Evaluation of ambulance offload delay at a university hospital emergency department

The multicenter impacts of an emergency physician lead on departmental flow and provider experiences

INTRODUCTION

Emergency department (ED) flow impacts patient safety, quality of care and ED provider satisfaction. Throughput interventions have been shown to improve flow, yet few studies have reported the impact of ED physician leadership roles on patient flow and provider experiences. The study objective was to evaluate the impacts of the emergency physician lead role on ED flow metrics and provider experiences.

METHODS

Quantitative data about patient flow metrics were collected from ED information systems in two tertiary hospital EDs and analyzed to compare ED length of stay, EMS hallway length of stay, physician initial assessment time, 72-h readmission and left without being seen rates three months before and following emergency physician lead role implementation. ED flow metrics for adult patients at each site were analyzed independently using descriptive and inferential statistics, t tests and multivariable regression analysis. Qualitative data were collected via surveys from ED providers (physicians, nurses, and EMS) about their experiences working with the emergency physician leads and analyzed for themes about emergency physician leads impact.

RESULTS

The number of ED visits was relatively stable pre-post at the Peter Lougheed Centre (Lougheed) but increased pre-post at the Foothills Medical Centre (Foothills). Post-intervention at Lougheed median ED length of stay decreased by 18 min (p < 0.001) and at Foothills ED length of stay increased by 8 min (p < 0.001). EMS length of stay at Lougheed decreased by 20 min (p < 0.001), and at Foothills length of stay increased by 17 min (p < 0.001). Themes in provider feedback were that emergency physician leads (1) facilitated patient flow, (2) impacted provider workload, and (3) supported patient flow and safety with early assessments, treatments and investigations.

CONCLUSION

In this study, the emergency physician lead impacted ED flow metrics variably at different sites, but important learnings from provider experiences can guide future emergency physician lead implementation.

Impacts of an EMS Hospital Liaison Program on Ambulance Offload Times: A Preliminary Analysis

INTRODUCTION

Ambulance patients who are unable to be quickly transferred to an emergency department (ED) bed represent a key contributing factor to ambulance offload delay (AOD). Emergency department crowding and associated AOD are exacerbated by multiple factors, including infectious disease outbreaks such as the coronavirus disease 2019 (COVID-19) pandemic. Initiatives to address AOD present an opportunity to streamline ambulance offload procedures while improving patient outcomes.

STUDY OBJECTIVE

The goal of this study was to evaluate the initial outcomes and impact of a novel Emergency Medical Service (EMS)-based Hospital Liaison Program (HLP) on ambulance offload times (AOTs).

METHODS

Ambulance offload times associated with EMS patients transported to a community hospital six months before and after HLP implementation were retrospectively analyzed using proportional significance tests, t-tests, and multiple regression analysis.

RESULTS

A proportional increase in incidents in the zero to <30 minutes time category after program implementation (+2.96%; P <.01) and a commensurate decrease in the proportion of incidents in the 30 to <60 minutes category (-2.65%; P <.01) were seen. The fully adjusted regression model showed AOT was 16.31% lower (P <.001) after HLP program implementation, holding all other variables constant.

CONCLUSION

The HLP is an innovative initiative that constitutes a novel pathway for EMS and hospital systems to synergistically enhance ambulance offload procedures. The greatest effect was demonstrated in patients exhibiting potentially life-threatening symptoms, with a reduction of approximately three minutes. While small, this outcome was a statistically significant decrease from the pre-intervention period. Ultimately, the HLP represents an additional strategy to complement existing approaches to mitigate AOD.

Predicting Ambulance Patient Wait Times: A Multicenter Derivation and Validation Study

STUDY OBJECTIVE

To derive and internally and externally validate machine-learning models to predict emergency ambulance patient door-to-off-stretcher wait times that are applicable to a wide variety of emergency departments.

METHODS

Nine emergency departments provided 3 years (2017 to 2019) of retrospective administrative data from Australia. Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine-learning models were developed to predict wait times at each site and were internally and externally validated.

