Objectives: In this study, we examine APP productivity to determine if a similar pattern applies to that of residents and attendings, with the hypothesis that hourly productivity decreases after the first few hours of the shift.

Background: APP productivity follows a similar pattern to previously described behaviors in both residents and attendings. This further confirms the results of prior studies that productivity is a dynamic process that needs to be considered when adjusting staffing models. Additionally, this pattern by APPs at a community hospital provides additional validation of this model outside of academic institutions or training environments.

Methods: This is a retrospective cohort study from 7/1/21 through 6/30/21 at a single suburban community hospital in the northeast. APPs work ten hour shifts from 10AM to 8PM, nearly every day. APPs also provide coverage for approximately 5 shifts per month from 3PM to 11PM. Timestamps of initial patient contact are automatically logged by the electronic health record, and then analyzed to determine in which hour of the shift this occurred. A mixed linear model was performed with the hour as a categorical variable, and day of the week, month and year as covariates. Data was grouped by individual shifts.

Results: A total of 345 10-hour shifts were worked by five APPs over the one year studied. There were 64 additional afternoon shifts which were excluded due to the shorter length and lower frequency. Two APPs worked the vast majority of the shifts, and the other three provided per-diem coverage. A mean of 13.3 patients (SD 2.7) were seen per shift. In the first hour, APPs saw an average of 2.67 patients (95% CI 2.59-2.76). Each hour demonstrated a statistically significant decrease relative to the first hour (p< 0.001), with the highest magnitude over the second (-0.58 (95% CI -0.69 — -0.47)) and third (-0.98 (95% CI -1.09 — -0.86)) hours. This downward trend continued throughout the rest of the shift; however, the magnitude of this difference after the sixth hour was not significant. There was no effect by day of week, month or year.

Conclusion: APP productivity follows a similar pattern to previously described behaviors in both residents and attendings. This further confirms the results of prior studies that productivity is a dynamic process that needs to be considered when adjusting staffing models. Additionally, this pattern by APPs at a community hospital provides additional validation of this model outside of academic institutions or training environments.

Objectives: To assess whether time-to-disposition is significantly different when a patient is seen by a provider during the early half or late half of a shift.

Background: 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 hours (IQR 1.90-5.04), and 2.62 hours (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.

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.

Objectives: To validate and expand upon prior data suggesting that visits shared with a midlevel or resident influence EP behavior as measured by resource use and propensity to admit a patient.

Background: Variability exists in Emergency Physicians’ (EP) resource utilization as measured by ordering practices, propensity to admit patients, and whether a visit is shared with a resident or midlevel provider (nurse practitioner or physician assistant).

Methods: This is a retrospective study of routinely gathered operational data from two community, suburban hospitals within an academic emergency network. We analyzed 34 EPs with 141,433 patient visits from July 1, 2016 to June 30, 2019. We collected individual EP data on advanced imaging (CT, US, MRI), admission rates, and whether a visit was shared with a midlevel or resident for each patient encounter. To investigate whether there might be distinct groups of practice patterns relating these resources, we used a Gaussian Mixture Model (GMM), a classification method used to determine the likelihood of distinct subgroups within a larger population. The total number of groups and covariance structure were determined by Bayesian Information Criteria.

Results: Our GMM revealed three distinct groups of physicians based on their ordering practices. The largest group is characterized by a homogenous pattern of neither high or low resource utilization (n=19, 58% female, median years’ experience: 9 [IQR 2-16]; rates of Advanced Imaging: 44%, Admission: 21%, Midlevel/Resident staffing 35% with a modest group of low-resource users (n=10, 0% female, median years’ experience: 7 [IQR 5-11]; rates of Advanced Imaging: 31%, Admission: 17%, Midlevel/Resident staffing 32%), and far fewer members of a high-resource use group (n=5, 20% female, median years’ experience: 15 [IQR 5-16]; rates of Advanced Imaging: 49%, Admission: 22%, Midlevel/Resident staffing 35%) [Figure 1]. This variation suggests that use of advanced imaging and propensity to admit may be influenced by whether a patient visit is shared with a midlevel or resident provider.

Conclusion: At two community EDs, three distinct subgroups of EP ordering practices exist based on advanced imaging use, propensity to admit a patient, and whether a visit was shared with a midlevel or resident. This data validates prior work showing that resource utilization and admission rates are related, while demonstrating that more nuanced patterns of EP ordering practices exist based on whether a visit is shared with a midlevel or resident provider. Further investigation is needed to understand the impact of EP characteristics and behavior on throughput and quality of care.

Objectives: The purpose of this study is to determine Emergency Medicine (EM) resident productivity (as measured by patients per hour) over the course of the COVID-19 pandemic compared to the prior training environment.

Background: The coronavirus disease 2019 (COVID-19) pandemic disrupted medical education throughout the United States. As a result, many EM residents began residency with atypical clinical experiences. In addition, Emergency Department (ED) patient volumes decreased during the early months of the pandemic.

Methods: This is a retrospective observational cohort study conducted at an urban, academic medical center with an established EM residency program. Data was collected from electronic medical records between July 1, 2017 and October 31, 2021. EM residents completing full, consecutive years of residency were included in the sample. Classes prior to 2020 were defined as a control group. Due to the structure of the residency, only shifts at the academic medical center during first and second year of residency (PGY1 and PGY2) were included. Productivity was defined as total primary patient encounters divided by aggregate scheduled shift hours. To allow comparison of the most recent data, analysis was performed on the first four months (July-October) of each academic year. The data was analyzed using descriptive statistics, including standard deviation and t-tests.

Results: A total of 63 residents were included in this analysis of the first four months of each training year from 2017-2021. Prior to COVID-19, PGY1 residents evaluated 0.75±0.23 patients per hour and PGY2 residents evaluated 1.46±0.11 patients per hour (p<0.001). Compared with the pre-COVID control group, PGY1 residents evaluated 0.64±0.22 patients per hour in 2020 (p=0.15) and 0.82±0.21 patients per hour in 2021 (p=0.34); PGY2 residents evaluated 1.44±0.17 patients per hour (p=0.65) in 2020 and 1.75±0.17 patients per hour in 2021 (p<0.001).

Conclusion: This analysis suggests that EM resident productivity at the medical center did not decrease significantly compared to prior years during the COVID-19 pandemic. While there was a trend towards fewer patients per hour in 2020, this did not reach statistical significance. These results may have applications to medical education and ED operations. The study is limited by single center, retrospective, and observational design.