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  • Using Prediction to Improve Elective Surgery Scheduling

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    86213_1.pdf (583.1Kb)
    Author(s)
    Kargar, ZS
    Khanna, S
    Sattar, A
    Griffith University Author(s)
    Sattar, Abdul
    Khanna, Sankalp
    Shahabi Kargar, Zahra
    Year published
    2012
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    Abstract
    Stochastic activity durations, uncertainty in the arrival process of patients, and coordination of multiple activities are some key features of surgery planning and scheduling. In this paper we provide an overview of challenges around elective surgery scheduling and propose a predictive model for elective surgery scheduling to be evaluated in a major tertiary hospital in Queensland. The proposed model employs waiting lists, peri-operative information, workload predictions, and improved procedure time estimation models, to optimise surgery scheduling. It is expected that the resulting improvement in scheduling processes will ...
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    Stochastic activity durations, uncertainty in the arrival process of patients, and coordination of multiple activities are some key features of surgery planning and scheduling. In this paper we provide an overview of challenges around elective surgery scheduling and propose a predictive model for elective surgery scheduling to be evaluated in a major tertiary hospital in Queensland. The proposed model employs waiting lists, peri-operative information, workload predictions, and improved procedure time estimation models, to optimise surgery scheduling. It is expected that the resulting improvement in scheduling processes will lead to more efficient use of surgical suites, higher productivity, and lower labour costs, and ultimately improve patient outcomes.
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    Conference Title
    CEUR Workshop Proceedings
    Volume
    941
    Publisher URI
    http://ceur-ws.org/Vol-941/
    Copyright Statement
    © The Editors 2012. The attached file is reproduced here in accordance with the copyright policy of the publisher. For information about this conference please refer to the conference’s website or contact the authors.
    Subject
    Artificial intelligence not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/52402
    Collection
    • Conference outputs

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