Redefining Cancer Survivorship Care: How AI Technology and Big Data are Contributing to Proactive Care Delivery - University of Miami

By: Monica Smith | May 26, 2026 | 12 min. read | 

For a growing number of cancer survivors, ringing the bell at the end of primary treatment marks a transition into a complex phase of care that is often less structured and harder to predict. Even after therapy concludes, patients may experience lingering physical symptoms, emotional distress or other unexpected medical needs that lead to emergency department or urgent care visits or even hospitalizations, along with worsening symptom burden.

A new multidisciplinary study from Sylvester Comprehensive Cancer Center, part of the University of Miami Miller School of Medicine, suggests that the key to anticipating those outcomes may lie in closely examining electronic health records and patient-reported data systematically, using novel artificial intelligence (AI) technologies.

Published in JCO–Cancer Clinical 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 survivorship. 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., Sylvester associate director for population sciences, director of Sylvester’s Survivorship and Supportive Care Institute and the study’s senior author.

“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?” Dr. Penedo said.

Listening to the Patient Experience

PROs capture patient experiences that traditional clinical data often miss or are infrequently assessed, including emotional well-being, fatigue, functional limitations and other practical needs that may interfere with adequate survivorship care. Over the past decade, PROs have become an increasingly important component of cancer care. Yet translating large volumes of patient-reported data and integrating them with vast amounts of medical record data to enable actionable insights, particularly across survivor populations, has remained a persistent challenge.

Led by Akina Natori, M.D., M.S.P.H., a Sylvester oncologist and assistant professor in the Division of Medical Oncology at the Miller School, the study reframed PROs not as retrospective descriptions of patient experience, but as prospective indicators of future need.

“PROs tell us how patients are actually feeling and functioning,” said Dr. Natori, first author of the study. “We wanted to know whether those self‑reported experiences, in combination with clinical data such as cancer and treatment type, could help us identify which survivors might be at higher risk for significant symptom burden or unplanned health care use down the line.”

Unplanned health care use can include emergency department visits or hospitalizations that arise outside of scheduled follow‑up. They often signal unmet needs or gaps in survivorship and supportive care. Being able to forecast that risk could allow care teams to intervene earlier, with targeted symptom management, psychosocial support or closer monitoring.

Applying Machine Learning to Survivorship Data

To explore that possibility, the research team analyzed data from more than 25,000 cancer survivors followed over three years, using machine learning to detect patterns that traditional statistical methods can miss. The advantage of such approaches is their ability to weigh many factors, including clinical history, treatments, symptoms, emotional well-being and patterns of health care use, and to find the subtle interactions that signal which patients are heading toward trouble.

The answers turned out to depend on what was being predicted. For acute events like emergency room visits and hospitalizations, recent clinical activity was the strongest signal. What was happening with a patient in the last few months mattered more than where they started. For symptom burden, longer-term trends told a clearer story. Adding patient-reported outcomes nearly doubled how well the models performed compared with clinical data alone. When the researchers flagged the highest-risk 10% of patients, that group accounted for roughly half of all subsequent health care events and elevated symptom episodes.

“This type of risk stratification problem is well-suited for machine learning,” said Jerry R. Bonnell, Ph.D., a postdoctoral associate at the University of Miami’s Frost Institute for Data Science and Computing. “The challenge is developing models that are not only accurate, but also interpretable and meaningful for clinicians making real‑world decisions.”

That emphasis on interpretability shaped the study’s design. Rather than treating the models as opaque systems, the team built them to show their reasoning. This surfaced which factors were driving a given patient’s risk score and how these factors shifted over time. The goal is a tool that gives clinicians not just a number, but a starting point for conversation about who needs closer follow-up, what they may need and when to step in before a problem escalates.

An Interdisciplinary Approach

The project brought together expertise from clinical oncology, psychosocial oncology, population sciences and data science, reflecting the multifaceted nature of survivorship care. Contributors included Vasileios Stathias, Ph.D., assistant director for data science at Sylvester, as well as collaborators across the University of Miami.

“Survivorship sits at the intersection of biology, behavior and health systems,” Dr. Stathias said. “By combining patient‑reported and clinical data with advanced analytics, we can begin to see patterns that might otherwise remain invisible and that can inform more proactive care strategies.”

Additional authors included

• Mitsunori Ogihara, Ph.D., program director of UM’s Big Data Analytics and Data Mining program

• Andrew Wang, A.B.

• Ravi Vadapalli, Ph.D., director of advanced computing for the Frost Institute for Data Science and Computnig

• Blanca Silvia Noriega Esquives, M.D., Ph.D., a Sylvester postdoctoral associate

Implications for Cancer Survivorship Care

While the authors emphasized that the findings are not intended to immediately change clinical practice, they highlighted the broader implications of the work. As cancer survivorship populations continue to grow, health systems face increasing pressure to deliver long‑term care that is precise, proactive and sustainable.

“This is about shifting from reactive to proactive survivorship care,” Dr. Penedo said. “If we can identify patients who are more likely to struggle, we can begin to align supportive resources earlier and more effectively.”

The team also noted the potential impact of clinical and PRO‑based predictive models for health care access. PROs reflect patient voices directly. They may help surface unmet needs that are less likely to be captured through routine clinical encounters alone.

Looking Ahead

Future research will focus on continuing to refine and validate these models across broader survivor populations, as well as exploring how electronic health record and PRO data-driven risk stratification could be integrated into survivorship standards of care.

“The expertise of our multidisciplinary team provides a unique opportunity to create a data ecosystem that facilitates the implementation of AI-powered analytics to guide proactive and precision care to reduce the burden of cancer on patients and health systems. This study is among several initiatives that are working towards this goal,” said Dr. Penedo.

“Our long-term goal is to ensure that survivorship care keeps pace with advances in treatment,” said Dr. Natori. “That means using data not only to describe outcomes, but to anticipate them, so we can more proactively support patients in the years after cancer.”

AI and the Miller School of Medicine

Tags: AI, Akina Natori, artificial intelligence, cancer research, cancer survivorship, data science, Division of Medical Oncology, Dr. Frank Penedo, Dr. Vasileios Stathias, Sylvester Comprehensive Cancer Center, Sylvester’s Survivorship and Supportive Care Institute, team science