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dc.contributor.authorBoyle, Justinen_US
dc.contributor.authorCrilly, Juliaen_US
dc.contributor.authorFitzgerald, Gerarden_US
dc.contributor.authorGreen, Daviden_US
dc.contributor.authorJessup, Melanieen_US
dc.contributor.authorLind, Jamesen_US
dc.contributor.authorMiller, Peteren_US
dc.contributor.authorWallis, Marianneen_US
dc.date.accessioned2017-04-04T15:35:09Z
dc.date.available2017-04-04T15:35:09Z
dc.date.issued2012en_US
dc.date.modified2014-01-15T21:53:15Z
dc.identifier.issn1472-0205en_US
dc.identifier.doi10.1136/emj.2010.103531en_US
dc.identifier.urihttp://hdl.handle.net/10072/44701
dc.description.abstractObjective 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.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.format.extent1430134 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherBMJ Publishing Groupen_US
dc.publisher.placeUnited Kingdomen_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom358en_US
dc.relation.ispartofpageto365en_US
dc.relation.ispartofissue5en_US
dc.relation.ispartofjournalEmergency Medicine Journalen_US
dc.relation.ispartofvolume29en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchClinical Sciences not elsewhere classifieden_US
dc.subject.fieldofresearchcode110399en_US
dc.titlePredicting emergency department admissionsen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.rights.copyrightCopyright remains with the authors 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.en_US
gro.date.issued2012
gro.hasfulltextFull Text


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