Hospital Determinants of Emergency Department Left Without Being Seen Rates

doi:10.1016/j.annemergmed.2011.01.009

Annals of Emergency Medicine

Volume 58, Issue 1, July 2011, Pages 24-32.e3

https://doi.org/10.1016/j.annemergmed.2011.01.009 Get rights and content

Study objective

The proportion of patients who leave without being seen in the emergency department (ED) is an outcome-oriented measure of impaired access to emergency care and represents the failure of an emergency care delivery system to meet its goals of providing care to those most in need. Little is known about variation in the amount of left without being seen or about hospital-level determinants. Such knowledge is necessary to target hospital-level interventions to improve access to emergency care. We seek to determine whether hospital-level socioeconomic status case mix or hospital structural characteristics are predictive of ED left without being seen rates.

Methods

We performed a cross-sectional study of all acute-care, nonfederal hospitals in California that operated an ED in 2007, using data from the California Office of Statewide Health Planning and Development database and the US census. Our outcome of interest was whether a visit to a given hospital ED resulted in left without being seen. The proportion of left without being seen was measured by the number of left without being seen cases out of the total number of visits.

Results

We studied 9.2 million ED visits to 262 hospitals in California. The percentage of left without being seen varied greatly over hospitals, ranging from 0% to 20.3%, with a median percentage of 2.6%. In multivariable analyses adjusting for hospital-level socioeconomic status case mix, visitors to EDs with a higher proportion of low-income and poorly insured patients experienced a higher risk of left without being seen. We found that the odds of an ED visit resulting in left without being seen increased by a factor of 1.15 for each 10-percentage-point increase in poorly insured patients, and odds of left without being seen decreased by a factor of 0.86 for each $10,000 increase in household income. When hospital structural characteristics were added to the model, county ownership, trauma center designation, and teaching program affiliation were positively associated with increased probability of left without being seen (odds ratio 2.09; 1.62, and 2.14, respectively), and these factors attenuated the association with insurance status.

Conclusion

Visitors to different EDs experience a large variation in their probability of left without being seen, and visitors to hospitals serving a high proportion of low-income and poorly insured patients are at disproportionately higher risk of leaving without being seen. Our findings suggest that there is room for substantial improvement in this outcome, and regional interventions can be targeted toward certain at-risk hospitals to improve access to emergency care.

Introduction

Chronicled by the Institute of Medicine,¹ lay press,2, 3, 4 and researchers,5, 6, 7, 8, 9 a decrease in access to emergency departments (EDs) and services has strained the acute care system to its breaking point. Patients who leave without being seen represent the failure of an emergency care delivery system to meet its goals of providing care to those most in need. The proportion of annual hospital-level left without being seen visits is the amalgamation of all individual ED visitors' decisions to leave without being seen or not and is often used as a marker of ED crowding and is associated with longer waits.10, 11 Previous studies suggest that a nontrivial proportion of left without being seen patients are seriously ill, require immediate evaluation, and are at risk of poorer outcomes.11, 12 As such, the proportion of visits resulting in left without being seen have been proposed by The Joint Commission (TJC) and Centers for Medicare & Medicaid Services as hospital quality indicators.13, 14

The proportion of left without being seen has increased dramatically in the past 15 years⁵ as strains in the emergency care system have mounted. Although it is reasonable to suspect that the burden of left without being seen has fallen disproportionately on vulnerable populations and the hospitals that serve them, the evidence for this is not clear.

Contemporary attempts to study left without being seen have been limited by the scarcity of data reporting it. Most of the pertinent literature focuses on the patient-level or operational determinants at individual (or single) hospitals.10, 11, 12, 15, 16, 17, 18, 19, 20, 21, 22, 23 A survey of Los Angeles County hospitals performed 20 years ago identified some facility-level determinants of left without being seen, including longer wait times, a higher proportion of uninsured patients, and teaching status.²³ One national survey of left without being seen analyzed hospital structural factors but did not include hospital socioeconomic status case mix.²⁴ This dearth of information has hindered the ability of policymakers to understand the effect of crowding on vulnerable communities and to design system-level interventions to improve access to emergency care.

The goal of the current study was to assess the association of socioeconomic status case mix and other hospital characteristics with left without being seen rates for patients presenting to California hospital EDs during 2007.

