Large Language Models' Simulation of Patient Comprehension of Discharge Summaries: Multimodel Evaluation Study.

Background: Large language models (LLMs) are increasingly used to generate and evaluate patient-facing medical materials. Through persona prompting, LLMs can be instructed to simulate responses from individuals with different demographic and experiential backgrounds, offering a scalable approach for approximating patient feedback. However, it remains unclear how closely these simulated persona-based responses reflect real patient perspectives, and whether this alignment is consistent across demographic subgroups. Objective: This study assessed whether contemporary LLMs can simulate diverse patient perspectives on hospital discharge summaries when prompted with personas defined by education, gender, and health care use. We further examined whether persona-based simulation performance was equitable across subgroups by comparing LLM-generated responses with responses from a general-population human cohort under a standardized evaluation framework. Methods: We evaluated 4 contemporary LLMs, GPT-4.1, GPT-5.2, Claude Sonnet 4.5, and Opus 4.6, against responses from 99 participants recruited through Prolific (Prolific Academic Ltd). Participants answered 10 questions for discharge summaries that varied in length and medical complexity. Questions included information-based items assessing factual comprehension and perception-based items assessing subjective understanding. Model-human alignment was measured using modal mean absolute error and expected absolute ordinal distance. We also conducted a chain-of-thought sensitivity analysis for cases with the greatest divergence between LLM-generated persona and matched human responses. Results: Across LLMs, persona responses aligned more closely with human responses on information-based items than on perception-based items. Education was the most consistent stratification axis: every model aligned more closely with the high-education subgroup than with the low-education subgroup on information items, suggesting that LLMs capture broad factual-comprehension differences associated with educational background. Perception items, in which subjective understanding and uncertainty dominate human responses, were simulated less reliably. Alignment also depended on discharge summary characteristics, with short but clinically complex summaries producing the largest divergence. Gender-related differences were smaller and inconsistent in direction, reflecting item-level modal flips rather than systematic distributional bias. Chain-of-thought improved alignment on some factual items but did not recover the full range of less common human responses. Across models, LLMs underselected "I don't know" and "Not provided" responses relative to humans, indicating a tendency to commit to determinate answers even when human participants expressed uncertainty. Conclusions: Contemporary LLMs can approximate common response patterns within demographic subgroups, particularly for factual comprehension and education-based personas. However, they are less reliable for capturing heterogeneous and perception-based patient responses. Equity performance varied substantially across models, making model selection an important design consideration. Persona-based LLM simulation should be used as an assistive screening tool for patient materials and should complement, not replace, human evaluation with subgroup validation.