JMIR Publications

ABSTRACT

Acute care utilization (ACU) stands for a major economic burden in oncology, which can ideally be prevented. Existing models effectively predict such events. We aim to quantify the cost savings achieved by implementing a model to predict ACU in oncology patients undergoing systemic therapy. This retrospective cohort study analyzed 20,556 patients at an academic medical center from 2010 to 2022. We included patients whose treatment began on day one of therapy, excluding those with known death dates within study period. Data on ACU-related expenses were gathered from Medicare claims and mapped to service codes in electronic health records, yielding average daily costs for each patient over 180 days. The exposure was an ACU event. The main outcome was the average daily cost per patient by the end of the first year of systemic therapy. The study included 20,556 patients. The average daily cost per patient with ACU was US $94.62 ± 72.54 (92.32, 96.92) (mean ± SD, 95% CI) and without ACU was $53.28 ± 59.92 (92.32, 54.19). The average total cost per ACU patient was US $17,031.92 ± 13,056.63 (16,616.74, 17,445.09), and $9,591.06 ± 10,785.83 (9,427.64, 9,754.48) for those without an ACU. Based on an average of 2,177 patients annually, the model predicts a savings of $910,000 in the first year, growing to $9.46 million over six years, with cumulative savings of $31.11 million. Predictive analytics can significantly reduce costs associated with ACU events, enhancing economic efficiency in cancer care. Further research is needed to explore potential health benefits.