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February 14, 2024Journal of Medical Systems102 citationsOpen Access

Artificial Intelligence in Operating Room Management

VBValentina BelliniMRMichele RussoTDTania Domenichetti

Structured PICO

Does artificial intelligence improve prediction accuracy and resource utilization in operating room management?

P
Population
22 selected studies focusing on operating room management
I
Intervention
Artificial intelligence, particularly machine learning algorithms (e.g., XGBoost, random forest, and neural networks)
O
Outcome
Predicting surgical case durations, optimizing post-anesthesia care unit resource allocation, and detecting surgical case cancellations

Artificial intelligence and machine learning algorithms show significant potential to enhance healthcare efficiency and resource utilization in operating room management.

Limitations

  • Data access concerns
  • Privacy concerns

Abstract

This systematic review examines the recent use of artificial intelligence, particularly machine learning, in the management of operating rooms. A total of 22 selected studies from February 2019 to September 2023 are analyzed. The review emphasizes the significant impact of AI on predicting surgical case durations, optimizing post-anesthesia care unit resource allocation, and detecting surgical case cancellations. Machine learning algorithms such as XGBoost, random forest, and neural networks have demonstrated their effectiveness in improving prediction accuracy and resource utilization. However, challenges such as data access and privacy concerns are acknowledged. The review highlights the evolving nature of artificial intelligence in perioperative medicine research and the need for continued innovation to harness artificial intelligence's transformative potential for healthcare administrators, practitioners, and patients. Ultimately, artificial intelligence integration in operative room management promises to enhance healthcare efficiency and patient outcomes.

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Cite This Study

Bellini et al. (2024) studied this question.

synapsesocial.com/papers/69dc37374f901957bec0fd58https://doi.org/10.1007/s10916-024-02038-2
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