Publications

2018

Joseph, Joshua W, Bryan A Stenson, Nicole M Dubosh, Matthew L Wong, David T Chiu, Jonathan Fisher, Larry A Nathanson, and Leon D Sanchez. (2018) 2018. “The Effect of Signed-Out Emergency Department Patients on Resident Productivity.”. The Journal of Emergency Medicine 55 (2): 244-51. https://doi.org/10.1016/j.jemermed.2018.05.020.

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.

Kairys, Norah, Keegan Skidmore, Jennifer Repanshek, and Wayne Satz. (2018) 2018. “An Unlikely Cause of Abdominal Pain.”. Clinical Practice and Cases in Emergency Medicine 2 (2): 139-42. https://doi.org/10.5811/cpcem.2018.2.37073.

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.

April, Michael D, and Calvin A Brown. (2018) 2018. “In Reply.”. Annals of Emergency Medicine 72 (4): 507-8. https://doi.org/10.1016/j.annemergmed.2018.06.043.
April, Michael D, Allyson Arana, Daniel J Pallin, Steven G Schauer, Andrea Fantegrossi, Jessie Fernandez, Joseph K Maddry, et al. (2018) 2018. “Emergency Department Intubation Success With Succinylcholine Versus Rocuronium: A National Emergency Airway Registry Study.”. Annals of Emergency Medicine 72 (6): 645-53. https://doi.org/10.1016/j.annemergmed.2018.03.042.

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.

2017

Joseph, Joshua W, Victor Novack, Matthew L Wong, Larry A Nathanson, and Leon D Sanchez. (2017) 2017. “Do Slow and Steady Residents Win the Race? Modeling the Effects of Peak and Overall Resident Productivity in the Emergency Department.”. The Journal of Emergency Medicine 53 (2): 252-59. https://doi.org/10.1016/j.jemermed.2017.03.019.

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.

Horng, Steven, David A Sontag, Yoni Halpern, Yacine Jernite, Nathan I Shapiro, and Larry A Nathanson. (2017) 2017. “Creating an Automated Trigger for Sepsis Clinical Decision Support at Emergency Department Triage Using Machine Learning.”. PloS One 12 (4): e0174708. https://doi.org/10.1371/journal.pone.0174708.

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.

Nelson, Philippa, Anthony J Bell, Larry Nathanson, Leon D Sanchez, Jonathan Fisher, and Philip D Anderson. (2017) 2017. “Ethnographic Analysis on the Use of the Electronic Medical Record for Clinical Handoff.”. Internal and Emergency Medicine 12 (8): 1265-72. https://doi.org/10.1007/s11739-016-1567-7.

The objective of this study was to understand the social elements of clinical and organizational interactions of the key stakeholders in the specific context of an electronic dashboard used by the emergency department (ED) and inpatient medicine teams at the time of clinical referral and handover. An electronic handover function is utilised at the ED-inpatient interface at this institution and has given clinicians the ability to better communicate, monitor the department and strive to improve patient safety in streamline the delivery of care in the acute phase. This study uses an ethnographic qualitative research design incorporating semistructured interviews, participant observation on the ED floor and fieldwork notes. The setting for this research was in the ED at a tertiary University affiliated hospital. Triangulation was used to combine information obtained from multiple sources and information from fieldwork and interviews refined into useable chunks culminating in a thematic analysis. Thematic analysis yielded five central themes that reflected how the clinical staff utilised this IT system and why it had become embedded in the culture of clinical referral and handover. Efficient time management for improved patient flow was demonstrated, value added communication (at the interpersonal level), the building trust at the ED-inpatient interface, the maintenance of mutual respect across medical cultures and an overall enhancement of the quality of ED communication (in terms of the information available). A robust electronic handover process, resulted in an integrated approach to patient care by removing barriers to admission for medical inpatients, admitted via ED. The value proposition for patients was a more complete information transfer, both within the ED and between departments.

Sanchez, Leon D, David T Chiu, Larry Nathanson, Steve Horng, Richard E Wolfe, Mark L Zeidel, Kirsten Boyd, et al. (2017) 2017. “A Model for Electronic Handoff Between the Emergency Department and Inpatient Units.”. The Journal of Emergency Medicine 53 (1): 142-50. https://doi.org/10.1016/j.jemermed.2017.03.027.

BACKGROUND: Patient handoffs between units can introduce risk and time delays. Verbal communication is the most common mode of handoff, but requires coordination between different parties.

OBJECTIVE: We present an asynchronous patient handoff process supported by a structured electronic signout for admissions from the emergency department (ED) to the inpatient medicine service.

METHODS: A retrospective review of patients admitted to the medical service from July 1, 2011 to June 30, 2015 at a tertiary referral center with 520 inpatient beds and 57,000 ED visits annually. We developed a model for structured electronic, asynchronous signout that includes an option to request verbal communication after review of the electronic handoff information.

RESULTS: During the 2010 academic year (AY) all admissions used verbal communication for signout. The following academic year, electronic signout was implemented and 77.5% of admissions were accepted with electronic signout. The rate increased to 87.3% by AY 2014. The rate of transfer from floor to an intensive care unit within 24 h for the year before and 4 years after implementation of the electronic signout system was collected and calculated with 95% confidence interval. There was no statistically significant difference between the year prior and the years after the implementation.

CONCLUSIONS: Our handoff model sought to maximize the opportunity for asynchronous signout while still providing the opportunity for verbal signout when deemed necessary. The process was rapidly adopted with the majority of patients being accepted electronically.