Section snippets

Theoretical Model of the Problem

We study left without being seen rates as a measure of impaired health care access, in which patients' behavior represents a failed attempt at entering the health care system.²⁵ Although literature on access spans aspects of potential measures of accessibility (eg, socio-organizational and geographic), as well as individuals' willingness to seek care, left without being seen is the end product of a system's inability to provide services to patients who have self-identified as needing care in a

Results

We identified 288 hospitals in our data set that operated an ED during the entire 2007 study period. Twenty-six facilities reported a left without being seen rate of 0%; those 262 hospital reporting a nonzero left without being seen rate represented a total of 9.2 million ED visits. The mean ED census for the remaining 262 hospitals reporting a nonzero left without being seen rate was 35,034 visits per year (median 31,079; range 5,721 to 133,968) compared with 28,126 visits (range 844 to

Limitations

The findings in this study may not be generalizable to the entire United States because it is limited to a geographically distinct state with a number of characteristics different from the rest of the United States. However, California represents 12% of the US population and 7% of the US hospital market⁴² and itself can provide insight to the scale of the problem. In fact, California has a lower number of ED visits per 1,000 population compared with national estimates (274 in 2007 in California

Discussion

We analyzed a large, statewide cohort of hospital EDs to identify variation and facility-level determinants of annual left without being seen rates. We found that there was a wide range of left without being seen rates and that visits to EDs serving a high proportion of poorly insured individuals and patients residing in areas with low income were associated with a higher probability of left without being seen. Hospitals with high proportions of such vulnerable populations are much more likely

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Cited by (75)

