Impact of an Electronic Clinical Decision Support Tool for Emergency Department Patients With Pneumonia.

STUDY OBJECTIVE: Despite evidence that guideline adherence improves clinical outcomes, management of pneumonia patients varies in emergency departments (EDs). We study the effect of a real-time, ED, electronic clinical decision support tool that provides clinicians with guideline-recommended decision support for diagnosis, severity assessment, disposition, and antibiotic selection. METHODS: This was a prospective, controlled, quasi-experimental trial in 7 Intermountain Healthcare hospital EDs in Utah's urban corridor. We studied adults with International Classification of Diseases, Ninth Revision codes and radiographic evidence for pneumonia during 2 periods: baseline (December 2009 through November 2010) and post-tool deployment (December 2011 through November 2012). The tool was deployed at 4 intervention EDs in May 2011, leaving 3 as usual care controls. We compared 30-day, all-cause mortality adjusted for illness severity, using a mixed-effect, logistic regression model. RESULTS: The study population comprised 4,758 ED pneumonia patients; 14% had health care-associated pneumonia. Median age was 58 years, 53% were female patients, and 59% were admitted to the hospital. Physicians applied the tool for 62.6% of intervention ED study patients. There was no difference overall in severity-adjusted mortality between intervention and usual care EDs post-tool deployment (odds ratio [OR]=0.69; 95% confidence interval [CI] 0.41 to 1.16). Post hoc analysis showed that patients with community-acquired pneumonia experienced significantly lower mortality (OR=0.53; 95% CI 0.28 to 0.99), whereas mortality was unchanged among patients with health care-associated pneumonia (OR=1.12; 95% CI 0.45 to 2.8). Patient disposition from the ED postdeployment adhered more to tool recommendations. CONCLUSION: This study demonstrates the feasibility and potential benefit of real-time electronic clinical decision support for ED pneumonia patients.

A systematic review of the implementation and impact of asthma protocols.

BACKGROUND: Asthma is one of the most common childhood illnesses. Guideline-driven clinical care positively affects patient outcomes for care. There are several asthma guidelines and reminder methods for implementation to help integrate them into clinical workflow. Our goal is to determine the most prevalent method of guideline implementation; establish which methods significantly improved clinical care; and identify the factors most commonly associated with a successful and sustainable implementation. METHODS: PUBMED (MEDLINE), OVID CINAHL, ISI Web of Science, and EMBASE. STUDY SELECTION: Studies were included if they evaluated an asthma protocol or prompt, evaluated an intervention, a clinical trial of a protocol implementation, and qualitative studies as part of a protocol intervention. Studies were excluded if they had non-human subjects, were studies on efficacy and effectiveness of drugs, did not include an evaluation component, studied an educational intervention only, or were a case report, survey, editorial, letter to the editor. RESULTS: From 14,478 abstracts, we included 101 full-text articles in the analysis. The most frequent study design was pre-post, followed by prospective, population based case series or consecutive case series, and randomized trials. Paper-based reminders were the most frequent with fully computerized, then computer generated, and other modalities. No study reported a decrease in health care practitioner performance or declining patient outcomes. The most common primary outcome measure was compliance with provided or prescribing guidelines, key clinical indicators such as patient outcomes or quality of life, and length of stay. CONCLUSIONS: Paper-based implementations are by far the most popular approach to implement a guideline or protocol. The number of publications on asthma protocol reminder systems is increasing. The number of computerized and computer-generated studies is also increasing. Asthma guidelines generally improved patient care and practitioner performance regardless of the implementation method.

Exploring the frontier of electronic health record surveillance: the case of postoperative complications.

BACKGROUND: The aim of this study was to build electronic algorithms using a combination of structured data and natural language processing (NLP) of text notes for potential safety surveillance of 9 postoperative complications. METHODS: Postoperative complications from 6 medical centers in the Southeastern United States were obtained from the Veterans Affairs Surgical Quality Improvement Program (VASQIP) registry. Development and test datasets were constructed using stratification by facility and date of procedure for patients with and without complications. Algorithms were developed from VASQIP outcome definitions using NLP-coded concepts, regular expressions, and structured data. The VASQIP nurse reviewer served as the reference standard for evaluating sensitivity and specificity. The algorithms were designed in the development and evaluated in the test dataset. RESULTS: Sensitivity and specificity in the test set were 85% and 92% for acute renal failure, 80% and 93% for sepsis, 56% and 94% for deep vein thrombosis, 80% and 97% for pulmonary embolism, 88% and 89% for acute myocardial infarction, 88% and 92% for cardiac arrest, 80% and 90% for pneumonia, 95% and 80% for urinary tract infection, and 77% and 63% for wound infection, respectively. A third of the complications occurred outside of the hospital setting. CONCLUSIONS: Computer algorithms on data extracted from the electronic health record produced respectable sensitivity and specificity across a large sample of patients seen in 6 different medical centers. This study demonstrates the utility of combining NLP with structured data for mining the information contained within the electronic health record.

