The association between hospital overcrowding and mortality among patients admitted via Western Australian emergency departments

Tags: overcrowding, emergency hospital, hospital admissions, increased mortality, occupancy, Western Australia, Australasian College for Emergency Medicine, emergency admission, ACEM, length of stay, hospital, study period, emergency department, Overcrowding Hazard Scale, emergency admissions, Hazard Scale, Julie-Ann Da Silva, Ann Emerg Med, Peter C Sprivulis, mortality, hospital admission, Western Australian, bed occupancy, Australia, ED Design, Ian G Jacobs, Department of Emergency Medicine, risk ratio, uncrowded conditions, overcrowded conditions, hospital readmission, Emergency Department Information Systems, George A Jelinek, Emergency Medicine, patient, Skogland P, Med J Aust, Acad Emerg Med, Eur J Emerg Med, Sprivulis P. Access, J Emerg Med, Emerg Med, hazard ratio, tertiary hospital, Zwemer F. Emergency department, Perth, Western Australia, inpatient, Emergency Care, Associate Professor Peter C Sprivulis, Health Information Centre, Western Australian Department of Health, Coopers & Lybrand Consultants, Meltzer D. Continuity, Western Australian emergency
The association between hospital overcrowding and mortality among patients admitted via Western Australian emergency departments Peter C Sprivulis, Julie-Ann Da Silva, Ian G Jacobs, Amanda R L Frazer and George A Jelinek
E mergency department (ED) overcrowding is common in North Amer- ica, the United Kingdom and Australasia.1-3 Overcrowding results in
ABSTRACT Objective: To examine the relationship between hospital and emergency department (ED) occupancy, as indicators of hospital overcrowding, and mortality after emergency admission.
ambulance diversion and impaired ED Design: Retrospective analysis of 62 495 probabilistically linked emergency hospital
responTshieveMneesdsi.c2a,4l,5Journal of Australia ISSN: admissions and death records.
Setting: Three tertiary metropolitan hospitals between July 2000 and June 2003. Participants: All patients 18 years or older whose first ED attendance resulted in
blockwiws wd.emfijna.ecdoma.sauthe proportion of ED hospital admission during the study period.
patienRtseseraerqcuhiring admission whose total time within the ED exceeds 8 hours.7 Access block is correlated with total hospital inpa- tient bed occupancy of 90% or more, as measured by a midnight bed census.7-9 A target occupancy of 85% has been suggested as a balance between unused bed capacity and efficient inpatient flow.8,10 Some studies have identified a relationship between high occupancy, access block and
Main outcome measures: Deaths on days 2, 7 and 30 were evaluated against an Overcrowding Hazard Scale based on hospital and ED occupancy, after adjusting for age, diagnosis, referral source, urgency and mode of transport to hospital. Results: There was a linear relationship between the Overcrowding Hazard Scale and deaths on Day 7 (r = 0.98; 95% CI, 0.79­1.00). An Overcrowding Hazard Scale > 2 was associated with an increased Day 2, Day 7 and Day 30 hazard ratio for death of 1.3 (95% CI, 1.1­1.6), 1.3 (95% CI, 1.2­1.5) and 1.2 (95% CI, 1.1­1.3), respectively. Deaths at 30 days associated with an Overcrowding Hazard Scale > 2 compared with one of < 3 were undifferentiated with respect to age, diagnosis, urgency, transport mode, referral source
adverse patient outcomes, as measured by inpatient length of stay, hospital readmission or reattendance for emergency care.11-13
or hospital length of stay, but had longer ED durations of stay (risk ratio per hour of ED stay, 1.1; 95% CI, 1.1­1.1; P < 0.001) and longer physician waiting times (risk ratio per hour of ED wait, 1.2; 95% CI, 1.1­1.3; P = 0.01).
Our study examines whether high hospi- Conclusions: Hospital and ED overcrowding is associated with increased mortality. The
tal occupancy and ED access block is also Overcrowding Hazard Scale may be used to assess the hazard associated with hospital
associated with increased patient mortality. and ED overcrowding. Reducing overcrowding may improve outcomes for patients
requiring emergency hospital admission.
