text: In recent years there has been an accelerated interest in using LLMs to automate clinical tasks 
  in an effort to unburden physicians and reduce burnout.22 Computer-generated text within clinical 
  notes using natural language processing (NLP) have been overall shown to improve note completion 
  rates, physician satisfaction, and patient outcomes.23 Since 2018, NLP has made rapid advancements 
  in health care with the discovery of the transformer model architecture, the building block of large 
  language models (LLMs). LLMs can automate workflows such as discharge summaries,24 radiology 
  reports,25 patient messaging,26 after-visit summaries,27 and ambient dictation28 with various levels 
  of perceived quality in each workflow.29 LLMs are particularly effective at summarizing large 
  unstructured clinical datasets, such as ED patient medical records.30 A common concern of LLMs is 
  their ability to hallucinate data, or LLMs generating output text that is not factually consistent with 
  the original source content.31 Much work has been done in health care to reduce hallucinations 
  through building larger-parameter models trained on trillions of datasets, and then instruction fine- 
  tuning the LLM on smaller, well-curated datasets.32,33 LLMs can also be designed with explainability 
  by citing inferred content back to the reference source notes.34 For short-context length notes, using 
  few-shot prompt engineering approaches with large language models like GPT-4 can produce 