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Updated: Sep 12, 2025

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Development of a Digital Case-Based Knowledge Base to Support Rapid Learning in Precision Oncology.

Jeffery Chan1, Minh Tran1, Shuang Liang1

  • 1Faculty of Medicine and Health, UNSW, Sydney, Australia.

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|August 8, 2025
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This study introduces a collaborative web platform for precision oncology, enabling rapid learning from physician-curated case studies to enhance cancer care decision-making.

Keywords:
Case-based learningclinical decision supportlearning health systemnatural language processingoncologyprecision medicine

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Area of Science:

  • Oncology
  • Medical Informatics
  • Health Services Research

Background:

  • Precision oncology relies on up-to-date knowledge, often lacking sufficient clinical trial evidence for rare cases.
  • Individual patient data is crucial for supplementing evidence gaps in cancer treatment.

Purpose of the Study:

  • To propose a novel collaborative web-based community platform to support a learning health concept in oncology.
  • To facilitate rapid knowledge sharing and analysis among physicians for improved cancer care.

Main Methods:

  • Developed a web platform organizing physician-directed analysis of case studies.
  • Utilized natural language processing (NLP) to extract and index concepts from curated case reports.
  • Structured case reports into clinical scenarios, questions, and solutions within a searchable database.

Main Results:

  • Created a searchable database of structured case reports.
  • Enabled clinicians to access insights through a web-based question-answering interface.
  • Facilitated asynchronous knowledge sharing and case critiques among oncologists.

Conclusions:

  • The platform supports a learning health concept by enabling rapid integration of new insights and case critiques.
  • Fosters collaboration within the oncology community to enhance decision-making.
  • Addresses the challenge of rapidly evolving knowledge in precision oncology.