Emergency physician resource utilization varies by years of experience.

Objectives: One of the most pivotal decisions an emergency physician (EP) makes is whether to admit or discharge a patient. The emergency department (ED) work-up leading to this decision involves several resource-intensive tests. Previous studies have demonstrated significant differences in EP resource utilization, measured by lab tests, advanced imaging (magnetic resonance imaging [MRI], computed tomography [CT], ultrasound), consultations, and propensity to admit a patient. However, how an EP's years of experience may impact their resource utilization and propensity to admit patients has not been well characterized. This study seeks to better understand how EPs' years of experience, post-residency, relates to their use of advanced imaging and patient disposition. Methods: Ten years of ED visits were analyzed for this study from a single, academic tertiary care center in the urban Northeast United States. The primary outcomes were utilization of advanced imaging during the visit (CT, MRI, or formal ultrasound) and whether the patient was admitted. EP years of experience was categorized into 0-2 years, 3-5 years, 6-8 years, 9-11 years, and 12 or more years. Patient age, sex, Emergency Severity Index (ESI), and the attending EP's years of experience were collected. The relationship between EP years of experience and each outcome was assessed with a linear mixed model with a random effect for provider and patient age, sex, and ESI as covariates. Results: A total of 460,937 visits seen by 65 EPs were included in the study. Over one-third (37.6%) of visits had an advanced imaging study ordered and nearly half (49.5%) resulted in admission. Compared to visits with EPs with 0-2 years of experience, visits with EPs with 3-5 or 6-8 years of experience had significantly lower odds of advanced imaging occurring. Visits seen by EPs with more than 2 years of experience had lower odds of admission than visits by EPs with 0-2 years of experience. Conclusion: More junior EPs tend to order more advanced imaging studies and have a higher propensity to admit patients. This may be due to less comfort in decision-making without advanced imaging or a lower risk tolerance. Conversely, the additional clinical experience of the most senior EPs, with greater than 9 years of experience, likely impacts their resource utilization patterns such that their use of advanced imaging does not significantly differ from the most junior EPs.

Root Cause Analysis of Delayed Emergency Department Computed Tomography Scans.

Introduction: A solution for emergency department (ED) congestion remains elusive. As reliance on imaging grows, computed tomography (CT) turnaround time has been identified as a major bottleneck. In this study we sought to identify factors associated with significantly delayed CT in the ED. Methods: We performed a retrospective analysis of all CT imaging completed at an urban, tertiary care ED from May 1-July 31, 2021. During that period, 5,685 CTs were performed on 4,344 patients, with a median time from CT order to completion of 108 minutes (Quartile 1 [Q1]: 57 minutes, Quartile 3 [Q3]: 182 minutes, interquartile range [IQR]: 125 minutes). Outliers were defined as studies that took longer than 369 minutes to complete (Q3 + 1.5 × IQR). We systematically reviewed outlier charts to determine factors associated with delay and identified five factors: behaviorally non-compliant or medically unstable patients; intravenous (IV) line issues; contrast allergies; glomerular filtration rate (GFR) concerns; and delays related to imaging protocol (eg, need for IV contrast, request for oral and/or rectal contrast). We calculated confidence intervals (CI) using the modified Wald method. Inter-rater reliability was assessed with a kappa analysis. Results: We identified a total of 182 outliers (4.2% of total patients). Fifteen (8.2%) cases were excluded for CT time-stamp inconsistencies. Of the 167 outliers analyzed, 38 delays (22.8%, 95% confidence interval [CI] 17.0-29.7) were due to behaviorally non-compliant or medically unstable patients; 30 (18.0%, 95% CI 12.8-24.5) were due to IV issues; 24 (14.4%, 95% CI 9.8-20.6) were due to contrast allergies; 21 (12.6%, 95% CI 8.3-18.5) were due to GFR concerns; and 20 (12.0%, 95% CI 7.8-17.9) were related to imaging study protocols. The cause of the delay was unknown in 55 cases (32.9%, 95% CI 26.3-40.4). Conclusion: Our review identified both modifiable and non-modifiable factors associated with significantly delayed CT in the ED. Patient factors such as behavior, allergies, and medical acuity cannot be controlled. However, institutional policies regarding difficult IV access, contrast administration in low GFR settings, and study protocols may be modified, capturing up to 42.6% of outliers.

