Retrospective Analysis of Quality Improvement Throughput Measures at a High-Volume Community Emergency Department

Milsten, Andrew; Klein, Joel S.; Qin, Liu; Vibhakar, Neel; Linder, Lawrence H.

doi:10.1111/jhq.12014

Cited by 11 publications

(7 citation statements)

References 24 publications

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“…Others have reported that LWBS can be significantly improved by placement of a physician or mid-level provider (MLP) at triage. 19 , 21 , 22 These programs seem to usually, although not invariably, result in a statistically significant reduction in LWBS. 23 Similarly favorable impact on LWBS has been reported with the institution of a “fast track” area of the ED, at which location less-critical patients are seen.…”

Section: Discussionmentioning

confidence: 96%

Identifying Patient Door-to-Room Goals to Minimize Left-Without-Being-Seen Rates

Pielsticker¹,

Whelan²,

Arthur³

et al. 2015

WestJEM

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IntroductionEmergency department (ED) patients in the leave-without-being-seen (LWBS) group risk problems of inefficiency, medical risk, and financial loss. The goal at our hospital is to limit LWBS to <1%. This study’s goal was to assess the influence on LWBS associated with prolonging intervals between patient presentation and placement in an exam room (DoorRoom time). This study’s major aim was to identify DoorRoom cutoffs that maximize likelihood of meeting the LWBS goal (i.e. <1%).MethodsWe conducted the study over one year (8/13–8/14) using operations data for an ED with annual census ~50,000. For each study day, the LWBS endpoint (i.e. was LWBS <1%: “yes or no”) and the mean DoorRoom time were recorded. We categorized DoorRoom means by intervals starting with ≤10min and ending at >60min. Multivariate logistic regression was used to assess for DoorRoom cutoffs predicting high LWBS, while adjusting for patient acuity (triage scores and admission %) and operations parameters. We used predictive marginal probability to assess utility of the regression-generated cutoffs. We defined statistical significance at p<0.05 and report odds ratio (OR) and 95% confidence intervals (CI).ResultsUnivariate results suggested a primary DoorRoom cutoff of 20′, to maintain a high likelihood (>85%) of meeting the LWBS goal. A secondary DoorRoom cutoff was indicated at 35′, to prevent a precipitous drop-off in likelihood of meeting the LWBS goal, from 61.1% at 35′ to 34.4% at 40′. Predictive marginal analysis using multivariate techniques to control for operational and patient-acuity factors confirmed the 20′ and 35′ cutoffs as significant (p<0.001). Days with DoorRoom between 21–35′ were 74% less likely to meet the LWBS goal than days with DoorRoom ≤20′ (OR 0.26, 95% CI [0.13–0.53]). Days with DoorRoom >35′ were a further 75% less likely to meet the LWBS goal than days with DoorRoom of 21–35′ (OR 0.25, 95% CI [0.15–0.41]).ConclusionOperationally useful DoorRoom cutoffs can be identified, which allow for rational establishment of performance goals for the ED attempting to minimize LWBS.

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Section: Discussionmentioning

confidence: 96%

Identifying Patient Door-to-Room Goals to Minimize Left-Without-Being-Seen Rates

Pielsticker¹,

Whelan²,

Arthur³

et al. 2015

WestJEM

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“…This practice has now changed and since ED operations performance can be significantly improved by placement of a physician or advanced practice provider at triage, the physician-out-front plan may be part of future operational (and tMD improvements). 19 , 26 , 27 …”

Section: Limitationsmentioning

confidence: 99%

“…Fortunately, there is evidence that focusing solely on tMD – while providing only a part of the overall picture – yields results of substantial operational importance. 26 …”

Section: Limitationsmentioning

confidence: 99%

Marginal analysis in assessing factors contributing time to physician in the Emergency Department using operations data

