Patient-Friendly Simplifications of Outpatient Reports Using Large Language Models via Few-Shot Prompting: Blinded Comparative Evaluation
Journal of Medical Internet Research ·
Background: Effective communication between clinicians and patients plays an important role in the successful delivery of health care. In geriatric medicine, where multimorbidity, polypharmacy, and functional limitations often make treatment plans complex, AI simplifications of clinical reports into patient-friendly language offer a promising way to improve communication. Objective: This study investigated whether AI could generate patient-friendly simplifications of outpatient reports that were comparable to those authored by human experts. Methods: Gemma-3-27B-it, an instruction fine-tuned open-source large language model (LLM) by Google, was used, and few-shot prompting was applied to enable the generation of patient-friendly simplifications of 64 deidentified geriatric outpatient reports written in German while preserving clinical meaning. LLM-generated simplifications were compared with those produced by 2 medical experts in a blinded evaluation. Quality was assessed using three approaches: (1) embedding-based semantic similarity to the original report, (2) blinded expert review by 2 clinicians, and (3) review by patient representatives, with the latter 2 using a standardized Likert-scale questionnaire. Results: AI-generated simplifications were comparable to geriatrician-authored simplifications regarding semantic similarity (=.02) and were preferred by patient representatives (<.001) as well as one expert reviewer (=.001). Expert reviewers perceived physician-authored simplifications as more complete (<.001), a finding that correlated with text length (<.001). Conclusions: This study demonstrates the feasibility of using LLMs via few-shot prompting to simplify medical reports for improved perceived patient comprehension in geriatric care. Future prospective studies should examine whether such AI-driven simplifications positively impact objective health-related outcomes, adherence, and patient engagement.