Toward Automated Clinical Transcriptions
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914955735859200 |
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| author | Klusty, Mitchell A. Logan, W. Vaiden Armstrong, Samuel E. Mullen, Aaron D. Leach, Caroline N. Talbert, Jeff Bumgardner, V. K. Cody |
| author_facet | Klusty, Mitchell A. Logan, W. Vaiden Armstrong, Samuel E. Mullen, Aaron D. Leach, Caroline N. Talbert, Jeff Bumgardner, V. K. Cody |
| contents | Administrative documentation is a major driver of rising healthcare costs and is linked to adverse outcomes, including physician burnout and diminished quality of care. This paper introduces a secure system that applies recent advancements in speech-to-text transcription and speaker-labeling (diarization) to patient-provider conversations. This system is optimized to produce accurate transcriptions and highlight potential errors to promote rapid human verification, further reducing the necessary manual effort. Applied to over 40 hours of simulated conversations, this system offers a promising foundation for automating clinical transcriptions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15378 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Toward Automated Clinical Transcriptions Klusty, Mitchell A. Logan, W. Vaiden Armstrong, Samuel E. Mullen, Aaron D. Leach, Caroline N. Talbert, Jeff Bumgardner, V. K. Cody Audio and Speech Processing Artificial Intelligence Computation and Language Sound Administrative documentation is a major driver of rising healthcare costs and is linked to adverse outcomes, including physician burnout and diminished quality of care. This paper introduces a secure system that applies recent advancements in speech-to-text transcription and speaker-labeling (diarization) to patient-provider conversations. This system is optimized to produce accurate transcriptions and highlight potential errors to promote rapid human verification, further reducing the necessary manual effort. Applied to over 40 hours of simulated conversations, this system offers a promising foundation for automating clinical transcriptions. |
| title | Toward Automated Clinical Transcriptions |
| topic | Audio and Speech Processing Artificial Intelligence Computation and Language Sound |
| url | https://arxiv.org/abs/2409.15378 |