RESULTS

There were 421,894 episodes analyzed, and median site off-load times varied from 13 (interquartile range [IQR], 9 to 20) to 29 (IQR, 16 to 48) minutes. The global site prediction model median absolute errors were 11.7 minutes (95% confidence interval [CI], 11.7 to 11.8) using linear regression and 12.8 minutes (95% CI, 12.7 to 12.9) using elastic net. The individual site model prediction median absolute errors varied from the most accurate at 6.3 minutes (95% CI, 6.2 to 6.4) to the least accurate at 16.1 minutes (95% CI, 15.8 to 16.3). The model technique performance was the same for linear regression, random forests, elastic net, and rolling average. The important variables were the last k-patient average waits, triage category, and patient age. The global model performed at the lower end of the accuracy range compared with models for the individual sites but was within tolerable limits.

CONCLUSION

Electronic emergency demographic and flow information can be used to estimate emergency ambulance patient off-stretcher times. Models can be built with reasonable accuracy for multiple hospitals using a small number of point-of-care variables.

A systematic mixed studies review of patient experiences in the ED

BACKGROUND

Understanding patient experiences is crucial to evaluating care quality in EDs. However, while previous reviews describe the determinants of ED patient experiences (ie, factors that influence patient experiences), few have described actual patient experiences. The aim of this systematic mixed studies review was to describe patient experiences in the ED from the patient's perspective.

METHODS

Embase, Medline, ProQuest Nursing and Allied Health, the Cumulative Index to Nursing and Allied Health Literature and the Cochrane Library electronic databases were searched, with publication dates limited between 1 January 2001 and 16 September 2019. Studies describing adult patient experiences in the ED were included. Studies describing patient satisfaction, proxy-reported experiences or child/adolescent experiences were excluded. The quality of included studies was appraised using the Mixed Methods Appraisal Tool (2018 version). An inductive, convergent qualitative synthesis of the extracted data was undertaken following Thomas and Harden's (2008) methods.

RESULTS

Fifty-four studies were included and of those, only five (9%) studies included a standardised definition of patient experience. Two inter-related themes emerged: Relationships between ED patients and care providers; and Spending time in the ED environment. The first theme included four subthemes regarding respect, communication, caring behaviours and optimising patient confidence. A key finding related to the potential for power imbalances between patients and their care providers. The second theme included two subthemes regarding physical aspects of the ED environment and patients' waiting experience. Patients attributed more importance to the waiting experience itself rather than the duration they had to wait.

CONCLUSIONS

Patients in the ED have unique and complex experiences. Greater research is needed to understand the relational and environmental factors that contribute to power imbalances between patients and care providers, how to support more positive waiting experiences, and developing a standardised definition of patient experience in the ED.

PROSPERO REGISTRATION NUMBER

CRD42020150154.

Validation of the National Emergency Department Overcrowding Score (NEDOCS) in a UK non-specialist emergency department

INTRODUCTION

Emergency department (ED) crowding has significant adverse consequences, however, there is no widely accepted tool to measure it. This study validated the National Emergency Department Overcrowding score (NEDOCS) (range 0-200 points), which uses routinely collected ED data.

METHODS

This prospective single-centre study sampled data during four periods of 2018. The outcome against which NEDOCS performance was assessed was a composite of clinician opinion of crowding (physician and nurse in charge). Area under the receiver operating characteristic curves (AUROCs) and calibration plots were produced. Six-hour stratified sampling was added to adjust for temporal correlation of clinician opinion. Staff inter-rater agreement and NEDOCS association with opinion of risk, safety and staffing levels were collected.

RESULTS

From 905 sampled hours, 448 paired observations were obtained, with the ED deemed crowded 18.5% of the time. Inter-rater agreement between staff was moderate (weighted kappa 0.57 (95% CI 0.56 to 0.60)). AUROC for NEDOCS was 0.81 (95% CI 0.77 to 0.86). Adjusted for temporal correlation, AUROC was 0.80 (95% CI 0.73 to 0.88). At a cut-off of 100 points sensitivity was 75.9% (95% CI 65.3% to 84.6%), specificity 72.1% (95% CI 67.1% to 76.6%), positive predictive value 38.2% (95% CI 30.7% to 46.1%) and negative predictive value 92.9% (95% CI 89.3% to 95.6%). NEDOCS underpredicted clinical opinion on Calibration assessment, only partially correcting with intercept updating. For perceived risk of harm, safety and insufficient staffing, NEDOCS AUROCs were 0.71 (95% CI 0.61 to 0.82), 0.71 (95% CI 0.63 to 0.80) and 0.70 (95% CI 0.64 to 0.76), respectively.

