Clinical Reasoning Prover MCP Connector for Claude
A+Forces AI to validate clinical treatment plans against US guidelines (AHA, ACC) using real differential exclusion, explicit pharmacokinetics, and objective triage scales instead of subjective descriptors and diagnostic anchoring.
AI agents generate clinical treatment plans that appear highly competent but suffer from catastrophic failure modes. They anchor prematurely on the first symptom, ignore crucial drug clearance metrics, and use subjective descriptors instead of clinical scoring systems. In medicine, 'looks sick' is not an assessment.
The Problem It Solves
AI-generated clinical reasoning fails for five specific reasons:
- Absent differential — Anchoring on a single diagnosis without ruling out immediate life-threats (VINDICATE).
- Ungrounded evidence — Vague appeals to 'clinical consensus' instead of citing specific US guidelines (e.g., AHA/ACC, USPSTF) or evidence levels.
- Ignored pharmacokinetics — Prescribing medications without explicit analysis of ADME, renal/hepatic clearance, or CYP450 interactions.
- Subjective triage — Failing to mandate objective scoring systems (ESI, GCS, qSOFA) to assess severity.
- Missed contraindications — Ignoring FDA black box warnings and patient-specific allergies.
How It Works
Clinical Reasoning Prover uses 5 Decision Pivots grounded in US clinical practice:
- differentialDiagnosisExplored — Are life-threatening conditions explicitly ruled out before anchoring?
- evidenceLevelGrounded — Are specific US guidelines and evidence levels explicitly cited?
- pharmacokineticsAnalyzed — Are ADME and organ clearance explicitly analyzed for the proposed treatment?
- triageSeverityAssessed — Is an objective clinical scale (e.g., GCS, ESI) applied to quantify acuity?
- contraindicationsChecked — Are FDA black box warnings and patient allergies explicitly verified?
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