The in-hospital interval: a description of EMT time spent in the emergency department

A brief history of ramping

'Ramping' is a commonly used term in contemporary Australian healthcare. It is also a part of the public and political zeitgeist. However, its precise definition varies among sources. In the published literature, there are distinctions between related terms, such as 'entry overload' and 'Patient Off Stretcher Time Delay'. How ramping is defined and how it came to be defined have significance for policies and procedures relating to the described phenomenon. Through examination of the history of the term, insights are obtained into the underlying issues contributing to ramping and, accordingly, associated possible solutions.

Machine learning-based forecasting of firemen ambulances' turnaround time in hospitals, considering the COVID-19 impact

When ambulances’ turnaround time (TT) in emergency departments is prolonged, it not only affects the victim severely but also causes unavailability of resources in emergency medical services (EMSs) and, consequently, leaves a locality unprotected. This problem may worsen with abnormal situations, e.g., the current coronavirus disease 2019 (COVID-19) pandemic. Taking this into consideration, this paper presents a first study on the COVID-19 impact on ambulances’ TT by analyzing historical data from the Departmental Fire and Rescue Service of the Doubs (SDIS 25), in France, for three hospitals. Because the TTs of SDIS 25 ambulances increased, this paper also calculated and analyzed the number of breakdowns in services, which augmented due to shortage of ambulances that return on service in time. It is, therefore, vital to have a decision-support tool to better reallocate resources by knowing the time EMSs ambulances and personnel will be in use. Thus, this paper proposes a novel two-stage methodology based on machine learning (ML) models to forecast the TT of each ambulance in a given time and hospital. The first stage uses a multivariate model of regularly spaced time series to predict the average TT (AvTT) per hour, which considers temporal variables and external ones (e.g., COVID-19 statistics, weather data). The second stage utilizes a multivariate irregularly spaced time series model, which considers temporal variables of each ambulance departure, type of intervention, external variables, and the previously predicted AvTT as inputs. Four state-of-the-art ML models were considered in this paper, namely, Light Gradient Boosted Machine, Multilayer Perceptron, Long Short-Term Memory, and Prophet. As shown in the results, the proposed methodology provided remarkable results for practical purposes. The AvTT accuracies obtained for the three hospitals were 90.16%, 97.02%, and 93.09%. And the TT accuracies were 74.42%, 86.63%, and 76.67%, all with an error margin of ±10 min.

Comparison of pre-hospital emergency services time intervals in patients with heart attack in Arak, Iran

Background:

After cardiac arrest, the possibility of death or irreversible complications will highly increase in the absence of cardiac resuscitation within 4 to 6 minutes. Accordingly, measuring the pre-hospital services time intervals is important for better management of emergency medical services delivery. The purpose of this study then was to investigate pre-hospital time intervals for patients with heart attack in Arak city, based on locations and time variables.

Methods:

This is a retrospective descriptive cross-sectional study, which was conducted at the Arak Emergency Medical Services (EMS) during 2017-2018. Data were analyzed by SPSS version 13.

Results:

The total number of heart attack patients registered in Arak emergency medical services was 2,659 of which 51% of patients were males. Six percent of patients were under 25 and about 49 percent were between 46 and 65 years old. The average of activation, response, on-scene, transportation, recovery and total time intervals were 3:30, 7:56, 15:15, 13:34, 11:07, 12:11, and 41:25, respectively. In the city area, the shortest and longest average response time intervals were in spring and winter, respectively. In out of the city area, the shortest average response time interval was in summer and the longest one in autumn. The shortest and the longest average response time intervals in the city area were in June and March, respectively, and in out of the city area, the shortest average response time interval was in June and the longest one in April.

Conclusions:

The shorter response and delivery time interval compared to the other studies may indicate improvement in the provision of EMS. Special attention should be paid to the facilities and equipment of vehicles during cold seasons to be in the shortest possible time. Also, training and informing the staff more about the code of cardiac patients along with general public education can help improve these intervals.

Disparities between Rural and Urban Areas of the Central Region of Saudi Arabia in the Utilization and Time-Centeredness of Emergency Medical Services

The purpose of this study was to explore differences in characteristics of missions dispatched by Emergency Medical Services (EMS) between rural and urban areas of Riyadh province in Saudi Arabia (SA). It also aimed at identifying weaknesses related to utilization and Response Time (RT). The study retrospectively evaluated 146,639 completed missions in 2018 by measuring the utilization rate in rural and urban areas. The study shows there are six times more ambulance crews available for rural areas compared to urban. There were 22.1 missions per 1000 urban inhabitants and 11.2 missions per 1000 in rural areas. The median RT for high urgent trauma cases was 20.2 min in rural compared to 15.2 min in urban areas (p < 0.001). In urban areas, the median RT for high urgent medical cases was 16.1 min, while it was 15.2 min for high urgent trauma cases. Around 62.3% of emergency cases in urban and 56.5% in rural areas were responded to within 20.00 min. Women utilized EMS less frequently. The RT was increased in urban areas compared to previous studies. The RT in the central region of SA has been identified as equal, or less than 20.00 min in 62.4% of all emergency cases. To further improve adherence to the 20' target, reorganizing the lowest urgent cases in the rural areas seems necessary.

