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The use of a hybrid mannequin for the modern high-fidelity simulation in the labor ward: The Italian experience of the EGEO group.

Ghi T, et al. Am J Obstet Gynecol. 2019.

Abstract

Regular training in the management of intrapartum emergencies has been demonstrated to yield measurable benefits in terms of maternal and perinatal outcomes. Thanks to technologic advances,computerized full-body mannequins have been created and made available for high-fidelity simulation in obstetrics. The technical skills subjected to training are conventionally represented by classical manual maneuvers, which are recommended in the case of instrumental vaginal delivery, shoulder dystocia, or postpartum hemorrhage. During the past few years, manual skills in the labor ward have been increasingly supported by the use of ultrasound, and this has substantially altered the practical management of intrapartum emergencies in real life. Based on this, a new generation of mannequins suitable for both clinical maneuvers and ultrasound examination seems to be the most appropriate tool for the modern high-fidelity simulation in the management of intrapartum complications. The use of these new hybrid clinical ultrasound mannequins may usher in a new era in high-fidelity obstetric simulation and can hopefully optimize the competencies and technical skills of labor ward professionals in the management of obstetric emergencies. It is from this background that at the beginning of 2018, the Ecografia Gestione Emergenze Ostetriche (EGEO) group was founded in Italy. This group has aggregated a multiprofessional labor ward team including obstetricians, midwives, and anesthesiologists under the common philosophy that ultrasound provides an essential added value in the management of obstetric emergencies. Thanks to the use of these mannequins, the multiprofessional Italian EGEO group has started to run practical workshops to promote the culture of extraordinary synergy of ultrasound and clinical skills as the best approach to handle intrapartum complications.

Copyright © 2019 Elsevier Inc. All rights reserved.

PMID

31323218 [ - as supplied by publisher]

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