Emergency medicine resident productivity across consecutive shifts.

Objectives: Clinical productivity is an important operational and educational metric for emergency medicine (EM) residents. It is unclear whether working consecutive days and circadian disruption impact resident productivity. The objective of this study was to determine whether there is a correlation between consecutive shifts and productivity. Methods: This was a single-site retrospective observational study using data from academic year 2021-2022 (July 1, 2021-June 23, 2022). Productivity was defined as primary resident encounters with patients per hour (PPH). Postgraduate year (PGY)-1 and PGY-2 productivity data and schedules were abstracted from the electronic medical record and scheduling software. Descriptive statistics, including arithmetic mean, standard deviation, and confidence interval (CI), were determined for each shift number and stratified by PGY level. Subgroup analysis of night shifts was performed. Analysis of variance and linear regression analysis were performed. Results: A total of 2950 shifts were identified, including 1328 PGY-1 shifts and 1622 PGY-2 shifts, which involved a total of 32,379 patient encounters. PGY-1 residents saw a mean of 0.88-0.96 PPH on sequential shifts 1-7, respectively (y-intercept 0.923, slope 0.001, 95% CI -0.008 to 0.009, p = 0.86). PGY-2 residents saw a mean of 1.61-1.75 PPH on Shifts 1-7, respectively (y-intercept 1.628, slope 0.004, 95% CI -0.007 to 0.015, p = 0.50). A subgroup analysis of 598 overnight shifts (11 p.m.-7 a.m.) was performed, in which residents saw a mean of 1.29-1.56 PPH on Sequential Shifts 1-7 (y-intercept 1.286, slope 0.011, 95% CI -0.011 to 0.033, p = 0.34). Conclusions: EM resident productivity remained relatively constant across consecutive shifts, including night shifts. These findings may have educational and operational implications. Further research is required to understand patient- and provider-oriented consequences of consecutive shift scheduling.

Does the number of pages received per hour affect resident productivity?

Background: Workflow interruptions are common for emergency physicians and are shown to have downstream consequences such as patient dissatisfaction, delay in clinical response, and increase in medical error. However, the impact of passive interruptions on physician productivity is unclear and has not been well studied. We sought to evaluate if the number of pages received per hour significantly affects the number of patients seen per hour. Methods: Retrospective data was collected on resident physician (RP) emergency department shifts from July 1st, 2021 to June 30th, 2022 at an academic medical center with an annual census of 55,000 patients. A total of 2865 RP shifts were collected among the 26 postgraduate year (PGY) 1 and PGY2 residents. For each RP shift, we identified the number of pages received per hour and the number of new patients seen per hour. Pages consist of any transmitted message that was sent to the RP's personal pager, which includes both automatic (eg, bed assignments, abnormal lab values) and personalized pages from other healthcare practicioners (eg, nursing, consultants). Data were analyzed using Poisson regression controlling for clustering at the physician level to determine if the number of patients seen per hour is significantly affected by the number of pages (divided into quartiles) received. Results: We found the number of pages received per hour did not decrease the number of patients seen per hour. Contrary to our hypothesis, there was a strong positive relationship between the number of pages received per hour and the number of patients seen by RPs in that hour and subsequent hours. During the middle of a shift (hours 3, 4, and 5), RPs receiving pages in the third and fourth quartile (top 50% of pages) saw significantly more patients during that same hour and the next hour (p <0.001). Conclusion: The number of pages received by RPs per hour did not decrease the number of patients seen per hour. When RPs receive a higher number of pages, there is a positive association with the number of patients they see in that hour and subsequent hours. Further studies will be needed to determine whether the content of pages affects resident productivity.

Twelve tips for practical clinical skills coaching.

