Revive and Refuse: Capacity, Autonomy, and Refusal of Care After Opioid Overdose.

Physicians generally recommend that patients resuscitated with naloxone after opioid overdose stay in the emergency department for a period of observation in order to prevent harm from delayed sequelae of opioid toxicity. Patients frequently refuse this period of observation despiteenefit to risk. Healthcare providers are thus confronted with the challenge of how best to protect the patient's interests while also respecting autonomy, including assessing whether the patient is making an autonomous choice to refuse care. Previous studies have shown that physicians have widely divergent approaches to navigating these conflicts. This paper reviews what is known about the effects of opioid use disorder on decision-making, and argues that some subset of these refusals are non-autonomous choices, even when patients appear to have decision making capacity. This conclusion has several implications for how physicians assess and respond to patients refusing medical recommendations after naloxone resuscitation.

Navigating Care Refusal and Noncompliance in Patients with Opioid Use Disorder.

BACKGROUND: For many emergency physicians (EPs), deciding whether or not to allow a patient suffering the ill effects of opioid use to refuse care is the most frequent and fraught situation in which they encounter issues of decision-making capacity, informed refusal, and autonomy. Despite the frequency of this issue and the well-known impacts of opioid use disorder on decision-making, the medical ethics community has offered little targeted analysis or guidance regarding these situations. DISCUSSION: As a result, EPs demonstrate significant variability in how they evaluate and respond to them, with highly divergent understandings and application of concepts such as decision-making capacity, informed consent, autonomy, legal repercussions, and strategies to resolve the clinical dilemma. In this paper, we seek to provide more clarity to this issue for the EPs. CONCLUSIONS: Successfully navigating this issue requires that EPs understand the specific effects that opioid use disorder has on decision-making, and how that in turn bears on the ethical concepts of autonomy, capacity, and informed refusal. Understanding these concepts can lead to helpful strategies to resolve these commonly-encountered dilemmas.

Practice as you Teach: Comparing Ordering Practices Between Shared and Physician-Only Visits in Academically Affiliated Community Emergency Departments.

BACKGROUND: Considerable variability exists in emergency physicians' (EPs) rates of resource utilization, which may cluster in distinct patterns. However, previous studies have focused on academic and tertiary care centers, and it is unclear whether similar patterns exist in community practice. OBJECTIVE: Our aim was to examine whether EPs practicing in community emergency departments (EDs) have practice patterns similar to those of academic EDs. Secondarily, we sought to investigate the effects of shared visits with advanced practice professionals and residents. METHODS: This was a retrospective study of two community EDs affiliated with an academic network. There were 62,860 visits among 50 EPs analyzed from October 1, 2018 through January 31, 2020 for rates of advanced imaging, admission, and shared visits. To classify practice patterns, we used a Gaussian Mixture Model (GMM), with groups and covariance determined by Bayesian Information Criteria. RESULTS: Our GMM revealed three groups. The largest had homogeneous patterns of resource use (n = 28; 50% were female; years of experience: 7; interquartile range [IQR] 2-11; advanced imaging: 28%; admission: 19%; shared: 34%), a small group with lower resource use (n = 4; 0% were female; years of experience: 6; IQR 4-10; advanced imaging: 28%; admission: 16%; shared: 8%), and a modest high-resource group (n = 18; 28% female; years of experience: 5; IQR 2-16; advanced imaging: 34%; admission: 23%; shared: 43%). Rates of shared visits had little direct correlation with imaging (r2 = 0.045) or admission (r2 = 0.093), and rates of imaging and admission were weakly correlated (r2 = 0.242). CONCLUSIONS: Our data suggest that community EPs may have multiple patterns of resource use, similar to those in academic EDs.

Frequency, compliance, and yield of cardiac testing after high-sensitivity troponin accelerated diagnostic protocol implementation.

