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Implementation of a real‑time qualitative app to evaluate resuscitation performance in an Advanced Cardiac Life Support course

Chao‑Hsiung Leea,b, Ming‑Yuan Huanga,b, Yi‑Kung Leec,d, Chen‑Yang Hsue, Yung‑Cheng Suc,d*

aDepartment of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan, bDepartment of Medicine, Mackay Medical College, New Taipei City, Taiwan, cDepartment of Emergency, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan, dSchool of Medicine, Tzu Chi University, Hualien, Taiwan, eDepartment of Public Heath, National Taiwan University, Taipei, Taiwan

 

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Open Access funded by Buddhist Compassion Relief Tzu Chi Foundation

 
 
Abstract
 
Objective: In addition to high‑quality chest compression, parameters of resuscitation efficiency such as early chest compression, early defibrillation, and decreased hands‑off time are also vital in the Advanced Cardiac Life Support (ACLS) protocol. However, because of limited time and equipment in ACLS courses, efficiency of performance is difficult to evaluate. Materials and Methods: A free, easy‑to‑use iOS and Android app (CodeTracer®) was developed for real‑time recording of cardiopulmonary resuscitation (CPR)  performance. Interventions performed during resuscitation were set up as buttons. When the simulated scenario in the ACLS course began, instructors recorded every intervention and the team performed by pushing the appropriate buttons. When the scenario ended, the CodeTracer® automatically computed parameters, including the percentage of no‑flow time, time to initiating CPR, and time to initiating defibrillation and also generated a graphic log for later discussion. Results: A total of 76 resuscitation episodes were recorded, 27 in the practice scenarios and 49 in the final Megacode simulations. After the course, the average percentage of no‑flow time decreased 5.79%, time to initiating CPR decreased 3.05 s, and time to initiating defibrillation decreased up to 20.27 s. Of note, physicians as leaders seem to have better performance after the ACLS course than before, but the results were insignificant except for the percentage of no‑flow time. Conclusions: CodeTracer® can record and calculate objective parameters for resuscitation performance in ACLS courses and can assist instructors in disseminating important concepts to participants. It can be a useful tool in ACLS courses.
 
Keywords: Advanced cardiac life support, Android, CodeTracer, iOS
 
 

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