Artificial intelligence models using electronic health records and patient-reported outcomes may help identify cancer survivors at increased risk for emergency department visits, hospitalizations and worsening symptoms after treatment, according to a new study from Sylvester Comprehensive Cancer Center, part of the University of Miami Miller School of Medicine. The findings suggest AI-driven risk forecasting could help care teams intervene earlier with more proactive survivorship support.
Published in JCO–Clinical Cancer Informatics, the study demonstrates how machine learning models applied to clinical data and patient‑reported outcomes, or PROs, can help identify survivors at increased risk for unplanned health care use and elevated symptom burden during the survivorship continuum. By transforming medical records and patient-reported data into predictive signals, the research offers a potential pathway toward more proactive, personalized survivorship care.
Cancer survivorship care is a dynamic, ongoing process, not a single phase of care, explained Frank J. Penedo, Ph.D., director of Sylvester's Survivorship and Supportive Care Institute, and the study's senior author, who led the multidisciplinary team. "For many patients, new or evolving challenges arise after treatment ends, just as routine clinical contact often tapers off, raising a critical question: how can we identify those at higher risk earlier, before these concerns intensify and become harder to address?"
