Joshua W. Joseph

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. Table 1 Characteristics of the study participants and shifts evaluated. 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.

The agreement between venous and arterial blood gases in critical care and ward patients: is there a need to stratify for shock?

To the Editor, We read Martin and Priestap’s paper addressing the agreement between venous and arterial blood gases in both critical care and ward patients with great interest. The topic is relevant to all clinicians who care for the critically ill. It clearly shows that there is a strong correlation between arterial and venous gas analysis results when considered in aggregate. Nevertheless, I think that their report could be strengthened if it employed a stratified analysis. That is, the relationship between arterial and venous laboratory values may not be uniform across patients with differing severities of illness. While arterial and venous blood gases may be nearly identical in patients with adequate perfusion, the differences may be exaggerated in patients with shock. For example, this could be determined by examining the bias and standard deviation of the differences in blood gas values for patients with mean lactate values[4 mEq L or with a mean pH \ 7.25, which we encourage authors to report.

A rules based algorithm to generate problem lists using emergency department medication reconciliation

OBJECTIVES To evaluate the sensitivity and specificity of a problem list automatically generated from the emergency department (ED) medication reconciliation. METHODS We performed a retrospective cohort study of patients admitted via the ED who also had a prior inpatient admission within the past year of an academic tertiary hospital. Our algorithm used the First Databank ontology to group medications into therapeutic classes, and applied a set of clinically derived rules to them to predict obstructive lung disease, hypertension, diabetes, congestive heart failure (CHF), and thromboembolism (TE) risk. This prediction was compared to problem lists in the last discharge summary in the electronic health record (EHR) as well as the emergency attending note. RESULTS A total of 603 patients were enrolled from 03/29/2013-04/30/2013. The algorithm had superior sensitivity for all five conditions versus the attending problem list at the 99% confidence level (Obstructive Lung Disease 0.93 vs 0.47, Hypertension 0.93 vs 0.56, Diabetes 0.97 vs 0.73, TE Risk 0.82 vs 0.36, CHF 0.85 vs 0.38), while the attending problem list had superior specificity for both hypertension (0.76 vs 0.94) and CHF (0.87 vs 0.98). The algorithm had superior sensitivity for all conditions versus the EHR problem list (Obstructive Lung Disease 0.93 vs 0.34, Hypertension 0.93 vs 0.30, Diabetes 0.97 vs 0.67, TE Risk 0.82 vs 0.23, CHF 0.85 vs 0.32), while the EHR problem list also had superior specificity for detecting hypertension (0.76 vs 0.95) and CHF (0.87 vs 0.99). CONCLUSION The algorithm was more sensitive than clinicians for all conditions, but less specific for conditions that are not treated with a specific class of medications. This suggests similar algorithms may help identify critical conditions, and facilitate thorough documentation, but further investigation, potentially adding alternate sources of information, may be needed to reliably detect more complex conditions.

Experience Within the Emergency Department and Improved Productivity for First-Year Residents in Emergency Medicine and Other Specialties

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.

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.

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 (Spearmans 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.