A review on ambulance offload delay literature

ABSTRACTObjectiveEmergency department (ED) and hospital overcrowding cause offload delays that remove emergency medical services (EMS) crews from service and compromise care delivery. Prolonged ED boarding and delays to inpatient care are associated with increased hospital length of stay (LOS) and patient mortality, but the effects of EMS offload delays have not been well studied.MethodsWe used administrative data to study all high-acuity Canadian Triage Acuity Scale 2–3 EMS arrivals to Calgary adult EDs from July 2013 to June 2016. Patients offloaded to a care space within 15 minutes were considered controls, whereas those delayed ≥ 60 minutes were considered “delayed.” Propensity matching was used to create comparable control and delayed cohorts. The primary outcome was 7-day mortality. Secondary outcomes included hospital LOS and 30-day mortality.ResultsOf 162,002 high-acuity arrivals, 70,711 had offload delays <15 minutes and 41,032 had delays > 60 minutes. Delayed patients were more likely to be female, older, to have lower triage acuity, to live in dependent living situations, and to arrive on weekdays and day or evening hours. Delayed patients less often required admission and, when admitted, were more likely to go to the hospitalist service. Main outcomes were similar for propensity-matched control and delayed cohorts, although delayed patients experienced longer ED LOS and slightly lower 7-day mortality rates.ConclusionIn this setting, high-acuity EMS arrivals exposed to offload delays did not have prolonged hospital LOS or higher mortality than comparable patients who received timely access.

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101 Impact of a Sobriety Emergency Response Unit on Ambulance Offload Delay in a Large Urban Hospital

Annals of Emergency Medicine ◽

10.1016/j.annemergmed.2019.08.106 ◽

2019 ◽

Vol 74 (4) ◽

pp. S41

Author(s):

S. Kashani ◽

C. Brahaney ◽

S. Sanko ◽

M. Eckstein

Keyword(s):

Emergency Response ◽

Urban Hospital ◽

Response Unit ◽

Offload Delay

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Predicting ambulance offload delay using a hybrid decision tree model

Socio-Economic Planning Sciences ◽

10.1016/j.seps.2021.101146 ◽

2021 ◽

pp. 101146

Author(s):

Mengyu Li ◽

Peter Vanberkel ◽

Xiang Zhong

Keyword(s):

Decision Tree ◽

Decision Tree Model ◽

Tree Model ◽

Offload Delay

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Real-Time Patient Transfer under Ambulance Offload Delay

SSRN Electronic Journal ◽

10.2139/ssrn.4003735 ◽

2022 ◽

Author(s):

Wenqian Xing ◽

Cheng Hua

Keyword(s):

Real Time ◽

Patient Transfer ◽

Offload Delay

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Analysis and optimization of an ambulance offload delay and allocation problem

Omega ◽

10.1016/j.omega.2016.01.006 ◽

2016 ◽

Vol 65 ◽

pp. 148-158 ◽

Cited By ~ 13

Author(s):

Eman Almehdawe ◽

Beth Jewkes ◽

Qi-Ming He

Keyword(s):

Allocation Problem ◽

Offload Delay

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Expanding emergency department capacity: a multisite study

Australian Health Review ◽

10.1071/ah13085 ◽

2014 ◽

Vol 38 (3) ◽

pp. 278 ◽

Cited By ~ 6

Author(s):

Julia L. Crilly ◽

Gerben B. Keijzers ◽

Vivienne C. Tippett ◽

John A. O'Dwyer ◽

Marianne C. Wallis ◽

...

Keyword(s):

Hospital Mortality ◽

Delay Time ◽

Service Outcomes ◽

Before And After ◽

Related Service ◽

Comparative Cohort Study ◽

Public Emergency ◽

Admission Requirement ◽

Offload Delay ◽

Respiratory Conditions

Objectives The aims of the present study were to identify predictors of admission and describe outcomes for patients who arrived via ambulance to three Australian public emergency departments (EDs), before and after the opening of 41 additional ED beds within the area. Methods The present study was a retrospective comparative cohort study using deterministically linked health data collected between 3 September 2006 and 2 September 2008. Data included ambulance offload delay, time to see doctor, ED length of stay (LOS), admission requirement, access block, hospital LOS and in-hospital mortality. Logistic regression analysis was undertaken to identify predictors of hospital admission. Results Almost one-third of all 286 037 ED presentations were via ambulance (n = 79 196) and 40.3% required admission. After increasing emergency capacity, the only outcome measure to improve was in-hospital mortality. Ambulance offload delay, time to see doctor, ED LOS, admission requirement, access block and hospital LOS did not improve. Strong predictors of admission before and after increased capacity included age >65 years, Australian Triage Scale (ATS) Category 1–3, diagnoses of circulatory or respiratory conditions and ED LOS >4 h. With additional capacity, the odds ratios for these predictors increased for age >65 years and ED LOS >4 h, and decreased for ATS category and ED diagnoses. Conclusions Expanding ED capacity from 81 to 122 beds within a health service area impacted favourably on mortality outcomes, but not on time-related service outcomes such as ambulance offload time, time to see doctor and ED LOS. To improve all service outcomes, when altering (increasing or decreasing) ED bed numbers, the whole healthcare system needs to be considered.

