From grimoire
Generates and systematically ranks differential diagnoses using anatomical, pathophysiological, and probabilistic frameworks to reduce diagnostic error.
How this skill is triggered — by the user, by Claude, or both
Slash command
/grimoire:design-differential-diagnosisThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Generate a comprehensive, prioritized differential diagnosis using systematic anatomical, physiological, and probabilistic frameworks, then narrow it with targeted evaluation.
Generate a comprehensive, prioritized differential diagnosis using systematic anatomical, physiological, and probabilistic frameworks, then narrow it with targeted evaluation.
Adopted by: USMLE clinical reasoning assessment, Royal College of Physicians and Surgeons certification (Canada, UK), Johns Hopkins/Mayo Clinic clinical reasoning curricula, WHO IMCI (Integrated Management of Childhood Illness) diagnostic algorithms.
Impact: Systematic differential diagnosis reduces diagnostic error — which affects 12 million Americans/year (IOM 2015) — by 40% compared to pattern-recognition-only approaches (Graber et al. Arch Int Med 2005); premature closure (settling on a diagnosis too early) accounts for 36% of diagnostic errors.
Why best: Structured differential generation forces clinicians to consider diagnoses outside their initial pattern match, preventing anchoring bias and premature closure — the two most common cognitive errors in diagnosis. Probabilistic ranking ensures the most dangerous conditions are evaluated first.
Sources: Harrison's 21st ed. Part 1; Kassirer & Kopelman (1991) ch. 3–5; Sackett et al. (2000) ch. 3; Graber et al. Arch Intern Med 165:1493–1499 (2005).
Extract the problem representation — create a one-sentence clinical summary: "A [age][sex] with [key risk factors] presents with [duration] [chief complaint] plus [2–3 key associated findings], most notable for [pivotal finding]." This activates the correct disease schema.
Generate the initial differential broadly — list all conditions that could explain the chief complaint; use a systematic framework to avoid omission:
Apply pre-test probability — for each candidate diagnosis, estimate base rate given: age, sex, risk factors, geographic prevalence, and referral context. High prior probability diagnoses deserve more prominent position even with fewer specific findings.
Identify pivot features — find 2–3 findings that most powerfully discriminate between diagnoses: features that are highly specific (LR+>10) for one diagnosis or highly sensitive (LR-<0.1) for ruling out another. Pivot features drive the diagnostic workup.
Rank the differential in three tiers:
Apply LR to update probabilities — for key clinical findings and test results: post-test odds = pre-test odds × LR+/−. Use published LR values (EvidenceAlerts, DynaMed); a LR+10 with 10% pre-test probability gives 53% post-test probability.
Identify the discriminating workup — order tests that have the highest LR+ for the top "must-not-miss" and most-likely diagnoses; avoid ordering tests that won't change management regardless of result.
Apply diagnostic thresholds — test when: uncertainty is high enough to warrant testing (>test threshold) but not high enough to treat without confirmation (below treatment threshold); treat without testing when probability is above treatment threshold in time-sensitive conditions.
Iteratively update after each result — each finding (positive or negative) updates probabilities; re-rank the differential with each new piece of information. Do not anchor to the initial most-likely diagnosis if contradictory evidence accumulates.
Document clinical reasoning — in the Assessment section of the note, state the leading diagnosis with supporting evidence, the differential with key distinguishing features, and the rationale for chosen workup. Undocumented reasoning is indistinguishable from no reasoning.
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