An Independent Evaluation of Four Quantitative Emergency Department Crowding Scales

Background Emergency department (ED) overcrowding has become a frequent topic of investigation. Despite a significant body of research, there is no standard definition or measurement of ED crowding. Four quantitative scales for ED crowding have been proposed in the literature: the Real‐time Emergency Analysis of Demand Indicators (READI), the Emergency Department Work Index (EDWIN), the National Emergency Department Overcrowding Study (NEDOCS) scale, and the Emergency Department Crowding Scale (EDCS). These four scales have yet to be independently evaluated and compared. Objectives The goals of this study were to formally compare four existing quantitative ED crowding scales by measuring their ability to detect instances of perceived ED crowding and to determine whether any of these scales provide a generalizable solution for measuring ED crowding. Methods Data were collected at two‐hour intervals over 135 consecutive sampling instances. Physician and nurse agreement was assessed using weighted κ statistics. The crowding scales were compared via correlation statistics and their ability to predict perceived instances of ED crowding. Sensitivity, specificity, and positive predictive values were calculated at site‐specific cut points and at the recommended thresholds. Results All four of the crowding scales were significantly correlated, but their predictive abilities varied widely. NEDOCS had the highest area under the receiver operating characteristic curve (AROC) (0.92), while EDCS had the lowest (0.64). The recommended thresholds for the crowding scales were rarely exceeded; therefore, the scales were adjusted to site‐specific cut points. At a site‐specific cut point of 37.19, NEDOCS had the highest sensitivity (0.81), specificity (0.87), and positive predictive value (0.62). Conclusions At the study site, the suggested thresholds of the published crowding scales did not agree with providers' perceptions of ED crowding. Even after adjusting the scales to site‐specific thresholds, a relatively low prevalence of ED crowding resulted in unacceptably low positive predictive values for each scale. These results indicate that these crowding scales lack scalability and do not perform as designed in EDs where crowding is not the norm. However, two of the crowding scales, EDWIN and NEDOCS, and one of the READI subscales, bed ratio, yielded good predictive power (AROC >0.80) of perceived ED crowding, suggesting that they could be used effectively after a period of site‐specific calibration at EDs where crowding is a frequent occurrence.     

Forecasting Daily Patient Volumes in the Emergency Department

Background: Shifts in the supply of and demand for emergency department (ED) resources make the efficient allocation of ED resources increasingly important. Forecasting is a vital activity that guides decision‐making in many areas of economic, industrial, and scientific planning, but has gained little traction in the health care industry. There are few studies that explore the use of forecasting methods to predict patient volumes in the ED. Objectives: The goals of this study are to explore and evaluate the use of several statistical forecasting methods to predict daily ED patient volumes at three diverse hospital EDs and to compare the accuracy of these methods to the accuracy of a previously proposed forecasting method. Methods: Daily patient arrivals at three hospital EDs were collected for the period January 1, 2005, through March 31, 2007. The authors evaluated the use of seasonal autoregressive integrated moving average, time series regression, exponential smoothing, and artificial neural network models to forecast daily patient volumes at each facility. Forecasts were made for horizons ranging from 1 to 30 days in advance. The forecast accuracy achieved by the various forecasting methods was compared to the forecast accuracy achieved when using a benchmark forecasting method already available in the emergency medicine literature. Results: All time series methods considered in this analysis provided improved in‐sample model goodness of fit. However, postsample analysis revealed that time series regression models that augment linear regression models by accounting for serial autocorrelation offered only small improvements in terms of postsample forecast accuracy, relative to multiple linear regression models, while seasonal autoregressive integrated moving average, exponential smoothing, and artificial neural network forecasting models did not provide consistently accurate forecasts of daily ED volumes. Conclusions: This study confirms the widely held belief that daily demand for ED services is characterized by seasonal and weekly patterns. The authors compared several time series forecasting methods to a benchmark multiple linear regression model. The results suggest that the existing methodology proposed in the literature, multiple linear regression based on calendar variables, is a reasonable approach to forecasting daily patient volumes in the ED. However, the authors conclude that regression‐based models that incorporate calendar variables, account for site‐specific special‐day effects, and allow for residual autocorrelation provide a more appropriate, informative, and consistently accurate approach to forecasting daily ED patient volumes.     

Regional Anesthesia for Children Undergoing Orthopedic Ambulatory Surgeries in the United States, 1996–2006

Background

Our objective was to evaluate national trends in regional anesthetic techniques among children undergoing ambulatory orthopedic procedures.

Purpose and Questions

We aimed to determine whether an increase in regional anesthetics was primarily driven by an increase in the number of peripheral nerve blocks performed rather than an increase in neuraxial techniques. We further aimed to determine whether the proportion of peripheral nerve blocks performed in conjunction with general anesthesia has increased over time.

Patients and Methods

Our study sample included any pediatric patient (i.e., <18 years old) who underwent an orthopedic ambulatory procedure in 1996 and 2006. We obtained data on ambulatory surgical procedures by accessing the Centers for Disease Control and Prevention’s National Survey of Ambulatory Surgery. Patient demographics (age, gender), procedure information, and anesthesia-related variables were analyzed for each year.

Results

The proportion of peripheral nerve blocks performed for ambulatory surgery more than doubled from 1996 (4.4 %) to 2006 (8.1 %). A significantly larger proportion of orthopedic procedures were being performed with a combination of peripheral nerve blocks and general anesthesia (1.2 % in 1996 and 43 % 2006). The use of neuraxial anesthesia for lower extremity surgeries decreased over the 10-year period (1.1 and 0.4 % in 1996 and 2006, respectively).

Conclusions

There was a significant increase in the use of peripheral nerve blocks for children undergoing ambulatory orthopedic procedures in the USA, while neuraxial techniques became less common over the 10-year period. The peripheral nerve blocks were frequently performed in conjunction with general anesthesia.     

Informing a home time measure reflective of quality of life: A data driven investigation of time frames and settings of health care utilization

Objective

To evaluate short- and long-term measures of health care utilization—days in the emergency department (ED), inpatient (IP) care, and rehabilitation in a post-acute care (PAC) facility—to understand how home time (i.e., days alive and not in an acute or PAC setting) corresponds to quality of life (QoL).

Data Sources

Survey data on community-residing veterans combined with multipayer administrative data on health care utilization.

Study Design

VA or Medicare health care utilization, quantified as days of care received in the ED, IP, and PAC in the 6 and 18 months preceding survey completion, were used to predict seven QoL-related measures collected during the survey. Elastic net machine learning was used to construct models, with resulting regression coefficients used to develop a weighted utilization variable. This was then compared with an unweighted count of days with any utilization.

Principal Findings

In the short term (6 months), PAC utilization emerged as the most salient predictor of decreased QoL, whereas no setting predominated in the long term (18 months). Results varied by outcome and time frame, with some protective effects observed. In the 6-month time frame, each weighted day of utilization was associated with a greater likelihood of activity of daily living deficits (0.5%, 95% CI: 0.1%–0.9%), as was the case with each unweighted day of utilization (0.6%, 95% CI: 0.3%–1.0%). The same was true in the 18-month time frame (for both weighted and unweighted, 0.1%, 95% CI: 0.0%–0.3%). Days of utilization were also significantly associated with greater rates of instrumental ADL deficits and fair/poor health, albeit not consistently across all models. Neither measure outperformed the other in direct comparisons.

Conclusions

These results can provide guidance on how to measure home time using multipayer administrative data. While no setting predominated in the long term, all settings were significant predictors of QoL measures.