Use Case
Patient Communication AI Evaluation
Overview
System Description
Patient communication AI generates letters, discharge summaries, patient information leaflets, and conversational responses aimed at patients and carers. These systems must communicate clinical information at an appropriate reading level, maintain empathy and sensitivity, avoid alarming language for routine findings, provide actionable next steps, and include appropriate safety-netting advice. Clinical evaluation ensures AI-generated patient communications are safe, clear, and appropriate.
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Risk Profile by Setting
In primary care, patient-facing communication errors include alarming language for benign findings, minimising serious diagnoses, and failing to provide clear action steps. Discharge summary AI carries risk around medication instruction accuracy and follow-up requirements. Patient information leaflets generated by AI must avoid conflicting advice, inappropriate reassurance about side effects, and must be accessible to patients with varying health literacy.
Methodology
Evaluation Workflow
Our patient communication evaluation tests AI-generated content against clinical communication best practices. Evaluators assess readability, clinical accuracy, tone appropriateness, safety-netting adequacy, and actionability. We test across communication types — results letters, discharge instructions, appointment summaries — with specific attention to how sensitive diagnoses and uncertain findings are communicated.
Safety
Top Failure Modes
The most common and dangerous failure modes for this type of medical AI system.
Related
Other Use Cases
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