Pathan

Bhutta

Moinudheen

et al. 2016

Qatar Medical Journal

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Background: Standard Emergency Department (ED) operations goals include minimization of the time interval (tMD) between patients' initial ED presentation and initial physician evaluation. This study assessed factors known (or suspected) to influence tMD with a two-step goal. The first step was generation of a multivariate model identifying parameters associated with prolongation of tMD at a single study center. The second step was the use of a study center-specific multivariate tMD model as a basis for predictive marginal probability analysis; the marginal model allowed for prediction of the degree of ED operations benefit that would be affected with specific ED operations improvements. Methods: The study was conducted using one month (May 2015) of data obtained from an ED administrative database (EDAD) in an urban academic tertiary ED with an annual census of approximately 500,000; during the study month, the ED saw 39,593 cases. The EDAD data were used to generate a multivariate linear regression model assessing the various demographic and operational covariates' effects on the dependent variable tMD. Predictive marginal probability analysis was used to calculate the relative contributions of key covariates as well as demonstrate the likely tMD impact on modifying those covariates with operational improvements. Analyses were conducted with Stata 14MP, with significance defined at p < 0.05 and confidence intervals (CIs) reported at the 95% level. Results: In an acceptable linear regression model that accounted for just over half of the overall variance in tMD (adjusted r 2 0.51), important contributors to tMD included shift census (p = 0.008), shift time of day (p = 0.002), and physician coverage n (p = 0.004). These strong associations remained even after adjusting for each other and other covariates. Marginal predictive probability analysis was used to predict the overall tMD impact (improvement from 50 to 43 minutes, p < 0.001) of consistent staffing with 22 physicians. Conclusions: The analysis identified expected variables contributing to tMD with regression demonstrating significance and effect magnitude of alterations in covariates including patient census, shift time of day, and number of physicians. Marginal analysis provided operationally useful demonstration of the need to adjust physician coverage numbers, prompting changes at the study ED. The methods used in this analysis may prove useful in other EDs wishing to analyze operations information with the goal of predicting which interventions may have the most benefit.

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“…As of January 2012, the annual reporting of LWBS rates as a quality metric is required by the Centers for Medicare and Medicaid Services [6]. LWBS may also represent a significant negative economic impact on hospitals in the form of lost revenue and exposure to increased risk [7]. To date, these authors have not yet found decisive data to demonstrate the extent of this cost or exposure.…”

Section: Introductionmentioning

confidence: 99%

“…LWBS has often been identified as a problem related to suboptimal ED efficiency. Approaches to decrease LWBS include placing a provider in triage, implementing nursing order protocols, streamlined communication between the ED/inpatient units, leasing mobile units to act as fast-track departments, and variations of direct bedding [6][7][8][9][10][11][12][13][14][15]. The provider in triage approach not only decreases LWBS but also increases ED throughput metrics especially in large urban hospitals [12].…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Multifactorial Strategy Including Direct Bedding Is Associated With a Rapid and Sustained Reduction in Left Without Being Seen

Martin¹,

Brunnell²,

Neulander³

et al. 2021

Cureus

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Improve left without being seen (LWBS) in our high volume, tertiary care trauma center. Prior to intervention, our LWBS rate was 4.4%. Including a direct bedding strategy, we successfully reduced our LWBS to <1%. Design and methodWe utilized a retrospective before and after model. We hired a clinical documentation specialist and tracked several metrics. These included daily census, admission rates, and door to provider, door to room, average boarding, and door to disposition times. Data were collected and disseminated daily. Reports were shared at organization quality meetings. Simultaneously, we implemented the direct bedding initiative in conjunction with quick registration. To accommodate higher numbers of patients and expediate movement to care spaces, all patient spaces were clearly designated and labeled. ResultsDirect bedding began in September 2015 and our LWBS was 4.4%. One-year post-intervention, our LWBS was <2%. Within four years, it was <0.5%. The LWBS rate for each year, 2016 to 2019, was significantly lower than the control period (p < 0.01) (2015 up to September). Improvement was also seen in door-to-provider time and with patient experience scores. ConclusionOur multifactorial approach was associated with a profound and sustained reduction in LWBS over a short time period.

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Retrospective Analysis of Quality Improvement Throughput Measures at a High-Volume Community Emergency Department

Abstract: Despite an increase in patient census, the LWBS rate and door-to-doctor time decreased. This study of one solution to the issue of ED crowding demonstrates how a process redesign can lead to successful changes in throughput metrics.

Cited by 11 publications

References 24 publications

Identifying Patient Door-to-Room Goals to Minimize Left-Without-Being-Seen Rates

Identifying Patient Door-to-Room Goals to Minimize Left-Without-Being-Seen Rates

Marginal analysis in assessing factors contributing time to physician in the Emergency Department using operations data

Implementation of a Multifactorial Strategy Including Direct Bedding Is Associated With a Rapid and Sustained Reduction in Left Without Being Seen

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