BACKGROUND: Among persons presenting to the emergency department with suspected acute myocardial infarction (MI), cardiac troponin (cTn) testing is commonly used to detect acute myocardial injury. Accelerated diagnostic protocols (ADPs) guide clinicians to integrate cTn results with other clinical information to decide whether to order further diagnostic testing. OBJECTIVE: To determine the change in the rate and yield of stress test or coronary CT angiogram following cTn measurement in patients with chest pain presenting to the emergency department pre- and post-transition to a high-sensitivity (hs-cTn) assay in an updated ADP. METHODS: Using electronic health records, we examined visits for chest pain at five emergency departments affiliated with an integrated academic health system 1-year pre- and post-hs-cTn assay transition. Outcomes included stress test or coronary imaging frequency, ADP compliance among those with additional testing, and diagnostic yield (ratio of positive tests to total tests). RESULTS: There were 7564 patient-visits for chest pain, including 3665 in the pre- and 3899 in the post-period. Following the updated ADP using hs-cTn, 862 (23.5 per 100 patient visits) visits led to subsequent testing versus 1085 (27.8 per 100 patient visits) in the pre-hs-cTn period, (P < 0.001). Among those who were tested, the protocol-compliant rate fell from 80.9% to 46.5% (P < 0.001), but the yield of those tests rose from 24.5% to 29.2% (P = 0.07). Among tests that were noncompliant with ADP guidance, yield was similar pre- and post-updated hs-cTn ADP implementation (pre 13.0%, post 15.4% (P = 0.43). CONCLUSION: Implementation of hs-cTn supported by an updated ADP was associated with a lower rate of stress testing and coronary CT angiogram.

Machine Learning Methods for Predicting Patient-Level Emergency Department Workload.

BACKGROUND: Work Relative Value Units (wRVUs) are a component of many compensation models, and a proxy for the effort required to care for a patient. Accurate prediction of wRVUs generated per patient at triage could facilitate real-time load balancing between physicians and provide many practical operational and clinical benefits. OBJECTIVE: We examined whether deep-learning approaches could predict the wRVUs generated by a patient's visit using data commonly available at triage. METHODS: Adult patients presenting to an urban, academic emergency department from July 1, 2016-March 1, 2020 were included. Deidentified triage information included structured data (age, sex, vital signs, Emergency Severity Index score, language, race, standardized chief complaint) and unstructured data (free-text chief complaint) with wRVUs as outcome. Five models were examined: average wRVUs per chief complaint, linear regression, neural network and gradient-boosted tree on structured data, and neural network on unstructured textual data. Models were evaluated using mean absolute error. RESULTS: We analyzed 204,064 visits between July 1, 2016 and March 1, 2020. The median wRVUs were 3.80 (interquartile range 2.56-4.21), with significant effects of age, gender, and race. Models demonstrated lower error as complexity increased. Predictions using averages from chief complaints alone demonstrated a mean error of 2.17 predicted wRVUs per visit (95% confidence interval [CI] 2.07-2.27), the linear regression model: 1.00 wRVUs (95% CI 0.97-1.04), gradient-boosted tree: 0.85 wRVUs (95% CI 0.84-0.86), neural network with structured data: 0.86 wRVUs (95% CI 0.85-0.87), and neural network with unstructured data: 0.78 wRVUs (95% CI 0.76-0.80). CONCLUSIONS: Chief complaints are a poor predictor of the effort needed to evaluate a patient; however, deep-learning techniques show promise. These algorithms have the potential to provide many practical applications, including balancing workloads and compensation between emergency physicians, quantify crowding and mobilizing resources, and reducing bias in the triage process.

Outcomes for Patients Discharged to Involuntary Commitment for Substance Use Disorder Directly from the Hospital.

To evaluate the outcomes of patients discharged to involuntary commitment for substance use disorders directly from the hospital. We performed a retrospective chart review of 22 patients discharged to involuntary commitment for substance use disorder from the hospital between October 2016 and February 2020. We collected demographic data, details about each commitment episode, and healthcare utilization outcomes 1 year following involuntary commitment. Nearly all patients had a primary alcohol use disorder (91%) and had additional medical (82%) and psychiatric comorbidities (71%). One year following involuntary commitment, all patients had relapsed to substance use and had at least one emergency department visit while 78.6% had at least one admission. These findings suggest that patients discharged to involuntary commitment directly from the hospital universally relapsed and experienced significant medical morbidity during the first year following their release. This study adds to a growing literature recognizing the harms of involuntary commitment for substance use disorder.

The Ethics of Procedural Education Under Pandemic Conditions.

