A Multidisciplinary Intubation Algorithm for Suspected COVID-19 Patients in the Emergency Department

• Main Authors: Lauren L. Trembley, Adam Z. Tobias, Gwendolyn Schillo, Nicholas von Foerster, Jordan Singer, Samantha L. Pavelka, Paul Phrampus
• Format: Article
• Language: English
• Published: eScholarship Publishing, University of California 2020-07-01
• Series: Western Journal of Emergency Medicine
• Online Access: https://escholarship.org/uc/item/1cc5w85g

Introduction: Intubation of patients suspected of having coronavirus disease 2019 (COVID-19) is considered to be a high-risk procedure due to the aerosolization of viral particles. In an effort to minimize the risk of exposure and optimize patient care, we sought to develop, test, provide training, and implement a standardized algorithm for intubating these high-risk patients at our institution. Methods: We developed an initial intubation algorithm, incorporating strategic use of equipment and incorporating emerging best practices. By combining simulation-based training sessions and rapid-cycle improvement methodology with physicians, nurses, and respiratory therapists, and incorporating their feedback into the development, we were able to optimize the process prior to implementation. Training sessions also enabled the participants to practice the algorithm as a team. Upon completion of each training session, participants were invited to complete a brief online survey about their overall experience. Results: An algorithm and training system vetted by simulation and actual practice were developed. A training video and dissemination package were made available for other emergency departments to adopt. Survey results were overall positive, with 97.92% of participants feeling confident in their role in the intubation process, and many participants citing the usefulness of the multidisciplinary approach to the training. Conclusion: A multidisciplinary, team-based approach to the development and training of a standardized intubation algorithm combining simulation and rapid-cycle improvement methodology is a useful, effective process to respond to rapidly evolving clinical information and experiences during a global pandemic.