CONCLUSIONS

NEDOCS demonstrated good discriminatory power for clinical perception of crowding. Prior to implementation, determining individual unit ED cut-off point(s) would be important as published thresholds may not be generalisable. Future studies could explore refinement of existing variables or addition of new variables, including acute physiological data, which may improve performance.

Impact of the Four-Hour Rule policy on emergency medical services delays in Australian EDs: a longitudinal cohort study

INTRODUCTION

Delayed handover of emergency medical services (EMS) patients to EDs is a major issue with hospital crowding considered a primary cause. We explore the impact of the 4-hour rule (the Policy) in Australia, focusing on ambulance and ED delays.

METHODS

EMS (ambulance), ED and hospital data of adult patients presenting to 14 EDs from 2002 to 2013 in three jurisdictions were linked. Interrupted time series 'Before-and-After' trend analysis was used for assessing the Policy's impact. Random effects meta-regression analysis was examined for associations between ambulance delays and Policy-associated ED intake, throughput and output changes.

RESULTS

Before the Policy, the proportion of ED ambulances delayed increased between 1.1% and 1.7% per quarter across jurisdictions. After Policy introduction, Western Australia's increasing trend continued but Queensland decreased by 5.1% per quarter. In New South Wales, ambulance delay decreased 7.1% in the first quarter after Policy introduction. ED intake (triage delay) improved only in New South Wales and Queensland. Each 1% ambulance delay reduction was significantly associated with a 0.91% reduction in triage delay (p=0.014) but not ED length of stay ≤4 hours (p=0.307) or access-block/boarding (p=0.605) suggesting only partial improvement in ambulance delay overall.

CONCLUSION

The Policy was associated with reduced ambulance delays over time in Queensland and only the immediate period in New South Wales. Associations may be due to local jurisdictional initiatives to improve ambulance performance. Strategies to alleviate ambulance delay may need to focus on the ED intake component. These should be re-examined with longer periods of post-Policy data.

Improving emergency department transfer for patients arriving by ambulance: A retrospective observational study

Objective

Extended delays in the transfer of patients from ambulance to ED can compromise patient flow. The present study aimed to describe the relationship between the use of an Emergency Department Ambulance Off‐Load Nurse (EDAOLN) role, ED processes of care and cost effectiveness.

Methods

This was a retrospective observational study over three periods of before (T1), during (T2) and after (T3) the introduction of the EDAOLN role in 2012. Ambulance, ED and cost data were linked and used for analysis. Processes of care measures analysed included: time to be seen by a doctor from ED arrival (primary outcome), ambulance‐ED offload compliance, proportion of patients seen within recommended triage timeframe, ED length of stay (LoS), proportion of patients transferred, admitted or discharged from the ED within 4 h and cost effectiveness.

Results

A total of 6045 people made 7010 presentations to the ED by ambulance over the study period. Several measures improved significantly between T1 and T2 including offload compliance (T1: 58%; T2: 63%), time to be seen (T1: 31 min; T2: 28 min), ED LoS (T1: 335 min; T2: 306 min), ED LoS <4 h (T1: 31%; T2: 33%). Some measures carried over into T3, albeit to a lesser extent. Post‐hoc analyses showed that outcomes improved most for less urgent patients. The annualised net cost of the EDAOLN (if funded from additional resources) of $130 721 could result in an annualised reduction of approximately 3912 h in waiting time to be seen by a doctor.

Conclusion

With the EDAOLN role in place, slight outcome improvements in several key ambulance and ED efficiency criteria were noted. During times of ED crowding, the EDAOLN role may be one cost‐effective strategy to consider.

Are emergency medical services offload delay patients at increased risk of adverse outcomes?

OBJECTIVE

Emergency 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.

METHODS

We 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.

RESULTS

Of 162,002 high-acuity arrivals, 70,711 had offload 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.

CONCLUSION

In 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.

