Cancer survivorship care plans (SCPs) are critical tools for guiding long-term follow-up care of cancer survivors. Yet, their widespread adoption remains hindered by the significant clinician burden and the time- and labor-intensive process of SCP creation. Current practices require clinicians to extract and synthesize treatment summaries from complex patient data, apply relevant survivorship guidelines, and generate a care plan with personalized recommendations, making SCP generation time-consuming. In this study, we systematically explore the potential of large language models (LLMs) for automating SCP generation and introduce Survivorship Navigator, a framework designed to streamline SCP creation and enhance integration with clinical systems. We evaluate our approach through automated assessments and a human expert study, demonstrating that Survivorship Navigator outperforms baseline methods, producing SCPs that are more accurate, guideline-compliant, and actionable.

The authors have declared no competing interest.

This study did not receive any funding

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The study used ONLY openly available human data that were originally located at: https://physionet.org/content/curated-oncology-reports/1.0/ The CORAL dataset is a fine-grained, expert-annotated collection of 40 de-identified breast and pancreatic cancer progress notes.

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All data utilized in this study are available at https://physionet.org/content/curated-oncology-reports/1.0/