MJA 2006; 184: 208­212
The Emergency Care, Hospitalisation and Outcome Study (ECHO) Western Australia's population at 30 June 2001 was 1.9 million, with 1.4 million people (77%) residing in metropolitan Perth.14 Perth has seven public and three private hospitals with EDs. The Emergency Care, Hospitalisation and Outcome Study Project (ECHO) links all metropolitan Perth's emergency care records, with sufficient information to allow linkage, to metropolitan prehospital care records and
hospitalisation and mortality records for the whole state. Data from the three 400- to 550-bed tertiary hospitals in metropolitan Perth accepting adult referrals were used. These hospitals accepted 81% of all metropolitan ambulance attendances, 67% of emergency inpatient admissions and were responsible for 74% of episodes of ambulance diversion during the study period. For the study, we used the emergency admission record of the
Department of Emergency Medicine, University of Western Australia, Perth, WA. Peter C Sprivulis, MB BS, PhD, FACEM, Clinical associate professor of Emergency Medicine; Harkness Fellow in Healthcare Policy; Julie-Ann Da Silva, BPsych, Project Officer; Ian G Jacobs, RN, PhD, Associate Professor of Emergency Medicine; George A Jelinek, MD, FACEM, Professor of Emergency Medicine. Women and Children's Health Service, Subiaco, WA. Amanda RL Frazer, MB BS, LLB, Executive Director, Women and Newborn Health. Reprints will not be available from the authors. Correspondence: Clinical Associate Professor Peter C Sprivulis, Department of Emergency Medicine, University of Western Australia, 2nd Floor R Block, Queen Elizabeth II, Medical Centre, Verdun Street, Nedlands, WA 6009. [email protected]
first ED attendance during the study period at any of the hospitals' EDs that resulted in the patient being formally admitted to the hospital. Data sources EDIS EDIS ( Emergency Department Information Systems, Version 10.0, Health Administration Solutions, Sydney) is the primary data source for ECHO. It is a patient tracking system, containing patient demographics, admissions, transfers, discharges, timetracking information and clinical information entered by clinical staff in real time. Hospital Morbidity Data System Information on length of stay was obtained from the Western Australian Hospital Morbidity Data System, which contains patient information for all hospital inpatient care episodes in Western Australia.
MJA · Volume 184 Number 5 · 6 March 2006
1 Hospital crowding states Uncrowded hospital
Crowded hospital
(90%) or to indicate absolute hospital overcrowding (100%). Access block occupancy was scored 1, 2 or 3 corresponding to < 10%, 10%­19% and 20% occupancy. The Overcrowding Hazard Scale score was calculated by multiplying the hospital occupancy score and the ED access block occupancy score, resulting in values ranging from 1 to 9 (eg, hospital occupancy 90%­ 99% and access block occupancy 10%­ 19% = 2 2 = 4). A second model for the effect of overcrowding on mortality was developed using the Overcrowding Hazard Scale instead of either hospital occupancy or access block.
Emergency room
Emergency room
Low ward occupancy: empty beds; no medical outliers; few ED boarders; good patient flow
High ward occupancy: no empty beds; medical outliers; many ED boarders; poor patient flow
ED = emergency department. Boarders = patients waiting for an inpatient bed. Outliers = patients unable to
be admitted to the "correct" ward (eg, medical patients on surgical wards).

