SoftTiger: A Clinical Foundation Model for Healthcare Workflows
This addresses the challenge of healthcare workflow automation by providing a foundation model for clinical data structuring, though it appears incremental as it builds on existing LLM methods with domain-specific adaptations.
The authors tackled the problem of structuring unstructured clinical notes into standardized clinical data by introducing SoftTiger, a clinical large language model, which outperformed other open-source models and GPT-3.5, with performance comparable to Gemini-pro and a mild gap from GPT-4 in evaluations.
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.