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volume; 3) number of ED admissions; 4) number of ED intensive care admissions; 5)number of ED resuscitation room cases; 6) day, evening or night shift. The followingindependent variables were measured over a 24-hour period and were assigned to eachof its component three 8-hour periods: 1) number of elective surgical admissions; 2)hospital medical-surgical occupancy (hospital occupancy) defined as the sum of thenumber of patients in a hospital bed at midnight and the number of patientsdischarged in the preceding 24 hours divided by the total number of staffed beds; and3) day of the week.
Results: Six factors had statistically significant associations with mean LOS in thetime series analysis: 1) RN workload; 2) hospital occupancy; 3) number of EDadmissions; 4) night shift; 5) day of the week; and 6) ED volume. The mean LOSincreased by: 6.65 (95% CI: 2.64, 10.67) minutes for every additional patientdischarged per nurse on duty per 8-hour period; 1.34 (95%CI 0.87, 1.82) minutesfor every percent increase in hospital occupancy; 3.85 (95%CI 3.31, 4.38) minutesfor every additional admission; 23.32 (95%CI 18.17, 28.47) minutes on the nightshift compared with days and evenings. Sunday was considered the baseline for theday of the week analysis. Except for Monday, all other weekdays were associated witha statistically significant increase in mean LOS. Mean LOS decreased 0.49 (95%CI0.82, 0.15) minutes for every additional visit. The number of elective surgicaladmissions was collinear with hospital occupancy and not significantly associated withmean LOS when adjusting for hospital occupancy. The final model parameters forthe ARIMA analysis were AR(1) MA(1) which adequately adjust for the serialcorrelation in the data.
Conclusions: RN workload is strongly associated with mean LOS.Autocorrelation exists between the mean LOS for consecutive 8-hour periods. Thesefindings suggest that administrative redesign in RN workload may have a directimpact on decreasing the mean LOS. Conversely, changes in hospital occupancy andthe number of ED admissions require broad hospital-wide redesign efforts.
6 Racial Disparities In ED Wait Time Prior To PhysicianEvaluation and Boarding Times in the United States
Pines JM, Schwartz JS, Hollander JE/University of Pennsylvania, Philadelphia, PA
Study Objectives: While ED crowding is prevalent and associated with poorerquality of care, little is known about the disparate effects of ED wait time and race.This study examined the impact of race on ED waiting room times and ED boardingtimes.
Methods: We performed a retrospective analysis of the 2003 National HospitalAmbulatory Medical Care Survey (NHAMCS), a probability sample of US ED visits.Primary outcomes included 1) waiting for �30 minutes to see a physician and 2) anED boarding time of � 4 hours (time from evaluation until transfer to an inpatientbed for admitted patients). We used logistic regression to determine the impact ofrace on the primary outcomes after adjusting for potential confounders: urgency ofvisit, urban location (metropolitan statistical area [MSA]), and hospital organization(for-profit v non-profit or government). Reported confidence intervals were calculatedafter clustering by the 408 separate hospitals reporting data in NHAMCS.
Results: In 2003, there were 40,253 ED visits representing 113.2 million EDvisits. Mean age was 36 �/� 24, 75% white, 21% black, and 53% female. Medianwaiting room time was 27 minutes (inter-quartile range [IQR] 12-58) and 22,999(57%) had to wait � 30 minutes to see a physician. A total of 5,454 (13%) wereadmitted to the hospital. For those, mean age was 56 �/� 24, 79% white, 17%black, and 54% female. Median boarding time was 160 minutes (IQR 75-290) and3,202 (58%) of admitted patients boarded for � 4 hours in the ED. In univariableanalysis, prolonged waiting time to be seen by a physician (�30 minutes) wasincreased in patients with black race (OR 1.36 [95% CI 1.30-1.44]), female sex (OR1.05 [95% CI 1.01-1.09]); MSA (OR 2.00 [95% CI 1.91-2.11]), and for-profitstatus (OR 0.65 [95% CI 0.60-0.68]). After adjusting for MSA, triage severity, andgender, black race (OR 1.19 [95% CI 1.03-1.36]) remained a significant predictor ofprolonged waiting room time. In univariable analysis of admitted patients, prolongedboarding times (� 4 hours) were associated with black race (OR 1.11 [95% CI 1.05-1.16]), MSA (OR 1.43 [95% CI 1.36-1.52]), and for-profit status (OR 0.49 [95%CI 0.46-0.53]). After adjusting for MSA and for-profit status, black race wasassociated with prolonged ED boarding times (OR 1.31 [95% CI 1.01-1.70]).
Conclusion: Black race is independently associated with prolonged ED waitingtimes to see a physician and prolonged times to be transferred to inpatient beds. Sinceprolonged ED waiting times and ED boarding have been associated with adverseoutcomes, this national pattern might partially explain the worse medical outcomesseen in black patients. Efforts to correct ED crowding might improve the care ofminority patients in the US.
