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Implementation of a real-time qualitative app to evaluate resuscitation performance in an Advanced Cardiac Life Support course
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Medknow Publications & Media Pvt Ltd
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6047333/ https://www.ncbi.nlm.nih.gov/pubmed/30069125 http://dx.doi.org/10.4103/tcmj.tcmj_103_17 |
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author | Lee, Chao-Hsiung Huang, Ming-Yuan Lee, Yi-Kung Hsu, Chen-Yang Su, Yung-Cheng |
author_facet | Lee, Chao-Hsiung Huang, Ming-Yuan Lee, Yi-Kung Hsu, Chen-Yang Su, Yung-Cheng |
author_sort | Lee, Chao-Hsiung |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6047333 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Medknow Publications & Media Pvt Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-60473332018-08-01 Implementation of a real-time qualitative app to evaluate resuscitation performance in an Advanced Cardiac Life Support course Lee, Chao-Hsiung Huang, Ming-Yuan Lee, Yi-Kung Hsu, Chen-Yang Su, Yung-Cheng Tzu Chi Med J Original Article 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. Medknow Publications & Media Pvt Ltd 2018 /pmc/articles/PMC6047333/ /pubmed/30069125 http://dx.doi.org/10.4103/tcmj.tcmj_103_17 Text en Copyright: © 2018 Tzu Chi Medical Journal http://creativecommons.org/licenses/by-nc-sa/4.0 This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms. |
spellingShingle | Original Article Lee, Chao-Hsiung Huang, Ming-Yuan Lee, Yi-Kung Hsu, Chen-Yang Su, Yung-Cheng Implementation of a real-time qualitative app to evaluate resuscitation performance in an Advanced Cardiac Life Support course |
title | Implementation of a real-time qualitative app to evaluate resuscitation performance in an Advanced Cardiac Life Support course |
title_full | Implementation of a real-time qualitative app to evaluate resuscitation performance in an Advanced Cardiac Life Support course |
title_fullStr | Implementation of a real-time qualitative app to evaluate resuscitation performance in an Advanced Cardiac Life Support course |
title_full_unstemmed | Implementation of a real-time qualitative app to evaluate resuscitation performance in an Advanced Cardiac Life Support course |
title_short | Implementation of a real-time qualitative app to evaluate resuscitation performance in an Advanced Cardiac Life Support course |
title_sort | implementation of a real-time qualitative app to evaluate resuscitation performance in an advanced cardiac life support course |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6047333/ https://www.ncbi.nlm.nih.gov/pubmed/30069125 http://dx.doi.org/10.4103/tcmj.tcmj_103_17 |
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