OSCE Patient Simulator-AI-powered clinical simulation tool.

OSCE Patient Simulator — concise introduction

OSCE Patient Simulator is an interactive, configurable virtual standardized-patient engine designed to help learners practice clinical encounters, history-taking, focused physical-exam reasoning, differential diagnosis construction, investigation selection, and management planning in a safe, repeatable environment. It creates lifelike patient profiles (name, age, DOB, appearance), an internally consistent hidden history (presenting complaint, HOPC, PMH, medications, allergies, family and social history, relevant systems review), and an externalized OSCE station brief that gives the learner a short stem and instructions without revealing diagnostic clues. Sessions can be run as: 1) a one-on-one conversation where the learner asks open and closed questions and the simulator replies as the patient would; 2) a timed OSCE station with an explicit task (e.g., 'take a focused history and propose initial investigations'); or 3) a practice suite where the system auto-scores performance against a customizable checklist. The system supports progressive disclosure (the simulated patient’s full background is kept hidden until the learner completes the encounter and asks for feedback) and produces structured feedback and suggested learning resources at the end. ExampleOSCE Patient Simulator overview scenario 1 — 'Chest pain station (history focus)': The simulator generates a 58-year-old male named Mr. Patel with substernal chest pain. The student receives a 2-line stem: '58-year-old male with chest pain for 2 hours. Take a focused history and provide differential diagnoses.' The simulated patient will answer truthfully to relevant questions (pain quality, radiation, exertional component, risk factors) and provide scripted distractors if asked unrelated questions. After the encounter the simulator reveals the true diagnosis (e.g., acute coronary syndrome) and gives feedback: missed red flags, recommended immediate investigations (ECG, troponin), and a short communication tip for breaking bad news. Example scenario 2 — 'Depression and safety assessment (communication + risk assessment)': The simulator creates a 23-year-old female college student with low mood and poor sleep. The stem instructs: 'Conduct a mental health risk assessment and safety plan if indicated.' The patient provides psychosocial context, substance use history, and direct answers about suicidal ideation only if asked using clear, nonjudgmental phrasing. The system evaluates whether the learner asked about safety, asked about intent/plan, and proposed appropriate immediate steps if risk is present.