Summary of Improving Clinical Note Generation From Complex Doctor-patient Conversation, by Yizhan Li et al.
Improving Clinical Note Generation from Complex Doctor-Patient Conversation
by Yizhan Li, Sifan Wu, Christopher Smith, Thomas Lo, Bang Liu
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Automated clinical note generation systems are crucial for healthcare professionals, allowing them to spend more time interacting with patients. This paper presents three key contributions to this field using large language models (LLMs). The first contribution is the CliniKnote dataset, consisting of 1,200 complex doctor-patient conversations paired with full clinical notes. This resource enables training and evaluating models for clinical note generation tasks. The second contribution is the K-SOAP note format, which enhances traditional SOAP notes by adding a keyword section for quick identification of essential information. The third contribution is an automatic pipeline to generate K-SOAP notes from doctor-patient conversations, benchmarking various modern LLMs using metrics such as ROUGE and METEOR. Results show significant improvements in efficiency and performance compared to standard LLM finetuning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Doctors need help writing important patient notes. This task takes up a lot of time, which means doctors can’t spend as much time with patients. To solve this problem, researchers are working on systems that can automatically write these notes. In this paper, the authors share three big ideas to make this happen. First, they created a huge dataset of conversations between doctors and patients, along with the notes written after each conversation. This dataset will help train computers to write better notes. Second, they came up with a new way to organize patient notes called K-SOAP. This format makes it easier for doctors to quickly find important information in their notes. Third, they developed a computer program that can take conversations and turn them into K-SOAP notes. The results show that this program is much faster and better at writing notes than other methods. |
Keywords
* Artificial intelligence * Rouge