Hospital admission decision for patients with community-acquired pneumonia: variability among physicians in an emergency department.

STUDY OBJECTIVE: We examine variability among emergency physicians in rate of hospitalization for patients with pneumonia and the effect of variability on clinical outcomes. METHODS: We studied 2,069 LDS Hospital emergency department (ED) patients with community-acquired pneumonia who were aged 18 years or older during 1996 to 2006, identified by International Classification of Diseases, Ninth Revision coding and compatible chest radiographs. We extracted vital signs, laboratory and radiographic results, hospitalization, and outcomes from the electronic medical record. We defined "low severity" as PaO(2)/FiO(2) ratio greater than or equal to 280 mm Hg, predicted mortality less than 5% by an electronic version of CURB-65 that uses continuous and weighted elements (eCURB), and less than 3 Infectious Disease Society of America-American Thoracic Society 2007 severe pneumonia minor criteria. We adjusted hospitalization decisions and outcomes for illness severity and patient demographics. RESULTS: Initial hospitalization rate was 58%; 10.7% of patients initially treated as outpatients were secondarily hospitalized within 7 days. Median age of admitted patients was 63 years; median eCURB predicted mortality was 2.65% (mean 6.8%) versus 46 years and 0.93% for outpatients. The 18 emergency physicians (average age 44.9 [standard deviation 7.6] years; years in practice 8.4 [standard deviation 6.9]) objectively calculated and documented illness severity in 2.7% of patients. Observed 30-day mortality for inpatients was 6.8% (outpatient mortality 0.34%) and decreased over time. Individual physician admission rates ranged from 38% to 79%, with variability not explained by illness severity, time of day, day of week, resident care in conjunction with an attending physician, or patient or physician demographics. Higher hospitalization rates were not associated with reduced mortality or fewer secondary hospital admissions. CONCLUSION: We observed a 2-fold difference in pneumonia hospitalization rates among emergency physicians, unexplained by objective data.

Relationships among initial hospital triage, disease progression and mortality in community-acquired pneumonia.

BACKGROUND AND OBJECTIVE: Appropriate triage of patients with community-acquired pneumonia (CAP) may improve morbidity, mortality and use of hospital resources. Worse outcomes from delayed intensive care unit (ICU) admission have long been suspected but have not been verified. METHODS: In a retrospective study of consecutive patients with CAP admitted from 1996-2006 to the ICUs of a tertiary care hospital, we measured serial severity scores, intensive therapies received, ICU-free days, and 30-day mortality. Primary outcome was mortality. We developed a regression model of mortality with ward triage (and subsequent ICU transfer within 72 h) as the predictor, controlled by propensity for ward triage and radiographic progression. RESULTS: Of 1059 hospital-admitted patients, 269 (25%) were admitted to the ICU during hospitalization. Of those, 167 were directly admitted to the ICU without current requirement for life support, while 61 (23%) were initially admitted to the hospital ward, 50 of those undergoing ICU transfer within 72 h. Ward triage was associated with increased mortality (OR 2.6, P = 0.056) after propensity adjustment. The effect was less (OR 2.2, P = 0.12) after controlling for radiographic progression. The effect probably increased (OR 4.1, P = 0.07) among patients with ≥ 3 severity predictors at admission. CONCLUSIONS: Initial ward triage among patients transferred to the ICU is associated with twofold higher 30-day mortality. This effect is most apparent among patients with ≥ 3 severity predictors at admission and is attenuated by controlling for radiographic progression. Intensive monitoring of ward-admitted patients with CAP seems warranted. Further research is needed to optimize triage in CAP.

Predictors of patient length of stay in 9 emergency departments.

