  We introduce SoftTiger, a clinical large language model (CLaM) designed as a
foundation model for healthcare workflows. The narrative and unstructured
nature of clinical notes is a major obstacle for healthcare intelligentization.
We address a critical problem of structuring clinical notes into clinical data,
according to international interoperability standards. We collect and annotate
data for three subtasks, namely, international patient summary, clinical
impression and medical encounter. We then supervised fine-tuned a
state-of-the-art LLM using public and credentialed clinical data. The training
is orchestrated in a way that the target model can first support basic clinical
tasks such as abbreviation expansion and temporal information extraction, and
then learn to perform more complex downstream clinical tasks. Moreover, we
address several modeling challenges in the healthcare context, e.g., extra long
context window. Our blind pairwise evaluation shows that SoftTiger outperforms
other popular open-source models and GPT-3.5, comparable to Gemini-pro, with a
mild gap from GPT-4. We believe that LLMs may become a step-stone towards
healthcare digitalization and democratization. Therefore, we publicly release
SoftTiger models at scales of 13 billion and 70 billion parameters, as well as
datasets and code for our innovative scalable evaluation, hopefully, making a
significant contribution to the healthcare industry.
