Summary of Ai Managed Emergency Documentation with a Pretrained Model, by David Menzies et al.
AI Managed Emergency Documentation with a Pretrained Model
by David Menzies, Sean Kirwan, Ahmad Albarqawi
First submitted to arxiv on: 17 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
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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 This study explores the potential of a large language model (LLM) to enhance efficiency and quality in emergency department discharge letter writing. Time constraints and infrastructure deficits hinder compliance with current targets. The researchers investigate whether an AI system can improve discharge letters by leveraging advanced techniques, such as fine-tuning models to generate summaries from short-hand inputs like voice, text, and electronic health records. Nineteen physicians evaluated the system’s text and voice-to-text interfaces against manual typing, revealing significant time savings with MedWrite LLM interfaces compared to traditional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at using a special kind of computer program to help doctors write better discharge letters for patients leaving emergency departments. Doctors often struggle to write these letters quickly enough because they have too much work to do and not enough time. The researchers tested an AI system that can generate discharge summaries from what doctors type or say. Nineteen experienced doctors tried the system and found it saved them a lot of time compared to writing by hand. |
Keywords
» Artificial intelligence » Fine tuning » Large language model
