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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.09193 |
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| _version_ | 1866916361147514880 |
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| author | Menzies, David Kirwan, Sean Albarqawi, Ahmad |
| author_facet | Menzies, David Kirwan, Sean Albarqawi, Ahmad |
| contents | This study investigates the use of a large language model system to improve efficiency and quality in emergency department (ED) discharge letter writing. Time constraints and infrastructural deficits make compliance with current discharge letter targets difficult. We explored potential efficiencies from an artificial intelligence software in the generation of ED discharge letters and the attitudes of doctors toward this technology. The evaluated system leverages advanced techniques to fine-tune a model to generate discharge summaries from short-hand inputs, including voice, text, and electronic health record data. Nineteen physicians with emergency medicine experience evaluated the system text and voice-to-text interfaces against manual typing. The results showed significant time savings with MedWrite LLM interfaces compared to manual methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_09193 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI Managed Emergency Documentation with a Pretrained Model Menzies, David Kirwan, Sean Albarqawi, Ahmad Artificial Intelligence Computation and Language This study investigates the use of a large language model system to improve efficiency and quality in emergency department (ED) discharge letter writing. Time constraints and infrastructural deficits make compliance with current discharge letter targets difficult. We explored potential efficiencies from an artificial intelligence software in the generation of ED discharge letters and the attitudes of doctors toward this technology. The evaluated system leverages advanced techniques to fine-tune a model to generate discharge summaries from short-hand inputs, including voice, text, and electronic health record data. Nineteen physicians with emergency medicine experience evaluated the system text and voice-to-text interfaces against manual typing. The results showed significant time savings with MedWrite LLM interfaces compared to manual methods. |
| title | AI Managed Emergency Documentation with a Pretrained Model |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2408.09193 |