Predictive variables of an emergency department quality and performance indicator: a 1-year prospective, observational, cohort study evaluating hospital and emergency census variables and emergency department time interval measurements. Issue 8 (20th August 2012)

• Record Type: Journal Article
• Authors: Casalino, Enrique; Choquet, Christophe; Bernard, Julien; Debit, Abigael; Doumenc, Benoit; Berthoumieu, Audrey; Wargon, Mathias
• Is Part Of: Emergency medicine journal. Volume 30:Issue 8(2013)
• Page Start: 638
• Page End: 645
• Publication Date: 2012-08-20
• Subjects: Acute coronary syndrome -- bacterial -- cardiac care -- efficiency -- emergency care systems -- emergency department management -- HIV -- infectious diseases -- neurology -- stroke; Emergency medicine -- Periodicals; 616.02505
• DOI: 10.1136/emermed-2012-201404
• Languages: English
• ISSNs: 1472-0205

Abstract

Abstract : Objective: Emergency department (ED) crowding impacts negatively on quality of care. The aim was to determine the association between ED quality and input, throughput and output-associated variables. Methods: This 1-year, prospective, observational, cohort study determined the daily percentage of patients leaving the ED in <4 h (ED quality and performance indicator; EDQPI). According to the median EDQPI two groups were defined: best-days and bad-days. Hospital and ED variables and time interval metrics were evaluated as predictors. Results: Data were obtained for 67 307 patients over 364 days. Differences were observed between the two groups in unadjusted analysis: number of daily visits, number of patients as a function of final disposition, number boarding in the ED, and time interval metrics including wait time to triage nurse and ED provider, time from ED admission to decision, time from decision to departure and length of stay (LOS) as a function of final disposition. Five variables remained significant predictors for bad-days in multivariate analysis: wait time to triage nurse (OR 2.36; 95% CI 1.36 to 4.11; p=0.002), wait time to ED provider (OR 1.93; 95% CI 1.05 to 3.54; p=0.03), number of patients admitted to hospital (OR 1.86; 95% CI 1.09 to 3.19; p=0.02), LOS of non-admitted patients (OR 9.5; 95% CI 5.17 to 17.48; p<0.000001) and LOS of patients admitted to hospital (OR 2.46; 95% CI 1.44 to 4.2; p=0.0009). Conclusions: Throughput is the major determinant of EDQPI, notably time interval reflecting the work dynamics of medical and nursing teams and the efficacy of fast-track routes for low-complexity patients. Output also significantly impacted on EDQPI, particularly the capacity to reduce the LOS of admitted patients.