Mortality Database Death records were obtained from the Western Australian Mortality Database. The records contain death certificate information, including date of death and principal and secondary causes of death.15 Patient-based data linkage EDIS, the Hospital Morbidity Data System, and the Mortality Database records were linked by the Western Australian Data Linkage Unit using probabilistic matching.15,16 EDIS records for the period 1 July 2000 to 30 June 2003 were linked to morbidity and mortality records until 31 March 2004. A minimum of 42 000 records were needed to identify a 30-day mortality hazard ratio of 1.2 with a power of 0.9. Statistical analysis The Statistical Package for the social sciences (SPSS, Version 12.0, Chicago, Ill, USA) was used for the analysis. Overcrowding and mortality analysis Our analysis examined the effect on mortality of overcrowding, as indicated by high hospital occupancy and high ED occupancy of patients waiting for an inpatient bed. Box 1 illustrates the difference between uncrowded and overcrowded conditions. Hospital occupancy was calculated from the admitted patient census at 23:59 on the
day of attendance, divided by the 99th centile 23:59 patient census for the hospital during the first 6 months of the calendar year (eg, 400/500 = 80%). Access block occupancy was calculated as the percentage of ED cubicles occupied by patients experiencing access block (ie, waiting 8 hours or more for an inpatient bed) at the time of emergency attendance. The relationship between hospital occupancy, access block occupancy and other risk factors hypothesised as likely to influence mortality by Day 2 (Day 1 = day of attendance), Day 7 and Day 30 were evaluated using Cox regression analysis. Risk factors other than those indicative of hospital or ED occupancy or flow were chosen on the basis of a known relationship with emergency admission deaths, cost or hospital length of stay.11,17 The model for the effect of overcrowding on mortality was developed manually. Deaths associated with overcrowding were calculated as the excess deaths in the exposed population. Interaction between hospital occupancy and access block: the Overcrowding Hazard Scale We developed an Overcrowding Hazard Scale to test the combined effects of hospital and ED overcrowding. Hospital occupancy was scored 1, 2 or 3 corresponding to occupancy levels < 90%, 90%­99% and 100%, levels known to affect ED function
Tests for confounding Two potential confounders were tested. · Confounding due to increased respiratory and cardiovascular diagnoses in winter was tested by removing admissions in the four peak respiratory/influenza months (June­ September) of each year from the models. · Confounding caused by admission selection (ie, hospitals operating at high occupancy may be less likely to admit patients at lower risk of death, resulting in a spurious association between overcrowding and mortality) was tested by assessing the relationship between overcrowding and the probability of admission for all adult index emergency attendances to the hospitals using binary logistic regression. Clinical characteristics of overcrowdingassociated deaths Binary logistic regression was also used to evaluate differences in demographic, clinical and attendance characteristics of patients who died by Day 30 after experiencing an Overcrowding Hazard Scale score < 3, in comparision with those who experienced a score > 2. Ethics approval Ethical and record linkage approvals were obtained from the Human Research Ethics Committee at the University of Western Australia and the Confidentiality of Health Information Committee of Western Australia. . RESULTS Sample characteristics There were 62 495 first Emergency Admissions and 3084 deaths by the Day 30 censoring date. The admission characteristics, grouped by hospital occupancy, are summarised in Box 2. Higher hospital occupancy was associated with a slightly higher propor-
MJA · Volume 184 Number 5 · 6 March 2006
tion of elderly, female, illness admissions, and was more likely during weekdays and during winter. However, the hospital occupancy groupings were undifferentiated with respect to the proportion of physicianreferred admissions, ambulance-transported admissions, triage urgency, or length of hospital stay.18 Hospital occupancy and mortality Box 2 shows a positive relationship between level of hospital occupancy and death by days 2, 7 and 30 after index ED attendance, with a relative increase in mortality by Day 7 of 18% (95% CI, 0.5%­38%) for hospital occupancy of 90%­99% and 46% (95% CI, 14%­85%) for hospital occupancy of 100% or more. Box 3 illustrates the 7-day survival stratified by hospital occupancy, adjusted for age, mode of transport, diagnosis (ICD-10-CM), triage urgency and referral source. In comparison with < 90% occupancy, the 7-day hazard ratio for 90%­99% hospital occupancy was 1.2 (95% CI, 1.1­1.3; P = 0.02), and for 100% hospital occupancy it was 1.3 (95% CI, 1.1­1.6; P = 0.001). Initially
