Evaluating the impact of emergency department crowding on disposition patterns and outcomes of discharged patients.

BACKGROUND: Crowding is a major challenge faced by EDs and is associated with poor outcomes. OBJECTIVES: Determine the effect of high ED occupancy on disposition decisions, return ED visits, and hospitalizations. METHODS: We conducted a retrospective analysis of electronic health records of patients evaluated at an adult, urban, and academic ED over 20 months between the years 2012 and 2014. Using a logistic regression model predicting admission, we obtained estimates of the effect of high occupancy on admission disposition, adjusted for key covariates. We then stratified the analysis based on the presence or absence of high boarder patient counts. RESULTS: Disposition decisions during a high occupancy hour decreased the odds of admission (OR = 0.93, 95% CI: [0.89, 0.98]). Among those who were not admitted, high occupancy was not associated with increased odds of return in the combined (OR = 0.94, 95% CI: [0.87, 1.02]), with-boarders (OR = 0.96, 95% CI: [0.86, 1.09]), and no-boarders samples (OR = 0.93, 95% CI: [0.83, 1.04]). Among those who were not admitted and who did return within 14 days, disposition during a high occupancy hour on the initial ED visit was not associated with a significant increased odds of hospitalization in the combined (OR = 1.04, 95% CI: [0.87, 1.24]), the with-boarders (OR = 1.12, 95% CI: [0.87, 1.44]), and the no-boarders samples (OR = 0.98, 95% CI: [0.77, 1.24]). CONCLUSION: ED crowding was associated with reduced likelihood of hospitalization without increased likelihood of 2-week return ED visit or hospitalization. Furthermore, high occupancy disposition hours with high boarder patient counts were associated with decreased likelihood of hospitalization.

False activation of the cardiac catheterization laboratory for primary PCI.

OBJECTIVES: We sought to evaluate trends in door-to-balloon (D2B) times and false activation rates for the cardiac catheterization laboratory (CCL) in patients presenting to the emergency department (ED) with acute ST-elevation myocardial infarction (STEMI). In patients with STEMI, national efforts have focused on reducing D2B times for primary percutaneous coronary intervention (P-PCI). This emphasis on time-to-treatment may increase the rate of false CCL activations and unnecessary healthcare utilization. STUDY DESIGN: Retrospective quality improvement chart review. METHODS: We examined all emergent CCL activations for P-PCI between 2007 and 2011 at the University of Michigan Hospital. False activation was defined as emergent CCL activation when the patient did not require CCL care or emergent cardiology evaluation in the ED. Pre-hospital or ED false activation rates and mean D2B time were retrospectively determined by chart review. RESULTS: The CCL was activated 717 times for suspected STEMI. The number of CCL activations increased from 96 in 2007 to 190 in 2011. False CCL activations accounted for 28% of all prehospital and 29% of all ED activations. The false activation rate increased from 15% of all cases in 2007 to 40% of all cases in 2011. The median D2B time decreased from 67 minutes in 2007 to 55 minutes in 2011. CONCLUSIONS: Over a 5-year period with a strong emphasis on reducing D2B times, there has been an increased CCL false activation rate for P-PCI.

Hypothenar hammer syndrome: case report and review.

BACKGROUND: Hypothenar hammer syndrome is a rare vascular overuse syndrome characterized by post-traumatic vascular insufficiency of the hand from repetitive compression over the hypothenar eminence. Patients with occupations predisposing to repetitive use of the hypothenar eminence as a tool are at high risk. Repetitive trauma compresses a superficial segment of the ulnar artery against the hook of hamate as it exits Guyon's canal, damaging the intimal layer of the artery and causing vasospasm, platelet aggregation, and thrombus formation, leading to pain and paresthesias that eventually progress to claudication and ischemia. OBJECTIVE: The main objectives of this case report are to describe a representative example of hypothenar hammer syndrome and review common aspects of this clinical entity. CASE REPORT: We describe the case of a 71-year-old man working as a car mechanic who presented with right-hand pain of several months' duration that was noted to have swelling and pain over the hypothenar eminence with associated paresthesias of the fifth digit. Duplex ultrasound imaging confirmed the presence of a dilated ulnar artery with arterial thrombus characteristic of hypothenar hammer syndrome. Conservative treatment, including cessation of activities that utilize the hypothenar region, was instituted with improvement in symptoms on follow-up evaluation. CONCLUSIONS: This case report illustrates a rare clinical condition that with prompt and early identification by Emergency Physicians can prevent significant morbidity and progression of disease. The epidemiology, clinical features, and diagnosis are discussed along with a review of the relevant literature regarding available treatment options.