Coaching is rapidly evolving in clinical medicine, including for clinical skills (CS) learning. Yet a schema is needed for how to coach students in the many CS that are pivotal to the practice of medicine. These twelve tips aim to provide practical strategies for teachers and educators to coach students for CS learning. The tips cover many important aspects of CS coaching, including establishing a safe space, ways to prepare to coach, setting goals, guiding a coaching relationship, fostering coaching conversations, and in-person or virtual approaches. Together, the tips align as seven key steps of an overall coaching process. The twelve tips apply equally to coaching struggling students and all students seeking to improve CS and offer a guide for coaching at an individual or program level.

The association between day of arrival, time of arrival, daily volume and the rate of patients that "left without being seen".

INTRODUCTION: Patients' left without being seen (LWBS) rate is used as an emergency department (ED) quality indicator. Prior research has investigated characteristics of these patients, but there are minimal studies assessing the impact of departmental variables. We evaluate the LWBS rate at a granular level, looking at its relationship to day of week, hour of arrival and total patient volume. METHODS: Retrospective cohort analysis of 109,983 cases from a single academic center. We captured patient disposition, day of week and hour of day of arrival, and total daily volume. Chi-squared test was performed to determine the difference in LWBS rates based on arrival variables. We ran a polynomial regression for LWBS rates by decile of daily patient volume. RESULTS: The overall LWBS rate was 1.82% over 2 years. This varied significantly by day of week and hour of day (p < 0.001). Day of week rates ranged from 0.73% on Sunday to 2.45% on Wednesday. Hour of day rates ranged from 0.26% between 8 AM-9 AM, to 3.71% between 10 PM-11 PM. As total daily patient volume increased, LWBS rates gradually increased until the 70th percentile, followed by significant exponential growth afterwards. DISCUSSION: LWBS rates are not static measurements, and vary greatly depending on ED circumstances. Weekdays and evenings have significantly higher rates. Additionally, LWBS rates climb above 2% as daily registrations reach the 70th percentile, increasing exponentially at each subsequent decile. Understanding these effects will allow for more effective, targeted interventions to minimize this rate and improve throughput.

Use of an Automated Discharge Instruction Module to Improve Outpatient Follow-Up for Emergency Department Patients with Elevated Blood Pressure.

INTRODUCTION: Hypertension is often incidentally discovered in the emergency department (ED); these patients may benefit from close follow-up. We developed a module to automatically include discharge instructions for patients with elevated blood pressure (BP) in the ED, aiming to improve 30-day follow-up. AIM: This study sought to determine if automated discharge instructions for patients with elevated blood pressure in the ED improved 30-day follow-up with a patient's primary care physician (PCP). METHODS: We developed an automated module with standardized instructions for patients with elevated BP. These were read upon discharge, and e-mailed to the PCP. We analyzed 193 patients during a 1-month interval after implementation, and 207 during 1-month the year prior. The groups were compared using Fisher's exact test. RESULTS: Thirty-day follow-up was 52.2% pre-implementation and 48.4% post-implementation, with no significant difference noted. For patients without known hypertension, follow-up slightly improved, but not significantly. For hypertensive patients, follow-up rates significantly decreased post-implementation. CONCLUSIONS: Despite implementation of automated discharge instructions, we found no improvement in 30-day follow-up. Patients without hypertension trended towards improved follow-up, possibly being more attentive to new abnormal BP readings. However, known hypertensive patients followed-up at a lower rate, which was unexpected and requires further investigation.

Not All Testers are Admitters: An Analysis of Emergency Physician Resource Utilization and Consultation Rates.

