Ambulance Offload Research Articles

Offload zone patient selection criteria to reduce ambulance offload delay

Emergency department overcrowding is a widespread problem and often leads to ambulance offload delay. If no bed is available when a patient arrives, the patient has to wait with the ambulance crew. A recent Canadian innovation is the offload zone—an area where multiple patients can wait with a single paramedic–nurse team allowing, the ambulance crew to return to service immediately. Although a reduction in offload delay was anticipated, it was observed that the offload zone is often at capacity. In this study we investigate why this is the case and use a continuous time Markov chain to evaluate how interventions can prevent congestion in the offload zone. Specifically we demonstrate conditions where the offload zone worsens offload delay and conditions where the offload zone can essentially eliminate offload delay.

Analysis and optimization of an ambulance offload delay and allocation problem

Ambulance offload delays have recently become one of the most significant operational challenges for Emergency Medical Services (EMS) providers. Offload delays occur when an ambulance arriving at a hospital Emergency Department (ED) is blocked until a bed becomes available for the patient. To formally investigate the effect of patient routing decisions on EMS offload delays, we introduce a stylized queueing network model with blocking. Following a decomposition approach, we develop an approximation scheme to find explicit solutions that can be used to find proper patient allocation policies to multiple hospitals in a region. We introduce a Markov chain representation for a single ED network and solve for its exact steady state distribution. A comprehensive numerical study is carried out to validate the approximation approaches and to gain insight into ambulance offload delays. By keeping the total offload delays at minimal levels, we observe that it is better to load larger EDs more heavily than smaller ones due to resource pooling.

The lived experiences of patients and ambulance ramping in a regional Australian emergency department: An interpretive phenomenology study

Internationally, the workload of emergency departments (ED) has increased, resulting in overcrowding and frequent delays in the offloading of patients arriving via ambulance--referred to in Australia as 'ambulance ramping'. Using interpretive phenomenology, this study sought to understand the experience of ambulance ramping from the perspective of patients. Semi-structured interviews were undertaken with seven patients who presented to a regional Queensland ED via ambulance, and experienced an ambulance offload delay of >30 min. Ambulance ramping in the ED was described as 'Being a patient, patient', and three major themes emerged: Understanding the emergency healthcare system; Making do within the emergency healthcare system; and Being 'in the dark' during ambulance ramping. Most participants did not understand the antecedents to ambulance ramping, but understood some of the consequences. Most were happy to wait with paramedics for a bed and, although without privacy, felt safe. However, most participants felt 'in the dark' during ambulance ramping, due to communication difficulties regarding bed availability, and this led to frustration. In light of the Australian Charter of Healthcare Rights, service improvement opportunities exist for patients arriving to the ED by ambulance to ensure delays are minimised and quality care is delivered.

Improved outcomes for emergency department patients whose ambulance off-stretcher time is not delayed.

ObjectiveTo describe and compare characteristics and outcomes of patients who arrive by ambulance to the ED. We aimed to (i) compare patients with a delayed ambulance offload time (AOT) >30 min with those who were not delayed; and (ii) identify predictors of an ED length of stay (LOS) of >4 h for ambulance-arriving patients.MethodsA retrospective, multi-site cohort study was undertaken in Australia using 12 months of linked health data (September 2007–2008). Outcomes of AOT delayed and non-delayed presentations were compared. Logistic regression analysis was undertaken to identify predictors of an ED LOS of >4 h.ResultsOf the 40 783 linked, analysable ambulance presentations, AOT delay of >30 min was experienced by 15%, and 63% had an ED LOS of >4 h. Patients with an AOT <30 min had better outcomes for: time to triage; ambulance time at hospital; time to see healthcare professional; proportion seen within recommended triage time frame; and ED LOS for both admitted and non-admitted patients. In-hospital mortality did not differ. Strong predictors of an ED LOS >4 h included: hospital admission, older age, triage category, and offload delay >30 min.ConclusionPatients arriving to the ED via ambulance and offloaded within 30 min experience better outcomes than those delayed. Given that offload delay is a modifiable predictor of an ED LOS of >4 h, targeted improvements in the ED arrival process for ambulance patients might be useful.

Expanding emergency department capacity: a multisite study.

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. 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. Almost one-third of all 286037 ED presentations were via ambulance (n=79196) 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 >4h. With additional capacity, the odds ratios for these predictors increased for age >65 years and ED LOS >4h, and decreased for ATS category and ED diagnoses. 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.

Can Emergency Medical Services Use Turnaround Time as a Proxy for Measuring Ambulance Offload Time?

Background“Offload delay” occurs when the transfer of care from paramedics to the emergency department (ED) is prolonged. Accurately measuring the delivery interval or “offload” is important, because it represents the time patients are waiting for definitive care. Because recording this interval presents a significant challenge, most emergency medical services systems only measure the complete at-hospital time or “turnaround interval,” and most offload delay research and policy is based on this proxy. ObjectiveThis study sought to test the validity of using the turnaround interval as a surrogate for the delivery interval. MethodsThis observational study examined levels of correspondence, or correlation, between delivery interval and turnaround interval, to assess whether turnaround is a reasonable surrogate for delivery. Delivery and turnaround intervals were logged by Richmond Ambulance Authority (RAA) in Richmond, Virginia, United States from April 1 to December 31, 2008. A total of 1732 ambulance runs from RAA were included. ResultsPearson's correlation analysis showed a good correlation between turnaround and actual offload time (delivery), with a coefficient (r) of 0.753. A post hoc analysis explored patterns in the relationship, which is quite complex. ConclusionThe results show that the correlation between the delivery and turnaround intervals is good. However, there remains much to be learned about the at-hospital time intervals and how to use these data to make decisions that will improve resource utilization and patient care. Efforts to establish a method to accurately record the delivery interval and to understand the at-hospital portion of the ambulance response are necessary.