Modeling abandonment behavior among patients
2023, European Journal of Operational Research
Researchers often utilize time-based models of queue abandonment. In such models, researchers determine the length of time until abandonment by drawing from an exogenous distribution with no dependence on the system state. Recent empirical work, however, suggests that queue abandonment times may differ depending on key parameters of the system. We build on these empirical findings herein. Specifically, we study three operational drivers of abandonment – waiting time, queue length, and service rate – within the context of a hospital emergency department (ED) where patients often leave without being seen by a physician. We examine the shape of the hazard function for these patients and identify a dynamic Weibull distribution that is parameterized by queue length and service rate, effectively characterizes abandonment behavior in this context. We conduct a numerical study and demonstrate the benefit of using state-dependent abandonment times. Specifically, we show that a scaled arrival model that overlooks abandonment behavior, a prevailing model in discrete-event simulations of the ED, results in significant bias in resource planning (e.g., staffing levels, bed capacity, etc.) and performs poorly compared to our proposed Weibull model. Our findings have implications for researchers modeling and simulating queue abandonment.
‘Swab and Go’ impact on emergency department left without being seen rates
2022, American Journal of Emergency Medicine
Crowding is the strongest predictor of left without being seen risk in a pediatric emergency department
2021, American Journal of Emergency Medicine
Citation Excerpt :
For these reasons, identifying patients at risk of becoming LWBS is of paramount importance to health systems. Studies conducted in both the pediatric and adult ED setting have shown that the LWBS disposition is associated with a number of social and behavioral patient attributes including insurance status, age, race, household income, and urbanicity. [11-15] There is additional evidence that LWBS risk is tied to elements of patients' medical care, including acuity and chief complaint. [12,13,16]
Emergency Department (ED) patients who leave without being seen (LWBS) are associated with adverse safety and medico-legal consequences. While LWBS risk has been previously tied to demographic and acuity related factors, there is limited research examining crowding-related risk in the pediatric setting. The primary objective of this study was to determine the association between LWBS risk and crowding, using the National Emergency Department Overcrowding Score (NEDOCS) and occupancy rate as crowding metrics.
We performed a retrospective observational study on electronic health record (EHR) data from the ED of a quaternary care children's hospital and trauma center during the 14-month study period. NEDOCS and occupancy rate were calculated for 15-min windows and matched to patient arrival time. We leveraged multiple logistic regression analyses to demonstrate the relationship between patientlevel LWBS risk and each crowding metric, controlling for characteristics drawn from the pre-arrival state. We performed a chi-squared test to determine whether a difference existed between the receiver operating characteristic (ROC) curves in the two models. Finally, we executed a dominance analysis using McFadden's pseudo-R 2 to determine the relative importance of each crowding metric in the models.
A total of 54,890 patient encounters were studied, 1.22% of whom LWBS. The odds ratio for LWBS risk was 1.30 (95% CI 1.27–1.33) per 10-point increase in NEDOCS and 1.23 (95% CI 1.21–1.25). per 10% increase in occupancy rate. Area under the curve (AUC) was 86.9% for the NEDOCS model and 86.7% for the occupancy rate model. There was no statistically significant difference between the AUCs of the two models (p-value 0.27). Dominance analysis revealed that in each model, the most important variable studied was its respective crowding metric; NEDOCS accounted for 55.6% and occupancy rate accounted for 53.9% of predicted variance in LWBS.
Not only was ED overcrowding positively and significantly associated with individual LWBS risk, but it was the single most important factor that determined a patient's likelihood of LWBS in the pediatric ED. Because occupancy rate and NEDOCS are available in real time, each could serve as a monitor for individual LWBS risk in the pediatric ED.
Characterization of emergency department abandonment using a real-time location system
2020, American Journal of Emergency Medicine
Citation Excerpt :
One study showed a discrepancy of roughly 25 min between the earliest recorded time a patient was noted to have left and the official end time of their medical chart [14]; this study shows an even larger gap, with a mean discrepancy of 92 (median 70) minutes between staff-recorded and RTLS departure time. This should be interpreted in the light of findings that LWBS rates vary significantly by the demographics of populations they serve – for example, hospitals serving patients who are younger, more often uninsured, or low income tend to have higher LWBS rates independent of wait times [17]. Our ED population as measured by RTLS waited a median of 68 min before leaving, which may represent a significantly higher tolerance to waiting than other institutions whose “latest possible” approach showed mean LWBS times of 70 min, [14] suggesting they actually left earlier than this.
Patients who present to emergency departments (EDs) for evaluation but are noted to have left without being seen (LWBS) are potentially at great risk. Governmental agencies, such as the Centers for Medicare and Medicaid, as well as hospitals and health organizations, are examining the factors which drive LWBS, including accurately quantifying patient tolerance to wait times and targeting interventions to improve patient tolerance to waiting.
Compare traditional methods of estimating time to LWBS with an objective method using a real-time location tracking system (RTLS); examine temporal factors associated with greater LWBS rates.
This is a retrospective cohort study of all ED visits to a large, suburban, quaternary care hospital in one calendar year. LWBS was calculated as patient registration to nurse recognition and documentation of patient abandonment (traditional method) vs registration to last onsite RTLS timestamp (study method). Descriptives of patterns of patient abandonment rates and patient demographic data were also included.
Our study shows that traditional methods of measuring LWBS times significantly overestimate actual patient tolerance to waiting times (median 70, mean 92 min). Patients triaged to resource intensive categories (Emergency Severity Index (ESI) 2, 3) wait longer than patients triaged to less resource intensive categories (ESI 4, 5).
Compared to traditional methods, RTLS is an efficient and accurate way to measure LWBS rates and helps set the stage for assessing the efficacy of interventions to reduce LWBS and reduce the gap between those seeking evaluation at emergency departments and those ultimately receiving it.
Patients Who Leave the Emergency Department Without Being Seen and Their Follow-Up Behavior: A Retrospective Descriptive Analysis
2019, Journal of Emergency Medicine
Citation Excerpt :
This may be because these patients tend to present repeatedly for complaints stemming from issues the ED may not be resourced to solve, including substance dependence or homelessness. In examining the return visits to the ED within the LWBS population, we also found that nearly one-quarter (24.8%) of LWBS visits were followed by an ED return visit within 7 days, with 59.6% returning to the ED within the first 24 h. Of those, the vast majority were seen by a physician and either discharged or left on their own accord, with 6.7% LWBS for a second time; 11.5% were admitted upon returning to the ED after LWBS, a rate towards the upper end of those found by most other single- and multi-institution studies nationally and internationally, with some exceptions (1,10,11,13,15,16). On the other hand, this rate compares favorably to our health system’s overall ED admission rate of 20.9%, meaning that the returning LWBS patient is only approximately half as likely to be admitted as the usual ED patient.
Past studies suggest that patients who leave without being seen (LWBS) by a physician from a hospital’s emergency department (ED) represent a quality and safety concern, and thus LWBS rates have often been used as an ED performance metric. There are few recent studies, however, that have examined the characteristics of the LWBS population at hospitals in the United States.
This study describes the LWBS population at a multi-hospital academic health system.
This was a retrospective study of electronic medical record data from EDs at two academic hospitals with a shared patient population that analyzed all LWBS visits during the 45-month period between July 2012 and March 2016. Demographic and clinical variables, including patient characteristics, chief complaint, acuity, and evidence of ongoing medical care, were assessed.
During the study period, 2.4% of patients presenting to the study EDs left without being seen. This population tended to have lower-acuity chief complaints and nearly triple the number of ED visits as the general ED patient; 7.8% sought follow-up care from outpatient clinics and 24.8% returned to the ED within 7 days. Of this latter group, 11.5% were subsequently admitted for inpatient care, representing 0.068% of the total ED census during the study period.
LWBS patients are high ED utilizers who may be effectively targeted by “hotspotting.” Our 11.5% admission rate at return after LWBS compares favorably with the overall 20.9% admission rate at the study EDs and represents a small minority of all LWBS visits. Given the paucity of return ED visits after interval clinic encounters, our data suggest that patients who were seen in clinic had their medical complaint adequately resolved on a non-emergent outpatient basis, and that increased LWBS rates may reflect poor access to timely clinic-based care rather than intrinsic systemic issues within the ED.
Development of relationship between triaging of patients and emergency department performance
2019, Procedia Manufacturing
Triage is a very important step in today’s Emergency Departments (EDs). While triage mainly focuses on prioritizing patients based on their medical conditions, this is also an effective management tool to organize, monitor and evaluate the ED patients and resources. As the waiting time of a patient is highly influenced by the triage category, there is a possibility that the length of stay in ED (EDLOS) and overall performance of ED have a relationship with this classification. The main aim of this study is to investigate how the actual ED performance is affected by the number of ED presentations in different triage categories. A comprehensive case study has been conducted and ED patient data of two hospitals in Brisbane have been collected. Based on a statistical analysis of the collected data, a relationship has been identified between the arrival of patients in each category and ED performance. Descriptive statistics have been used to analyze the patients’ data for each category. This result will help in managing the necessary resources to avoid the long waiting times as well as a drop in the performance level. Eventually, the outcome will facilitate the ED practitioners to maintain the quality of care in the health sector and also the patients’ satisfaction.

