Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230174
Authors: 
Schiele, Julian
Koperna, Thomas
Brunner, Jens O.
Year of Publication: 
2020
Citation: 
[Journal:] Naval Research Logistics (NRL) [ISSN:] 1520-6750 [Volume:] [Issue:] [Pages:] n/a-n/a
Abstract: 
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.
Subjects: 
artificial neural network
downstream units
intensive care unit
machine learning
master surgery scheduling
operations research
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by-nc-nd/4.0/
Document Type: 
Article
Document Version: 
Published Version

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