OBJECTIVES: Prolonged emergency department (ED) length of stay (LOS) is linked to adverse outcomes, decreased patient satisfaction, and ED crowding. This multicenter study identified factors associated with increased LOS. METHODS: This retrospective study included 9 EDs from across the United States. Emergency department daily operational metrics were collected from calendar year 2009. A multivariable linear population average model was used with log-transformed LOS as the dependent variable to identify which ED operational variables are predictors of LOS for ED discharged, admitted, and overall ED patient categories. RESULTS: Annual ED census ranged from 43,000 to 101,000 patients. The number of ED treatment beds ranged from 27 to 95. Median overall LOS for all sites was 5.4 hours. Daily percentage of admitted patients was found to be a significant predictor of discharged and admitted patient LOS. Higher daily percentage of discharged and eloped patients, more hours on ambulance diversion, and weekday (vs weekend) of patient presentation were significantly associated with prolonged LOS for discharged and admitted patients (P < .05). For each percentage of increase in discharged patients, there was a 1% associated decrease in overall LOS, whereas each percentage of increase in eloped patients was associated with a 1.2% increase in LOS. CONCLUSIONS: Length of stay was increased on days with higher percentage daily admissions, higher elopements, higher periods of ambulance diversion, and during weekdays, whereas LOS was decreased on days with higher numbers of discharges and weekends. This is the first study to demonstrate this association across a broad group of hospitals.

Placing physician orders at triage: the effect on length of stay.

STUDY OBJECTIVE: Emergency department (ED) crowding is a significant problem nationwide, and numerous strategies have been explored to decrease length of stay. Placing a physician in the triage area to rapidly disposition low-acuity patients and begin evaluations on more complex patients is one strategy that can be used to lessen the effect of ED crowding. The goal of this study is to assess the effect of order placement by a triage physician on length of stay for patients ultimately treated in a bed within the ED. METHODS: We conducted a pre-experimental study with retrospective data to evaluate patients with and without triage physician orders at a single academic institution. A matched comparison was performed by pairing patients with the same orders and similar propensity scores. Propensity scores were calculated with demographic and triage data, chief complaint, and ED capacity on the patient's arrival. RESULTS: During the 23-month study period, a total of 66,909 patients were sent to the waiting room after triage but still eventually spent time in an ED bed. A quarter of these patients (23%) had physician orders placed at triage. After a matched comparison, patients with triage orders had a 37-minute (95% confidence interval 34 to 40 minutes) median decrease in time spent in an ED bed, with an 11-minute (95% confidence interval 7 to 15 minutes) overall median increase in time until disposition. CONCLUSION: Our study suggests that early orders placed by a triage physician have an effect on ED operations by reducing the amount of time patients spend occupying an ED bed.

Factors affecting stress in emergency medicine residents while working in the ED.

OBJECTIVES: The aim of this study was to identify factors other than work hours in the emergency department (ED) work environment contributing to resident stress. METHODS: This study involved a prospective cohort evaluation of emergency medicine residents in the ED. Twelve surveys were collected from 18 subjects, 4 each from the day, evening, and night shifts. The Perceived Stress Questionnaire and a visual analog stress scale were administered. Data collected included the shift number of a given consecutive sequence of shifts, number of procedures performed, number of adverse events, average age of the patients seen by the resident, triage nurse-assigned acuities of the patients seen by the resident during the shift, the number of patients seen during a shift, the number of patients admitted by the resident during the shift, anticipated overtime after a shift, and shift-specific metrics related to overcrowding, including average waiting room time both for the individual residents and for all patients, average waiting room count for all patients, and average occupancy of the ED for all patients. RESULTS: Among the 216 studied shifts, there was considerable variability in stress both within and between residents. In the multivariate mixed-effect regression analysis, only anticipated overtime and process failures were correlated with stress. Factors related to ED overcrowding had no significant effect on resident stress. CONCLUSIONS: Resident stress was most impacted by anticipation of overtime and adverse events. Overcrowding in the ED and traditional measures of workload did not seem to affect stress as much.

The effect of physician triage on emergency department length of stay.