BACKGROUND: The COVID-19 pandemic significantly disrupted emergency medicine residents' education. Early in the pandemic, many facilities lacked adequate personal protective equipment (PPE), and intubation was considered particularly high risk for transmission to physicians, leading hospitals to limit the number of individuals present during the procedure. This posed difficulties for residents and academic faculty, as opportunities to perform endotracheal intubation during residency are limited, but patients with COVID-19 requiring intubation are unstable and have difficult airways. Case Scenario: When PPE is being rationed, who should be the one to perform an intubation on a patient with respiratory failure from severe COVID-19? DISCUSSION: We examined this case scenario using the ethical frameworks of bioethical principles and virtue ethics. Bioethical principles include justice, beneficence, nonmalfeasance, and autonomy, and virtue ethics emphasizes the provision of moral exemplars and opportunities to exercise practical wisdom. Arguments for an attending-only strategy include the role of the attending as a truly autonomous decision maker and the importance of providing residents with a moral exemplar. A resident-only strategy benefits a resident's future patients and provides opportunities for residents to exercise character. Strategies preserving the dyad of attending and resident maintain these advantages and mitigate some drawbacks, while intubation teams may provide the most parsimonious use of PPE, but may elide resident involvement. CONCLUSIONS: There exist compelling motivations for involving senior residents and attendings in high-risk intubations during the COVID-19 pandemic. A just strategy will preserve residents' role whenever possible, while maximizing supervision and providing alternative routes for intubation practice.

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.

Association of resident characteristics with patterns of patient self-assignment.

OBJECTIVE: We hypothesized that resident characteristics impact patterns of patient self-assignment in the emergency department (ED). Our goal was to determine if male residents would be less likely than their female colleagues to see patients with sensitive (e.g. breast-related or gynecologic) chief complaints (CCs). We also investigated whether resident specialty was associated with preferentially choosing patients with more familiar chief complaints. METHODS: We performed a retrospective cross-sectional study at a tertiary academic medical center using data from all adult patients presenting to the ED between 2010 and 2019 with one of six CC categories (vaginal bleeding, breast-related concerns, male genitourinary [GU] concerns, gastrointestinal bleeding, epistaxis, and laceration). These CCs were chosen as they each require either an invasive medical exam or procedure, and cannot easily be evaluated with an exam in a hallway bed. We used logistic regression to assess the likelihood of being treated by a male resident compared to a female resident for each CC, adjusting for candidate variables of patient age, race, primary language, ESI score, bed location, time of day, day of week, calendar month, and resident specialty. We also similarly analyzed patterns of patient self-assignment according to resident specialty. RESULTS: Male residents were significantly less likely than female residents to treat patients with breast-related CCs (adjusted OR 0.67, 95% CI 0.54-0.83, p < 0.001) or vaginal bleeding (adjusted OR 0.73, 95% CI 0.63-0.84, p < 0.001, reference group: epistaxis). Off-service residents were more likely to assign themselves to familiar chief complaints, for example surgery residents were more likely to see patients with lacerations (adjusted OR 2.11, 95% CI 1.71-2.61, p < 0.001) and OB/GYN residents were less likely to see patients with male GU concerns (adjusted OR 0.21, 95% CI 0.05-0.85, p = 0.029), compared to emergency medicine residents. CONCLUSION: In a single facility, resident characteristics were associated with preferential patient self-assignment. Further work is necessary to determine the underlying reasons for patient avoidance, and to create work environments in which preferentially choosing patients is discouraged.

Decreased hospital admissions through emergency departments during the COVID-19 pandemic.

STUDY OBJECTIVE: Emergency Department (ED) visits decreased significantly in the United States during the COVID-19 pandemic. A troubling proportion of this decrease was among patients who typically would have been admitted to the hospital, suggesting substantial deferment of care. We sought to describe and characterize the impact of COVID-19 on hospital admissions through EDs, with a specific focus on diagnosis group, age, gender, and insurance coverage. METHODS: We conducted a retrospective, observational study of aggregated third-party, anonymized ED patient data. This data included 501,369 patient visits from twelve EDs in Massachusetts from 1/1/2019-9/9/2019, and 1/1/2020-9/8/2020. We analyzed the total arrivals and hospital admissions and calculated confidence intervals for the change in admissions for each characteristic. We then developed a Poisson regression model to estimate the relative contribution of each characteristic to the decrease in admissions after the statewide lockdown, corresponding to weeks 11 through 36 (3/11/2020-9/8/2020). RESULTS: We observed a 32% decrease in admissions during weeks 11 to 36 in 2020, with significant decreases in admissions for chronic respiratory conditions and non-orthopedic needs. Decreases were particularly acute among women and children, as well as patients with Medicare or without insurance. The most common diagnosis during this time was SARS-CoV-2. CONCLUSION: Our findings demonstrate decreased hospital admissions through EDs during the pandemic and suggest that several patient populations may have deferred necessary care. Further research is needed to determine the clinical and operational consequences of this delay.

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

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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.

Emergency physicians' active patient queues over the course of a shift.

