ArticleJMIR medical informatics2025
Cost-Benefit Analysis of Preventing Acute Care Use in Oncology Patients Following Systemic Therapy Using Medicare Claims Data: Retrospective Cohort Study.
Article in JMIR medical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
Background: Acute care use (ACU) represents a major economic burden in oncology, which can ideally be prevented. Existing models effectively predict such events. Objective: We aimed 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 patients with cancer at an academic medical center from 2010 to 2022. We included patients who received systemic therapy and identified ACU events occurring after treatment initiation, excluding those with known death dates within the 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 at the end of the first 180 days of systemic therapy. We observed that expense accumulation flattened earlier and more rapidly among non-ACU patients. This study included 20,556 patients, of whom 3820 (18.58%) experienced at least 1 ACU. The average daily cost per patient for those with and without ACU was US $94.62 (SD US $72.54; 95% CI US $92.32-$96.92) and US $53.28 (SD US $59.92; 95% CI US $52.37-$54.19), respectively. The average total cost per ACU and non-ACU patient was US $17,031.92 (SD US $13,056.63; 95% CI US $16,616.74-$17,445.09) and US $9591.06 (SD US $10,785.83; 95% CI US $9427.64-$9754.48), respectively. To estimate the long-term financial impact of deploying the predictive model, we conducted a cost-benefit analysis based on an annual cohort size of 2177 patients. In the first year alone, the model yielded projected savings of US $910,000. By year 6, projected savings grew to US $9.46 million annually. The cumulative avoided costs over a 6-year deployment period totaled approximately US $31.11 million. These estimates compared the baseline cost model to the intervention model assuming a prevention rate of 35% for preventable ACU events and an average ACU cost of US $17,031.92 (SD US $13,037). 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.
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