title: Can medical admission and length of stay be accurately predicted by emergency staff, patients or relatives?
abstract: OBJECTIVE
  To determine the accuracy of predictions of the need for hospital admission and, if admitted, length of stay (LOS) made early in an emergency attendance by emergency department (ED) doctors, nurses, patients and relatives, and the characteristics of ED presentations predictive of admission and short stays (</= 3 days).
  
  
  METHODS
  Prospective collection of predictions by medical and nursing staff, patients and relatives of ED departure status and LOS (1 day, 2-3 days, 4-7 days or longer) of a convenience sample of adults presenting with medical symptoms. Predictions were made before full medical assessment and matched against actual departure status and LOS. Vital signs and demographics were recorded.
  
  
  RESULTS
  Seventy five percent (2159/2904; CI 73%-77%) of all admission predictions in 704 patients were correct with 85% (575/673; CI 81%-88%) of doctors' predictions correct. Thirty-five percent (361/1024) of all LOS predictions for 331 patients were correct with 46% (122/268; CI 40%-52%) of doctors' predictions correct. Risk factors for short-stay over longer admission included age less than 65, normal oxygen saturations and self-referral.
  
  
  CONCLUSIONS
  Emergency admissions can be predicted with reasonable accuracy but LOS is difficult to predict. Development of a prediction tool may facilitate streaming and appropriate use of short-stay units.
authors.affiliation: Emergency Practice Innovation Centre, Department of Emergency Medicine, St. Vincent's Hospital, Melbourne, Melbourne, VIC.
authors.forename: Andrew W
authors.initials: AW
authors.lastname: Dent
authors.forename: Tracey J
authors.initials: TJ
authors.lastname: Weiland
authors.forename: Lisa
authors.initials: L
authors.lastname: Vallender
authors.forename: Nicola E
authors.initials: NE
authors.lastname: Oettel
mesh_terms: D000368:Aged; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005190:Family; D005260:Female; D005544:Forecasting; D006801:Humans; D007902:Length of Stay / Q000639:trends*; D008297:Male; D008505:Medical Staff, Hospital; D008875:Middle Aged; D020380:Needs Assessment / Q000592:standards* / Q000706:statistics & numerical data; D010343:Patient Admission / Q000639:trends*; D010361:Patients; D011379:Prognosis; D011446:Prospective Studies; D014739:Victoria