DeepScore: A Comprehensive Approach to Measuring Quality in AI-Generated Clinical Documentation

• URL: http://arxiv.org/abs/2409.16307v1
• Date: Tue, 10 Sep 2024 23:06:48 GMT
• Title: DeepScore: A Comprehensive Approach to Measuring Quality in AI-Generated Clinical Documentation
• Authors: Jon Oleson,
• Abstract summary: This paper presents an overview of DeepScribe's methodologies for assessing and managing note quality. These methodologies aim to enhance the quality of patient care documentation through accountability and continuous improvement.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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