Question-Answering Based Summarization of Electronic Health Records
  using Retrieval Augmented Generation
Walid Saba Suzanne Wendelken James. Shanahan
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. We propose here a method that
mitigates these shortcomings by combining semantic search, retrieval augmented
generation (RAG) and question-answering using the latest LLMs. In our approach
summarization is the extraction of answers to specific questions that are
deemed important by subject-matter experts (SMEs). Our approach is quite
efficient; requires minimal to no training; does not suffer from the
'hallucination' problem of LLMs; and it ensures diversity, since the summary
will not have repeated content but diverse answers to specific questions.