Effect of testing and treatment on emergency department length of stay using a national database.

OBJECTIVES: Testing and treatment are essential aspects of the delivery of emergency care. Recognition of the effects of these activities on emergency department (ED) length of stay (LOS) has implications for administrators planning efficient operations, providers, and patients regarding expectations for length of visit; researchers in creating better models to predict LOS; and policy-makers concerned about ED crowding. METHODS: A secondary analysis was performed using years 2006 through 2008 of the National Hospital Ambulatory Medical Care Survey (NHAMCS), a nationwide study of ED services. In univariate and bivariate analyses, the authors assessed ED LOS and frequency of testing (blood test, urinalysis, electrocardiogram [ECG], radiograph, ultrasound, computed tomography [CT], or magnetic resonance imaging [MRI]) and treatment (providing a medication or performance of a procedure) according to disposition (discharged or admitted status). Two sets of multivariable models were developed to assess the contribution of testing and treatment to LOS, also stratified by disposition. The first was a series of logistic regression models to provide an overview of how testing and treatment activity affects three dichotomized LOS cutoffs at 2, 4, and 6 hours. The second was a generalized linear model (GLM) with a log-link function and gamma distribution to fit skewed LOS data, which provided time costs associated with tests and treatment. RESULTS: Among 360 million weighted ED visits included in this analysis, 227 million (63%) involved testing, 304 million (85%) involved treatment, and 201 million (56%) involved both. Overall, visits with any testing were associated with longer LOS (median = 196 minutes; interquartile range [IQR] = 125 to 305 minutes) than those with any treatment (median = 159 minutes; IQR = 91 to 262 minutes). This difference was more pronounced among discharged patients than admitted patients. Obtaining a test was associated with an adjusted odds ratio (OR) of 2.29 (95% confidence interval [CI] = 1.86 to 2.83) for experiencing a more than 4-hour LOS, while performing a treatment had no effect (adjusted OR = 0.84; 95% CI = 0.68 to 1.03). The most time-costly testing modalities included blood test (adjusted marginal effects on LOS = +72 minutes; 95% CI = 66 to 78 minutes), MRI (+64 minutes; 95% CI = 36 to 93 minutes), CT (+59 minutes; 95% CI = 54 to 65 minutes), and ultrasound (US; +56 minutes; 95% CI = 45 to 67 minutes). Treatment time costs were less substantial: performing a procedure (+24 minutes; 95% CI = 20 to 28 minutes) and providing a medication (+15 minutes; 95% CI = 8 to 21 minutes). CONCLUSIONS: Testing and less substantially treatment were associated with prolonged LOS in the ED, particularly for blood testing and advanced imaging. This knowledge may better direct efforts at streamlining delivery of care for the most time-costly diagnostic modalities or suggest areas for future research into improving processes of care. Developing systems to improve efficient utilization of these services in the ED may improve patient and provider satisfaction. Such practice improvements could then be examined to determine their effects on ED crowding.

Use of an administrative data set to determine optimal scheduling of an alcohol intervention worker.

