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Starts 13 June 2025 13:26

Ends 13 June 2025

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Toward Relieving Clinician Burden by Automatically Generating Progress Notes

Discover how automated progress note generation from EHR data can reduce clinician burden, as Dr. Sarvesh Soni presents a novel framework using LLMs to process structured patient data while addressing privacy concerns.
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Overview

Discover how automated progress note generation from EHR data can reduce clinician burden, as Dr. Sarvesh Soni presents a novel framework using LLMs to process structured patient data while addressing privacy concerns.

Syllabus

  • Introduction to Clinician Burden
  • Overview of clinician workload and the role of documentation
    Impact of EHR documentation on clinician stress and efficiency
  • Basics of Electronic Health Records (EHRs)
  • Structure and types of EHR data
    Current EHR documentation practices
  • Fundamentals of Large Language Models (LLMs)
  • Introduction to LLMs and their capabilities
    Application of LLMs in healthcare
  • Framework for Automated Progress Note Generation
  • Overview of the novel framework proposed by Dr. Sarvesh Soni
    Steps in processing structured patient data using LLMs
    Integration of LLMs with EHR systems
  • Addressing Privacy Concerns
  • Key privacy issues in handling patient data
    Techniques for ensuring data privacy and security
    Regulatory compliance (HIPAA, GDPR)
  • Implementation and Evaluation
  • Setting up an automated progress note system in healthcare settings
    Evaluating the effectiveness of the framework
    Case study: Real-world application and outcomes
  • Ethical and Practical Considerations
  • Ethical implications of automation in clinical settings
    Training and adaptability for clinicians
    Future opportunities and challenges
  • Conclusion and Future Directions
  • Recap of potential benefits in clinician burden reduction
    Advances in LLMs and potential future enhancements

Subjects

Computer Science