OBJECTIVES: When emergency physicians see new patients in an ad libitum system, they see fewer patients as the shift progresses. However, it is unclear if this reflects a decreasing workload, as patient assessments often span many hours. We sought to investigate whether the size of a physician's queue of active patients similarly declines over a shift. METHODS: Retrospective cohort study, conducted over two years in three community hospitals in the Northeastern United States, with 8 and 9-h shifts. Timestamps of all encounters were recorded electronically. Generalized estimating equations were constructed to predict the number of active patients a physician concurrently managed per hour. RESULTS: We evaluated 64 physicians over a two-year period, with 9822 physician-shifts. Across all sites, physicians managed an increasing queue of active patients in the first several hours. This queue plateaued in the middle of the shift, declining in the final hours, independently of other factors. Physicians' queues of active patients increased slightly with greater volume and acuity, but did not affect the overall pattern of work. Similarly, working alone or with colleagues had little effect on the number of active patients managed. CONCLUSIONS: Emergency physicians in an ad libitum system tend to see new patients until reaching a stable roster of active patients. This pattern may help explain why physicians see fewer new patients over the course of a shift, should be factored into models of throughput, and suggests new avenues for evaluating relationships between physician workload, patient safety, physicians' well-being, and the quality of care.

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.

How Emergency Physicians Approach Refusal of Observation after Naloxone Resuscitation.

BACKGROUND: Patients who are resuscitated with naloxone frequently refuse a period of observation, even though they may be suffering from a variety of medical and psychiatric comorbidities. Emergency physicians (EPs) are then confronted with the challenge of how best to serve patients' interests while respecting autonomy. OBJECTIVES: We sought to characterize how EPs think about this kind of dilemma and the strategies they use to resolve them. METHODS: We conducted qualitative semi-structured interviews with a convenience sample of 59 emergency physicians attending the American College of Emergency Physicians' Scientific Assembly in October 2018. Three case vignettes highlighting different clinical and ethical features served as prompts. Interviews were analyzed using a constant comparative method to identify patterns of responses and derive key themes. RESULTS: Across the vignettes, EPs demonstrated diverse approaches to observation, assessing decision-making capacity and encouraging compliance. Some EPs refused to comply with a patient's wishes even when they had determined a patient demonstrated capacity. Conversely, a few EPs were willing to allow patients to leave the emergency department (ED) without assessing capacity, or despite determining that the patient lacked capacity. Common reasons for complying with patients' demands were concerns about the patients' rights and concerns about the safety of staff. Most physicians interviewed reported no institutional guidelines or education on the topic, and many physicians expressed an interest in providing medication for addiction treatment in the ED. CONCLUSIONS: EPs approach this clinical and ethical dilemma in widely divergent ways. Consensus about strategies for navigating patients' wishes relative to clinical concerns are needed to help EPs manage these challenging cases.

Grit, anxiety, and stress in emergency physicians.

INTRODUCTION: The personality traits of emergency physicians are infrequently studied, though interest in physician wellness is increasing. The objective of this study is to acquire pilot data about the amount of grit, anxiety, and stress in emergency physicians using established psychological survey instruments, and to examine their associations of each of these traits with each other. METHODS: Thirty-six emergency medicine resident and attending physicians from an urban academic medical center consented for enrollment. Participants were administered the Duckworth 12-point Grit Scale, the State-Trait Anxiety Inventory (STAI), and the Perceived Stress Scale (PSS), which measure grit, anxiousness, and perceived stress, respectively. These are the gold standard psychological instruments for each of their areas. We analyzed the results with descriptive statistics, Spearman correlations, and linear regression. RESULTS: Nineteen residents and 17 attending physicians completed the surveys during the first quarter of a new academic year. The mean grit score was 3.7 (95% CI 3.5-3.8, SD: 0.56), the mean trait-anxiety score was 32.61 (95% CI 30.15-35.07, SD: 7.26), and the mean PSS score was 12.28 (95% CI 10.58-13.97, SD: 4.99). Only trait-anxiety and perceived stress were significantly correlated (Spearman's rho: 0.70, p<0.01). CONCLUSIONS: In this pilot study at a single institution, emergency physicians demonstrated a range of grit, trait-anxiety, and perceived stress. Trait-anxiety and stress were strongly associated, and individuals who were more anxious reported more stress. Levels of grit were not associated with trait-anxiety. These psychological concepts should be studied further as they relate to the function and health of emergency medicine providers.

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.

Modelling attending physician productivity in the emergency department: a multicentre study.

