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a b s t r a c tBackground: N95 filtering facepiece respirators (N95 respirators) may not provide adequate protection against respiratory infections during chest compression due to inappropriate fitting. Methods: This was a single-center simulation study performed from December 1, 2016, to December 31, 2016. Each participant underwent quantitative fit test (QNFT) of N95 respirators according to the Occupational Safety and Health Administration protocol. Adequacy of respirator fit was represented by the fit factor (FF), which is calculated as the number of ambient particles divided by the number inside the respirator. We divided all participants into the group that passed the overall fit test but failed at least one individual exercise (partially passed group [PPG]) and the group that passed all exercises (all passed group [APG]). Then, the participants performed three sessions of continuous chest compressions, each with a duration of 2 min, while undergoing real-time fit testing. The primary outcome was any failure (FF < 100) of the fit test during the three bouts of chest compression. Results: Forty-four participants passed the QNFT. Overall, 73% (n = 32) of the participants failed at least one of the three sessions of chest compression; the number of participants who failed was significantly higher in the PPG than in the APG (94% vs. 61%; p = 0.02). Approximately 18% (n = 8) of the participants experienced mask fit failures, such as strap slipping. Conclusions: Even if the participants passed the QNFT, the N95 respirator did not provide adequate protection against respiratory infections during chest compression.

BackgroundThe task of monitoring and managing the entire emergency department (ED) is becoming more important due to increasing pressure on the ED. Recently, dashboards have received the spotlight as health information technology to support these tasks.ObjectiveThis study aimed to describe the development of a real-time autonomous dashboard for the ED and to evaluate perspectives of clinical staff on its usability.MethodsWe developed a dashboard based on three principles—“anytime, anywhere, at a glance;” “minimal interruption to workflow;” and “protect patient privacy”—and 3 design features—“geographical layout,” “patient-level alert,” and “real-time summary data.” Items to evaluate the dashboard were selected based on the throughput factor of the conceptual model of ED crowding. Moreover, ED physicians and nurses were surveyed using the system usability scale (SUS) and situation awareness index as well as a questionnaire we created on the basis of the construct of the Situation Awareness Rating Technique.ResultsThe first version of the ED dashboard was successfully launched in 2013, and it has undergone 3 major revisions since then because of geographical changes in ED and modifications to improve usability. A total of 52 ED staff members participated in the survey. The average SUS score of the dashboard was 67.6 points, which indicates “OK-to-Good” usability. The participants also reported that the dashboard provided efficient “concentration support” (4.15 points), “complexity representation” (4.02 points), “variability representation” (3.96 points), “information quality” (3.94 points), and “familiarity” (3.94 points). However, the “division of attention” was rated at 2.25 points.ConclusionsWe developed a real-time autonomous ED dashboard and successfully used it for 5 years with good evaluation from users.