DeepScore: A Comprehensive Approach to Measuring Quality in AI-Generated Clinical Documentation
Fuente:
arXiv
Enregistré dans:
| Auteur principal: | |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866913516911329280 |
|---|---|
| author | Oleson, Jon |
| author_facet | Oleson, Jon |
| contents | Medical practitioners are rapidly adopting generative AI solutions for clinical documentation, leading to significant time savings and reduced stress. However, evaluating the quality of AI-generated documentation is a complex and ongoing challenge. This paper presents an overview of DeepScribe's methodologies for assessing and managing note quality, focusing on various metrics and the composite "DeepScore", an overall index of quality and accuracy. These methodologies aim to enhance the quality of patient care documentation through accountability and continuous improvement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_16307 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DeepScore: A Comprehensive Approach to Measuring Quality in AI-Generated Clinical Documentation Oleson, Jon Computation and Language Artificial Intelligence Applications Medical practitioners are rapidly adopting generative AI solutions for clinical documentation, leading to significant time savings and reduced stress. However, evaluating the quality of AI-generated documentation is a complex and ongoing challenge. This paper presents an overview of DeepScribe's methodologies for assessing and managing note quality, focusing on various metrics and the composite "DeepScore", an overall index of quality and accuracy. These methodologies aim to enhance the quality of patient care documentation through accountability and continuous improvement. |
| title | DeepScore: A Comprehensive Approach to Measuring Quality in AI-Generated Clinical Documentation |
| topic | Computation and Language Artificial Intelligence Applications |
| url | https://arxiv.org/abs/2409.16307 |