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.

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

BACKGROUND

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'.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

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.

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.

OBJECTIVE

This study sought to test the validity of using the turnaround interval as a surrogate for the delivery interval.

METHODS

This 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.

RESULTS

Pearson'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.

CONCLUSION

The 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.

Analysis and impact of delays in ambulance to emergency department handovers

OBJECTIVES

Delays in the clinical handover of patient care from emergency medical services (EMS) to the ED because of ED crowding are a substantial problem for many EMS systems. This study was conducted to quantify handover delays experienced by the Ambulance Service of New South Wales (ASNSW), and to investigate patient and system factors associated with handover delay.

METHODS

A retrospective study of EMS dispatch and ambulance patient care records was conducted for all patients transported by ASNSW in January/April/July/October 2009. Patient characteristics and time intervals were summarised using descriptive statistics, with handover delay categorised as <30 min, 30-60 min and ≥60 min. Times are reported as HH:MM:SS. Partial proportional odds models were used to investigate factors associated with delays.

RESULTS

Of 141 381 transports, 12.5% of patients experienced a handover delay of 30-60 min, and 5% a delay of ≥60 min. The median handover interval was 00:15:46 (IQR 00:08:58-00:24:52, maximum 08:43:13). Patients transported to large hospitals were more likely to experience a delay of ≥30 min (odds ratio [OR] 14.57, 95% CI 11.41-18.60) or ≥60 min (OR 15.75, 95% CI 12.27-20.23) than those transported to small hospitals. Patients in major cities were more likely to experience delays than those in other areas, and patients ≥65 years were more likely to experience delays than those <16 years. Delays were most likely in winter. Cardiac and major trauma patients had the lowest likelihood of experiencing delays.

CONCLUSIONS

Handover delays are relatively common at the EMS/ED interface in New South Wales, and are most pronounced at large hospitals, in urban areas and during winter.

Impact of an emergency department closure on the local emergency medical services system

BACKGROUND

On July 12, 2010, Boston Medical Center (BMC), the busiest emergency department (ED) in Massachusetts, with more than 100,000 adult patient visits per year, consolidated its two fully functional EDs into one. In preparation for this consolidation, BMC implemented systems changes to mitigate potential negative effects on both BMC and emergency medical services (EMS) providers, including Boston Emergency Medical Services (Boston EMS), the provider of 9-1-1 EMS to the City of Boston.

OBJECTIVE

To examine the impact of the closure of an ED on an urban EMS system in a setting where ambulance diversion is not allowed.

METHODS

We performed a before-and-after study that examined the effects of an ED closure on BMC and Boston EMS. We examined ED and Boston EMS volumes and ambulance turnaround intervals from June 1, 2010, to July 11, 2010 (preclosure) as compared with July 12, 2010, to August 26, 2010 (postclosure). Mean ED and Boston EMS volumes and Boston EMS turnaround intervals were calculated in four-hour shifts. We used multivariate analysis to analyze electronic medical systems data from BMC and Boston EMS and linear regression. We used autoregressive integrated moving average (ARIMA) models to determine the effect of the ED closure on turnaround intervals, ED volumes, and transport volumes. All analyses were adjusted for shift, ED volume, day of the week, and citywide EMS transport volumes.

RESULTS

After ED closure, there was a statistically significant increase of 0.89 minutes (p = 0.02) in the mean EMS turnaround intervals. Additionally, the total ED volume decreased by 3.67 visits per shift (p < 0.001). The ratio of patients transported by Boston EMS to BMC remained unchanged (p = 0.11) for two weeks before and two weeks after the closure.

CONCLUSIONS

The closure of one ED resulted in a statistically significant increase in turnaround intervals and a significant decrease in ED volume independent of EMS volumes. In the absence of ambulance diversion, ratios of EMS turnaround intervals and EMS volumes according to hospital destination can be used as alternatives to ambulance diversion times to examine the effects of system-level changes such as closure of an ED on an urban EMS system.

The application of forecasting techniques to modeling emergency medical system calls in Calgary, Alberta

We develop and evaluate time-series models of call volume to the emergency medical service of a major Canadian city. Our objective is to offer simple and effective models that could be used for realistic simulation of the system and for forecasting daily and hourly call volumes. Notable features of the analyzed time series are: a positive trend, daily, weekly, and yearly seasonal cycles, special-day effects, and positive autocorrelation. We estimate models of daily volumes via two approaches: (1) autoregressive models of data obtained after eliminating trend, seasonality, and special-day effects; and (2) doubly-seasonal ARIMA models with special-day effects. We compare the estimated models in terms of goodness-of-fit and forecasting accuracy. We also consider two possibilities for the hourly model: (3) a multinomial distribution for the vector of number of calls in each hour conditional on the total volume of calls during the day and (4) fitting a time series to the data at the hourly level. For our data, (1) and (3) are superior.