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Survivorship Navigator: Personalized Survivorship Care Plan Generation using Large Language Models.

Jathurshan Pradeepkumar1, Shivam Pankaj Kumar1, Courtney Bryce Reamer2

  • 1University of Illinois Urbana-Champaign, Urbana, IL.

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Large language models (LLMs) can automate cancer survivorship care plan (SCP) generation, reducing clinician burden. Survivorship Navigator demonstrates improved accuracy and guideline compliance in creating these essential follow-up tools.

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Area of Science:

  • Oncology
  • Health Informatics
  • Artificial Intelligence

Background:

  • Cancer survivorship care plans (SCPs) are vital for long-term patient follow-up.
  • Current SCP creation is time-consuming and places a significant burden on clinicians.
  • Manual data extraction and guideline application complicate the process.

Purpose of the Study:

  • To explore the potential of large language models (LLMs) for automating SCP generation.
  • To introduce Survivorship Navigator, a framework for streamlining SCP creation.
  • To enhance the integration of SCPs with clinical systems.

Main Methods:

  • Systematic exploration of LLMs for automated SCP generation.
  • Development and implementation of the Survivorship Navigator framework.
  • Evaluation using automated assessments and human expert review.

Main Results:

  • Survivorship Navigator significantly outperforms baseline methods in SCP generation.
  • The framework produces more accurate and guideline-compliant SCPs.
  • Generated SCPs are more actionable for clinicians and survivors.

Conclusions:

  • LLMs offer a promising solution for automating SCP creation.
  • Survivorship Navigator effectively reduces clinician burden and improves SCP quality.
  • Automated SCP generation can enhance cancer survivorship care delivery.