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dc.contributor.authorBoyle, Justin
dc.contributor.authorJessup, Melanie
dc.contributor.authorCrilly, Julia
dc.contributor.authorGreen, David
dc.contributor.authorLind, James
dc.contributor.authorWallis, Marianne
dc.contributor.authorMiller, Peter
dc.contributor.authorFitzgerald, Gerard
dc.date.accessioned2017-05-03T12:00:15Z
dc.date.available2017-05-03T12:00:15Z
dc.date.issued2012
dc.date.modified2014-01-15T21:53:15Z
dc.identifier.issn1472-0205
dc.identifier.doi10.1136/emj.2010.103531
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.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent1430134 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherBMJ Publishing Group
dc.publisher.placeUnited Kingdom
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom358
dc.relation.ispartofpageto365
dc.relation.ispartofissue5
dc.relation.ispartofjournalEmergency Medicine Journal
dc.relation.ispartofvolume29
dc.rights.retentionY
dc.subject.fieldofresearchClinical Sciences not elsewhere classified
dc.subject.fieldofresearchClinical Sciences
dc.subject.fieldofresearchNursing
dc.subject.fieldofresearchPublic Health and Health Services
dc.subject.fieldofresearchcode110399
dc.subject.fieldofresearchcode1103
dc.subject.fieldofresearchcode1110
dc.subject.fieldofresearchcode1117
dc.titlePredicting emergency department admissions
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.rights.copyright© 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.
gro.date.issued2012
gro.hasfulltextFull Text
gro.griffith.authorWallis, Marianne
gro.griffith.authorCrilly, Julia
gro.griffith.authorJessup, Melanie
gro.griffith.authorGreen, David W.


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