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Transfer of care and offload delay: continued resistance or integrative thinking?

CJEM ◽

10.1017/cem.2014.62 ◽

2015 ◽

Vol 17 (6) ◽

pp. 679-684 ◽

Cited By ~ 6

Author(s):

Brian Schwartz

Keyword(s):

Emergency Department ◽

Prehospital Care ◽

Transfer Of Care ◽

Emergency Department Overcrowding ◽

The Past ◽

Innovative Solutions ◽

Integrative Process ◽

Thinking Process ◽

Trauma Teams ◽

Offload Delay

AbstractThe disciplines of paramedicine and emergency medicine have evolved synchronously over the past four decades, linked by emergency physicians with expertise in prehospital care. Ambulance offload delay (OD) is an inevitable consequence of emergency department overcrowding (EDOC) and compromises the care of the patient on the ambulance stretcher in the emergency department (ED), as well as paramedic emergency medical service response in the community. Efforts to define transfer of care from paramedics to ED staff with a view to reducing offload time have met with resistance from both sides with different agendas. These include the need to return paramedics to serve the community versus the lack of ED capacity to manage the patient. Innovative solutions to other system issues, such as rapid access to trauma teams, reducing door-to-needle time, and improving throughput in the ED to reduce EDOC, have been achieved by involving all stakeholders in an integrative thinking process. Only by addressing this issue in a similar integrative process will solutions to OD be realized.

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117 Can NEDOCS Score Be Used to Predict Ambulance Offload Delay?

Annals of Emergency Medicine ◽

10.1016/j.annemergmed.2011.06.144 ◽

2011 ◽

Vol 58 (4) ◽

pp. S217 ◽

Cited By ~ 2

Author(s):

D.R. Cooney ◽

S. Wojcik ◽

N. Seth

Keyword(s):

Offload Delay

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LO10: Associations between ED crowding metrics and 72h-hour ED re-visits: Which crowding metrics are most highly associated with patient-oriented adverse outcomes?

CJEM ◽

10.1017/cem.2019.53 ◽

2019 ◽

Vol 21 (S1) ◽

pp. S10

Author(s):

A. McRae ◽

G. Innes ◽

M. Schull ◽

E. Lang ◽

E. Grafstein ◽

...

Keyword(s):

Patient Outcomes ◽

Regression Models ◽

Information Criterion ◽

Patient Characteristics ◽

Adverse Outcomes ◽

Logistic Regression Models ◽

Return Visits ◽

Offload Delay ◽

Ed Crowding ◽

Meaningful Measure

Introduction: Emergency Department (ED) crowding is a pervasive problem and is associated with adverse patient outcomes. Yet, there are no widely accepted, universal ED crowding metrics. The objective of this study is to identify ED crowding metrics with the strongest association to the risk of ED revisits within 72 hours, which is a patient-oriented adverse outcome. Methods: Crowding metrics, patient characteristics and outcomes were obtained from administrative data for all ED encounters from 2011-2014 for three adult EDs in Calgary, AB. The data were randomly divided into three partitions for cross-validation, and further divided by CTAS category 1, 2/3 and 4/5. Twenty unique ED crowding metrics were calculated and assigned to each patient seen on each calendar day or shift, to standardize the exposure. Logistic regression models were fitted with 72h ED revisit as the dependent variable, and an individual crowding metric along with a common list of confounders as independent variables. Adjusted odds ratios (OR) for the 72h return visits were obtained for each crowding metric. The strength of associations between 72h revisits and crowding metrics were compared using Akaike's Information Criterion and Akaike weights. Results: This analysis is based on 1,149,939 ED encounters. Across all CTAS groups, INPUT metrics (ED census, ED occupancy, waiting time, EMS offload delay, LWBS%) were only weakly associated with the risk of 72h re-visit. Among THROUGHPUT metrics, ED Length of Stay and MD Care Time had similar adjusted ORs for 72h ED re-visit (range 0.99-1.15). Akaike weights ranging from 0.3/1.00 to 0.4/1.00 indicate that both THROUGHPUT metrics are reasonable predictors of 72h ED re-visits. All OUTPUT metrics (boarding time, # of boarded patients, % of beds occupied by boarded patients, hospital occupancy) had statistically significant ORs for 72h ED re-visits. The median boarding time had the highest adjusted OR for 72h ED re-visit (adjusted OR 1.40, 95% CI 1.33-1.47) and highest Akaike weight (0.97/1.00) compared to all other OUTPUT metrics, indicating that median boarding time had the strongest association with 72h re-visits. Conclusion: ED THROUGHPUT and OUTPUT metrics had consistent associations with 72h ED re-visits, while INPUT metrics had little to no association with 72h re-visits. Median boarding time is the strongest predictor of 72h re-visits, indicating that this may be the most meaningful measure of ED crowding.

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