BACKGROUND: Emergency Department (ED) overcrowding is a serious public health issue, but few solutions exist. OBJECTIVES: We sought to determine the impact of physician triage on ED length of stay for discharged and admitted patients, left-without-being-seen (LWBS) rates, and ambulance diversion. METHODS: This was a pre-post study performed using retrospective data at an urban, academic tertiary care, Level I trauma center. On July 11, 2005, physician triage was initiated from 1:00 p.m. to 9:00 p.m., 7 days a week. An additional physician was placed in triage so that the ED diagnostic evaluation and treatment could be started in waiting room patients. Using the hospital information system, we obtained individual patient data, ED and waiting room statistics, and diversion status data from a 9-week pre-physician triage (May 11, 2005 to July 10, 2005) and a 9-week physician triage (July 11, 2005 to September 9, 2005) period. RESULTS: We observed that overall ED length of stay decreased by 11 min, but this decrease was entirely attributed to non-admitted patients. No difference in ED length of stay was observed in admitted patients. LWBS rates decreased from 4.5% to 2.5%. Total time spent on ambulance diversion decreased from 5.6 days per month to 3.2 days per month. CONCLUSION: Physician triage was associated with a decrease in LWBS rates, and time spent on ambulance diversion. However, its effect on ED LOS was modest in non-admitted ED patients and negligible in admitted patients.

Factors Influencing Time-Dependent Quality Indicators for Patients With Suspected Acute Coronary Syndrome.

OBJECTIVES: Rapid risk stratification and timely treatment are critical to favorable outcomes for patients with acute coronary syndrome (ACS). Our objective was to identify patient and system factors that influence time-dependent quality indicators (QIs) for patients with unstable angina/non-ST elevation myocardial infarction (NSTEMI) in the emergency department (ED). METHODS: A retrospective, cohort study was conducted during a 42-month period of all patients 24 years or older suspected of having ACS as defined by receiving an electrocardiogram and at least 1 cardiac biomarker test. Cox regression was used to model the effects of patient characteristics, ancillary service use, staffing provisions, equipment availability, and ED and hospital crowding on ACS QIs. RESULTS: Emergency department adherence rates to national standards for electrocardiogram readout time and biomarker turnaround time were 42% and 37%, respectively. Cox regression models revealed that chief complaints without chest pain and the timing of stress testing and medication administration were associated with the most significant delays. CONCLUSIONS: Patient and system factors both significantly influenced QI times in this cohort with unstable angina/NSTEMI. These results illustrate both the complexity of diagnosing patients with NSTEMI and the competing effects of clinical and system factors on patient flow through the ED.

Forecasting emergency department crowding: a prospective, real-time evaluation.

OBJECTIVE: Emergency department crowding threatens quality and access to health care, and a method of accurately forecasting near-future crowding should enable novel ways to alleviate the problem. The authors sought to implement and validate the previously developed ForecastED discrete event simulation for real-time forecasting of emergency department crowding. DESIGN AND MEASUREMENTS: The authors conducted a prospective observational study during a three-month period (5/1/07-8/1/07) in the adult emergency department of a tertiary care medical center. The authors connected the forecasting tool to existing information systems to obtain real-time forecasts of operational data, updated every 10 minutes. The outcome measures included the emergency department waiting count, waiting time, occupancy level, length of stay, boarding count, boarding time, and ambulance diversion; each forecast 2, 4, 6, and 8 hours into the future. RESULTS: The authors obtained crowding forecasts at 13,239 10-minute intervals, out of 13,248 possible (99.9%). The R(2) values for predicting operational data 8 hours into the future, with 95% confidence intervals, were 0.27 (0.26, 0.29) for waiting count, 0.11 (0.10, 0.12) for waiting time, 0.57 (0.55, 0.58) for occupancy level, 0.69 (0.68, 0.70) for length of stay, 0.61 (0.59, 0.62) for boarding count, and 0.53 (0.51, 0.54) for boarding time. The area under the receiver operating characteristic curve for predicting ambulance diversion 8 hours into the future, with 95% confidence intervals, was 0.85 (0.84, 0.86). CONCLUSIONS: The ForecastED tool provides accurate forecasts of several input, throughput, and output measures of crowding up to 8 hours into the future. The real-time deployment of the system should be feasible at other emergency departments that have six patient-level variables available through information systems.

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 emergency department crowding: an external, multicenter evaluation.

