Question-Answering Based Summarization of Electronic Health Records using Retrieval Augmented Generation

Understand

Summarization of electronic health records (EHRs) can substantially minimize 'screen time' for both patients as well as medical personnel.

• In recent years summarization of EHRs have employed machine learning pipelines using state of the art neural models.
• However, these models have produced less than adequate results that are attributed to the difficulty of obtaining sufficient annotated data for training.
• Moreover, the requirement to consider the entire content of an EHR in summarization has resulted in poor performance due to the fact that attention mechanisms in modern large language models (LLMs) adds a quadratic complexity in terms of the size of the input.