Feasibility, safety and clinician-assessed quality of a large language model-based system for automated patient intake and clinical summarization in outpatient uveitis care

Background: Clinical documentation represents a major source of inefficiency in outpatient care. While artificial intelligence (AI) has advanced image-based diagnostics in ophthalmology, workflow-integrated applications targeting the documentation burden remain largely unexplored. Large language models (LLMs) offer new opportunities for automating structured data collection and clinical summarization, but the feasibility and safety of integrating such systems into subspecialty outpatient workflows have not been evaluated.

Methods: We developed and deployed a clinician-in-the-loop system in an outpatient tertiary uveitis clinic. Patients completed a structured intake questionnaire via QR code prior to the visit. Responses were processed through an orchestration layer (n8n) and summarized by a constrained large language model (GPT-4o-mini, OpenAI). Prompt constraints prohibited generation of diagnoses, treatment recommendations, or unsupported clinical inferences. Feasibility metrics included completion rate and time. Output quality was evaluated by two independent clinicians (8 and 12 years of experience) using a structured rubric assessing completeness, clinical fidelity, and usability (total score 0–6). Safety was assessed against six predefined categories of unsafe output. Inter-rater agreement was measured using Cohen’s kappa (κ).

Results: Of 62 consecutive eligible visits, 50 (80.6%) completed the questionnaire. Patients had a median age of 47 years (range 22–74); 56% were female. Median completion time was 85 seconds (IQR 48). Median summary quality score was 5.0/6 (IQR 1), with inter-rater agreement of 0.74 (κ). Overall, 88% of summaries required no or only minor edits for clinical use. No predefined unsafe outputs—including hallucinated diagnoses, medications, autonomous recommendations, or contradictions of source inputs—were identified.

Conclusions: A workflow-integrated LLM-based system for automated patient intake and clinical summarization is feasible and produces clinician-reviewable outputs under explicit safety constraints. Structured evaluation frameworks are essential for responsible deployment of such systems in clinical practice.