Effects of Real-time EMS Direction on Optimizing EMS Turnaround and Load-balancing Between Neighboring Hospital Campuses

Background: Implemented in September 2017, the "nurse navigator program" identified the preferred emergency department (ED) destination within a single healthcare system using real-time assessment of hospital and ED capacity and crowding metrics. Objective: The primary objective of the navigator program was to improve load-balancing between two closely situated emergency departments, both of which feed into the same inpatient facilities of a single healthcare system. A registered nurse in the hospital command center made real-time recommendations to emergency medical services (EMS) providers via radio, identifying the preferred destination for each transported patient based on such factors as chief complaint, ED volume, and waiting room census. The destination decision was made via the utilization of various real-time measures of health system capacity in conjunction with existing protocols dictating campus-specific clinical service availability. The objective of this study was to evaluate the efficacy of this real-time ambulance destination direction program as reflected in changes to emergency medical services (EMS) turnaround time and the incidence of intercampus transports. Methods: A before-and-after time series was performed to determine if program implementation resulted in a change in EMS turnaround time or incidence of intercampus transfers. Results: Implementation of the nurse navigator program was associated with a statistically significant decrease in EMS turnaround times for all levels of dispatch and transport at both hospital campuses. Intercampus transfers also showed significant improvement following implementation of the intervention, although this effect lagged behind implementation by several months. Conclusion: A proactive approach to EMS destination control using a nurse navigator with access to real-time hospital and ED capacity metrics appears to be an effective method of decreasing EMS turnaround time.

A review on ambulance offload delay literature

Ambulance offload delay (AOD) occurs when care of incoming ambulance patients cannot be transferred immediately from paramedics to staff in a hospital emergency department (ED). This is typically due to emergency department congestion. This problem has become a significant concern for many health care providers and has attracted the attention of many researchers and practitioners. This article reviews literature which addresses the ambulance offload delay problem. The review is organized by the following topics: improved understanding and assessment of the problem, analysis of the root causes and impacts of the problem, and development and evaluation of interventions. The review found that many researchers have investigated areas of emergency department crowding and ambulance diversion; however, research focused solely on the ambulance offload delay problem is limited. Of the 137 articles reviewed, 28 articles were identified which studied the causes of ambulance offload delay, 14 articles studied its effects, and 89 articles studied proposed solutions (of which, 58 articles studied ambulance diversion and 31 articles studied other interventions). A common theme found throughout the reviewed articles was that this problem includes clinical, operational, and administrative perspectives, and therefore must be addressed in a system-wide manner to be mitigated. The most common intervention type was ambulance diversion. Yet, it yields controversial results. A number of recommendations are made with respect to future research in this area. These include conducting system-wide mitigation intervention, addressing root causes of ED crowding and access block, and providing more operations research models to evaluate AOD mitigation interventions prior implementations. In addition, measurements of AOD should be improved to assess the size and magnitude of this problem more accurately.

Concepts, antecedents and consequences of ambulance ramping in the emergency department: A scoping review

BACKGROUND

Patients arriving at the Emergency Department (ED) via ambulance can experience a delay in receiving definitive care. In Australia, this phenomenon is referred to as 'Ambulance Ramping', 'Patient Off Stretcher Time Delay' or 'Offload Delay'. As a direct consequence of crowding, and in the context of a worldwide increase in ED and ambulance usage, hospital and ambulance service function is hampered. The aim of this review was to synthesize the literature with respect to the conceptualisation, meaning, antecedents and consequences of Ambulance Ramping.

METHODS

This was a scoping review and synthesis of the literature. Six search terms were employed: emergency medical technician; paramedic; ambulance; hospital emergency services; delay; and ambulance ramping. Journal articles that discussed Ambulance Ramping (or similar terms), and were published in English between 1983 and March 2015 were included. PubMed and CINAHL Plus databases were searched, with secondary searches of reference lists and grey literature also undertaken.

RESULTS

Thirteen papers were selected and inform this review. Several terms are used internationally to describe phenomena similar to Ambulance Ramping, where there is a delay in patient handover from paramedics to ED clinicians. Antecedents of Ambulance Ramping included reduction/limitation of ambulance diversion, patient acuity, the time of day, the day of the week, insufficient ED staff, insufficient ED beds, and high ED workload. Consequences of Ambulance Ramping include: further delays in patients' ability to receive definitive care and workforce stressors such as missed meal breaks, sick leave and staff attrition.

