The effect of a nursing strike on emergency department operational metrics.

BACKGROUND: In July of 2017, after more than 15 months of negotiations, an academic teaching hospital in Boston failed to reach an agreement on the terms of contract renewal with its nursing union resulting in a strike. Replacement nurses were hired by the hospital to fulfill nursing duties for five days. OBJECTIVES: This study aims to measure the effects of this nursing strike on the patients seen in the emergency department (ED) by examining operational metrics before and during the strike. METHODS: Retrospective analysis of patient visits occurring for the five days of the strike (July 12-16, 2017) compared with the analogous five-day period immediately preceding that of the strike (July 5-9, 2017). RESULTS: During the strike, ED volume decreased by 23.6% (691 vs. 528 visits), and the decrease was more pronounced for adult vs. pediatric visits. There were no differences in patient sex, race/ethnicity or age groups. EMS transports decreased by 49.1% (171 vs. 87 transports). Although patient dispositions were similar in both periods, length of stay decreased for discharged patients (median 204 vs 178 minutes, p=0.01), and did not change significantly for admitted patients (median 322 vs. 320 minutes, p=0.33). There was one patient death in each of the periods. CONCLUSION: Although rare, nursing strikes do occur. These data may be useful for hospitals preparing for a strike.

An example of medical device-based projection of clinical trial enrollment: Use of electrocardiographic data to identify candidates for a trial in acute coronary syndromes.

BACKGROUND: To identify potential participants for clinical trials, electronic health records (EHRs) are searched at potential sites. As an alternative, we investigated using medical devices used for real-time diagnostic decisions for trial enrollment. METHODS: To project cohorts for a trial in acute coronary syndromes (ACS), we used electrocardiograph-based algorithms that identify ACS or ST elevation myocardial infarction (STEMI) that prompt clinicians to offer patients trial enrollment. We searched six hospitals' electrocardiograph systems for electrocardiograms (ECGs) meeting the planned trial's enrollment criterion: ECGs with STEMI or > 75% probability of ACS by the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI). We revised the ACI-TIPI regression to require only data directly from the electrocardiograph, the e-ACI-TIPI using the same data used for the original ACI-TIPI (development set n = 3,453; test set n = 2,315). We also tested both on data from emergency department electrocardiographs from across the US (n = 8,556). We then used ACI-TIPI and e-ACI-TIPI to identify potential cohorts for the ACS trial and compared performance to cohorts from EHR data at the hospitals. RESULTS: Receiver-operating characteristic (ROC) curve areas on the test set were excellent, 0.89 for ACI-TIPI and 0.84 for the e-ACI-TIPI, as was calibration. On the national electrocardiographic database, ROC areas were 0.78 and 0.69, respectively, and with very good calibration. When tested for detection of patients with > 75% ACS probability, both electrocardiograph-based methods identified eligible patients well, and better than did EHRs. CONCLUSION: Using data from medical devices such as electrocardiographs may provide accurate projections of available cohorts for clinical trials.

A comparison of resource utilization between emergency physicians and pediatric emergency physicians.

OBJECTIVES: Pediatric patients in the emergency department (ED) are typically seen either by general emergency physicians (EPs) or by pediatric emergency physicians (PEPs) who have completed either a fellowship in pediatric emergency medicine or both pediatric and emergency medicine residencies. This study evaluates admission rates, turnaround times, and test and medication utilization for EPs versus PEPs. METHODS: A retrospective chart analysis was conducted at an academic tertiary care hospital with a dedicated pediatric ED. When the pediatric ED is open (from noon to midnight), it is always staffed with dedicated pediatric nurses and residents. In our ED, the only variable is the attending physician, who can either be an EP or a PEP. All visits for patients younger than 18 years who presented during the time the pediatric ED was open from July 1, 2007, to June 30, 2010, were eligible for inclusion. Only patients seen by physicians who saw more than 400 patients during this period were included. Disposition outcomes for patients who were either admitted or discharged were compared between EPs and PEPs. Complete blood count, Chem 7, urinalysis, chest radiography ordering rates, and intravenous fluid and ondansetron administration were used as surrogates for general conclusions about test utilization. RESULTS: There were 13,347 patient visits eligible for inclusion, of which 8330 (62.4%) were seen by 2 PEPs, and 5017 (37.6%) were seen by 9 EPs. There was a difference in mean patient age (6.9 vs 7.1 years, P = 0.01), whereas sex (53.6% vs 53.9% male, P = 0.72), race (P = 0.13), acuity (mean Emergency Severity Index 3.35 vs 3.33, P = 0.99), and mode of arrival (10.6% vs 12.3% emergency medical services transport, P = 0.06) were not significantly different. Overall admission rates were similar (17.1% PEP vs 17.5% EP, P = 0.50), as were critical care admissions (2.9% PEP vs 2.7% EP of total admissions, P = 0.40). Turnaround times were significantly different (146.0 ± 2.5 minutes PEP vs 149.7 ± 3.2 minutes EP, P = 0.04). Ordering rates of Chem 7, urinalyses, chest radiographs, and ondansetron were lower by PEPs. CONCLUSIONS: In our pediatric ED, which represents a natural experiment where the type of physician is the only variable, PEPs and EPs have similar rates of admission to floor beds and critical care. Pediatric EPs are slightly faster at throughput and order fewer tests and medication.