title: Assessing the impact of streaming in a regional emergency department.
abstract: OBJECTIVE
  To evaluate the impact of a streaming model, previously validated in metropolitan EDs, on selected performance indicators in a regional ED.
  
  
  METHODS
  Multiple linear regression models were applied to monthly time series data from 43 months prior to the intervention and 15 months following the intervention to measure the impact of the streaming model on the following performance indicators: (i) percentage of emergency patients admitted to an inpatient bed within 8 h; (ii) percentage of non-admitted emergency patients with a length of stay of less than 4 h; and (iii) percentage of emergency patients who left without being seen by a doctor or nurse practitioner.
  
  
  METHODS
  Bendigo Health ED in regional Victoria.
  
  
  RESULTS
  Prior to the introduction of streaming, there was a downward trend in both the percentage of emergency patients admitted to an inpatient bed within 8 h, and the percentage of non-admitted emergency patients with a length of stay of less than 4 h. After the introduction of streaming, these trends were reversed (P = 0.008 and P = 0.004, respectively). There was no statistically significant change in the trend associated with the percentage of emergency patients who left without being seen (P = 0.904).
  
  
  CONCLUSIONS
  The implementation of the streaming model had an impact on the two performance indicators associated with length of stay in this regional ED, but did not have a significant impact (positive or negative) on the percentage of patients who did not wait to be seen. These results might interest other EDs in regional hospitals.
authors.affiliation: Monash University School of Rural Health, Bendigo, Victoria, Australia. leigh.kinsman@med.monash.edu.au
authors.forename: Leigh
authors.initials: L
authors.lastname: Kinsman
authors.forename: Robert
authors.initials: R
authors.lastname: Champion
authors.forename: Geraldine
authors.initials: G
authors.lastname: Lee
authors.forename: Mary
authors.initials: M
authors.lastname: Martin
authors.forename: Kevin
authors.initials: K
authors.lastname: Masman
authors.forename: Elizabeth
authors.initials: E
authors.lastname: May
authors.forename: Terry
authors.initials: T
authors.lastname: Mills
authors.forename: Michael D
authors.initials: MD
authors.lastname: Taylor
authors.forename: Paulett
authors.initials: P
authors.lastname: Thomas
authors.forename: Ruth J
authors.initials: RJ
authors.lastname: Williams
authors.forename: Salomon
authors.initials: S
authors.lastname: Zalstein
mesh_terms: D004636:Emergency Service, Hospital / Q000592:standards* / Q000706:statistics & numerical data; D006801:Humans; D007902:Length of Stay; D016014:Linear Models; D015233:Models, Statistical; D008962:Models, Theoretical; D010343:Patient Admission / Q000706:statistics & numerical data; D011787:Quality of Health Care / Q000706:statistics & numerical data; D013997:Time Factors; D013996:Time and Motion Studies; D014739:Victoria