significant univariate associations between mortality and winter season, month of year, individual day of week and time of day were rendered non-significant after adjustment for the above variables. Adjustment for hospital attended (including use of an interaction term "hospital occupancy") or length of hospital stay did not significantly change the hazard associated with hospital occupancY. Relationship of hospital occupancy to access block Mean ED access block occupancy at the time of ED attendance was 4.6% (95% CI, 4.5%­ 4.7%) for patients attending when hospital occupancy was < 90% and increased to 6.8% (95% CI, 6.7%­6.9%) for 90%­99% hospital occupancy and 9.7% (95% CI, 9.5%­10%) for 100% hospital occupancy. The Overcrowding Hazard Scale and mortality Box 4 presents the 7-day hazard ratios associated with the Overcrowding Hazard Scale, using an identical model to that used for
Box 3, but with the Overcrowding Hazard Scale substituted for hospital occupancy. A linear relationship between the Overcrowding Hazard Scale and 7-day mortality hazard was demonstrated (r = 0.98; 95% CI, 0.79­ 1.00; P = 0.001), indicating that an Overcrowding Hazard Scale score > 2 (defining "overcrowded conditions") is associated with increased patient mortality. Box 5 presents the hazard ratios associated with overcrowded conditions and the other factors associated with Day 7 deaths, and Box 6 presents the deaths associated with overcrowded conditions, censoring survival at 2, 7 and 30 days: 2.3 deaths per 1000 emergency admissions were associated with overcrowded conditions by Day 30 (95% CI, 1.2­ 3.2), or an estimated 120 deaths (95% CI, 60­170) among the 53 025 tertiary hospital emergency admissions (including non-index admissions) in Perth in 2003. Tests for confounding Testing for winter seasonal confounding revealed no significant effect. The Day 7 hazard ratio for overcrowded conditions after exclusion of 22 582 June to September
2 Characteristics of emergency hospital admissions grouped by hospital occupancy
Occupancy < 90%
Sample characteristic Sex (% female) Age (% 50 years)
n (%) or mean 7 464 (45.0%) 9 969 (60.1%)
95% CI 44.3%­45.8% 59.4%­60.9%
Diagnosis (% injury)
4 130 (24.9%)
Shift (% 08:00­15:59 hours)
7 148 (43.1%)
Day (% Mon­Fri)
10 287 (62.0%)
Winter attendance (% Jun­Sep)
1 776 (10.7%)
Referral source (% physician-referred)
5 543 (33.4%)
Transport to emergency 8 141 (49.1%) (% ambulance)
Triage urgency (% resuscitation cases)
662 (4.0%)
Mean length of stay
Mean length of stay,
weighted for deaths
Day 2 deaths (%)
179 (1.1%)
Day 7 deaths (%)
375 (2.3%)
Day 30 deaths (%)
725 (4.4%)
Total (% of 62 495 admissions)
16 579 (26.5%)
* For all between-group tests.
24.3%­25.6% 42.4%­43.9% 61.3%­62.8% 10.2%­11.2% 32.7%­34.2% 48.3%­49.9% 3.7%­4.3% 6.44­6.76 6.67­7.00 0.9%­1.2% 2.0%­2.5% 4.1%­4.7%
Occupancy 90%­99%
n (%) or mean 19 023 (47.5%) 25 805 (64.4%)
95% CI 47.0%­48.0% 63.9%­64.9%
9 453 (23.6%) 18 123 (45.2%)
23.2%­24.0% 44.7%­45.7%
30 043 (75.0%) 16 192 (40.4%)
74.6%­75.4% 39.9%­40.9%
13 603 (34.0%) 33.5%­34.4%
20 653 (51.5%) 51.1%­52.0%
1 546 (3.9%)
532 (1.3%) 1 065 (2.7%) 2 001 (5.0%) 40 067 (64.1%)
1.2%­1.4% 2.5%­2.8% 4.8%­5.2%
Occupancy 100%
n (%) or mean 2 959 (50.6%) 4 220 (72.1%)
95% CI 49.3%­51.9% 71.0%­73.3%
1 343 (23.0%) 2 902 (49.6%)
21.9%­24.0% 48.3%­50.9%
5 110 (87.4%) 4 614 (78.9%)
86.5%­88.2% 77.8%­79.9%
2 129 (36.4%) 35.2%­37.6%
3 212 (54.9%) 53.6%­56.2%
251 (4.3%)
91 (1.6%) 193 (3.3%) 358 (6.1%) 5 849 (9.4%)
1.2%­1.9% 2.8%­3.8% 5.5%­6.7%
P 0.05 < 0.001 < 0.001 0.12 < 0.001 < 0.001 0.36 0.09 0.27 > 0.2* > 0.1* 0.06 0.002 0.001
MJA · Volume 184 Number 5 · 6 March 2006
3 Seven-day survival* after emergency admission stratified by hospital occupancy on the day of admission 1.000
Cumulative survival
0.995 0.990
< 90% 90-99% 100% +
5 Hazard ratios for variables used in 7-day mortality Overcrowding Hazard Scale model
Variable Age 50 years or older Mode of transport (ambulance v not ambulance) Diagnosis (illness v injury) Australasian Triage Scale urgency Category 1 (resuscitation v less urgent categories 3, 4, 5)18 Australasian Triage Scale Category 2 (emergency v less urgent categories 3, 4, 5)18 Overcrowding Hazard Scale > 2 Referral source (physician v non-medical)
Hazard ratio 3.3 3.4 2.2 14.0 1.6 1.3 1.2
95% CI 2.8­4.0 2.9­4.0 1.8­2.6 12.0­16.0 1.4­1.8 1.2­1.5 1.1­1.3
P < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.001
0.985 0 12 34 5 6 7 Days survived * Adjusted for age, mode of transport, diagnosis (ICD-10-CM), triage urgency, and referral source.
