Patient friendly summaries of oncology consultations generated by large language models - A pilot study of patient and provider satisfaction

The expanding capacity of large language models allow for improvements in patient and provider healthcare quality and experience. The medical oncology consultation often includes a discussion of a life-limiting diagnosis and complex treatment protocols. Patient recall from the discussion may be limited, and it is possible that a patient specific written summary could help with understanding, recall, and overall experience. Using a privacy compliant large language model, a prompt was instructed to rewrite an ambulatory medical consultation note as a patient friendly summary, capturing key details from a diagnosis and treatment plan. The summary was provided to both provider and patient for review and a 5-point Likert survey was administered inquiring on the output’s accuracy, clarity, and helpfulness. Patients reported agreement in 100%, 100%, and 87% on each topic respectively. 93% of patients recommended the use of similar summaries in the future. Providers reported agreement in 98%, 91%, and 96% for accuracy, clarity, and empathy respectively. All providers (100%) recommended similar summaries to be used in the future. Some of the summaries retained jargon and results from this study will be used to optimize the prompt for an expanded study. In conclusion, a patient-friendly summary derived from a medical note using a large language model prompt was helpful to patients, and found to be useful for providers