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

arXiv: https://arxiv.org/abs/2409.16307 · HTML: https://mlsanity.com/paper/2409.16307/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 2 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 2 of 2)

- [Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations](https://mlsanity.com/paper/2007.08749/) (2020)
- [Improving Clinical Note Generation from Complex Doctor-Patient Conversation](https://mlsanity.com/paper/2408.14568/) (2024)

## Built on by (most cited first, 2 of 2)

- [From Feedback to Checklists: Grounded Evaluation of AI-Generated Clinical Notes](https://mlsanity.com/paper/2507.17717/) (2025)
- [One note in three: a verified census of three deployed AI scribes, and the instrument that counted it](https://mlsanity.com/paper/2608.31017/) (2026)

## Abstract (arXiv)

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.