Enhancing Clinical Efficiency through LLM: Discharge Note Generation for
  Cardiac Patients
HyoJe Jung Yunha Kim Heejung Choi Hyeram Seo Minkyoung Kim JiYe Han Gaeun Kee Seohyun Park Soyoung Ko Byeolhee Kim Suyeon Kim Tae Joon Jun Young-Hak Kim
Medical documentation, including discharge notes, is crucial for ensuring
patient care quality, continuity, and effective medical communication. However,
the manual creation of these documents is not only time-consuming but also
prone to inconsistencies and potential errors. The automation of this
documentation process using artificial intelligence (AI) represents a promising
area of innovation in healthcare. This study directly addresses the
inefficiencies and inaccuracies in creating discharge notes manually,
particularly for cardiac patients, by employing AI techniques, specifically
large language model (LLM). Utilizing a substantial dataset from a cardiology
center, encompassing wide-ranging medical records and physician assessments,
our research evaluates the capability of LLM to enhance the documentation
process. Among the various models assessed, Mistral-7B distinguished itself by
accurately generating discharge notes that significantly improve both
documentation efficiency and the continuity of care for patients. These notes
underwent rigorous qualitative evaluation by medical expert, receiving high
marks for their clinical relevance, completeness, readability, and contribution
to informed decision-making and care planning. Coupled with quantitative
analyses, these results confirm Mistral-7B's efficacy in distilling complex
medical information into concise, coherent summaries. Overall, our findings
illuminate the considerable promise of specialized LLM, such as Mistral-7B, in
refining healthcare documentation workflows and advancing patient care. This
study lays the groundwork for further integrating advanced AI technologies in
healthcare, demonstrating their potential to revolutionize patient
documentation and support better care outcomes.