STUDY OBJECTIVE: We apply a previously described tool to forecast emergency department (ED) crowding at multiple institutions and assess its generalizability for predicting the near-future waiting count, occupancy level, and boarding count. METHODS: The ForecastED tool was validated with historical data from 5 institutions external to the development site. A sliding-window design separated the data for parameter estimation and forecast validation. Observations were sampled at consecutive 10-minute intervals during 12 months (n=52,560) at 4 sites and 10 months (n=44,064) at the fifth. Three outcome measures-the waiting count, occupancy level, and boarding count-were forecast 2, 4, 6, and 8 hours beyond each observation, and forecasts were compared with observed data at corresponding times. The reliability and calibration were measured following previously described methods. After linear calibration, the forecasting accuracy was measured with the median absolute error. RESULTS: The tool was successfully used for 5 different sites. Its forecasts were more reliable, better calibrated, and more accurate at 2 hours than at 8 hours. The reliability and calibration of the tool were similar between the original development site and external sites; the boarding count was an exception, which was less reliable at 4 of 5 sites. Some variability in accuracy existed among institutions; when forecasting 4 hours into the future, the median absolute error of the waiting count ranged between 0.6 and 3.1 patients, the median absolute error of the occupancy level ranged between 9.0% and 14.5% of beds, and the median absolute error of the boarding count ranged between 0.9 and 2.8 patients. CONCLUSION: The ForecastED tool generated potentially useful forecasts of input and throughput measures of ED crowding at 5 external sites, without modifying the underlying assumptions. Noting the limitation that this was not a real-time validation, ongoing research will focus on integrating the tool with ED information systems.

Performance of an algorithm for assessing smallpox risk among patients with rashes that may be confused with smallpox.

After the 2001 anthrax bioterror attacks, the Centers for Disease Control and Prevention developed an algorithm to evaluate patients rapidly for suspected smallpox. A prospective, multicenter study examined the performance of this algorithm in assessing patients with an acute, generalized vesicular or pustular rash (AGVPR) admitted to emergency departments and inpatient units of 12 acute-care hospitals in 6 states. Of 26,747 patients (3.5% of all admissions) with rashlike conditions screened, 89 (1.2 patients per 10,000 admissions) had an AGVPR. Physicians or study staff classified none of 73 enrolled patients as being at high risk for having smallpox; 72 (99%) were classified as being at low risk, and 1 was classified as being at moderate risk. The discharge diagnosis for 55 (75%) of these 73 participants was varicella illness. Use of the algorithm did not result in misclassification of AGVPR as high risk for smallpox. The algorithm is a highly specific tool for clinical evaluation of suspected smallpox disease.

Optimizing cardiology capacity to reduce emergency department boarding: a systems engineering approach.

BACKGROUND: Patient safety and emergency department (ED) functionality are compromised when inefficient coordination between hospital departments impedes ED patients' access to inpatient cardiac care. The objective of this study was to determine how bed demand from competing cardiology admission sources affects ED patients' access to inpatient cardiac care. METHODS: A stochastic discrete event simulation of hospital patient flow predicted ED patient boarding time, defined as the time interval between cardiology admission request to inpatient bed placement, as the primary outcome measure. The simulation was built and tested from 1 year of patient flow data and was used to examine prospective strategies to reduce cardiology patient boarding time. RESULTS: Boarding time for the 1,591 ED patients who were admitted to the cardiac telemetry unit averaged 5.3 hours (median 3.1, interquartile range 1.5-6.9). Demographic and clinical patient characteristics were not significant predictors of boarding time. Measurements of bed demand from competing admission sources significantly predicted boarding time, with catheterization laboratory demand levels being the most influential. Hospital policy required that a telemetry bed be held for each electively scheduled catheterization patient, yet the analysis revealed that 70.4% (95% CI 51.2-92.5) of these patients did not transfer to a telemetry bed and were discharged home each day. Results of simulation-based analyses showed that moving one afternoon scheduled elective catheterization case to before noon resulted in a 20-minute reduction in average boarding time compared to a 9-minute reduction achieved by increasing capacity by one additional telemetry bed. CONCLUSIONS: Scheduling and bed management practices based on measured patient transfer patterns can reduce inpatient bed blocking, optimize hospital capacity, and improve ED patient access.

Supporting patient care in the emergency department with a computerized whiteboard system.

Efficient information management and communication within the emergency department (ED) is essential to providing timely and high-quality patient care. The ED whiteboard (census board) usually serves as an ED's central access point for operational and patient-related information. This article describes the design, functionality, and experiences with a computerized ED whiteboard, which has the ability to display relevant operational and patient-related information in real time. Embedded functionality, additional whiteboard views, and the integration with ED and institutional information system components, such as the computerized patient record or the provider order entry system, provide rapid access to more detailed information. As an information center, the computerized whiteboard supports our ED environment not only for providing patient care, but also for operational, educational, and research activities.