7 The Impact Of Emergency Department Crowding On CardiacOutcomes In ED Patients With Potential Acute CoronarySyndromes
Pines JM, Hollander JE/University of Pennsylvania, Philadelphia, PA
Study Objectives: ED crowding been associated with poorer performance on timeto antibiotics in pneumonia and poorer time to thrombolytics in acute myocardialinfarction (AMI). However, the effects of ED crowding on objective post-EDdownstream outcomes such as complications, re-hospitalization or survival have notbeen studied. We sought to determine the impact of ED crowding on cardiovascularcomplications, 30-day re-hospitalization and 30-day mortality for ED patientspresenting with potential acute coronary syndromes (ACS).
Methods: We performed a prospective cohort study of patients presenting to anurban, tertiary care ED with potential ACS. The main outcome was inpatientcardiovascular complications (new congestive heart failure, AMI, ventriculartachycardia/ventricular fibrillation, supraventricular tachycardia, hypotension,bradycardia, stroke, or CPR), 30-day re-hospitalization and 30-day mortality. Severitywas adjusted using the TIMI Risk Score. Several validated ED crowding measureswere assigned to each patient at the time of triage. Logistic regression analysis wasused to determine the impact of ED crowding on 30 day adverse events.
Results: A total of 6,869 patients were included. Mean age was 52 �/� 15, 69%black, 27% white, 57% female. A total of 301 patients (4%) had cardiovascularcomplications after hospital arrival and within 30 days, and 72 (1%) died within 30-days of arrival. Data on re-hospitalization was collected over the most recent subsetand was complete on 3,806 patients: 389 (10%) of whom were re-hospitalized within30-days. With respect to crowding measures, median patient-care hours were 98(Inter-quartile range [IQR] 63-142), ED occupancy rate was 60% (IQR 45-73%),waiting room number was 8 (IQR 4-12), and the number of admitted patientsboarding in the ED was 9 (IQR 5-12). Median TIMI Score was 1 (IQR 0-2). Inadjusted analysis, the following crowding measures were all associated with increasedrates of cardiovascular complications: ED occupancy rate (OR 1.16 for each 10%increase in occupancy [95% CI 1.09-1.24]), waiting room number (OR 1.06 [95%CI 1.04-1.08]), and total patient-care hours (OR 1.03 for each 10 patient-care hours[95% CI 1.02-1.03]). There were trends toward higher levels of 30-day re-hospitalization in adjusted analysis in patients that presenting during periods with ahigher occupancy rate (OR 1.03 per 10% increase [95 CI 0.98-1.09]) and moreadmitted patients boarding (OR 1.02 [95% CI 1.00-1.04]). We could notdemonstrate a relationship between ED crowding and 30 day mortality, although wewere underpowered to do so.
Conclusion: ED crowding was associated with an increased incidence ofcardiovascular complications in ED patients presenting with potential acute coronarysyndromes. If we want to improve the care of patients with potential ACS and reducecardiovascular complications, ED crowding should be addressed.
8 The Effects of Emergency Department (ED) AmbulanceDiversion on Pediatric Mortality in a Large Metropolitan Area
Shenoi R, Ma L, Shah M, Jones J, Seo M, Begley C/Texas Children’s Hospital,Houston, TX; University of Texas Health Science Center, Houston, TX
Study Objectives: To determine the association between ED ambulance diversionand pediatric mortality.
Methods: The number of hours per day when each of four Houston pediatrichospitals was on diversion was obtained for the period August 2002 - December2004. Texas Health Care Information Council (THCIC) hospital inpatient dischargedata from the same hospitals and time period was used to examine the morbidity andmortality of children through age 18. Demographic data, date and source ofadmission, length of stay, discharge and transfer status, primary and secondarydiagnoses, diagnosis-related group [DRG] codes, payment source and procedurecodes were collected. Severity of illness was based on a 4 level all patient-refined DRG(APR-DRG) scale: minor, moderate, major or extreme. Only inter-hospital transferswere counted and were assumed to occur on the day of diagnosis of illness or injury.Significant ambulance diversion days were defined as days when two or morehospitals were on diversion for more than 8 hours. Mortality (those who died beforedischarge) was measured according to day of admission. Data was combined into asingle hospital-specific database.
Frequency of deaths and patients with varying levels of severity admitted onsignificant and nonsignificant diversion days were compared. Pearson chi-square andodds ratios with 95% confidence intervals were derived for those who died or weredischarged alive. Comparisons were made for all transfers and nontransfers, length of
Research Forum Abstracts
Volume , . : September Annals of Emergency Medicine S3