4 Relationship between the Overcrowding Hazard Scale and the 7-day mortality hazard for emergency admissions 2.5
Relative 7-day mortality hazard
2.0 1.5 1.4 1.3 1.1 1.0
R2= 0.95 1.7 1.4
0.5 1 2 3 4 5 6 7 8 9 10 Overcrowding Hazard Scale admissions (36.1%) (hazard ratio, 1.3; 95% CI, 1.1­1.5; P = 0.002) was essentially identical to the Day 7 hazard ratio for the total study population in overcrowded conditions reported in Box 5. In testing confounding caused by admission selection, neither hospital occupancy (risk ratio of admission when there was a 10% increase in occupancy: 1.0; 95% CI, 1.0­1.1) nor the presence of overcrowded conditions (risk ratio of admission with an Overcrowding Hazard Scale > 2: 1.0; 95% CI, 1.0­1.1) was associated with a reduction in the probability of admission among 194 362 ED attendees when adjusted for age, mode of transport, diagnosis, triage urgency, referral source and hospital attended.
6 Cumulative deaths per 1000 new emergency hospital admissions associated with an Overcrowding Hazard Scale > 2
Censoring date Day 2 Day 7 Day 30
Hazard ratio (95% CI) 1.3 (1.1­1.6) 1.3 (1.2­1.5) 1.2 (1.1­1.3)
Deaths per 1000 emergency hospi-
tal admissions (95% CI)
1.0 (0.4­1.4)
1.9 (0.7­2.5)
< 0.001
2.3 (1.2­3.2)
< 0.001
Deaths associated with overcrowding Deaths by Day 30 associated with overcrowded conditions were undifferentiated with respect to age, diagnosis, urgency, mode of transport, referral source or hospital length of stay compared with uncrowded conditions. However, patients dying who experienced overcrowded conditions were more likely to be male (risk ratio, 1.3; 95% CI, 1.1­1.5; P = 0.007) and to have attended during winter (risk ratio, 2.9; 95% CI, 2.4­ 3.5; P < 0.001), between Monday and Friday (risk ratio, 2.1; 95% CI, 1.7­2.6; P < 0.001) and between 08:00 and 15:59 (risk ratio, 1.7; 95% CI, 1.3­2.2; P < 0.001), consistent with the known weekly and seasonal variation in hospital overcrowding and ambulance diversion in metropolitan Perth (see Box 2). Patients dying who experienced overcrowded conditions had longer total durations of stay in the ED (risk ratio per hour of ED stay, 1.1; 95% CI, 1.1­1.1; P < 0.001) and slightly longer physician waiting times (risk ratio per hour of ED wait, 1.2; 95% CI, 1.1­1.3; P = 0.01). DISCUSSION Overcrowding is associated with increased mortality Our study showed that hospital and ED overcrowding is associated with a 30% relative increase in mortality by Day 2 and Day 7 for patients requiring admission via the ED to
an inpatient bed. This increase in mortality appears to be independent of patient age, season, diagnosis or urgency. The estimate of 120 deaths per annum associated with overcrowding in metropolitan Perth hospitals suggests that overcrowding should be regarded as a patient safety issue rather than simply an issue of hospital workflow. The finding of increased mortality associated with overcrowding is consistent with the known effects of overcrowding on emergency hospital admissions. Hospital occupancy above 90% has been demonstrated in our study to be closely associated with ED access block and is associated with an increased duration of ED stay.9 The duration of stay in the ED was longer for patients who experienced overcrowded conditions and died. The positive relationship between overcrowding and mortality is not explained by seasonal or admission selection confounding. For admission selection confounding, this counterintuitive finding may reflect the fact that hospital occupancy was measured at 23:59 each day and represents an outcome of all admission decisions made during the preceding 24 hours rather than perceived occupancy at the time of decision making about admission. Understanding the relationship between overcrowding and patient harm Our study did not examine the mechanisms by which overcrowding is associated with
MJA · Volume 184 Number 5 · 6 March 2006