BACKGROUND: Brief alcohol interventions are efficacious in reducing alcohol-related consequences among emergency department (ED) patients. Use of non-clinical staff may increase alcohol screening and intervention; however, optimal scheduling of an alcohol intervention worker (AIW) is unknown. OBJECTIVES: Determine optimal scheduling of an AIW based on peak discharge time of alcohol-related ED visits. METHODS: Discharge times for consecutive patients with an alcohol-related diagnosis were abstracted from an urban ED's administrative data set from September 2005 through August 2007. Queuing theory was used to identify optimal scheduling. Data for weekends and weekdays were analyzed separately. Stationary independent period-by-period analysis was performed for hourly periods. An M/M/s queuing model, for Markovian inter-arrival time/Markovian service time/and potentially more than one server, was developed for each hour assuming: 1) a single unlimited queue; 2) 75% of patients waited no longer than 30 min for intervention; 3) AIW spent an average 20 min/patient. Estimated average utilization/hour was calculated; if utilization/hour exceeded 25%, AIW staff was considered necessary. RESULTS: There were 2282 patient visits (mean age 38 years, range 11-84 years). Weekdays accounted for 45% of visits; weekends 55%. On weekdays, one AIW from 6:00 a.m.-9:00 a.m. (max utilization 42%/hour) would accommodate 28% of weekday alcohol-related patients. On weekends, 5:00 a.m.-11:00 a.m. (max utilization 50%), one AIW would cover 54% of all weekend alcohol-related visits. During other hours the utilization rate falls below 25%/hour. CONCLUSIONS: Evaluating 2 years of discharge data revealed that 30 h of dedicated AIW time--18 weekend hours (5:00 a.m.-11:00 a.m.), 12 weekday hours (6:00 a.m.-9:00 a.m.)--would allow maximal patient alcohol screening and intervention with minimal additional burden to clinical staff.

Little's law flow analysis of observation unit impact and sizing.

Expanding hospital capacity by developing an observation unit may be an important strategy in congested hospitals. Understanding the principles for evaluating the potential impact and appropriate sizing of an observation unit is important. The objective of this paper is to contrast two approaches to determining observation unit sizing and profitability, real options, and a flow analysis based on Little's Law. Both methods have validity and use similar data sets. The Little's Law approach has the advantage of providing an estimate of appropriate size for the unit and a natural internal consistency check on data. The benefits of an observation unit can depend critically on assumptions regarding backfill patients, and minor changes in data or assumptions can translate into significant changes in annual financial consequences. Using both the real options and the Little's Law approaches provides some internal consistency checks on data and assumptions. Both are sufficiently simple to be easily mastered and conducted. Using these two simple and accessible methods in parallel for computing the size and financial consequences for an observation unit is recommended.

Characterizing waiting room time, treatment time, and boarding time in the emergency department using quantile regression.

OBJECTIVES: The objective was to characterize service completion times by patient, clinical, temporal, and crowding factors for different phases of emergency care using quantile regression (QR). METHODS: A retrospective cohort study was conducted on 1-year visit data from four academic emergency departments (EDs; N = 48,896-58,316). From each ED's clinical information system, the authors extracted electronic service information (date and time of registration; bed placement, initial contact with physician, disposition decision, ED discharge, and disposition status; inpatient medicine bed occupancy rate); patient demographics (age, sex, insurance status, and mode of arrival); and clinical characteristics (acuity level and chief complaint) and then used the service information to calculate patients' waiting room time, treatment time, and boarding time, as well as the ED occupancy rate. The 10th, 50th, and 90th percentiles of each phase of care were estimated as a function of patient, clinical, temporal, and crowding factors using multivariate QR. Accuracy of models was assessed by comparing observed and predicted service completion times and the proportion of observations that fell below the predicted 10th, 50th, and 90th percentiles. RESULTS: At the 90th percentile, patients experienced long waiting room times (105-222 minutes), treatment times (393-616 minutes), and boarding times (381-1,228 minutes) across the EDs. We observed a strong interaction effect between acuity level and temporal factors (i.e., time of day and day of week) on waiting room time at all four sites. Acuity level 3 patients waited the longest across the four sites, and their waiting room times were most influenced by temporal factors compared to other acuity level patients. Acuity level and chief complaint were important predictors of all phases of care, and there was a significant interaction effect between acuity and chief complaint. Patients with a psychiatric problem experienced the longest treatment times, regardless of acuity level. Patients who presented with an injury did not wait as long for an ED or inpatient bed. Temporal factors were strong predictors of service completion time, particularly waiting room time. Mode of arrival was the only patient characteristic that substantially affected waiting room time and treatment time. Patients who arrived by ambulance had shorter wait times but longer treatment times compared to those who did not arrive by ambulance. There was close agreement between observed and predicted service completion times at the 10th, 50th, and 90th percentile distributions across the four EDs. CONCLUSIONS: Service completion times varied significantly across the four academic EDs. QR proved to be a useful method for estimating the service completion experience of not only typical ED patients, but also the experience of those who waited much shorter or longer. Building accurate models of ED service completion times is a critical first step needed to identify barriers to patient flow, begin the process of reengineering the system to reduce variability, and improve the timeliness of care provided.

