Developing Semi-Automated Approaches for Generating Survivorship Care Plans for Pediatric Cancer Survivors

Andrew Hornback, Rebecca Williamson Lewis, Wayne Haowei Liang, Karen E. Effinger, Naveen Muthu, Harinishree Sathu, Yuanda Zhu, Benoit Louis Marteau, May Dongmei Wang

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Keywords: Artificial intelligence, artificial intelligence implementation science, clinical informatics, data harmonization and integration, hematopoietic stem cell transplant, oncology, survivorship care plans

Abstract:

Survivorship Care Plans (SCPs) are clinical documents that summarize treatments, long-term health risks, and evidence-based recommendations for cancer and hematopoietic stem cell transplantation (HSCT) survivors. Despite their clinical value, SCPs remain underutilized due to limited automation, high documentation burden, and workflow misalignment. Manual SCP generation is time-consuming, error-prone, and burdensome—particularly in complex cases requiring hours of chart review and manual calculation.
To address these challenges, we developed a semi-automated SCP generation system grounded in principles of Artificial Intelligence (AI) implementation science, focusing on clinical context-aware integration, sustainable workflow alignment, and human-centered design. The system employs an Extract, Transform, and Load (ETL) pipeline to extract survivorship-relevant data from Epic Clarity, processing both structured and unstructured Electronic Health Record (EHR) data. Structured data are processed using deterministic rules, whose outputs are reviewed by clinical experts in an iterative, human-in-the-loop process to validate accuracy and refine rule logic. Unstructured notes are analyzed using a BERT-based NLP model to identify documentation of radiation therapy. In collaboration with a large pediatric healthcare system in the United States, we retrospectively identified a cohort of patients less than age 30 treated for cancer or HSCT between January 2011 and December 2021. Using a validation cohort of 864 patients, our system achieved 99.5% concordance for 53 out of 57 chemotherapy agent exposures, with most discrepancies attributable to human abstraction errors. Ongoing work includes usability testing with clinicians, co-design with survivorship coordinators, and evaluation of implementation outcomes such as trust, safety, and integration into clinical workflows.

Track: 4. Clinical Informatics

Registration Id: 6BNC68W8PGX

Submission Number: 332