Prompting clinicians about preventive care measures: a systematic review of randomized controlled trials.

Preventive care measures remain underutilized despite recommendations to increase their use. The objective of this review was to examine the characteristics, types, and effects of paper- and computer-based interventions for preventive care measures. The study provides an update to a previous systematic review. We included randomized controlled trials that implemented a physician reminder and measured the effects on the frequency of providing preventive care. Of the 1,535 articles identified, 28 met inclusion criteria and were combined with the 33 studies from the previous review. The studies involved 264 preventive care interventions, 4,638 clinicians and 144,605 patients. Implementation strategies included combined paper-based with computer generated reminders in 34 studies (56%), paper-based reminders in 19 studies (31%), and fully computerized reminders in 8 studies (13%). The average increase for the three strategies in delivering preventive care measures ranged between 12% and 14%. Cardiac care and smoking cessation reminders were most effective. Computer-generated prompts were the most commonly implemented reminders. Clinician reminders are a successful approach for increasing the rates of delivering preventive care; however, their effectiveness remains modest. Despite increased implementation of electronic health records, randomized controlled trials evaluating computerized reminder systems are infrequent.

Systematic review of emergency department crowding: causes, effects, and solutions.

Emergency department (ED) crowding represents an international crisis that may affect the quality and access of health care. We conducted a comprehensive PubMed search to identify articles that (1) studied causes, effects, or solutions of ED crowding; (2) described data collection and analysis methodology; (3) occurred in a general ED setting; and (4) focused on everyday crowding. Two independent reviewers identified the relevant articles by consensus. We applied a 5-level quality assessment tool to grade the methodology of each study. From 4,271 abstracts and 188 full-text articles, the reviewers identified 93 articles meeting the inclusion criteria. A total of 33 articles studied causes, 27 articles studied effects, and 40 articles studied solutions of ED crowding. Commonly studied causes of crowding included nonurgent visits, "frequent-flyer" patients, influenza season, inadequate staffing, inpatient boarding, and hospital bed shortages. Commonly studied effects of crowding included patient mortality, transport delays, treatment delays, ambulance diversion, patient elopement, and financial effect. Commonly studied solutions of crowding included additional personnel, observation units, hospital bed access, nonurgent referrals, ambulance diversion, destination control, crowding measures, and queuing theory. The results illustrated the complex, multifaceted characteristics of the ED crowding problem. Additional high-quality studies may provide valuable contributions toward better understanding and alleviating the daily crisis. This structured overview of the literature may help to identify future directions for the crowding research agenda.

Forecasting emergency department crowding: a discrete event simulation.

STUDY OBJECTIVE: To develop a discrete event simulation of emergency department (ED) patient flow for the purpose of forecasting near-future operating conditions and to validate the forecasts with several measures of ED crowding. METHODS: We developed a discrete event simulation of patient flow with evidence from the literature. Development was purely theoretical, whereas validation involved patient data from an academic ED. The model inputs and outputs, respectively, are 6-variable descriptions of every present and future patient in the ED. We validated the model by using a sliding-window design, ensuring separation of fitting and validation data in time series. We sampled consecutive 10-minute observations during 2006 (n=52,560). The outcome measures--all forecast 2, 4, 6, and 8 hours into the future from each observation--were the waiting count, waiting time, occupancy level, length of stay, boarding count, boarding time, and ambulance diversion. Forecasting performance was assessed with Pearson's correlation, residual summary statistics, and area under the receiver operating characteristic curve. RESULTS: The correlations between crowding forecasts and actual outcomes started high and decreased gradually up to 8 hours into the future (lowest Pearson's r for waiting count=0.56; waiting time=0.49; occupancy level=0.78; length of stay=0.86; boarding count=0.79; boarding time=0.80). The residual means were unbiased for all outcomes except the boarding time. The discriminatory power for ambulance diversion remained consistently high up to 8 hours into the future (lowest area under the receiver operating characteristic curve=0.86). CONCLUSION: By modeling patient flow, rather than operational summary variables, our simulation forecasts several measures of near-future ED crowding, with various degrees of good performance.

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.