OBJECTIVES: Emergency physician productivity, often defined as new patients evaluated per hour, is essential to planning clinical operations. Prior research in this area considered this a static quantity; however, our group's study of resident physicians demonstrated significant decreases in hourly productivity throughout shifts. We now examine attending physicians' productivity to determine if it is also dynamic. METHODS: This is a retrospective cohort study, conducted from 2014 to 2016 across three community hospitals in the north-eastern USA, with different schedules and coverage. Timestamps of all patient encounters were automatically logged by the sites' electronic health record. Generalised estimating equations were constructed to predict productivity in terms of new patients per shift hour. RESULTS: 207 169 patients were seen by 64 physicians over 2 years, comprising 9822 physician shifts. Physicians saw an average of 15.0 (SD 4.7), 20.9 (SD 6.4) and 13.2 (SD 3.8) patients per shift at the three sites, with 2.97 (SD 0.22), 2.95 (SD 0.24) and 2.17 (SD 0.09) in the first hour. Across all sites, physicians saw significantly fewer new patients after the first hour, with more gradual decreases subsequently. Additional patient arrivals were associated with greater productivity; however, this attenuates substantially late in the shift. The presence of other physicians was also associated with slightly decreased productivity. CONCLUSIONS: Physician productivity over a single shift follows a predictable pattern that decreases significantly on an hourly basis, even if there are new patients to be seen. Estimating productivity as a simple average substantially underestimates physicians' capacity early in a shift and overestimates it later. This pattern of productivity should be factored into hospitals' staffing plans, with shifts aligned to start with the greatest volumes of patient arrivals.

Modeling Hourly Resident Productivity in the Emergency Department.

STUDY OBJECTIVE: Resident productivity, defined as new patients per hour, carries important implications for emergency department operations. In high-volume academic centers, essential staffing decisions can be made on the assumption that residents see patients at a static rate. However, it is unclear whether this model mirrors reality; previous studies have not rigorously examined whether productivity changes over time. We examine residents' productivity across shifts to determine whether it remained consistent. METHODS: This was a retrospective cohort study conducted in an urban academic hospital with a 3-year emergency medicine training program in which residents acquire patients ad libitum throughout their shift. Time stamps of all patient encounters were automatically logged. A linear mixed model was constructed to predict productivity per shift hour. RESULTS: A total of 14,364 8- and 9-hour shifts were worked by 75 residents between July 1, 2010, and June 20, 2015. This comprised 6,127 (42.7%) postgraduate year (PGY) 1 shifts, 7,236 (50.4%) PGY-2 shifts, and 998 (6.9%) PGY-3 nonsupervisory shifts (Table 1). Overall, residents treated a mean of 10.1 patients per shift (SD 3.2), with most patients at Emergency Severity Index level 3 or more acute (93.8%). In the initial hour, residents treated a mean of 2.14 patients (SD 1.2), and every subsequent hour was associated with a significant decrease, with the largest in the second, third, and final hours. CONCLUSION: Emergency medicine resident productivity during a single shift follows a reliable pattern that decreases significantly hourly, a pattern preserved across PGY years and types of shifts. This suggests that resident productivity is a dynamic process, which should be considered in staffing decisions and studied further.

Do Slow and Steady Residents Win the Race? Modeling the Effects of Peak and Overall Resident Productivity in the Emergency Department.

BACKGROUND: Emergency medicine residents need to be staffed in a way that balances operational needs with their educational experience. Key to developing an optimal schedule is knowing a resident's expected productivity, a poorly understood metric. OBJECTIVE: We sought to measure how a resident's busiest (peak) workload affects their overall productivity for the shift. METHODS: We conducted a retrospective, observational study of resident productivity at an urban, tertiary care center with a 3-year Accreditation Council for Graduate Medical Education-approved emergency medicine training program, with 55,000 visits annually. We abstracted resident productivity data from a database of patient assignments from July 1, 2010 to June 20, 2015, utilizing a generalized estimation equation method to evaluate physician shifts. Our primary outcome measure was the total number of patients seen by a resident over a shift. The secondary outcome was the number of patients seen excluding those in the peak hour. RESULTS: A total of 14,361 shifts were evaluated. Multivariate analysis showed that the total number of patients seen was significantly associated with the number of patients seen during the peak hour, level of training, the timing of the shift, but most prominently, lower variance in patients seen per hour (coefficient of variation < 0.10). CONCLUSIONS: A resident's peak productivity can be a strong predictor of their overall productivity, but the substantial negative effect of variability favors a steadier pace. This suggests that resident staffing and patient assignments should generally be oriented toward a more consistent workload, an effect that should be further investigated with attending physicians.