Survivorship Navigator: Personalized Survivorship Care Plan Generation using Large Language Models
Pradeepkumar, J.; Pankaj Kumar, S.; Reamer, C. B.; Dreyer, M.; Patel, J.; Liebovitz, D.; Sun, J.
Show abstract
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.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Evaluating Semantic Similarity Methods for Comparison of Text-derived Phenotype Profiles 93%
- MelAnalyze: Fact-Checking Melatonin claims using Large Language Models and Natural Language Inference 93%
- Ontology-based expansion of virtual gene panels to improve diagnostic efficiency for rare genetic diseases 92%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.