How Long Are Patients Willing to Wait in the Emergency Department Before Leaving Without Being Seen

Shaikh, Sanober; Jerrard, David A.; Witting, Michael D.; Winters, Michael E.; Brodeur, Michael N.

doi:10.5811/westjem.2012.3.6895

Cited by 39 publications

(30 citation statements)

References 6 publications

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“…Our conclusions are in line with previous research by [15], who use survey data to show that wait times are an important determinant of the decision to leave without seeing a doctor.…”

Section: Introductionsupporting

confidence: 92%

Does overcrowding and health insurance type impact patient outcomes in emergency departments?

2013

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We examine the impact of Emergency Department (ED) overcrowding on wait times and patient outcomes using a unique cross section of about 32,000 patients for an ED located in the Southwestern United States. We construct a measure of a patient’s outcome and estimate the extent to which it is worsened by long waits in the ED. We find that waiting at an ED due to overcrowding tends to generate a negative outcome for all patients. We also find that this negative outcome is larger for those on Medicaid or who have no insurance and smaller for those with private insurance or Medicare.JEL Classification CodesI12; I13

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“…Our conclusions are in line with previous research by [15], who use survey data to show that wait times are an important determinant of the decision to leave without seeing a doctor.…”

Section: Introductionsupporting

confidence: 92%

Does overcrowding and health insurance type impact patient outcomes in emergency departments?

2013

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“…These results also suggest that the rate of leaving without being seen may increase as HIV testing rates increase, because patients who wait longer in the ED are more likely to leave without being seen (Shaikh, Jerrard, Witting, Winters, & Brodeur, ). Patients who seek ED care and leave without being seen may be unsatisfied with the service provided, and more importantly may face increased risk when serious conditions have not been assessed or treated (Kennedy, MacBean, Brand, Sundararajan, & Taylor, ).…”

Section: Discussionmentioning

confidence: 92%

Impact of Mandatory HIV Screening in the Emergency Department: A Queuing Study

Liu

Schnall

2016

Research in Nursing & Health

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To improve HIV screening rates, New York State in 2010 mandated that all persons 13-64 years receiving health care services, including care in emergency departments (EDs), be offered HIV testing. Little attention has been paid to the effect of screening on patient flow. Time-stamped ED visit data from patients eligible for HIV screening, 7,844 of whom were seen by providers and 767 who left before being seen by providers, were retrieved from electronic health records in one adult ED. During day shifts, 10% of patients left without being seen, and during evening shifts, 5% left without being seen. All patients seen by providers were offered testing, and 6% were tested for HIV. Queuing models were developed to evaluate the effect of HIV screening on ED length of stay, patient waiting time, and rate of leaving without being seen. Base case analysis was conducted using actual testing rates, and sensitivity analyses were conducted to evaluate the impact of increasing the testing rate. Length of ED stay of patients who received HIV tests was 24 minutes longer on day shifts and 104 minutes longer on evening shifts than for patients not tested for HIV. Increases in HIV testing rate were estimated to increase waiting time for all patients, including those who left without being seen. Our simulation suggested that incorporating HIV testing into ED patient visits not only adds to practitioner workload but also increases patient waiting time significantly during busy shifts, which may increase the rate of leaving without being seen.

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“…In recent years, these technologies have been applied to emergency care settings to forecast crowding [22][23][24][25][26], quantify the effects of patients who leave without being seen (LWBS) [27][28][29][30][31], assess triage and patient streaming mechanisms [4,5,[32][33][34], optimize staffing [1,[35][36][37][38][39], examine the impact of reducing boarding times [33,40], and analyze the financial consequences of crowding [14][15][16]. However, resources that provide this support in a single toolkit are not typically available to ED managers, and due to high technical barriers to entry, systems methods and tools remain broadly underutilized by decision-makers in emergency care settings [20,41].…”

Section: Introduction Backgroundmentioning

confidence: 99%

Predictive analytics in practice: A novel simulation application for addressing patient flow challenges in today's emergency departments

Hurwitz

Lopiano

Bohrmann

et al. 2016

Preprint

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Objectives: To develop a flexible software application that uses predictive analytics to enable emergency department (ED) decision-makers in virtually any environment to predict the effects of operational interventions and enhance continual process improvement efforts. To demonstrate the ability of the application's core simulation model to recreate and predict sitespecific patient flow in two very different EDs: a large academic center and a freestanding ED. To describe how the application was used by a freestanding ED medical director to match ED resources to patient demand. Methods:The application was developed through a public-private partnership between University of Florida Health and Roundtable Analytics, Inc., supported by a National Science Foundation Small Business Technology Transfer (STTR) grant. The core simulation technology was designed to be quickly adaptable to any ED using data routinely collected by most electronic health record systems. To demonstrate model accuracy, Monte Carlo studies were performed to predict the effects of management interventions in two distinct ED settings. At one ED, the medical director conducted simulation studies to evaluate the sustainability of the current staffing strategy and inform his decision to implement specific interventions that better match ED resources to patient demand. After implementation of one intervention, the fidelity of the model's predictions was evaluated.Results: A flexible, cloud-based software application enabling ED decision-makers to predict the effects of operational decisions was developed and deployed at two qualitatively distinct EDs. The application accurately recreated each ED's throughput and faithfully predicted the effects of specific management interventions. At one site, the application was used to identify when increasing arrivals will dictate that the current staffing strategy will be less effective than an alternative strategy. As actual arrivals approached this point, decision-makers used the application to simulate a variety different interventions; this directly informed their decision to implement a new strategy. The observed outcomes resulting from this intervention fell within the range of predictions from the model. Conclusion:This application overcomes technical barriers that have made simulation modeling inaccessible to key decision-makers in emergency departments. Using this technology, ED managers with no programming experience can conduct customized simulation studies regardless of their ED's volume and complexity. In two very different case studies, the fidelity of the application was established and the application was shown to have a direct positive effect on patient flow. The effective use of simulation modeling promises to replace inefficient trial-anderror approaches and become a useful and accessible tool for healthcare managers challenged to make operational decisions in environments of increasingly scarce resources.

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How Long Are Patients Willing to Wait in the Emergency Department Before Leaving Without Being Seen

Cited by 39 publications

References 6 publications

Does overcrowding and health insurance type impact patient outcomes in emergency departments?

Does overcrowding and health insurance type impact patient outcomes in emergency departments?

Impact of Mandatory HIV Screening in the Emergency Department: A Queuing Study

Predictive analytics in practice: A novel simulation application for addressing patient flow challenges in today's emergency departments

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