Validation of an algorithm for categorizing the severity of hospital emergency department visits.
BACKGROUND
Differentiating between appropriate and inappropriate resource use represents a critical challenge in health services research. The New York University Emergency Department (NYU ED) visit severity algorithm attempts to classify visits to the ED based on diagnosis, but it has not been formally validated.


OBJECTIVE
To assess the validity of the NYU algorithm.


METHODS
A longitudinal study in a single integrated delivery system from January 1999 to December 2001.


METHODS
A total of 2,257,445 commercial and 261,091 Medicare members of an integrated delivery system.


METHODS
ED visits were classified as emergent, nonemergent, or intermediate severity, using the NYU ED algorithm. We examined the relationship between visit-severity and the probability of future hospitalizations and death using a logistic model with a general estimating equation approach.


RESULTS
Among commercially insured subjects, ED visits categorized as emergent were significantly more likely to result in a hospitalization within 1-day (odds ratio = 3.37, 95% CI: 3.31-3.44) or death within 30-days (odds ratio = 2.81, 95% CI: 2.62-3.00) than visits categorized as nonemergent. We found similar results in Medicare patients and in sensitivity analyses using different probability thresholds. ED overuse for nonemergent conditions was not related to socio-economic status or insurance type.


CONCLUSIONS
The evidence presented supports the validity of the NYU ED visit severity algorithm for differentiating ED visits based on need for hospitalization and/or mortality risk; therefore, it can contribute to evidence-based policies aimed at reducing the use of the ED for nonemergencies.
Ballard
Dustin W
DW
Emergency Department, Kaiser Permanente San Rafael, San Rafael, CA 94903, USA. Dustin.ballard@kp.org
Price
Mary
M
Fung
Vicki
V
Brand
Richard
R
Reed
Mary E
ME
Fireman
Bruce
B
Newhouse
Joseph P
JP
Selby
Joseph V
JV
Hsu
John
J
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D015897:Comorbidity; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D006305:Health Status Indicators; D017052:Hospital Mortality; D006785:Hospitals, University / Q000706:statistics & numerical data; D006801:Humans; D019458:Insurance Coverage / Q000706:statistics & numerical data*; D007348:Insurance, Health / Q000706:statistics & numerical data*; D008137:Longitudinal Studies; D008297:Male; D017057:Medically Uninsured / Q000706:statistics & numerical data*; D006278:Medicare / Q000706:statistics & numerical data; D008875:Middle Aged; D009519:New York City; D017063:Outcome Assessment, Health Care; D015203:Reproducibility of Results; D012959:Socioeconomic Factors; D014481:United States; D055815:Young Adult