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  • Using prediction to improve elective surgery scheduling

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    93856_1.pdf (455.6Kb)
    Author(s)
    Kargar, ZS
    Khanna, S
    Sattar, A
    Griffith University Author(s)
    Sattar, Abdul
    Year published
    2013
    Metadata
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    Abstract
    Background An ageing population and higher rates of chronic disease increase the demand on health services. The Australian Institute of Health and Welfare reports a 3.6% per year increase in total elective surgery admissions over the past four years.1 The newly introduced National Elective Surgery Target (NEST) stresses the need for efficiency and necessitates the development of improved planning and scheduling systems in hospitals. Aims To provide an overview of the challenges of elective surgery scheduling and develop a prediction based methodology to drive optimal management of scheduling processes. Method Our proposed ...
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    Background An ageing population and higher rates of chronic disease increase the demand on health services. The Australian Institute of Health and Welfare reports a 3.6% per year increase in total elective surgery admissions over the past four years.1 The newly introduced National Elective Surgery Target (NEST) stresses the need for efficiency and necessitates the development of improved planning and scheduling systems in hospitals. Aims To provide an overview of the challenges of elective surgery scheduling and develop a prediction based methodology to drive optimal management of scheduling processes. Method Our proposed two stage methodology initially employs historic utilisation data and current waiting list information to manage case mix distribution. A novel algorithm uses current and past perioperative information to accurately predict surgery duration. A NEST-compliance guided optimisation algorithm is then used to drive allocation of patients to the theatre schedule. Results 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. Conclusion Accurate prediction of workload and surgery duration, retrospective and current waitlist as well as perioperative information, and NEST-compliance driven allocation of patients are employed by our proposed methodology in order to deliver further improvement to hospital operating facilities.
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    Journal Title
    Australasian Medical Journal
    Volume
    6
    Issue
    5
    DOI
    https://doi.org/10.4066/AMJ.2013.1652
    Copyright Statement
    © The Author(s) 2013. The attached file is reproduced here in accordance with the copyright policy of the publisher. For information about this journal please refer to the journal’s website or contact the authors.
    Subject
    Clinical sciences
    Clinical sciences not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/60430
    Collection
    • Journal articles

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