Publications

INTRODUCTION: Resident productivity is an important educational and operational measure in emergency medicine (EM). The ability to continue effectively seeing new patients throughout a shift is fundamental to an emergency physician's development, and residents are integral to the workforce of many academic emergency departments (ED). Our previous work has demonstrated that residents make gains in productivity over the course of intern year; however, it is unclear whether this is from experience as a physician in general on all rotations, or specific to experience in the ED.

METHODS: This was a retrospective cohort study, conducted in an urban academic hospital ED, with a three-year EM training program in which first-year residents see new patients ad libitum. We evaluated resident shifts for the total number of new patients seen. We constructed a generalized estimating equation to predict productivity, defined as the number of new patients seen per shift, as a function of the week of the academic year, the number of weeks spent in the ED, and their interaction. Off-service residents' productivity in the ED was analyzed in a secondary analysis.

RESULTS: We evaluated 7,779 EM intern shifts from 7/1/2010 to 7/1/2016. Interns started at 7.16 (95% confidence interval [CI] [6.87 - 7.45]) patients per nine-hour shift, with an increase of 0.20 (95% CI [0.17 - 0.24]) patients per shift for each week in the ED, over 22 weeks, leading to 11.5 (95% CI [10.6 - 12.7]) patients per shift at the end of their training in the ED. The effects of the week of the academic year and its interaction with weeks in the ED were not significant. We evaluated 2,328 off-service intern shifts, in which off-service residents saw 5.43 (95% CI [5.02 - 5.84]) patients per nine-hour shift initially, with 0.46 additional patients per week in the ED (95% CI [0.25 - 0.68]). The weeks of the academic year were not significant.

CONCLUSION: Intern productivity in EM correlates with time spent training in the ED, and not with experience on other rotations. Accordingly, an EM intern's productivity should be evaluated relative to their aggregate time in the ED, rather than the time in the academic year.

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.

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.

Cecal bascule is a rare subtype of cecal volvulus where the cecum folds anterior to the ascending colon causing intestinal obstruction. It is a challenging diagnosis to make in the emergency department, as the mobile nature of the cecum leads to a great deal of variation in its clinical presentation. Our discussion of a 78-year-old female who presented with abdominal pain and was found to have a cecal bascule requiring right hemicolectomy, demonstrates how emergency physicians must expand their differential diagnosis for patients reporting signs of intestinal obstruction. Though cecal bascule does not present often, the need for early surgical intervention necessitates a high level of clinical suspicion to prevent life-threatening complications.

STUDY OBJECTIVE: Although both succinylcholine and rocuronium are used to facilitate emergency department (ED) rapid sequence intubation, the difference in intubation success rate between them is unknown. We compare first-pass intubation success between ED rapid sequence intubation facilitated by succinylcholine versus rocuronium.

METHODS: We analyzed prospectively collected data from the National Emergency Airway Registry, a multicenter registry collecting data on all intubations performed in 22 EDs. We included intubations of patients older than 14 years who received succinylcholine or rocuronium during 2016. We compared the first-pass intubation success between patients receiving succinylcholine and those receiving rocuronium. We also compared the incidence of adverse events (cardiac arrest, dental trauma, direct airway injury, dysrhythmias, epistaxis, esophageal intubation, hypotension, hypoxia, iatrogenic bleeding, laryngoscope failure, laryngospasm, lip laceration, main-stem bronchus intubation, malignant hyperthermia, medication error, pharyngeal laceration, pneumothorax, endotracheal tube cuff failure, and vomiting). We conducted subgroup analyses stratified by paralytic weight-based dose.

RESULTS: There were 2,275 rapid sequence intubations facilitated by succinylcholine and 1,800 by rocuronium. Patients receiving succinylcholine were younger and more likely to undergo intubation with video laryngoscopy and by more experienced providers. First-pass intubation success rate was 87.0% with succinylcholine versus 87.5% with rocuronium (adjusted odds ratio 0.9; 95% confidence interval 0.6 to 1.3). The incidence of any adverse event was also comparable between these agents: 14.7% for succinylcholine versus 14.8% for rocuronium (adjusted odds ratio 1.1; 95% confidence interval 0.9 to 1.3). We observed similar results when they were stratified by paralytic weight-based dose.

CONCLUSION: In this large observational series, we did not detect an association between paralytic choice and first-pass rapid sequence intubation success or peri-intubation adverse events.

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.

OBJECTIVE: To demonstrate the incremental benefit of using free text data in addition to vital sign and demographic data to identify patients with suspected infection in the emergency department.

METHODS: This was a retrospective, observational cohort study performed at a tertiary academic teaching hospital. All consecutive ED patient visits between 12/17/08 and 2/17/13 were included. No patients were excluded. The primary outcome measure was infection diagnosed in the emergency department defined as a patient having an infection related ED ICD-9-CM discharge diagnosis. Patients were randomly allocated to train (64%), validate (20%), and test (16%) data sets. After preprocessing the free text using bigram and negation detection, we built four models to predict infection, incrementally adding vital signs, chief complaint, and free text nursing assessment. We used two different methods to represent free text: a bag of words model and a topic model. We then used a support vector machine to build the prediction model. We calculated the area under the receiver operating characteristic curve to compare the discriminatory power of each model.

RESULTS: A total of 230,936 patient visits were included in the study. Approximately 14% of patients had the primary outcome of diagnosed infection. The area under the ROC curve (AUC) for the vitals model, which used only vital signs and demographic data, was 0.67 for the training data set, 0.67 for the validation data set, and 0.67 (95% CI 0.65-0.69) for the test data set. The AUC for the chief complaint model which also included demographic and vital sign data was 0.84 for the training data set, 0.83 for the validation data set, and 0.83 (95% CI 0.81-0.84) for the test data set. The best performing methods made use of all of the free text. In particular, the AUC for the bag-of-words model was 0.89 for training data set, 0.86 for the validation data set, and 0.86 (95% CI 0.85-0.87) for the test data set. The AUC for the topic model was 0.86 for the training data set, 0.86 for the validation data set, and 0.85 (95% CI 0.84-0.86) for the test data set.

CONCLUSION: Compared to previous work that only used structured data such as vital signs and demographic information, utilizing free text drastically improves the discriminatory ability (increase in AUC from 0.67 to 0.86) of identifying infection.