title: Predicting admission of patients by their presentation to the emergency department.
abstract: OBJECTIVE
  The present study aims to determine the importance of certain factors in predicting the need of hospital admission for a patient in the ED.
  
  
  METHODS
  This is a retrospective observational cohort study between January 2010 and March 2012. The characteristics, including blood test results, of 100,123 patients who presented to the ED of a tertiary referral urban hospital, were incorporated into models using logistic regression in an attempt to predict the likelihood of patients' disposition on leaving the ED. These models were compared with triage nurses' prediction of patient disposition.
  
  
  RESULTS
  Patient age, their initial presenting symptoms or diagnosis, Australasian Triage Scale category, mode of arrival, existence of any outside referral, triage time of day and day of the week were significant predictors of the patient's disposition (P < 0.001). The ordering of blood tests for any patient and the extent of abnormality of those tests increased the likelihood of admission. The accuracy of triage nurses' admission prediction was similar to that offered by a model that used the patients' presentation characteristics. The addition of blood tests to that model resulted in only 3% greater accuracy in prediction of patient disposition.
  
  
  CONCLUSIONS
  Certain characteristics of patients as they present to hospital predict their admission. The accuracy of the triage nurses' prediction for disposition of patients is the same as that afforded by a model constructed from these characteristics. Blood test results improve disposition accuracy only slightly so admission decisions should not always wait for these results.
authors.affiliation: Flinders Centre for Epidemiology and Biostatistics, School of Medicine, Flinders University, Adelaide, South Australia, Australia.
authors.forename: Susan W
authors.initials: SW
authors.lastname: Kim
authors.forename: Jordan Y
authors.initials: JY
authors.lastname: Li
authors.forename: Paul
authors.initials: P
authors.lastname: Hakendorf
authors.forename: David Jo
authors.initials: DJ
authors.lastname: Teubner
authors.forename: David I
authors.initials: DI
authors.lastname: Ben-Tovim
authors.forename: Campbell H
authors.initials: CH
authors.lastname: Thompson
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001315:Australia; D002983:Clinical Competence / Q000592:standards; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D006403:Hematologic Tests / Q000706:statistics & numerical data; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009730:Nursing Assessment / Q000379:methods / Q000706:statistics & numerical data; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D013997:Time Factors; D014218:Triage
keywords: hospital emergency service; length of stay; patient admission; routine diagnostic test; triage