Parotid swelling and facial nerve palsy: an uncommon presentation of sarcoidosis.

Sarcoidosis is a granulomatous disease of unknown etiology that can affect virtually any organ system. Diagnosing sarcoidosis can be challenging, since manifestations of the disease will vary depending on the organ systems involved. The present case describes a 24-year-old woman with bilateral jaw pain and swelling, who developed bilateral facial nerve palsies secondary to sarcoidosis. Consideration of sarcoidosis in the differential diagnosis of facial nerve palsy or parotid swelling is important to ensure proper treatment.

Crowding delays treatment and lengthens emergency department length of stay, even among high-acuity patients.

STUDY OBJECTIVE: We determine the effect of crowding on emergency department (ED) waiting room, treatment, and boarding times across multiple sites and acuity groups. METHODS: This was a retrospective cohort study that included ED visit and inpatient medicine occupancy data for a 1-year period at 4 EDs. We measured crowding at 30-minute intervals throughout each patient's ED stay. We estimated the effect of crowding on waiting room time, treatment time, and boarding time separately, using discrete-time survival analysis with time-dependent crowding measures (ie, number waiting, number being treated, number boarding, and inpatient medicine occupancy rate), controlling for patient demographic and clinical characteristics. RESULTS: Crowding substantially delayed patients' waiting room and boarding times but not treatment time. During the day shift, when the number boarding increased from the 50th to the 90th percentile, the adjusted median waiting room time (range 26 to 70 minutes) increased by 6% to 78% (range 33 to 82 minutes), and the adjusted median boarding time (range 250 to 626 minutes) increased by 15% to 47% (range 288 to 921 minutes), depending on the site. Crowding delayed the care of high-acuity level 2 patients at all sites. During crowded periods (ie, 90%), the adjusted median waiting room times of high-acuity level 2 patients were 3% to 35% higher than during normal periods, depending on the site and crowding measure. CONCLUSION: Using discrete-time survival analysis, we were able to dynamically measure crowding throughout each patient's ED visit and demonstrate its deleterious effect on the timeliness of emergency care, even for high-acuity patients.

Forecasting models of emergency department crowding.

OBJECTIVES: The authors investigated whether models using time series methods can generate accurate short-term forecasts of emergency department (ED) bed occupancy, using traditional historical averages models as comparison. METHODS: From July 2005 through June 2006, retrospective hourly ED bed occupancy values were collected from three tertiary care hospitals. Three models of ED bed occupancy were developed for each site: 1) hourly historical average, 2) seasonal autoregressive integrated moving average (ARIMA), and 3) sinusoidal with an autoregression (AR)-structured error term. Goodness of fits were compared using log likelihood and Akaike's Information Criterion (AIC). The accuracies of 4- and 12-hour forecasts were evaluated by comparing model forecasts to actual observed bed occupancy with root mean square (RMS) error. Sensitivity of prediction errors to model training time was evaluated, as well. RESULTS: The seasonal ARIMA outperformed the historical average in complexity adjusted goodness of fit (AIC). Both AR-based models had significantly better forecast accuracy for the 4- and the 12-hour forecasts of ED bed occupancy (analysis of variance [ANOVA] p < 0.01), compared to the historical average. The AR-based models did not differ significantly from each other in their performance. Model prediction errors did not show appreciable sensitivity to model training times greater than 7 days. CONCLUSIONS: Both a sinusoidal model with AR-structured error term and a seasonal ARIMA model were found to robustly forecast ED bed occupancy 4 and 12 hours in advance at three different EDs, without needing data input beyond bed occupancy in the preceding hours.