CONCLUSION

While the existing research literature indicates that Ambulance Ramping is problematic, little is known about the patient's experience of Ambulance Ramping; this is required so that an enhanced understanding of its implications, including those for emergency nurses, can be identified.

Optimal Measurement Interval for Emergency Department Crowding Estimation Tools

STUDY OBJECTIVE

Emergency department (ED) crowding is a barrier to timely care. Several crowding estimation tools have been developed to facilitate early identification of and intervention for crowding. Nevertheless, the ideal frequency is unclear for measuring ED crowding by using these tools. Short intervals may be resource intensive, whereas long ones may not be suitable for early identification. Therefore, we aim to assess whether outcomes vary by measurement interval for 4 crowding estimation tools.

METHODS

Our eligible population included all patients between July 1, 2015, and June 30, 2016, who were admitted to the JPS Health Network ED, which serves an urban population. We generated 1-, 2-, 3-, and 4-hour ED crowding scores for each patient, using 4 crowding estimation tools (National Emergency Department Overcrowding Scale [NEDOCS], Severely Overcrowded, Overcrowded, and Not Overcrowded Estimation Tool [SONET], Emergency Department Work Index [EDWIN], and ED Occupancy Rate). Our outcomes of interest included ED length of stay (minutes) and left without being seen or eloped within 4 hours. We used accelerated failure time models to estimate interval-specific time ratios and corresponding 95% confidence limits for length of stay, in which the 1-hour interval was the reference. In addition, we used binomial regression with a log link to estimate risk ratios (RRs) and corresponding confidence limit for left without being seen.

RESULTS

Our study population comprised 117,442 patients. The time ratios for length of stay were similar across intervals for each crowding estimation tool (time ratio=1.37 to 1.30 for NEDOCS, 1.44 to 1.37 for SONET, 1.32 to 1.27 for EDWIN, and 1.28 to 1.23 for ED Occupancy Rate). The RRs of left without being seen differences were also similar across intervals for each tool (RR=2.92 to 2.56 for NEDOCS, 3.61 to 3.36 for SONET, 2.65 to 2.40 for EDWIN, and 2.44 to 2.14 for ED Occupancy Rate).

CONCLUSION

Our findings suggest limited variation in length of stay or left without being seen between intervals (1 to 4 hours) regardless of which of the 4 crowding estimation tools were used. Consequently, 4 hours may be a reasonable interval for assessing crowding with these tools, which could substantially reduce the burden on ED personnel by requiring less frequent assessment of crowding.

The Medical Duty Officer: An Attempt to Mitigate the Ambulance At-Hospital Interval

Introduction

A lack of coordination between emergency medical services (EMS), emergency departments (ED) and systemwide management has contributed to extended ambulance at-hospital times at local EDs. In an effort to improve communication within the local EMS system, the Baltimore City Fire Department (BCFD) placed a medical duty officer (MDO) in the fire communications bureau. It was hypothesized that any real-time intervention suggested by the MDO would be manifested in a decrease in the EMS at-hospital time.

Methods

The MDO was implemented on November 11, 2013. A senior EMS paramedic was assigned to the position and was placed in the fire communication bureau from 9 a.m. to 9 p.m., seven days a week. We defined the pre-intervention period as August 2013 – October 2013 and the post-intervention period as December 2013 – February 2014. We also compared the post-intervention period to the “seasonal match control” one year earlier to adjust for seasonal variation in EMS volume. The MDO was tasked with the prospective management of city EMS resources through intensive monitoring of unit availability and hospital ED traffic. The MDO could suggest alternative transport destinations in the event of ED crowding. We collected and analyzed data from BCFD computer-aided dispatch (CAD) system for the following: ambulance response times, ambulance at-hospital interval, hospital diversion and alert status, and “suppression wait time” (defined as the total time suppression units remained on scene until ambulance arrival). The data analysis used a pre/post intervention design to examine the MDO impact on the BCFD EMS system.

