OBJECTIVES: No single universal definition of emergency department (ED) overcrowding exists. The authors hypothesize that a previously developed site-sampling form for academic ED overcrowding is a valid model to quantify overcrowding in academic institutions and can be used to develop a validated short form that correlates with overcrowding. METHODS: A 23-question site-sampling form was designed based on input from academic physicians at eight medical schools representative of academic EDs nationwide. A total of 336 site-samplings at eight academic medical centers were conducted at 42 computer-generated random times over a three-week period by independent observers at each site. These sampling times ranged from very slow to severely overcrowded. The outcome variable was the degree of overcrowding as assessed by the charge nurse and ED physicians. The full model consisted of objective data that were obtained by counting the number of patients, determining patients' waiting times, and obtaining information from registration, triage, and ancillary services. Specific objective data were indexed to site-specific demographics. The outcome and objective data were compared using a multiple linear regression to determine predictive validity of the full model. A five-question reduced model was calculated using a backward stepdown procedure. Predictive validity and relationships between the outcome and objective data were assessed using a mixed-effects linear regression model, treating center as random effect. RESULTS: Overcrowding occurred 12% to 73% of the time (mean, 35%), with two hospitals being overcrowded more than 50% of the time. Comparison of objective and outcome data resulted in an R(2) of 0.49 (p < 0.001), indicating a good degree of predictive validity. A reduced five-question model predicted the full model with 88% accuracy. CONCLUSIONS: Overcrowding varied widely between academic centers during the study period. Results of a five-question reduced model are valid and accurate in predicting the degree of overcrowding in academic centers.
OBJECTIVES: No single universal definition of emergency department (ED) overcrowding exists. The authors hypothesize that a previously developed site-sampling form for academic ED overcrowding is a valid model to quantify overcrowding in academic institutions and can be used to develop a validated short form that correlates with overcrowding. METHODS: A 23-question site-sampling form was designed based on input from academic physicians at eight medical schools representative of academic EDs nationwide. A total of 336 site-samplings at eight academic medical centers were conducted at 42 computer-generated random times over a three-week period by independent observers at each site. These sampling times ranged from very slow to severely overcrowded. The outcome variable was the degree of overcrowding as assessed by the charge nurse and ED physicians. The full model consisted of objective data that were obtained by counting the number of patients, determining patients' waiting times, and obtaining information from registration, triage, and ancillary services. Specific objective data were indexed to site-specific demographics. The outcome and objective data were compared using a multiple linear regression to determine predictive validity of the full model. A five-question reduced model was calculated using a backward stepdown procedure. Predictive validity and relationships between the outcome and objective data were assessed using a mixed-effects linear regression model, treating center as random effect. RESULTS: Overcrowding occurred 12% to 73% of the time (mean, 35%), with two hospitals being overcrowded more than 50% of the time. Comparison of objective and outcome data resulted in an R(2) of 0.49 (p < 0.001), indicating a good degree of predictive validity. A reduced five-question model predicted the full model with 88% accuracy. CONCLUSIONS: Overcrowding varied widely between academic centers during the study period. Results of a five-question reduced model are valid and accurate in predicting the degree of overcrowding in academic centers.
Authors: Saul J Weiner; Jonathan B Vangeest; Richard I Abrams; Arthur Moswin; Richard Warnecke Journal: J Urban Health Date: 2006-03 Impact factor: 3.671
Authors: Nathan R Hoot; Larry J Leblanc; Ian Jones; Scott R Levin; Chuan Zhou; Cynthia S Gadd; Dominik Aronsky Journal: J Am Med Inform Assoc Date: 2009-03-04 Impact factor: 4.497
Authors: Nathan R Hoot; Larry J LeBlanc; Ian Jones; Scott R Levin; Chuan Zhou; Cynthia S Gadd; Dominik Aronsky Journal: Ann Emerg Med Date: 2008-04-03 Impact factor: 5.721
Authors: Charleen Hsuan; Renee Y Hsia; Jill R Horwitz; Ninez A Ponce; Thomas Rice; Jack Needleman Journal: Health Serv Res Date: 2019-04-02 Impact factor: 3.402
Authors: Nathan R Hoot; Stephen K Epstein; Todd L Allen; Spencer S Jones; Kevin M Baumlin; Neal Chawla; Anna T Lee; Jesse M Pines; Amandeep K Klair; Bradley D Gordon; Thomas J Flottemesch; Larry J LeBlanc; Ian Jones; Scott R Levin; Chuan Zhou; Cynthia S Gadd; Dominik Aronsky Journal: Ann Emerg Med Date: 2009-08-29 Impact factor: 5.721