The emergency department occupancy rate: a simple measure of emergency department crowding?

STUDY OBJECTIVE: We examine the validity of the emergency department (ED) occupancy rate as a measure of crowding by comparing it to the Emergency Department Work Index Score (EDWIN), a previously validated scale. METHODS: A multicenter validation study was conducted according to ED visit data from 6 academic EDs for a 3-month period in 2005. Hourly ED occupancy rate (ie, total number of patients in ED divided by total number of licensed beds) and EDWIN scores were calculated. The correlation between the scales was determined and their validity evaluated by their ability to discriminate between hours when 1 or more patients left without being seen and hours when the ED was on ambulance diversion, using area under the curve (AUC) statistics estimated from the bootstrap method. RESULTS: We calculated the ED occupancy rate and EDWIN for 2,208 consecutive hours at each of the 6 EDs. The overall correlation between the 2 scales was 0.58 (95% confidence interval [CI] 0.56 to 0.60). The ED occupancy rate (AUC=0.73; 95% CI 0.65 to 0.80) and the EDWIN (AUC=0.65; 95% CI 0.58 to 0.72) did not differ significantly in correctly identifying hours when patients left without being seen. The ED occupancy rate (AUC=0.78; 95% CI 0.75 to 0.80) and the EDWIN (AUC=0.70; 95% CI 0.59 to 0.81) performed similarly for ED diversion hours. CONCLUSION: The ED occupancy rate and the EDWIN classified leaving without being seen and ambulance diversion hours with moderate accuracy. Although the ED occupancy rate is not ideal, its simplicity makes real-time assessment of crowding feasible for more EDs nationwide.

Academic radiology and the emergency department: does it need changing?

RATIONAL AND OBJECTIVES: The increasing importance of imaging for both diagnosis and management in patient care has resulted in a demand for radiology services 7 days a week, 24 hours a day, especially in the emergency department (ED). We hypothesized the resident preliminary reports were better than generalist radiology interpretations, although inferior to subspecialty interpretations. MATERIALS AND METHODS: Total radiology volume through our Level I pediatric and adult academic trauma ED was obtained from the radiology information system. We conducted a literature search for error and discordant rates between radiologists of varying experience. For a 2-week prospective period, all preliminary reports generated by the residents and final interpretations were collected. Significant changes in the report were tabulated. RESULTS: The ED requested 72,886 imaging studies in 2004 (16% of the total radiology department volume). In a 2-week period, 12 of 1929 (0.6%) preliminary reports by residents were discordant to the final subspecialty dictation. In the 15 peer-reviewed publications documenting error rates in radiology, the error rate between American Board of Radiology (ABR)-certified radiologists is greater than that between residents and subspecialists in the literature and in our study. However, the perceived error rate by clinicians outside radiology is significantly higher. CONCLUSION: Sixteen percent of the volume of imaging studies comes through the ED. The residents handle off-hours cases with a radiology-detected error rate below the error rate between ABR-certified radiologists. To decrease the perceived clinician-identified error rate, we need to change how academic radiology handles ED cases.

Establishing the scope and methodological approach to out-of-hospital outcomes and effectiveness research.