Results

There were a total of 15,567 EMS calls during the pre-intervention period, 13,921 in the post-intervention period and 14,699 in the seasonal match control period one year earlier. The average at-hospital time decreased by 1.35 minutes from pre- to post-intervention periods and 4.53 minutes from the pre- to seasonal match control, representing a statistically significant decrease in this interval. There was also a statistically significant decrease in hospital alert time (approximately 1,700 hour decrease pre- to post-intervention periods) and suppression wait time (less than one minute decrease from pre- to post- and pre- to seasonal match control periods). The decrease in ambulance response time was not statistically significant.

Conclusion

Proactive deployment of a designated MDO was associated with a small, contemporaneous reduction in at-hospital time within an urban EMS jurisdiction. This project emphasized the importance of better communication between EMS systems and area hospitals as well as uniform reporting of variables for future iterations of this and similar projects.

Use of Radio Frequency Identification to Establish Emergency Medical Service Offload Times

Emergency medical services (EMS) crews often wait for emergency department (ED) beds to become available to offload their patients. Presently there is no national benchmark for EMS turnaround or offload times, or method for objectively and reliably measuring this. This study introduces a novel method for monitoring offload times and identifying variance. We performed a descriptive, observational study in a large urban community teaching hospital. We affixed radio frequency identification (RFID) tags (Confidex Survivor™, Confidex, Inc., Glen Ellyn, IL) to 65 cots from 19 different EMS agencies and placed a reader (CaptureTech Weatherproof RFID Interpreter, Barcoding Inc., Baltimore, Maryland) in the ED ambulance entrance, allowing for passive recording of traffic. We recorded data for 16 weeks starting December 2009. Offload times were calculated for each visit and analyzed using STATA to show variations in individual and cumulative offload times based on the time of day and day of the week. Results are presented as median times, confidence intervals (CIs), and interquartile ranges (IQRs). We collected data on 2,512 visits. Five hundred and ninety-two were excluded because of incomplete data, leaving 1,920 (76%) complete visits. Average offload time was 13.2 minutes. Median time was 10.7 minutes (IQR 8.1 minutes to 15.4 minutes). A total of 43% of the patients (833/1,920, 95% CI 0.41-0.46) were offloaded in less than 10 minutes, while 27% (513/1,920, 95% CI 0.25-0.29) took greater than 15 minutes. Median times were longest on Mondays (11.5 minutes) and shortest on Wednesdays (10.3 minutes). Longest daily median offload time occurred between 1600 and 1700 (13.5 minutes), whereas the shortest median time was between 0800 and 0900 (9.3 minutes). Cumulative time spent waiting beyond 15 minutes totaled 72.5 hours over the study period. RFID monitoring is a simple and effective means of monitoring EMS traffic and wait times. At our institution, most squads are able to offload their patients within 15 minutes, with many in less than 10 minutes. Variations in wait times are seen and are a topic for future study.

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

OBJECTIVE

To 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.

METHODS

A 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.

RESULTS

Of 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.

CONCLUSION

Patients 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.

Offload zones to mitigate emergency medical services (EMS) offload delay in the emergency department: a process map and hazard analysis

Introduction Offload delay is a prolonged interval between ambulance arrival in the emergency department (ED) and transfer of patient care, typically occurring when EDs are crowded. The offload zone (OZ), which manages ambulance patients waiting for an ED bed, has been implemented to mitigate the impact of ED crowding on ambulance availability. Little is known about the safety or efficiency. The study objectives were to process map the OZ and conduct a hazard analysis to identify steps that could compromise patient safety or process efficiency.

METHODS

A Health Care Failure Mode and Effect Analysis was conducted. Failure modes (FM) were identified. For each FM, a probability to occur and severity of impact on patient safety and process efficiency was determined, and a hazard score (probability X severity) was calculated. For any hazard score considered high risk, root causes were identified, and mitigations were sought.

RESULTS

The OZ consists of six major processes: 1) patient transported by ambulance, 2) arrival to the ED, 3) transfer of patient care, 4) patient assessment in OZ, 5) patient care in OZ, and 6) patient transfer out of OZ; 78 FM were identified, of which 28 (35.9%) were deemed high risk and classified as impact on patient safety (n=7/28, 25.0%), process efficiency (n=10/28, 35.7%), or both (n=11/28, 39.3%). Seventeen mitigations were suggested.

CONCLUSION

This process map and hazard analysis is a first step in understanding the safety and efficiency of the OZ. The results from this study will inform current policy and practice, and future work to reduce offload delay.