|---|
| Role | Specialty + expertise level |
| Task | Exactly what you need |
| Format | How you want the output |
RTF Example
"You are a board-certified retina specialist [Role]. Generate a ranked differential for a 68-year-old with acute painless vision loss and a cherry-red spot on fundoscopy [Task]. Present as a table: Diagnosis | Probability | Supporting Evidence | Must-Not-Miss [Format]."
Best for: Straightforward differentials, quick lookups, pattern-recognition questions, single-system problems.
Detailed Domain-Specific Framework (BRAIN)
Use when the case is complex, multi-system, or high-stakes. BRAIN builds in evidence standards, safety constraints, and patient-specific nuance from the very first prompt.
| Element | You Write… |
|---|---|
| Background | Clinical context, reasoning style, and what stage you’re at |
| Role | Specific persona (attending, consultant, subspecialist…) |
| Approach | Evidence standards and CoT method (guidelines to cite, reasoning style) |
| Instructions | Task details, constraints, safety rules, output requirements |
| Nuance | Comorbidities, allergies, organ function, patient preferences |
BRAIN Example — Diagnostic Phase
[B] Neuro-ophthalmology consult. 52-yo F with sudden monocular vision loss, APD, and a swollen disc. I need a structured differential before ordering workup.
[R] You are a fellowship-trained neuro-ophthalmologist at an academic center.
[A] Use Bayesian reasoning. Cite current AAO PPPs and NANOS guidelines. Show probability updates as you weigh each finding.
[I] Generate a top-5 differential with:
• Estimated probability for each
• Key supporting and refuting evidence
• Must-not-miss flag
• Recommended first-line diagnostic test for each
[N] PMH: type 2 diabetes (A1c 8.2), HTN, obesity. Allergy: sulfa. eGFR 45. Current meds: metformin, lisinopril, atorvastatin. Patient is anxious about vision prognosis.
Best for: Multi-system cases, patients with many comorbidities, high-stakes decisions, cases requiring specific guideline citations, unfamiliar subspecialty territory.
↓ Both paths converge here — now load your clinical data ↓
+
Load Structured Clinical Context
Present data the way you would hand off to a senior colleague. Include all that is relevant:
| History & Exam | Diagnostics | Patient Factors |
|---|---|---|
| Chief complaint & HPI Physical exam findings Relevant PMH, FH, SH |
Lab results Imaging results Special studies / pathology |
Medications & allergies Renal / hepatic function Patient preferences |
RTF users: Paste your clinical data after the RTF prompt. BRAIN users: Much of this is already in your [B] and [N] elements — add any remaining data here.
The Clinical Reasoning Sequence
3
Generate a Differential Diagnosis
Use Two-Step Prompting to separate analysis from ranking — this reduces premature anchoring:
First Prompt — Analyze
"Analyze the following clinical data. Weigh each finding, identify patterns, and note red flags. Do NOT generate a differential yet.
[Paste your de-identified case data here]"
Second Prompt — Rank
"Now generate a ranked differential (top 5–7). For each diagnosis, provide:
• Estimated probability
• Key supporting evidence
• Key refuting evidence
• Must-not-miss flag (Y/N)
• One atypical presentation to watch for"
Choose a Chain-of-Thought (CoT) reasoning style to include in your prompt:
| CoT Style | Add This to Your Prompt | Best For |
|---|---|---|
| Bayesian | “Assign a prior probability, then update it as each piece of evidence is considered.” | Differential with labs/imaging |
| Hierarchical | “Reason from broad organ systems down to specific diagnoses.” | Undifferentiated presentations |
| Causal Abduction | “Generate hypotheses, then seek confirming AND disconfirming evidence.” | Complex / atypical cases (best overall) |
| Skip CoT | Don’t add a reasoning instruction. | Simple pattern-recognition, quick look-ups |
Tip: CoT can actually reduce accuracy on simple pattern-recognition tasks (NEJM AI, 2025). Match complexity to the question. BRAIN users: you already specified your CoT style in the [A] element.
