Clinical Reasoning Education

Patient Reports Nurse Gave Wrong Medication — Nurse Denies, You Are the Charge RN

Clinical reasoning simulation for healthcare students and educators

Pharmacology Urgent Nursing

Practice This Case

Work through the full clinical encounter with AI patient and attending. Free, no signup required.

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About This Case

This clinical reasoning case presents a patient with patient reports nurse gave wrong medication / nurse denies / you are the charge rn in a pharmacology context. Learners work through a structured 10-phase simulation covering initial differential, history-taking, physical examination, labs and imaging, and management planning.

"A patient tells you the night nurse gave them a white pill instead of their usual yellow metoprolol. The night nurse documents 'patient refused metoprolol, given education.' The patient insists they took the pill they were given. The patient's morning BP is 168/104. What do you investigate first — and what does the discordant BP suggest?"

How the Simulation Works

  1. Read the patient presentation and form your initial differential diagnosis
  2. Interview the AI patient to gather history and explore your hypotheses
  3. Perform a focused physical examination based on your differential
  4. Order appropriate labs and imaging, then interpret the results
  5. Revise your diagnosis and develop a management plan
  6. Receive personalized teaching feedback from your AI attending, Dr. Patel

What You'll Learn

This case builds skills in systematic clinical reasoning, hypothesis-driven history-taking, appropriate test ordering, and evidence-based management. It is designed for Nursing students and practicing clinicians seeking to sharpen diagnostic thinking in pharmacology.

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About ReasonDx

ReasonDx is an AI-powered clinical reasoning education platform developed by Dr. Lauren Fine, MD, FAAAAI, Associate Professor and Assistant Dean of Clinical Skills Education at NSU Dr. Kiran C. Patel College of Allopathic Medicine. The platform features 394 simulation cases across 10 health professions, designed to train the cognitive processes underlying accurate diagnosis.