increased mortality. Examination of delays in the initiation of time-Critical Care, such as the administration of antibiotics in sepsis, may be a fruitful line of enquiry.19 The longer physician waiting times and ED durations of stay among patients in our study who experienced overcrowded conditions and died may be acting as proxies for delays in the initiation of care. The presence of patients experiencing access block is strongly correlated with longer physician waiting times in EDs in both metropolitan Perth (r = 0.86) and internationally.4,6 Human error theory predicts that errors occur more often when systems are stressed by constraining resources; such as when a hospital is overcrowded.20 Overcrowding is often associated with placing inpatients on an incorrect ward (eg, medical patients placed on surgical wards) where staff may be less familiar with standard service guidelines for care of the patient's condition or the clinical cues associated with potential adverse events. Such patient "outlying" may be a mediator of the association between overcrowding and increased mortality. Given the association between the Overcrowding Hazard Scale and increased mortality, we suggest that the scale could be used to monitor the hazard associated with overcrowding in real-time. An Overcrowding Hazard Scale score > 2 may be considered prima facie evidence of an increased Day 7 mortality hazard.8 Solutions to overcrowding Hospital overcrowding is a complex phenomenon. The prevalence of overcrowding may rise in health services in developed economies as age-related demand for hospital services grows over the next 10­15 years.21 In addition, economic incentives tend to favour high occupancy.21 Solutions may include the realignment of incentives that favour high levels of hospital occupancy at the expense of emergency access. Other solutions may include strategies that reduce waste, misuse and overuse of health services, and improved chronic disease management to reduce hospital bed demand.22 In addition, better matching of bed supply with predictable emergency demand and optimisation of hospital inpatient flow are required.22-24 Limitations This study used data from only one health system. Confirmation of a relationship between overcrowding and mortality and validation of the Overcrowding Hazard Scale requires replication of the findings of our study in other health care systems.
The estimate of the 7-day hazard ratio, and particularly the 30-day hazard ratio, should be considered conservative. The study methods only allowed a patient to enter the dataset once, at the first (index) hospital ED attendance. The mortality hazard associated with overcrowding could increase with repeated exposure. In addition, no estimate has been made of the mortality hazard associated with overcrowding among patients discharged from EDs. Finally, despite showing an association between overcrowding and mortality, further studies are needed to examine the mediators of the relationship of hospital overcrowding and patient mortality. Research is required that examines specifically the impact of delays in care associated with overcrowding on patient outcomes; and the impact on adverse event rates and patient outcomes of placing patients in wards or corridor locations inappropriate for their care during overcrowded conditions. CONCLUSION Hospital and ED overcrowding is associated with increased mortality. The Overcrowding Hazard Scale may be used to assess the mortality hazard to patients associated with hospital and ED overcrowding. Reducing overcrowding may improve outcomes for patients requiring emergency hospital admission. COMPETING INTERESTS None identified. ACKNOWLEDGEMENTS Peter Sprivulis acknowledges the support of the Commonwealth Fund, New York, during preparation of this manuscript. The views presented are those of the authors and not necessarily those of the Fund. We thank Dr David Bates, Dr Michael Schull, Dr Chaim Bell, Dr Stephen Schoenbaum and Dr Donald Goldmann for comments on an earlier version of this manuscript. The Emergency Care, Hospitalisation and Outcome Study (ECHO) is supported by the Australian Health Ministers' Advisory Council Priority Driven Research Funding Program. The ECHO Investigators are: Neil Banham, Simon Wood, Judith Finn, Gary Geelhoed, Adrian Goudie, Tom Hitchcock, Jack Hodge, Andrew Jan, Michelle Johnston, Debra O'Brien, Alan O'Connor, Paul Mark, David Mountain, Yusuf Nagree, Greg Sweetman, and Garry Wilkes. ECHO acknowledges the staff of Perth's emergency departments, the St John Ambulance Service, and the WA Data Linkage Unit. REFERENCES 1 Fatovich DM, Hirsch RL. Entry overload, emergency department overcrowding, and ambulance bypass. Emerg Med J 2003; 20: 406-409.
