Artikel

Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks

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In a master surgery scheduling (MSS) problem, a hospital's operating room (OR) capacity is assigned to different medical specialties. This task is critical since the risk of assigning too much or too little OR time to a specialty is associated with overtime or deficit hours of the staff, deferral or delay of surgeries, and unsatisfied—or even endangered—patients. Most MSS approaches in the literature focus only on the OR while neglecting the impact on downstream units or reflect a simplified version of the real-world situation. We present the first prediction model for the integrated OR scheduling problem based on machine learning. Our three-step approach focuses on the intensive care unit (ICU) and reflects elective and urgent patients, inpatients and outpatients, and all possible paths through the hospital. We provide an empirical evaluation of our method with surgery data for Universitätsklinikum Augsburg, a German tertiary care hospital with 1700 beds. We show that our model outperforms a state-of-the-art model by 43% in number of predicted beds. Our model can be used as supporting tool for hospital managers or incorporated in an optimization model. Eventually, we provide guidance to support hospital managers in scheduling surgeries more efficiently.

Language: Englisch

Bibliographic citation: Journal: Naval Research Logistics (NRL) ; ISSN: 1520-6750 ; Volume: 68 ; Year: 2021 ; Issue: 1 ; Pages: 65-88 ; Hoboken, USA: John Wiley & Sons, Inc.

Classification: Management

Subject: artificial neural network
downstream units
intensive care unit
machine learning
master surgery scheduling
operations research

Event: Geistige Schöpfung

(who): Schiele, Julian
Koperna, Thomas
Brunner, Jens O.

Event: Veröffentlichung

(who): John Wiley & Sons, Inc.

(where): Hoboken, USA

(when): 2021

DOI: doi:10.1002/nav.21929

Handle: http://hdl.handle.net/10419/230174

Last update: 10.03.2025, 11:41 AM CET

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Object type

Artikel

Associated

Schiele, Julian
Koperna, Thomas
Brunner, Jens O.
John Wiley & Sons, Inc.

Time of origin

2021

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Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks

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Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks

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Innovative operating room scheduling metric for creating surgical lists with desirable room utilisation rates

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Correction: Innovative operating room scheduling metric for creating surgical lists with desirable room utilisation rates

Periodic table of operating room and non-operating room anesthesia

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Recursive neural networks in hospital bed occupancy forecasting

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Recursive neural networks in hospital bed occupancy forecasting

Recursive neural networks in hospital bed occupancy forecasting

Ergonomics in the operating room

Ergonomics in the operating room

Introducing the “Twilight” operating room concept: a feasibility study to improve operating room utilization

Introducing the “Twilight” operating room concept: a feasibility study to improve operating room utilization

Surgery Scheduling in Flexible Operating Rooms by using a Convex Surrogate Model of Second-Stage Costs

Surgery Scheduling in Flexible Operating Rooms by using a Convex Surrogate Model of Second-Stage Costs

Obstetric operating room staffing and operating efficiency using queueing theory

Obstetric operating room staffing and operating efficiency using queueing theory

Artificial Intelligence in Operating Room Management

Artificial Intelligence in Operating Room Management

Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks

Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks

Innovative operating room scheduling metric for creating surgical lists with desirable room utilisation rates

Innovative operating room scheduling metric for creating surgical lists with desirable room utilisation rates

Correction: Innovative operating room scheduling metric for creating surgical lists with desirable room utilisation rates

Correction: Innovative operating room scheduling metric for creating surgical lists with desirable room utilisation rates

Periodic table of operating room and non-operating room anesthesia

Periodic table of operating room and non-operating room anesthesia

Operating room workforce planning and scheduling : A multi-agent simulation for decision support

Operating room workforce planning and scheduling : A multi-agent simulation for decision support

Recursive neural networks in hospital bed occupancy forecasting

Recursive neural networks in hospital bed occupancy forecasting

Recursive neural networks in hospital bed occupancy forecasting

Recursive neural networks in hospital bed occupancy forecasting

Ergonomics in the operating room

Ergonomics in the operating room

Introducing the “Twilight” operating room concept: a feasibility study to improve operating room utilization

Introducing the “Twilight” operating room concept: a feasibility study to improve operating room utilization

Surgery Scheduling in Flexible Operating Rooms by using a Convex Surrogate Model of Second-Stage Costs

Surgery Scheduling in Flexible Operating Rooms by using a Convex Surrogate Model of Second-Stage Costs

Obstetric operating room staffing and operating efficiency using queueing theory

Obstetric operating room staffing and operating efficiency using queueing theory

Artificial Intelligence in Operating Room Management

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