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: Supervising editor: David J. Magid, MD, MPH

: By Annals policy, submissions authored by faculty in the department of the editor in chief (Dr. Callaham) are handled entirely by other senior editors, and Dr. Callaham plays no role in their decisionmaking nor is informed of any details during the process.

: Author contributions: BCS conceived the study. BCS, SA, DZ, RW, and RYH designed the study analysis. BCS obtained research funding. HM, WH, RW, and LL supervised the data collection and managed and analyzed the data, and RW and LL provided statistical advice on study design. RYH and BCS drafted the manuscript, and all authors contributed substantially to its revision. RYH takes responsibility for the paper as a whole.

: Funding and support: By Annals policy, all authors are required to disclose any and all commercial, financial, and other relationships in any way related to the subject of this article as per ICMJE conflict of interest guidelines (see www.icmje.org). This publication was supported by the Emergency Medicine Foundation (Dr. Sun), Agency for Healthcare Research and Quality (R03 HS18098) (Dr. Sun), UCLA Older Americans Independence Center NIH/NIA grant P30-AG028748 (Ms. Han and Drs. McCreath and Sun), NIH/NCRR/OD UCSF-CTSI grant number KL2 RR024130 (Dr. Hsia), and the Robert Wood Johnson Foundation Physician Faculty Scholars (Dr. Hsia). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of any of the funding agencies.

: Publication date: Available online February 21, 2011.

: Please see page 25 for the Editor's Capsule Summary of this article.

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Health policy/original researchHospital Determinants of Emergency Department Left Without Being Seen Rates

Study objective

Methods

Results

Conclusion

Introduction

Section snippets

Theoretical Model of the Problem

Results

Limitations

Discussion

J Emerg Med

Ann Emerg Med

Ann Emerg Med

Ann Emerg Med

Jt Comm J Qual Patient Saf

Ann Emerg Med

Am J Emerg Med

Ann Emerg Med

Ann Emerg Med

Am J Emerg Med

Ann Emerg Med

Ann Emerg Med

Ann Emerg Med

Health Policy

Ann Emerg Med

Ann Emerg Med

Hospital-Based Emergency Care: At the Breaking Point

Overcrowding in emergency rooms common

USA Today

Ailing ERs threaten patients, leave communities vulnerable

USA Today

Emergency room delays

New York Times

Emergency department overcrowding: analysis of the factors of renege rate

Acad Emerg Med

Consequences of queuing for care at a public hospital emergency department

JAMA

Characteristics of patients who leave emergency departments without being seen

Acad Emerg Med

Patients who leave a public hospital emergency department without being seen by a physicianCauses and consequences

JAMA

Specification Manual for National Hospital Quality Measures

Effect of an emergency department (ED) managed acute care unit on ED overcrowding and emergency medical services diversion

Acad Emerg Med

A population follow-up study of patients who left an emergency department without being seen by a medical officer

Emerg Med J

Factors associated with patients who leave without being seen

Acad Emerg Med

Health policy/original research
Hospital Determinants of Emergency Department Left Without Being Seen Rates