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

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.