  Proprietary Large Language Models (LLMs) such as GPT-4 and Gemini have
demonstrated promising capabilities in clinical text summarization tasks.
However, due to patient data privacy concerns and computational costs, many
healthcare providers prefer using small, locally-hosted models over external
generic LLMs. This study presents a comprehensive domain- and task-specific
adaptation process for the open-source LLaMA-2 13 billion parameter model,
enabling it to generate high-quality clinical notes from outpatient
patient-doctor dialogues. Our process incorporates continued pre-training,
supervised fine-tuning, and reinforcement learning from both AI and human
feedback. We introduced a new approach, DistillDirect, for performing on-policy
reinforcement learning with Gemini 1.0 Pro as the teacher model. Our resulting
model, LLaMA-Clinic, can generate clinical notes comparable in quality to those
authored by physicians. In a blinded physician reader study, the majority
(90.4%) of individual evaluations rated the notes generated by LLaMA-Clinic as
"acceptable" or higher across all three criteria: real-world readiness,
completeness, and accuracy. In the more challenging "Assessment and Plan"
section, LLaMA-Clinic scored higher (4.2/5) in real-world readiness than
physician-authored notes (4.1/5). Our cost analysis for inference shows that
our LLaMA-Clinic model achieves a 3.75-fold cost reduction compared to an
external generic LLM service. Additionally, we highlight key considerations for
future clinical note-generation tasks, emphasizing the importance of
pre-defining a best-practice note format, rather than relying on LLMs to
determine this for clinical practice. We have made our newly created synthetic
clinic dialogue-note dataset and the physician feedback dataset publicly
available to foster future research.
