Training emergency department (ED) personnel in the care of victims of mass-casualty incidents (MCIs) is a highly challenging task requiring unique and innovative approaches. The purpose of this study was to retrospectively explore the value of high-fidelity simulators in an exercise that incorporates time and resource limitation as an optimal method of training health care personnel in mass-casualty care.

Mass-casualty injury patterns from an explosive blast event were simulated for 12 victims using high-fidelity computerized simulators (HFCS). Programmed outcomes, based on the nature of injuries and conduct of participants, ranged from successful resuscitation and survival to death. The training exercise was conducted five times with different teams of health care personnel (n = 42). The exercise involved limited time and resources such as blood, ventilators, and imaging capability. Medical team performance was observed and recorded. Following the exercise, participants completed a survey regarding their training satisfaction, quality of the exercise, and their prior experiences with MCI simulations. The Likert scale responses from the survey were evaluated using mean with 95% confidence interval, as well as median and inter-quartile range. For the categorical responses, the frequency, proportions, and associated 95% confidence interval were calculated.

The mean rating on the quality of experiences related trainee survey questions (n = 42) was between 4.1 and 4.6 on a scale of 5.0. The mean ratings on a scale of 10.0 for quality, usefulness, and pertinence of the program were 9.2, 9.5, and 9.5, respectfully. One hundred percent of respondents believed that this type of exercise should be required for MCI training and would recommend this exercise to colleagues. The five medical team (n = 5) performances resulted in the number of deaths ranging from two (including the expectant victims) to six. Eighty percent of medical teams attempted to resuscitate the “expectant” infant and exhausted the O- blood supply. Sixty percent of medical teams depleted the supply of ventilators. Forty percent of medical teams treated “delayed” victims too early.

A training exercise using HFCS for mass casualties and employing limited time and resources is described. This exercise is a preferred method of training among participating health care personnel.

To identify clinical signs and symptoms (ie, “terms”) that accurately predict laboratory-confirmed influenza cases and thereafter generate and evaluate various influenza-like illness (ILI) case definitions for detecting influenza. A secondary objective explored whether surveillance of data beyond the chief complaint improves the accuracy of predicting influenza.

Retrospective, cross-sectional study.

Large urban academic medical center hospital.

A total of 1,581 emergency department (ED) patients who received a nasopharyngeal swab followed by rRT-PCR testing between August 30, 2009, and January 2, 2010, and between November 28, 2010, and March 26, 2011.

An electronic surveillance system (GUARDIAN) scanned the entire electronic medical record (EMR) and identified cases containing 29 clinical terms relevant to influenza. Analyses were conducted using logistic regressions, diagnostic odds ratio (DOR), sensitivity, and specificity.

The best predictive model for identifying influenza for all ages consisted of cough (DOR=5.87), fever (DOR=4.49), rhinorrhea (DOR=1.98), and myalgias (DOR=1.44). The 3 best case definitions that included combinations of some or all of these 4 symptoms had comparable performance (ie, sensitivity=89%–92% and specificity=38%–44%). For children <5 years of age, the addition of rhinorrhea to the fever and cough case definition achieved a better balance between sensitivity (85%) and specificity (47%). For the fever and cough ILI case definition, using the entire EMR, GUARDIAN identified 37.1% more influenza cases than it did using only the chief complaint data.

A simplified case definition of fever and cough may be suitable for implementation for all ages, while inclusion of rhinorrhea may further improve influenza detection for the 0–4-year-old age group. Finally, ILI surveillance based on the entire EMR is recommended.

Infect Control Hosp Epidemiol 2015;00(0): 1–8