Append these anti-bias safety lines to every differential prompt:
| Safety Check | Copy-Paste This Language |
|---|---|
| Must-Not-Miss | “Always consider dangerous diagnoses that could be fatal if missed, regardless of probability.” |
| Atypical Presentations | “For each top diagnosis, list at least one atypical presentation the clinician should watch for.” |
| Counter-Evidence | “List the strongest evidence AGAINST each of your top 3 diagnoses.” |
4
Plan & Iterate the Diagnostic Workup
Use Iterative Stepwise Prompting — request a workup, feed back results, and let the AI refine:
Workup Prompt
"Based on the differential above, recommend a stepwise diagnostic workup:
1. Labs, imaging, and special studies ordered by pre-test probability and cost-effectiveness
2. Account for these patient constraints: [eGFR, pregnancy, contrast allergy, etc.]
3. For each test, state what result would confirm or rule out each top diagnosis"
Iterative Loop — Repeat as Results Come In
Feed-Back Prompt (use each time new results arrive)
"Results are back: [paste new results here].
Update the differential: remove ruled-out diagnoses, adjust probabilities, and recommend the next diagnostic step."
Repeat this feed-back cycle until you reach a working diagnosis or the differential is sufficiently narrowed.
5
Develop the Treatment Plan
If you started with RTF:
Treatment decisions carry the highest implementation risk. Switch to BRAIN now to add the precision layer — evidence standards, safety constraints, and patient-specific nuance — before recommending action.
If you started with BRAIN:
Continue with your BRAIN structure. Update the [B]ackground with workup results and confirmed diagnosis, then refine your [I]nstructions for the treatment task.
BRAIN Framework for Treatment Prompts
| Element | What to Write at This Stage |
|---|---|
| Background | Summarize the case journey: working diagnosis, key findings, workup results |
| Role | Specific treating persona: attending, subspecialist, clinical pharmacist, etc. |
| Approach | Evidence standards to follow (e.g., “Cite current AAO PPP / AHA / IDSA guidelines”) |
| Instructions | Treatment task + safety rules: dosing, monitoring, interaction checks, contraindications |
| Nuance | Patient-specific factors: comorbidities, allergies, renal/hepatic function, preferences |
BRAIN Treatment Prompt Example
[B] Working diagnosis: anterior ischemic optic neuropathy (arteritic) in a 72-yo F. ESR 88, CRP 54, temporal artery biopsy positive. eGFR 38. Current meds: metformin, lisinopril, atorvastatin.
[R] You are a neuro-ophthalmologist managing this patient with rheumatology co-management.
[A] Follow current AAO and ACR guidelines. Cite specific guideline recommendations.
[I] Propose a treatment plan including:
• 1st-line, 2nd-line, and alternative therapies with dosing
• Renal-adjusted dosing given eGFR 38
• Drug interactions with current medications
• Contraindication check against comorbidities
• Monitoring parameters and follow-up timeline
• Rate your confidence in each recommendation (high/moderate/low)
[N] Patient is anxious about long-term steroid side effects. Has a history of GI bleed 2 years ago. Lives alone, limited mobility.
Evidence: Metacognitive prompting (asking the AI to evaluate its own confidence) reduced harmful recommendations by 45% (Esmaeilzadeh). Pharmacist + LLM co-pilot showed strongest medication safety outcomes across 16 specialties.
6
Verify — NEVER Skip This Step
Run these self-critique prompts on the AI output before taking any clinical action:
| Ask the AI | What It Catches |
|---|---|
| “What diagnoses might I be missing?” | Blind spots, rare differentials |
| “What are the strongest arguments against this treatment plan?” | Forces adversarial self-evaluation |
| “Identify any inconsistencies in the reasoning above.” | Logical errors, contradictions |
| “Are any of the cited guidelines outdated? What is the most current recommendation?” | Stale protocol recommendations |
| “Cross-check the full medication list for interactions I may have missed.” | Incomplete drug-interaction screening |
Watch for these failure modes: Hallucinated diagnostic criteria • Outdated guidelines • Missed drug interactions • Overconfident probability estimates • Fabricated references • Consistent ≠ Correct
Decision: Are you satisfied with the output?
Yes Output is clinically sound
Apply your clinical judgment. The AI output is a first draft — never a final order. You are always the decision-maker.
No Needs refinement
Feed back corrections, add missing context, or try a different CoT style. Return to the relevant step above.
Remember: Clinical reasoning is iterative. New data at any stage can send you back to update the differential, refine the workup, or adjust the treatment plan.
Safety / Privacy
Differential & Context
Workup & Iteration
Treatment Plan
Verification
Decision Point
Companion to: LLMs as Your Diagnostic and Treatment Assistant — Eduardo Mayorga, MD — 2026