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Doccurate: A Curation-Based Approach for Clinical Text Visualization.

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    Physicians struggle with large patient charts. Doccurate offers a novel curation-based text visualization system, preserving original clinical notes and providing customizable, auditable summaries for efficient review.

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    Area of Science:

    • Clinical Informatics
    • Health Information Technology
    • Medical Visualization

    Background:

    • Physicians require comprehensive patient medical history overviews.
    • Large clinical text datasets pose challenges for efficient review and summarization.
    • Existing text visualization tools have limitations, including error rates and obscuring original text.

    Purpose of the Study:

    • To introduce Doccurate, a novel system for visualizing large clinical text datasets.
    • To address limitations of automated text summarization in clinical practice.
    • To provide physicians with customizable, auditable, and original-text-preserving visualization tools.

    Main Methods:

    • Development of Doccurate, a curation-based approach for clinical text visualization.
    • Integration of automation auditing and customizability features for physicians.
    • Conducting a formal qualitative evaluation with 6 domain experts.

    Main Results:

    • The evaluation provided insights into physicians' information needs and preferences for visualization tools.
    • Domain experts highlighted the importance of customization and preserving original clinical text.
    • Doccurate demonstrated potential for improving the review of large clinical text datasets.

    Conclusions:

    • Doccurate offers a promising solution to the challenges of visualizing large clinical text datasets.
    • The system's curation-based approach balances automation with physician control and data integrity.
    • Further adoption in clinical practice is supported by its focus on usability and preservation of original text.