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Background <p>Operational loss, defined as unanticipated financial deficits in intensive care unit (ICU) management, is challenging to predict yet critical for hospital sustainability. This study aimed to evaluate whether machine-learning models can predict financial loss events in postoperative ICU patients.</p> Methods <p>We conducted a retrospective analysis of postoperative patients admitted to the ICU at Tohoku University Hospital between April 2017 and March 2021. A total of 22 clinical and administrative variables collected within 24&#xa0;h of ICU admission were used to develop machine-learning models. The outcome was defined as financial loss events, determined by a negative contribution margin below the break-even threshold of − 909 USD. The dataset was randomly split into training (70%) and test (30%) sets. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUC) and accuracy.</p> Results <p>Among 6743 postoperative ICU patients, 425 (6.3%) experienced financial loss events. The random forest classifier demonstrated high predictive performance, with an AUC of 0.859 and accuracy of 0.785.</p> Conclusions <p>Machine-learning models may accurately predict financial loss events in postoperative ICU patients, potentially supporting efficient resource allocation and hospital financial planning.</p>

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Prediction of financial deficits of postoperative patients in the intensive care unit using machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月21日

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  • Saori Ikumi,
  • Takuya Shiga,
  • Eichi Takaya,
  • Shinya Sonobe,
  • Yu Kaiho,
  • Yukiko Ito,
  • Masanori Yamauchi

摘要

Background

Operational loss, defined as unanticipated financial deficits in intensive care unit (ICU) management, is challenging to predict yet critical for hospital sustainability. This study aimed to evaluate whether machine-learning models can predict financial loss events in postoperative ICU patients.

Methods

We conducted a retrospective analysis of postoperative patients admitted to the ICU at Tohoku University Hospital between April 2017 and March 2021. A total of 22 clinical and administrative variables collected within 24 h of ICU admission were used to develop machine-learning models. The outcome was defined as financial loss events, determined by a negative contribution margin below the break-even threshold of − 909 USD. The dataset was randomly split into training (70%) and test (30%) sets. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUC) and accuracy.

Results

Among 6743 postoperative ICU patients, 425 (6.3%) experienced financial loss events. The random forest classifier demonstrated high predictive performance, with an AUC of 0.859 and accuracy of 0.785.

Conclusions

Machine-learning models may accurately predict financial loss events in postoperative ICU patients, potentially supporting efficient resource allocation and hospital financial planning.