Leveraging Synthetic Data for Enhanced Clinical Documentation

EasyChair Preprint no. 14159

Abstract

In the rapidly evolving landscape of healthcare, clinical documentation plays a crucial role in ensuring accurate patient records, facilitating effective communication among healthcare professionals, and enhancing overall patient care. However, the creation and management of clinical documentation are often hindered by challenges such as data privacy concerns, the labor-intensive nature of manual data entry, and the variability in documentation quality. This paper explores the potential of leveraging synthetic data to address these challenges and improve clinical documentation processes.

By generating synthetic patient records that mimic real-world data, we aim to create a robust and scalable framework that can be used for training, validating, and testing clinical documentation systems without compromising patient privacy. We employ advanced machine learning techniques, including generative adversarial networks (GANs) and variational autoencoders (VAEs), to generate high-fidelity synthetic data that retains the statistical properties and complex patterns of real clinical datasets.

Our findings indicate that the use of synthetic data can significantly enhance the performance of natural language processing (NLP) models in extracting, summarizing, and generating clinical notes. Furthermore, synthetic data facilitates the development of automated documentation tools, reducing the burden on healthcare providers and ensuring consistency and accuracy in patient records.

This study underscores the transformative potential of synthetic data in the healthcare domain, paving the way for innovative solutions that safeguard patient privacy while promoting efficiency and accuracy in clinical documentation. Future work will focus on refining synthetic data generation techniques and exploring their integration into electronic health record (EHR) systems to further optimize clinical workflows.

Keyphrases: Clinical Documentation, data privacy, Generative Adversarial Networks, machine learning, synthetic data, Variational Autoencoders

@Booklet{EasyChair:14159,
  author = {John Owen},
  title = {Leveraging Synthetic Data for Enhanced Clinical Documentation},
  howpublished = {EasyChair Preprint no. 14159},

  year = {EasyChair, 2024}}