Enhancing doctor-patient communication using large language models for pathology report interpretation

Large language models (LLMs) are increasingly utilized in healthcare settings. Postoperative pathology reports, which are essential for diagnosing and determining treatment strategies for surgical patients, frequently include complex data that can be challenging for patients to comprehend. This complexity can adversely affect the quality of communication between doctors and patients about their diagnosis and treatment options, potentially impacting patient outcomes such as understanding of their condition, treatment adherence, and overall satisfaction. This study analyzed text pathology reports from four hospitals between October and December 2023, focusing on malignant tumors. Using GPT-4, we developed templates for interpretive pathology reports (IPRs) to simplify medical terminology for non-professionals. We randomly selected 70 reports to generate these templates and evaluated the remaining 628 reports for consistency and readability. Patient understanding was measured using a custom-designed pathology report understanding level assessment scale, scored by volunteers with no medical background. The study also recorded doctor-patient communication time and patient comprehension levels before and after using IPRs. Among 698 pathology reports analyzed, the interpretation through LLMs significantly improved readability and patient understanding. The average communication time between doctors and patients decreased by over 70%, from 35 to 10 min (P