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  • Predicting emergency department admissions

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    Author(s)
    Boyle, Justin
    Jessup, Melanie
    Crilly, Julia
    Green, David
    Lind, James
    Wallis, Marianne
    Miller, Peter
    Fitzgerald, Gerard
    Griffith University Author(s)
    Wallis, Marianne
    Crilly, Julia
    Jessup, Melanie
    Green, David W.
    Year published
    2012
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    Abstract
    Objective To develop and validate models to predict emergency department (ED) presentations and hospital admissions for time and day of the year. Methods Initial model development and validation was based on 5 years of historical data from two dissimilar hospitals, followed by subsequent validation on 27 hospitals representing 95% of the ED presentations across the state. Forecast accuracy was assessed using the mean average percentage error (MAPE) between forecasts and observed data. The study also determined a daily sample size threshold for forecasting subgroups within the data. Results Presentations to the ...
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    Objective To develop and validate models to predict emergency department (ED) presentations and hospital admissions for time and day of the year. Methods Initial model development and validation was based on 5 years of historical data from two dissimilar hospitals, followed by subsequent validation on 27 hospitals representing 95% of the ED presentations across the state. Forecast accuracy was assessed using the mean average percentage error (MAPE) between forecasts and observed data. The study also determined a daily sample size threshold for forecasting subgroups within the data. Results Presentations to the ED and subsequent admissions to hospital beds are not random and can be predicted. Forecast accuracy worsened as the forecast time intervals became smaller: when forecasting monthly admissions, the best MAPE was approximately 2%, for daily admissions, 11%; for 4-hourly admissions, 38%; and for hourly admissions, 50%. Presentations were more easily forecast than admissions (daily MAPE w7%). When validating accuracy at additional hospitals, forecasts for urban facilities were generally more accurate than regional forecasts (accuracy is related to sample size). Subgroups within the data with more than 10 admissions or presentations per day had forecast errors statistically similar to the entire dataset. The study also included a software implementation of the models, resulting in a data dashboard for bed managers. Conclusions Valid ED prediction tools can be generated from access to de-identified historic data, which may be used to assist elective surgery scheduling and bed management. The paper provides forecasting performance levels to guide similar studies.
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    Journal Title
    Emergency Medicine Journal
    Volume
    29
    Issue
    5
    DOI
    https://doi.org/10.1136/emj.2010.103531
    Copyright Statement
    © The Author(s) 2011. 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 not elsewhere classified
    Clinical Sciences
    Nursing
    Public Health and Health Services
    Publication URI
    http://hdl.handle.net/10072/44701
    Collection
    • Journal articles

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