Effective response to a mass-casualty incident (MCI) entails the activation of hospital MCI plans. Unfortunately, there are no tools available in the literature to support hospital responders in predicting the proper level of MCI plan activation. This manuscript describes the scientific-based approach used to develop, test, and validate the PEMAAF score (Proximity, Event, Multitude, Overcrowding, Temporary Ward Reduction Capacity, Time Shift Slot [Prossimità, Evento, Moltitudine, Affollamento, Accorpamento, Fascia Oraria], a tool able to predict the required level of hospital MCI plan activation and to facilitate a coordinated activation of a multi-hospital network.

Three study phases were performed within the Metropolitan City of Milan, Italy: (1) retrospective analysis of past MCI after action reports (AARs); (2) PEMAAF score development; and (3) PEMAAF score validation. The validation phase entailed a multi-step process including two retrospective analyses of past MCIs using the score, a focus group discussion (FGD), and a prospective simulation-based study. Sensitivity and specificity of the score were analyzed using a regression model, Spearman’s Rho test, and receiver operating characteristic/ROC analysis curves.

Results of the retrospective analysis and FGD were used to refine the PEMAAF score, which included six items–Proximity, Event, Multitude, Emergency Department (ED) Overcrowding, Temporary Ward Reduction Capacity, and Time Shift Slot–allowing for the identification of three priority levels (score of 5-6: green alert; score of 7-9: yellow alert; and score of 10-12: red alert). When prospectively analyzed, the PEMAAF score determined most frequent hospital MCI plan activation (>10) during night and holiday shifts, with a score of 11 being associated with a higher sensitivity system and a score of 12 with higher specificity.

The PEMAAF score allowed for a balanced and adequately distributed response in case of MCI, prompting hospital MCI plan activation according to real needs, taking into consideration the whole hospital response network.

The concept of response time with minimal interval is intimately related to the practice of emergency medicine. The factors influencing this time interval are poorly understood.

In a process of improvement of response time, the impact of the patient’s age on ambulance departure intervals was investigated.

This was a 3-year observational study. Departure intervals of ambulances, according to age of patients, were analyzed and a multivariate analysis, according to time of day and suspected medical problem, was performed.

A total of 44,113 missions were included, 2,417 (5.5%) in the pediatric group. Mean departure delay for the adult group was 152.9 seconds, whereas it was 149.3 seconds for the pediatric group (P =.018).

A statistically significant departure interval difference between missions for children and adults was found. The difference, however, probably was not significant from a clinical point of view (four seconds).

SchneggB, PasquierM, CarronPN, YersinB, DamiF. Prehospital Emergency Medical Services Departure Interval: Does Patient Age Matter?Prehosp Disaster Med. 2016;31(6):608–613.