Improving patient communication and understanding of medical reports through text simplification with ChatGPT: a comparative study

Abstract

PURPOSE: Medical documentation is essential for clinical communication but is often intended for professional audiences, limiting patient understanding. This linguistic complexity can reduce health literacy and hinder shared decision-making. Large language models offer new opportunities to simplify medical texts while maintaining factual accuracy, thereby improving accessibility and comprehension. This study aimed to evaluate whether ChatGPT can simplify medical reports while preserving clinical content and to assess whether these simplifications improve patient comprehension, readability, and perceived communication quality. METHODS: Five document types were analysed, including MRI, CT, surgical, pathology reports, and discharge summaries. Each original physician-written document was simplified using a standardized ChatGPT prompt instructing full content preservation and patient-oriented phrasing. Ten simplifications per text were reviewed for completeness. Readability was measured using Flesch Reading Ease (FRE) and LIX indices. A total of 576 participants without medical knowledge evaluated either a simplified or original report version and completed a standardized questionnaire assessing clarity, structure, and applicability, followed by comprehension testing. RESULTS: Simplified reports achieved significantly higher readability (FRE 48.4 ± 5.0 vs. 22.9 ± 5.2; p < 0.0001; LIX 48.3 ± 3.2 vs. 59.2 ± 2.5; p = 0.004). Across all document types, patients rated simplified texts significantly higher in clarity, structure, and usefulness (p < 0.001), with significantly improved comprehension accuracy (e.g., MRI 80.3% vs. 53.7%; p < 0.001). No loss of medical information was observed. CONCLUSIONS: ChatGPT appears to be capable of simplifying complex medical documents while preserving clinically relevant information, leading to improvements in both perceived readability and objective understanding under the conditions of this study. AI-driven text simplification thus represents a promising tool to enhance patient communication and health literacy.