BACKGROUND: Variability exists in emergency physician (EP) resource utilization as measured by ordering practices, rate of consultation, and propensity to admit patients. OBJECTIVE: To validate and expand upon previous data showing that resource utilization as measured by EP ordering patterns is positively correlated with admission rates. METHODS: This is a retrospective study of routinely gathered operational data from the ED of an urban academic tertiary care hospital. We collected individual EP data on advanced imaging, consultation, and admission rates per patient encounter. To investigate whether there might be distinct groups of practice patterns relating these 3 resources, we used a Gaussian mixture model, a classification method used to determine the likelihood of distinct subgroups within a larger population. RESULTS: Our Gaussian mixture model revealed 3 distinct groups of EPs based on their ordering practices. The largest group is characterized by a homogenous pattern of neither high or low resource utilization (n = 37, 27% female, median years' experience: 6 [interquartile ratio {IQR} 3-18]; rates of advanced imaging, 38.9%; consultation, 45.1%; and admission 39.3%), with a modest group of low-resource users (n = 15, 60% female, median years' experience: 6 [IQR 5-14]; rates of advanced imaging, 37%; consultation, 42.6%; and admission 37.3%), and far fewer members of a high-resource use group (n = 6, 0% female, median years' experience: 6 [IQR 4-16]; rates of advanced imaging, 42.2%; consultation, 45.8%; and admission 40.6%). This variation suggests that not "all testers are admitters," but that there exist wider practice variations among EPs. CONCLUSIONS: At our academic tertiary center, 3 distinct subgroups of EP ordering practices exist based on consultation rates, advanced imaging use, and propensity to admit a patient. These data validate previous work showing that resource utilization and admission rates are related, while demonstrating that more nuanced patterns of EP ordering practices exist. Further investigation is needed to understand the impact of EP characteristics and behavior on throughput and quality of care. © 2022 Elsevier Inc.

Analysis of time-to-disposition intervals during early and late parts of an emergency department shift.

INTRODUCTION: Time-to-disposition is an important metric for emergency department throughput. We hypothesized that providers view the shift end as a key timepoint and attempt to leave as few dispositions as possible to the oncoming team, thereby making quicker decisions later in the shift. This study evaluates disposition distribution relative to when patients are assigned a provider during the course of a shift. METHODS: 50,802 cases were analyzed over the one-year study interval. 31,869 patients were seen in the early half of a shift (hours 1-4) and 18,933 were seen in the later half (hours 5+). We ran a linear mixed model that adjusted for age, gender, emergency severity index score, time of day, weekend arrivals, quarter of arrival and shift type. RESULTS: Median time-to-disposition for the early group was 3.25 h (IQR 1.90-5.04), and 2.62 h (IQR 1.51-4.31) for the late group. From our mixed model, we conclude that in the later parts of the shift, providers take on average 15.1% less time to make a disposition decision than in the earlier parts of the shift. CONCLUSION: Patients seen during the latter half of a shift were more likely to have a shorter time-to-disposition than similar patients seen in the first half of a shift. This may be influenced by many factors, such as providers spending the early hours of a shift seeing new patients which generate new tasks and delay dispositions, and viewing the end of shift as a landmark with a goal to maximize dispositions prior to sign-out.

A retrospective multistate analysis: Do regional football games impact emergency department patient volume?

OBJECTIVE: We sought to assess the effect of National Football League (NFL) games played by a regional sports team, the New England Patriots, on emergency department (ED) patient volume. METHODS: We conducted a multicenter, retrospective chart review at the following 3 tertiary centers in New England from 2012 to 2019: Beth Israel Deaconess Medical Center, Boston, MA; Dartmouth Hitchcock Medical Center, Lebanon, NH; and Maine Medical Center, Portland, ME. RESULTS: Within the NFL season, we observed a 2.6% overall decrease (-10.4 patients) in average total daily volume across the study sites on Sundays when Patriots games were played compared with Sundays when games were not played (P = 0.07; 95% confidence interval [CI], -22.37 to 1.62). We observed a 4.3% reduction (-19.0 patients) in average total daily volume across the study sites on Mondays during which Patriots games were played compared with Mondays without games (P = 0.15; 95% CI, -43.51 to 5.47). Subanalyses on the 5-hour period corresponding with each Patriots game showed reductions in mean patient volume per hour. Although our primary and subanalyses showed reductions in patient volume during Patriots games, these results were not statistically significant. CONCLUSIONS: Our data support prior studies that showed a minimal impact of major sporting events on ED patient volume at tertiary centers. These results add to the limited data on this topic and can inform administrators whether staffing adjustments are necessary during similar types of sporting events.

