JMIR Publications

ABSTRACT

Background:

Acute care utilization (ACU) represents a major economic burden in oncology, which can ideally be prevented. Existing models effectively predict such events.

Objective:

We aim to quantify the cost savings achieved by implementing a model to predict ACU in oncology patients undergoing systemic therapy.

Methods:

This retrospective cohort study analyzed cancer patients at an academic medical center from 2010 to 2022. We included patients and counted ACU events starting from the first day following the initiation of systemic 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 following the start of therapy. The exposure was an ACU event.

Results:

The main outcome was the average daily cost per patient and the total cost per patient at the end of the first 180 days of systemic therapy. We see that expense accumulation flattens earlier and more rapidly for Non-ACU patients. The study included 20,556 patients. The average daily cost per patient with ACU was US $94.62 (92.32, 96.92) (95% CI) and US $53.28 (52.37, 54.19) without ACU. The average total cost per ACU patient was US $17,031.92 (16,616.74, 17,445.09), and US $9,591.06 (9,427.64, 9,754.48) for those without an ACU. Based on the average number of patients in systemic therapy annually, 2,177, the model predicts a savings of US $910,000 in the first year, growing to US $9.46 million over six years, with cumulative savings of US $31.11 million.

Conclusions:

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.