Ambulance Diversion Reduction and Elimination: The 3-2-1 Plan

Patel, Pankaj B.; Vinson, David R.

doi:10.1016/j.jemermed.2012.01.031

Cited by 11 publications

(8 citation statements)

References 31 publications

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“…These measures led to a 73–82% decrease of diversion hours. Following up on the successful reduction of diversion hours by 74% found in the Sacramento study [ 27 , 29 ] further protocols incorporating a web-based, region-wide emergency medical service software were introduced, similar to IVENA in Munich. As a result, diversion hours could be reduced by 87.4% annually while completely eliminating diversion hours in one county.…”

Section: Discussionmentioning

confidence: 99%

Closure simulation for reduction of emergency patient diversion: a discrete agent-based simulation approach to minimizing ambulance diversion

et al. 2018

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BackgroundThe city of Munich uses web-based information system IVENA to promote exchange of information regarding hospital offerings and closures between the integrated dispatch center and hospitals to support coordination of the emergency medical services. Hospital crowding resulting in closures and thus prolonged transportation time poses a major problem. An innovative discrete agent model simulates the effects of novel policies to reduce closure times and avoid crowding.MethodsFor this analysis, between 2013 and 2017, IVENA data consisting of injury/disease, condition, age, estimated arrival time and assigned hospital or hospital-closure statistics as well as underlying reasons were examined. Two simulation experiments with three policy variations are performed to gain insights on the influence of diversion policies onto the outcome variables.ResultsA total of 530,000+ patients were assigned via the IVENA system and 200,000+ closures were requested during this time period. Some hospital units request a closure on more than 50% of days. The majority of hospital closures are not triggered by the absolute number of patient arrivals, but by a sudden increase within a short time period. Four of the simulations yielded a specific potential for shortening of overall closure time in comparison to the current status quo.ConclusionEffective solutions against crowding require common policies to limit closure status periods based on quantitative thresholds. A new policy in combination with a quantitative arrival sensor system may reduce closing hours and optimize patient flow.

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Section: Discussionmentioning

confidence: 99%

Closure simulation for reduction of emergency patient diversion: a discrete agent-based simulation approach to minimizing ambulance diversion

et al. 2018

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“…Of note, ambulance diversion reduction efforts were ongoing prior to this study. 28,38 Regulatory agencies have delved into the issue of overcrowding and admitted patients who are boarded in EDs. The IOM called for an end to ED boarding through strong standards set by The Joint Commission.…”

Section: Discussionmentioning

confidence: 99%

Reduction of Admit Wait Times: The Effect of a Leadership‐based Program

Patel

Combs

Vinson

2014

Academic Emergency Medicine

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Objectives: Prolonged admit wait times in the emergency department (ED) for patients who require hospitalization lead to increased boarding time in the ED, a significant cause of ED congestion. This is associated with decreased quality of care, higher morbidity and mortality, decreased patient satisfaction, increased costs for care, ambulance diversion, higher numbers of patients who leave without being seen (LWBS), and delayed care with longer lengths of stay (LOS) for other ED patients. The objective was to assess the effect of a leadership-based program to expedite hospital admissions from the ED.Methods: This before-and-after observational study was undertaken from 2006 through 2011 at one community hospital ED. A team of ED and hospital leaders implemented a program to reduce admit wait times, using a computerized hospital-wide tracking system to monitor inpatient and ED bed status. The team collaboratively and consistently moved ED patients to their inpatient beds within an established goal of 60 minutes after an admission decision was reached. Top leadership actively intervened in real time by contacting staff whenever delays occurred to expedite immediate solutions to achieve the 60-minute goal. The primary outcome measures were the percentage of ED patients who were admitted to inpatient beds within 60 minutes from the time the beds were requested and ED boarding time. LOS, patient satisfaction, LWBS rate, and ambulance diversion hours were also measured.Results: After ED census, hospital admission rates, and ED bed capacity were controlled for using a multivariable linear regression analysis, the admit wait time reduction program contributed to an increase in patients being admitted to the hospital within 60 minutes by 16 percentage points (95% confidence intervals [CI] = 10 to 22 points; p < 0.0001) and a decrease in boarding time per admission of 46 minutes (95% CI = 63 to 82 minutes; p < 0.0001). LOS decreased for admitted patients by 79 minutes (95% CI = 55 to 104 minutes; p < 0.0001), for discharged patients by 17 minutes (95% CI = 12 to 23 minutes; p < 0.0001), and for all patients by 34 minutes (95% CI = 25 to 43 minutes; p < 0.0001). Patient satisfaction increased 4.9 percentage points (95% CI = 3.8 to 6.0 points; p < 0.0001). LWBS patients decreased 0.9 percentage points (95% CI = 0.6 to 1.2 points; p < 0.0001) and monthly ambulance diversion decreased 8.2 hours (95% CI = 4.6 to 11.8 hours; p < 0.0001).Conclusions: A leadership-based program to reduce admit wait times and boarding times was associated with a significant increase in the percentage of patients admitted to the hospital within 60 minutes and a significant decrease in boarding time. Also associated with the program were decreased ED LOS, LWBS rate, and ambulance diversion, as well as increased patient satisfaction.ACADEMIC EMERGENCY MEDICINE 2014; 21:266-273

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“…It is important for administrators to understand the significance of AOD and for stake-holders to avoid confusing ambulance diversion with AOD. A number of papers have been published addressing strategies to reduce diversion hours [8-11]. These studies have shown the diversion hours can be reduced.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of ambulance offload delay at a university hospital emergency department

et al. 2013

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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.

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Ambulance Diversion Reduction and Elimination: The 3-2-1 Plan

Cited by 11 publications

References 31 publications

Closure simulation for reduction of emergency patient diversion: a discrete agent-based simulation approach to minimizing ambulance diversion

Closure simulation for reduction of emergency patient diversion: a discrete agent-based simulation approach to minimizing ambulance diversion

Reduction of Admit Wait Times: The Effect of a Leadership‐based Program

Evaluation of ambulance offload delay at a university hospital emergency department

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