Outcomes research offers out-of-hospital medicine a valuable methodology for studying the effectiveness of services provided in the out-of hospital setting. A clear understanding of the history and constructs of outcomes research is necessary for its integration into emergency medical services research. This report describes the conceptual framework of outcomes research and key methodological considerations for the successful implementation of out-of-hospital outcomes research. Illustrations of the specific applications of outcomes research and implications to existing methodologies are given, as well as suggestions for improved interdisciplinary research.

Risk adjustment and outcome measures for out-of-hospital respiratory distress.

The purpose of the Emergency Medical Services Outcomes Project (EMSOP) is to develop a foundation and framework for out-of-hospital outcomes research. In prior work, this group delineated the priority conditions, described conceptual models, suggested core and risk adjustment measures potentially useful to emergency medical services research, and summarized out-of-hospital pain measurement. In this fifth article in the EMSOP series, the authors recommend specific risk-adjustment measures and outcome measures for use in out-of-hospital research on patients presenting with respiratory distress. The methodology included systematic literature searches and a structured review by an expert panel. The EMSOP group recommends use of pulse oximetry, peak expiratory flow rate, and the visual analog dyspnea scale as potential risk-adjustment measures and outcome measures for out-of-hospital research in patients with respiratory distress. Furthermore, using mortality as an outcome measure is also recommended. Future research is needed to alleviate the paucity of validated tools for out-of-hospital outcomes research.

Emergency Medical Services Outcomes Project (EMSOP) IV: pain measurement in out-of-hospital outcomes research.

The purpose of the Emergency Medical Services Outcomes Project (EMSOP) is to develop a foundation and framework for out-of-hospital outcomes research. In prior work (EMSOP I), discomfort had the highest weighted score among outcome categories for the top 3 adult conditions (ie, minor trauma, respiratory distress, chest pain) and the first and third highest rankings for children's conditions (ie, minor trauma, respiratory distress). In this fourth article in the EMSOP series, we discuss issues relevant to the measurement of pain in the out-of-hospital setting, recommended pain measures that require evaluation, and implications for outcomes research focusing on pain. For adults, adolescents, and older children, 2 verbal pain-rating scales are recommended for out-of-hospital evaluation: (1) the Adjective Response Scale, which includes the responses "none," "slight," "moderate," "severe," and "agonizing," and (2) the Numeric Response Scale, which includes responses from 0 (no pain) to 100 (worst pain imaginable). The Oucher Scale, combining a visual analog scale with pictures, seems most promising for out-of-hospital use among younger children. Future research in out-of-hospital care should be conducted to determine the utility and feasibility of these measures, as well as the effectiveness of interventions for pain relief.

Emergency Medical Services Outcomes Project III (EMSOP III): the role of risk adjustment in out-of-hospital outcomes research.

The purpose of the Emergency Medical Services Outcomes Project (EMSOP) is to develop a foundation and framework for out-of-hospital outcomes research. Fundamental to that purpose is the identification of priority conditions, risk-adjustment measures (RAMs), and outcome measures. In this third EMSOP article, we examine the topic of risk adjustment, discuss the relevance of risk adjustment for out-of-hospital outcomes research, and recommend RAMs that should be evaluated for potential use in emergency medical services (EMS) research. Risk adjustment allows better judgment about the effectiveness and quality of alternative therapies; it fosters a better comparison of potentially dissimilar groups of patients. By measuring RAMs, researchers account for an important source of variation in their studies. Core RAMs are those measures that might be necessary for out-of-hospital outcomes research involving any EMS condition. Potential core RAMs that should be evaluated for their feasibility, validity, and utility in out-of-hospital research include patient age and sex, race and ethnicity, vital signs, level of responsiveness, Glasgow Coma Scale, standardized time intervals, and EMS provider impression of the presenting condition. Potential core RAMs that could be obtained through linkage to other data sources and that should be evaluated for their feasibility, validity, and utility include principal diagnosis and patient comorbidity. We recommend that these potential core RAMs be systematically evaluated for use in risk adjustment of out-of-hospital patient groups that might be used for outcomes research