Understanding Demand and Capacity Mismatch in an Academic Emergency Department Using a Staircase Model of Provider Capacity and Staggered Shift Start Times.

BACKGROUND: Staffing and provider capacity are essential components of emergency department (ED) throughput. Patient flow is dependent on matching patient arrivals with provider capacity. Current models assume a static rate of patients per hour for providers; however, this metric has been shown to decrease throughout a shift in a pattern we describe as a staircase. OBJECTIVE: We sought to analyze the demand capacity mismatch based on both a static and staircase model of resident productivity. We then suggest a new staggered staffing model that would improve flow in the ED. METHODS: This was a retrospective analysis of patient demand and productivity, analyzing both static and staircase models of provider capacity. An alternative staggered shift model was then suggested, and a 2-sample t test was performed to assess if a new model reduces the amount of demand/capacity mismatch. RESULTS: Seventeen thousand five hundred twenty data points were analyzed over the 2-year interval, comparing the difference between actual patients placed into a treatment space at each hour and projected resident capacity based on the staircase model, using both the existing schedule and a new staggered schedule. Mean absolute values for the disparity in coverage was 2.69 (95% confidence interval 2.65-2.72) for the staircase scheduling model, and 2.14 (95% confidence interval 2.12-2.17) when staggering provider start times. The mean difference between these data sets was 0.54 (95% confidence interval 0.52-0.57; p < 0.0001). CONCLUSIONS: Academic EDs may find value in using a staircase model to analyze provider capacity because it is more reflective of actual capacity. EDs may benefit from visualizing their capacity curves to identify mismatches and staggering resident shifts to improve throughput and flow.

Estimating maximum patient throughput at an ED scanner: Is three patients per hour the magic number?

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Staffing and Provider Productivity in the Emergency Department.

Staffing and productivity are key concepts to understand when managing an emergency department. Provider productivity is not static, starts out high, and decreases throughout the shift in a stepwise manner. It is commonly measured by patients per hour or relative value units per hour, and is impacted by factors from the presence of residents to shift length. Appropriate staffing requires thorough understanding of the workforce and the variable patient demand of the department. Matching capacity to this demand potentially improves overall throughput and efficiency. Once knowledgeable about these factors, we provide a case study to showcase their application.

The Effect of Signed-Out Emergency Department Patients on Resident Productivity.

BACKGROUND: Transitions of care and patient hand-offs between physicians have important implications for patient care. However, what effect caring for signed-out patients has on providing care to new patients and education is unclear. OBJECTIVE: We sought to determine whether the number of patients a physician receives in sign-out affects productivity. METHODS: This was a retrospective cohort study, conducted at an emergency medicine residency program. A general estimation equation was constructed to model productivity, defined as new patients evaluated and relative value units (RVUs) generated per shift, relative to the number of sign-outs received, and training year. A secondary analysis evaluated the effect of signed-out patients in observation. RESULTS: We evaluated 19,389 shifts from July 1, 2010 to July 1, 2017. Postgraduate year (PGY)-1 residents without sign-out evaluated 10.3 patients (95% confidence interval [CI] 9.83 to 10.7), generating 31.6 RVUs (95% CI 30.5 to 32.7). Each signed-out patient was associated with -0.07 new patients (95% CI -0.12 to -0.01), but no statistically significant decrease in RVUs (95% CI -0.07 to 0.28). PGY-2 residents without sign-out evaluated 13.6 patients (95% CI 12.6 to 14.6), generating 47.7 RVUs (95% CI 45.1 to 50.3). Each signed-out patient was associated with -0.25 (95% CI -0.40 to -0.10) new patients, and -0.89 (95% CI -1.22 to -0.55) RVUs. For all residents, observation patients were associated with more substantial decreases in new patients (-0.40; 95% CI -0.47 to -0.33) and RVUs (-1.11; 95% CI -1.40 to -0.82). CONCLUSIONS: Overall, sign-out burden is associated with a small decrease in resident productivity, except for observation patients. Program faculty should critically examine how signed-out patients are distributed to address residents' educational needs, throughput, and patient safety.