Objectives: The objective was to derive and validate a novel queuing theory-based model that predicts the effect of various patient crowding scenarios on patient left without being seen (LWBS) rates.Methods: Retrospective data were collected from all patient presentations to triage at an urban, academic, adult-only emergency department (ED) with 87,705 visits in calendar year 2008. Data from specific time windows during the day were divided into derivation and validation sets based on odd or even days. Patient records with incomplete time data were excluded. With an established call center queueing model, input variables were modified to adapt this model to the ED setting, while satisfying the underlying assumptions of queueing theory. The primary aim was the derivation and validation of an ED flow model. Chi-square and Student's t-tests were used for model derivation and validation. The secondary aim was estimating the effect of varying ED patient arrival and boarding scenarios on LWBS rates using this model. Results:The assumption of stationarity of the model was validated for three time periods (peak arrival rate = 10:00 a.m. to 12:00 p.m.; a moderate arrival rate = 8:00 a.m. to 10:00 a.m.; and lowest arrival rate = 4:00 a.m. to 6:00 a.m.) and for different days of the week and month. Between 10:00 a.m. and 12:00 p.m., defined as the primary study period representing peak arrivals, 3.9% (n = 4,038) of patients LWBS. Using the derived model, the predicted LWBS rate was 4%. LWBS rates increased as the rate of ED patient arrivals, treatment times, and ED boarding times increased. A 10% increase in hourly ED patient arrivals from the observed average arrival rate increased the predicted LWBS rate to 10.8%; a 10% decrease in hourly ED patient arrivals from the observed average arrival rate predicted a 1.6% LWBS rate. A 30-minute decrease in treatment time from the observed average treatment time predicted a 1.4% LWBS. A 1% increase in patient arrivals has the same effect on LWBS rates as a 1% increase in treatment time. Reducing boarding times by 10% is expected to reduce LWBS rates by approximately 0.8%. Conclusions:This novel queuing theory-based model predicts the effect of patient arrivals, treatment time, and ED boarding on the rate of patients who LWBS at one institution. More studies are needed to validate this model across other institutions.ACADEMIC EMERGENCY MEDICINE 2013; 20:939-946

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A simple intervention of retaining only 'urinalysis with reflex to microscopy' and removing all other urine tests from the 'frequently ordered' window of the ED electronic order set decreased urine cultures ordered by 46.6% after accounting for temporal trends. Given the injudicious use of antimicrobial therapy for asymptomatic bacteriuria, findings from our study suggest that proper design of electronic order sets plays a vital role in reducing excessive ordering of urine cultures.

Objectives: All services provided by physicians to patients during an emergency department (ED) visit, including procedures and ''cognitive work,'' are described by common procedural terminology (CPT) codes that are translated by coders into total professional (physician) charges for the visit. These charges do not include the technical (facility) charges. The objectives of this study were to characterize associations between Emergency Severity Index (ESI) acuity level, ED Evaluation and Management (E&M) billing codes 99281-99285 and 99291, and total ED provider charges (sum of total procedure and E&M professional charges). Secondary objectives were to identify factors that might affect these associations and to evaluate the performance of ESI and identified variables to predict E&M code and average total professional charges. Methods:The authors reviewed 276,824 patient records for calendar year 2007, of which 193,952 adult ED visits from three different ED types (community, university-based academic, and non-universitybased academic) met inclusion criteria. Correlations between 1) ESI level and E&M billing code per visit by institution and 2) ESI and total professional charges were analyzed using Spearman rank correlation. Linear regression analysis was performed to identify variables that significantly affected these correlations.Results: ESI level and E&M codes were moderately correlated (Spearman r = 0.51). ESI levels corresponded proportionately to higher E&M codes. ESI 1, 2, and 3 most frequently corresponded with E&M level 5 (50, 62, and 45%, respectively), and ESI 4 and 5 most frequently corresponded with E&M level 3 (56 and 67%, respectively). Only age by decade significantly affected the association between ESI level and E&M billing code. The mean total professional charge for all patient encounters was $421 (SD ± $204) with increasing mean charges per patient by increasing ESI acuity. Race and E&M code significantly affected the relationship between ESI level and total ED professional charges per patient (adjusted r 2 = 0.66).Conclusions: A moderate, nonlinear correlation exists between ESI acuity levels and ED E&M billing codes. Increasing age affects this correlation. Race and E&M code affect the correlation between ESI level and total professional charges. As such, basic triage data can be used to estimate E&M code and total professional charges. Future studies are needed to validate these findings across other institutional settings.