  This study aims to leverage state of the art language models to automate
generating the "Brief Hospital Course" and "Discharge Instructions" sections of
Discharge Summaries from the MIMIC-IV dataset, reducing clinicians'
administrative workload. We investigate how automation can improve
documentation accuracy, alleviate clinician burnout, and enhance operational
efficacy in healthcare facilities. This research was conducted within our
participation in the Shared Task Discharge Me! at BioNLP @ ACL 2024. Various
strategies were employed, including few-shot learning, instruction tuning, and
Dynamic Expert Selection (DES), to develop models capable of generating the
required text sections. Notably, utilizing an additional clinical
domain-specific dataset demonstrated substantial potential to enhance clinical
language processing. The DES method, which optimizes the selection of text
outputs from multiple predictions, proved to be especially effective. It
achieved the highest overall score of 0.332 in the competition, surpassing
single-model outputs. This finding suggests that advanced deep learning methods
in combination with DES can effectively automate parts of electronic health
record documentation. These advancements could enhance patient care by freeing
clinician time for patient interactions. The integration of text selection
strategies represents a promising avenue for further research.