2 Schneider SM, Gallery ME, Schafermeyer R, Zwemer F. Emergency department crowding: a point in time. Ann Emerg Med 2003; 42: 167-172. 3 Vilke GM, Brown L, Skogland P, et al. Approach to decreasing emergency department ambulance diversion hours. J Emerg Med 2004; 26: 189-192. 4 Schull MJ, Lazier K, Vermeulen M, et al. Emergency department contributors to ambulance diversion: a quantitative analysis. Ann Emerg Med 2003; 41: 467476. 5 Glaser CA, Gilliam S, Thompson WW, et al. medical care capacity for influenza outbreaks, Los Angeles. Emerg Infect Dis 2002; 8: 569-574. 6 Fatovich DM, Nagree Y, Sprivulis P. Access block causes emergency department overcrowding and ambulance diversion in Perth, Western Australia. Emerg Med J 2005; 22: 351-354. 7 Australasian College for Emergency Medicine. Access block and overcrowding in emergency departments. Melbourne: ACEM, 2004. 8 Bagust A, Place M, Posnett JW. Dynamics of bed use in accommodating emergency admissions: stochastic simulation model. BMJ 1999; 319: 155-158. 9 Forster AJ, Stiell I, Wells G, et al. The effect of hospital occupancy on emergency department length of stay and patient disposition. Acad Emerg Med 2003; 10: 127-133. 10 Green LV. How many hospital beds? Inquiry 20022003; 39 (4): 400-412. 11 Richardson DB. The access-block effect: relationship between delay to reaching an inpatient bed and inpatient length of stay. Med J Aust 2002; 177: 492495. 12 Miro O, Antonio MT, Jimenez S, et al. Decreased health care quality associated with emergency department overcrowding. Eur J Emerg Med 1999; 6: 105-107. 13 Liew D, Liew D, Kennedy MP. Emergency department length of stay independently predicts excess inpatient length of stay. Med J Aust 2003; 179: 524526. 14 Health Information Centre. Population characteristics of residents of the Perth metropolitan health region. Perth: Western Australian Department of Health; 2001. 15 Holman CDJ, Bass AJ, Rouse IL, Hobbs MST. Population-based linkage of health records in Western Australia: development of a health services research linked database. Aust N Z J Public Health 1999; 23: 453-459. 16 Jaro MA. Probabilistic linkage of large public health data files. Stat Med 1995; 14: 491-498. 17 Coopers & Lybrand Consultants. Outpatient Costing and Classification Study incorporating the Developmental Ambulatory Classification System Evaluation. Report for the Commonwealth Department of Health and Family Services, Canberra. Adelaide: Coopers & Lybrand Consultants, 1998. 18 Australasian College for Emergency Medicine. Policy Document -- The Australasian Triage Scale. Melbourne: ACEM, 2000. 19 Shah MN, Schmit J, Croley WC, Meltzer D. Continuity of antibiotic therapy in patients admitted from the emergency department. Ann Emerg Med 2003; 42: 117-123. 20 Reason JT. Human error: causes and consequences. Cambridge: Cambridge University Press; 1990. 21 Goetghebeur MM, Forrest S, Hay JW. Understanding the underlying drivers of inpatient cost growth: a literature review. Am J Manag Care 2003; Spec No 1: SP3-12. 22 Leatherman S, Berwick D, Iles D, et al. The business case for quality: case studies and an analysis. Health Affairs 2003; 22: 17. 23 Institute of Medicine's Committee on Quality of Health Care in America. Crossing the quality chasm: a new health system for the 21st century. Washington DC: National Academy Press. 24 Haraden C, Resar R. Patient flow in hospitals: understanding and controlling it better. Front Health Serv Manage 2004; 20: 3-15.
(Received 24 May 2005, accepted 21 Nov 2005)

MJA · Volume 184 Number 5 · 6 March 2006

File: the-association-between-hospital-overcrowding-and-mortality-among.pdf
Author: peterho
Published: Fri Jan 27 09:17:33 2006
Pages: 5
File size: 0.34 Mb

, pages, 0 Mb

Eclipse of the Kai, 159 pages, 0.53 Mb

, pages, 0 Mb

, pages, 0 Mb

, pages, 0 Mb
Copyright © 2018