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Towards optimizing hospital patient transports by automatically identifying interpretable causes of delays

Pieter Bonte (UGent) , Femke Ongenae (UGent) and Filip De Turck (UGent)
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Abstract
The continuous financial pressure on hospitals forces them to rethink various workflows. We focus on optimizing hospital transports, within the hospital, as they count up to 30% of the overall hospital cost. In this paper, we discuss a self-learning platform that learns the causes of transport delays, in order to avoid these kinds of delays in the future. We pay special attention to the explainability of the self-learning system, such that management understands the learned causes and remains in control over the automated process. This is achieved by providing the learned causes as sentences that can be understood by non-technical personnel and allowing these causes to first be supervised before the system takes them into account. Once approved, the system will calculate how much more time should be assigned to these transports in order to avoid future delays. As a result, the scheduling of patient transportation can be automatically optimized, while management remains in full control of the process.
Keywords
DYNAMIC TRANSPORTATION, ONTOLOGIES, HEALTH, Rule learning, ontology, hospital transport

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Citation

Please use this url to cite or link to this publication:

Chicago
Bonte, Pieter, Femke Ongenae, and Filip De Turck. 2019. “Towards Optimizing Hospital Patient Transports by Automatically Identifying Interpretable Causes of Delays.” International Journal of Software Engineering and Knowledge Engineering 29 (6): 819–847.
APA
Bonte, P., Ongenae, F., & De Turck, F. (2019). Towards optimizing hospital patient transports by automatically identifying interpretable causes of delays. INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING, 29(6), 819–847.
Vancouver
1.
Bonte P, Ongenae F, De Turck F. Towards optimizing hospital patient transports by automatically identifying interpretable causes of delays. INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING. Singapore: World Scientific Publ Co Pte Ltd; 2019;29(6):819–47.
MLA
Bonte, Pieter, Femke Ongenae, and Filip De Turck. “Towards Optimizing Hospital Patient Transports by Automatically Identifying Interpretable Causes of Delays.” INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 29.6 (2019): 819–847. Print.
@article{8623325,
  abstract     = {The continuous financial pressure on hospitals forces them to rethink various workflows. We focus on optimizing hospital transports, within the hospital, as they count up to 30% of the overall hospital cost. In this paper, we discuss a self-learning platform that learns the causes of transport delays, in order to avoid these kinds of delays in the future. We pay special attention to the explainability of the self-learning system, such that management understands the learned causes and remains in control over the automated process. This is achieved by providing the learned causes as sentences that can be understood by non-technical personnel and allowing these causes to first be supervised before the system takes them into account. Once approved, the system will calculate how much more time should be assigned to these transports in order to avoid future delays. As a result, the scheduling of patient transportation can be automatically optimized, while management remains in full control of the process.},
  author       = {Bonte, Pieter and Ongenae, Femke and De Turck, Filip},
  issn         = {0218-1940},
  journal      = {INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING},
  keywords     = {DYNAMIC TRANSPORTATION,ONTOLOGIES,HEALTH,Rule learning,ontology,hospital transport},
  language     = {eng},
  number       = {6},
  pages        = {819--847},
  publisher    = {World Scientific Publ Co Pte Ltd},
  title        = {Towards optimizing hospital patient transports by automatically identifying interpretable causes of delays},
  url          = {http://dx.doi.org/10.1142/S0218194019500281},
  volume       = {29},
  year         = {2019},
}

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