# Enhancing Clinical Documentation with Synthetic Data: Leveraging Generative Models for Improved Accuracy

arXiv: https://arxiv.org/abs/2406.06569 · HTML: https://mlsanity.com/paper/2406.06569/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 57 references that are in the map.
- First posted on arXiv 3 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://mlsanity.com/paper/1609.05473/) (2016)
- [A global analysis of metrics used for measuring performance in natural language processing](https://mlsanity.com/paper/2204.11574/) (2022)
- [Improving Vietnamese Named Entity Recognition from Speech Using Word Capitalization and Punctuation Recovery Models](https://mlsanity.com/paper/2010.00198/) (2020)
- [VAIS Hate Speech Detection System: A Deep Learning based Approach for System Combination](https://mlsanity.com/paper/1910.05608/) (2019)
- [Language Models: A Guide for the Perplexed](https://mlsanity.com/paper/2311.17301/) (2023)
- [Three Variations on Variational Autoencoders](https://mlsanity.com/paper/2212.04451/) (2022)

## Built on by (most cited first, 1 of 1)

- [Embedding-Driven Diversity Sampling to Improve Few-Shot Synthetic Data Generation](https://mlsanity.com/paper/2501.11199/) (2025)

## Abstract (arXiv)

Accurate and comprehensive clinical documentation is crucial for delivering high-quality healthcare, facilitating effective communication among providers, and ensuring compliance with regulatory requirements. However, manual transcription and data entry processes can be time-consuming, error-prone, and susceptible to inconsistencies, leading to incomplete or inaccurate medical records. This paper proposes a novel approach to augment clinical documentation by leveraging synthetic data generation techniques to generate realistic and diverse clinical transcripts. We present a methodology that combines state-of-the-art generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), with real-world clinical transcript and other forms of clinical data to generate synthetic transcripts. These synthetic transcripts can then be used to supplement existing documentation workflows, providing additional training data for natural language processing models and enabling more accurate and efficient transcription processes. Through extensive experiments on a large dataset of anonymized clinical transcripts, we demonstrate the effectiveness of our approach in generating high-quality synthetic transcripts that closely resemble real-world data. Quantitative evaluation metrics, including perplexity scores and BLEU scores, as well as qualitative assessments by domain experts, validate the fidelity and utility of the generated synthetic transcripts. Our findings highlight synthetic data generation's potential to address clinical documentation challenges, improving patient care, reducing administrative burdens, and enhancing healthcare system efficiency.