Patient characteristics associated with longer emergency department stay: a rapid review.
BACKGROUND
Prolonged emergency department (ED) stays make a disproportionate contribution to ED overcrowding, but the factors associated with longer stays have not been systematically reviewed.


OBJECTIVE
To identify the patient characteristics associated with ED length of stay (LOS) and ascertain whether a predictive model existed.


METHODS
This rapid systematic review included published, English-language studies that assessed at least one patient-level predictor of ED LOS (defined as a continuous or dichotomous variable) in an adult or mixed adult/paediatric population within an Organization for Economic Cooperation and Development country. Findings were synthesised narratively.


RESULTS
We identified 35 relevant studies; most included multiple predictors, but none developed a predictive model. The factors most commonly associated with long ED LOS were need for admission (10 of 10 studies) and older age (which may be a proxy for age-related differences in health condition and severity; 9 of 10), receipt of diagnostic tests or consults (8 of 8) and ambulance arrival (4 of 5). Acuity often showed a bell-shaped relationship with LOS (ie, patients with moderate acuity stayed longest).


CONCLUSIONS
Methodological choices made in the interests of rapidity limited the review's comprehensiveness and depth.


CONCLUSIONS
Despite a sizeable body of literature, the available information is insufficiently precise to inform clinical or service-planning decisions; there is a need for a predictive model, including specific patient complaints. Deeper understanding of the determinants of ED LOS could help to identify patients and/or populations who require special intervention or resources to prevent a protracted stay.
Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada Evaluation Platform, George & Fay Yee Centre for Healthcare Innovation, Winnipeg, Manitoba, Canada.
Sara A
SA
Kreindler
Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada Evaluation Platform, George & Fay Yee Centre for Healthcare Innovation, Winnipeg, Manitoba, Canada Winnipeg Regional Health Authority, Winnipeg, Manitoba, Canada.
Yang
Y
Cui
Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada Evaluation Platform, George & Fay Yee Centre for Healthcare Innovation, Winnipeg, Manitoba, Canada Winnipeg Regional Health Authority, Winnipeg, Manitoba, Canada.
Colleen J
CJ
Metge
Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada Winnipeg Regional Health Authority, Winnipeg, Manitoba, Canada.
Melissa
M
Raynard
D000367:Age Factors; D003441:Crowding; D003955:Diagnostic Tests, Routine / Q000706:statistics & numerical data; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D006304:Health Status; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data*; D062072:Patient Acuity; D012307:Risk Factors; D012720:Severity of Illness Index; D013997:Time Factors
crowding; emergency care systems, emergency departments