The use of scripting at triage and its impact on elopements.
OBJECTIVE
The objective of this study was to measure the effect of scripting language at triage on the likelihood of elopements, controlling for patient volume and other potential confounding variables.


METHODS
This was a pre- and postintervention cohort study using the same 5-month period (November 2007-March 2008 and November 2008-March 2009, respectively) that included in the analysis all patients 21 years of age and older, who presented to the triage window in the emergency department (ED) waiting room (not by ambulance). As part of the scripting, triage nurses informed patients of the longest waiting time (at that point in time) for any patients still waiting to be brought back from the waiting room into the ED. Rates of elopement were compared between patients who did and did not receive the scripting, controlling for individual and daily ED variables.


RESULTS
A total of 24,390 ED visits were included in this analysis. The elopement rate was 4.4% among ED patients in the prescripting period, compared to 2.3% in the postscripting period. In a multivariate logistic regression model, the use of scripting was significantly associated with decreased odds of elopement, compared to the nonscripting group (odds ratio [OR] = 0.61, 95% confidence interval [CI] = 0.46 to 0.80).


CONCLUSIONS
The use of triage scripting was found to significantly reduce elopement rates in patients placed in the ED waiting room, even after controlling for other confounding variables. Scripting is a simple and underutilized technique that can have a positive effect for patients and the ED.
Handel
Daniel A
DA
Department of Emergency Medicine, Oregon Health and Science University, Portland, OR, USA. handeld@ohsu.edu
Fu
Rongwei
R
Daya
Mohamud
M
York
Jodie
J
Larson
Eric
E
John McConnell
K
K
D000328:Adult; D015331:Cohort Studies; D004636:Emergency Service, Hospital / Q000458:organization & administration*; D005260:Female; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010353:Patient Education as Topic; D017060:Patient Satisfaction; D013997:Time Factors; D014218:Triage / Q000379:methods*; D014850:Waiting Lists