Patient friendly summaries of oncology consultations generated by large language models - A pilot study of patient and provider satisfaction
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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
Author Summary
As medical oncology providers, our new patient consultation appointments often require disclosing the diagnosis of a cancer, and a discussion on prognosis, complex treatment plans, the potential for significant side effects, and a number of tests/procedures that are required prior to initiation of the care plan. Patients often benefit from friends or family who take notes during an appointment, however this is not always possible. Technological advances in natural language processing with large language models such as Chat GPT allow for translation of medical language into plain language. In this study, we used a prompt to rewrite a medical note into a summary of the patient’s oncologic diagnosis and care plan. We then provided this summary to patients and provider to assess their feedback on the value of these summaries. We found that both providers and patients found these summaries to be accurate and understandable. Both groups recommended further development of these summaries. We intend to optimize our summary production for future studies using findings and feedback from this project.
