  The discharge summary is a one of critical documents in the patient journey,
encompassing all events experienced during hospitalization, including multiple
visits, medications, tests, surgery/procedures, and admissions/discharge.
Providing a summary of the patient's progress is crucial, as it significantly
influences future care and planning. Consequently, clinicians face the
laborious and resource-intensive task of manually collecting, organizing, and
combining all the necessary data for a discharge summary. Therefore, we propose
"NOTE", which stands for "Notable generation Of patient Text summaries through
an Efficient approach based on direct preference optimization". NOTE is based
on Medical Information Mart for Intensive Care- III dataset and summarizes a
single hospitalization of a patient. Patient events are sequentially combined
and used to generate a discharge summary for each hospitalization. In the
present circumstances, large language models' application programming
interfaces (LLMs' APIs) are widely available, but importing and exporting
medical data presents significant challenges due to privacy protection policies
in healthcare institutions. Moreover, to ensure optimal performance, it is
essential to implement a lightweight model for internal server or program
within the hospital. Therefore, we utilized DPO and parameter efficient fine
tuning (PEFT) techniques to apply a fine-tuning method that guarantees superior
performance. To demonstrate the practical application of the developed NOTE, we
provide a webpage-based demonstration software. In the future, we will aim to
deploy the software available for actual use by clinicians in hospital. NOTE
can be utilized to generate various summaries not only discharge summaries but
also throughout a patient's journey, thereby alleviating the labor-intensive
workload of clinicians and aiming for increased efficiency.