Impact of opening a new emergency department on healthcare service and patient outcomes: analyses based on linking ambulance, emergency and hospital databases

Emergency department (ED) crowding caused by access block is an increasing public health issue and has been associated with impaired healthcare delivery, negative patient outcomes and increased staff workload. To investigate the impact of opening a new ED on patient and healthcare service outcomes. A 24-month time series analysis was employed using deterministically linked data from the ambulance service and three ED and hospital admission databases in Queensland, Australia. Total volume of ED presentations increased 18%, while local population growth increased by 3%. Healthcare service and patient outcomes at the two pre-existing hospitals did not improve. These outcomes included ambulance offload time: (Hospital A PRE: 10 min, POST: 10 min, P < 0.001; Hospital B PRE: 10 min, POST: 15 min, P < 0.001); ED length of stay: (Hospital A PRE: 242 min, POST: 246 min, P < 0.001; Hospital B PRE: 182 min, POST: 210 min, P < 0.001); and access block: (Hospital A PRE: 41%, POST: 46%, P < 0.001; Hospital B PRE: 23%, POST: 40%, P < 0.001). Time series modelling indicated that the effect was worst at the hospital furthest away from the new ED. An additional ED within the region saw an increase in the total volume of presentations at a rate far greater than local population growth, suggesting it either provided an unmet need or a shifting of activity from one sector to another. Future studies should examine patient decision making regarding reasons for presenting to a new or pre-existing ED. There is an inherent need to take a 'whole of health service area' approach to solve crowding issues.

Analysis of Ambulance Offload Delay at an Academic Level 1 Trauma Center With Adult and Pediatric Emergency Departments

Analysis of Ambulance Offload Delay at an Academic Level 1 Trauma Center With Adult and Pediatric Emergency Departments

Evaluation of ambulance offload delay at a university hospital emergency department

BackgroundAmbulance offload delay (AOD) has been recognized by the National Association of EMS Physicians (NAEMSP) as an important quality marker. AOD is the time between arrival of a patient by EMS and the time that the EMS crew has given report and moved the patient off of the EMS stretcher, allowing the EMS crew to begin the process of returning to service. The AOD represents a potential delay in patient care and a delay in the availability of an EMS crew and their ambulance for response to emergencies. This pilot study was designed to assess the AOD at a university hospital utilizing direct observation by trained research assistants.FindingsA convenience sample of 483 patients was observed during a 12-month period. Data were analyzed to determine the AOD overall and for four groups of National Emergency Department Overcrowding Scale (NEDOCS) score ranges. The AOD ranged from 0 min to 157 min with a median of 11 min. When data were grouped by NEDOCS score, there was a statistically significant difference in median AOD between the groups (p < 0.001), indicating the relationship between ED crowding and AOD.ConclusionThe median AOD was considered significant and raised concerns related to patient care and EMS system resource availability. The NEDOCS score had a positive correlation with AOD and should be further investigated as a potential marker for determining diversion status or for destination decision-making by EMS personnel.

Effect of an electronic medical record information system on emergency department performance

To determine whether implementation of the Cerner FirstNet electronic medical record system was associated with any change in emergency department (ED) performance. A retrospective observational study conducted during a 6-03 period in 2009 after the introduction of FirstNet and a corresponding 6-03 control period in 2008 when the Emergency Department Information System (EDIS) was operational. Data from all patients presenting to the ED during each period were extracted from each system and analysed for changes in key performance indicators (KPIs). Potential confounding variables, including total number of ED presentations, ED occupancy, ED staffing levels and triage category distribution, were also collected. Waiting time for all patients; waiting time, treatment time and total time for patients discharged from the ED; proportion of patients who did not wait to be seen by a doctor (DNW rate); and proportion of ambulance offload waiting times longer than 30 minutes. We found a reduction in performance with respect to ED KPIs after implementation of the FirstNet system. There were increases in the waiting time for all patients (median, 40 min v 78 min), and the waiting time (median, 49 min v 87 min), treatment time (median, 128 min v 147 min) and total time (median, 214 min v 280 min) for patients discharged from the ED. There were increases in the DNW rate (8.3% v 15.6%) and the proportion of ambulance offload times longer than 30 minutes (10.5% v 13.3%). All differences were statistically significant (P < 0.05). Implementation of the FirstNet electronic medical record system was associated with deterioration in ED KPIs.

A Markovian queueing model for ambulance offload delays

Ambulance offload delays are a growing concern for health care providers in many countries. Offload delays occur when ambulance paramedics arriving at a hospital Emergency Department (ED) cannot transfer patient care to staff in the ED immediately. This is typically caused by overcrowding in the ED. Using queueing theory, we model the interface between a regional Emergency Medical Services (EMS) provider and multiple EDs that serve both ambulance and walk-in patients. We introduce Markov chain models for the system and solve for the steady state probability distributions of queue lengths and waiting times using matrix-analytic methods. We develop several algorithms for computing performance measures for the system, particularly the offload delays for ambulance patients. Using these algorithms, we analyze several three-hospital systems and assess the impact of system resources on offload delays. In addition, simulation is used to validate model assumptions.

Ambulance Diversion and Emergency Department Offload Delay

The National Association of EMS Physicians (NAEMSP) believes that health care systems need to collectively work to minimize ambulance offload delay. This paper is the official position of the National Association of EMS Physicians.