The Impact of Ambulance Diversion on Heart Attack Deaths

Yankovic, Natalia; Glied, Sherry; Green, Linda V.; Grams, Morgan

doi:10.5034/inquiryjrnl_47.01.81

Cited by 25 publications

(13 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As we show at a granular level in this paper, this is not a tenable assumption. In addition, there have been various condition specific studies in the medical community demonstrating that delays can result in an increase in mortality (de Luca et al 2004, Chan et al 2008, Buist et al 2002, Yankovic et al 2010) and/or extend patient Length-of-stay (LOS) (Chalfin et al 2007, Renaud et al 2009, Rivers et al 2001). …”

Section: Standard Queueing Models Fall Shortmentioning

confidence: 99%

“…They find that the median hospital LOS and 28-day mortality rate is nearly twice as high for delayed patients. The order of magnitude for delay can be in minutes as in the case of cardiac patients (de Luca et al 2004, Buist et al 2002, Yankovic et al 2010, Chan et al 2008 or up to 5 days for burn-injured patients (Sheridan et al 1999). …”

Section: Related Literaturementioning

confidence: 99%

See 1 more Smart Citation

The Impact of Delays on Service Times in the Intensive Care Unit

Chan¹,

Farias

Escobar

2017

Management Science

View full text Add to dashboard Cite

Mainstream queueing models are frequently employed in modeling healthcare delivery in a number of settings, and further used in making operational decisions for the same. The vast majority of these queueing models assume that the service requirements of a job are independent of the state of the queue upon its arrival. In a healthcare setting, this assumption is equivalent to ignoring the effects of delay experienced by a patient awaiting care. However, it is only natural to conjecture that long delays may have adverse effects on patient outcomes and can potentially lead to longer lengths of stay (LOS) when the patient ultimately does receive care. At a very coarse level, prior research confirms these natural conjectures. This work sets out to understand these delay issues from an operational perspective. In particular, using data of nearly 6,000 Emergency Department (ED) visits, we use an instrumental variable approach to empirically measure how congestion in the Intensive Care Unit (ICU) can lead to delays in boarding from the ED to the ICU and measure the impact on the patient's ICU LOS.Capturing these empirically observed effects in a queueing model is challenging as the effect introduces potentially long range correlations in service and inter-arrival times. As such, we consider the problem of how to incorporate these measured delay effects into a queueing model and characterize approximations to various quantities of interest when the service time of a job is adversely impacted by the delay experienced by that job. Our findings suggest that this delay effect can be substantial and ignoring it when using queueing models to model healthcare delivery systems may result in significant under-provisioning.

show abstract

Section: Standard Queueing Models Fall Shortmentioning

confidence: 99%

Section: Related Literaturementioning

confidence: 99%

The Impact of Delays on Service Times in the Intensive Care Unit

Chan¹,

Farias

Escobar

2017

Management Science

View full text Add to dashboard Cite

show abstract

“…We identified a simulation study of a community hospital ED which found that the effect of diversion on waiting room times is quite small (2 minute reduction for every percentage point increase of time on diversion) 30. If this finding can be confirmed in other simulation and observational studies, it is unlikely that the small decrease in waiting room times from ambulance diversion could outweigh the unintended consequences of prolonged transport times and possibly worse outcomes of diverted patients 6,7,10…”

Section: Discussionmentioning

confidence: 99%

Reducing Ambulance Diversion at the Hospital and Regional Levels: Systemic Review of Insights from Simulation Models

Delgado

Meng²,

Mercer³

et al. 2013

WestJEM

View full text Add to dashboard Cite

Introduction:Optimal solutions for reducing diversion without worsening emergency department (ED) crowding are unclear. We performed a systematic review of published simulation studies to identify: 1) the tradeoff between ambulance diversion and ED wait times; 2) the predicted impact of patient flow interventions on reducing diversion; and 3) the optimal regional strategy for reducing diversion.Methods:Data Sources: Systematic review of articles using MEDLINE, Inspec, Scopus. Additional studies identified through bibliography review, Google Scholar, and scientific conference proceedings. Study Selection: Only simulations modeling ambulance diversion as a result of ED crowding or inpatient capacity problems were included. Data extraction: Independent extraction by two authors using predefined data fields.Results:We identified 5,116 potentially relevant records; 10 studies met inclusion criteria. In models that quantified the relationship between ED throughput times and diversion, diversion was found to only minimally improve ED waiting room times. Adding holding units for inpatient boarders and ED-based fast tracks, improving lab turnaround times, and smoothing elective surgery caseloads were found to reduce diversion considerably. While two models found a cooperative agreement between hospitals is necessary to prevent defensive diversion behavior by a hospital when a nearby hospital goes on diversion, one model found there may be more optimal solutions for reducing region wide wait times than a regional ban on diversion.Conclusion:Smoothing elective surgery caseloads, adding ED fast tracks as well as holding units for inpatient boarders, improving ED lab turnaround times, and implementing regional cooperative agreements among hospitals are promising avenues for reducing diversion.

show abstract

“…AD is known to reduce hospital revenues, increase patient transport times, and reduce ambulance availability (9)(10)(11)(12)(13)(14)(15). More recently, AD has been linked to increased mortality and a delay in the time to thrombolysis in patients with acute myocardial infarction (16,17). AD may have greater negative impact on vulnerable populations like the uninsured, those on public insurance, the elderly, and the critically ill, as these populations disproportionately use ambulance transport (18).…”

Section: Introductionmentioning

confidence: 98%