Can a voice assistant help bystanders save lives? A feasibility pilot study chatbot in beta version to assist OHCA bystanders

ObjectiveEvaluating the usefulness of a chat bot as an assistant during CPR care by laypersons.MethodsTwenty-one university graduates and university students naive in basic life support participated in this quasi-experimental simulation pilot trial. A version beta chatbot was designed to guide poten...

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Veröffentlicht in:The American journal of emergency medicine 2022-11, Vol.61, p.169-174
Hauptverfasser: Otero-Agra, Martín, Jorge-Soto, Cristina, Cosido-Cobos, Óscar J., Blanco-Prieto, Jorge, Alfaya-Fernández, Cristian, García-Ordóñez, Enrique, Barcala-Furelos, Roberto
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container_end_page 174
container_issue
container_start_page 169
container_title The American journal of emergency medicine
container_volume 61
creator Otero-Agra, Martín
Jorge-Soto, Cristina
Cosido-Cobos, Óscar J.
Blanco-Prieto, Jorge
Alfaya-Fernández, Cristian
García-Ordóñez, Enrique
Barcala-Furelos, Roberto
description ObjectiveEvaluating the usefulness of a chat bot as an assistant during CPR care by laypersons.MethodsTwenty-one university graduates and university students naive in basic life support participated in this quasi-experimental simulation pilot trial. A version beta chatbot was designed to guide potential bystanders who need help in caring for cardiac arrest victims. Through a Question-Answering (Q&A) flowchart, the chatbot uses Voice Recognition Techniques to transform the user's audio into text. After the transformation, it generates the answer to provide the necessary help through machine and deep learning algorithms. A simulation test with a Laerdal Little Anne manikin was performed. Participants initiated the chatbot, which guided them through the recognition of a cardiac arrest event. After recognizing the cardiac arrest, the chatbot indicated the start of chest compressions for 2 min. Evaluation of the cardiac arrest recognition sequence was done via a checklist and the quality of CPR was collected with the Laerdal Instructor App.Results91% of participants were able to perform the entire sequence correctly. All participants checked the safety of the scene and made sure to call 112. 62% place their hands on the correct compression point. A media time of 158 s (IQR: 146–189) was needed for the whole process. 33% of participants achieved high-quality CPR with a median of 60% in QCPR (IQR: 9–86). Compression depth had a median of 42 mm (IQR: 33–53) and compression rate had a median of 100 compressions/min (IQR: 97–100).ConclusionThe use of a voice assistant could be useful for people with no previous training to perform de out-of-hospital cardiac arrest recognition sequence. Chatbot was able to guide all participants to call 112 and to perform continuous chest compressions. The first version of the chatbot for potential bystanders naive in basic life support needs to be further developed to reduce response times and be more effective in giving feedback on chest compressions.
doi_str_mv 10.1016/j.ajem.2022.09.013
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A feasibility pilot study chatbot in beta version to assist OHCA bystanders</title><source>ScienceDirect Journals (5 years ago - present)</source><source>ProQuest Central UK/Ireland</source><creator>Otero-Agra, Martín ; Jorge-Soto, Cristina ; Cosido-Cobos, Óscar J. ; Blanco-Prieto, Jorge ; Alfaya-Fernández, Cristian ; García-Ordóñez, Enrique ; Barcala-Furelos, Roberto</creator><creatorcontrib>Otero-Agra, Martín ; Jorge-Soto, Cristina ; Cosido-Cobos, Óscar J. ; Blanco-Prieto, Jorge ; Alfaya-Fernández, Cristian ; García-Ordóñez, Enrique ; Barcala-Furelos, Roberto</creatorcontrib><description>ObjectiveEvaluating the usefulness of a chat bot as an assistant during CPR care by laypersons.MethodsTwenty-one university graduates and university students naive in basic life support participated in this quasi-experimental simulation pilot trial. A version beta chatbot was designed to guide potential bystanders who need help in caring for cardiac arrest victims. Through a Question-Answering (Q&amp;A) flowchart, the chatbot uses Voice Recognition Techniques to transform the user's audio into text. After the transformation, it generates the answer to provide the necessary help through machine and deep learning algorithms. A simulation test with a Laerdal Little Anne manikin was performed. Participants initiated the chatbot, which guided them through the recognition of a cardiac arrest event. After recognizing the cardiac arrest, the chatbot indicated the start of chest compressions for 2 min. Evaluation of the cardiac arrest recognition sequence was done via a checklist and the quality of CPR was collected with the Laerdal Instructor App.Results91% of participants were able to perform the entire sequence correctly. All participants checked the safety of the scene and made sure to call 112. 62% place their hands on the correct compression point. A media time of 158 s (IQR: 146–189) was needed for the whole process. 33% of participants achieved high-quality CPR with a median of 60% in QCPR (IQR: 9–86). Compression depth had a median of 42 mm (IQR: 33–53) and compression rate had a median of 100 compressions/min (IQR: 97–100).ConclusionThe use of a voice assistant could be useful for people with no previous training to perform de out-of-hospital cardiac arrest recognition sequence. Chatbot was able to guide all participants to call 112 and to perform continuous chest compressions. The first version of the chatbot for potential bystanders naive in basic life support needs to be further developed to reduce response times and be more effective in giving feedback on chest compressions.</description><identifier>ISSN: 0735-6757</identifier><identifier>EISSN: 1532-8171</identifier><identifier>DOI: 10.1016/j.ajem.2022.09.013</identifier><language>eng</language><publisher>Philadelphia: Elsevier Limited</publisher><subject>Algorithms ; Cardiopulmonary resuscitation ; Chest ; Colleges &amp; universities ; Compression ; CPR ; Deep learning ; Emergency medical care ; Emergency services ; Feasibility studies ; Heart ; Participation ; Simulation ; University students ; Verbal communication</subject><ispartof>The American journal of emergency medicine, 2022-11, Vol.61, p.169-174</ispartof><rights>2022. The Author(s)</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c352t-77fd4cc962571e0d09aff72ed08d5595858ef6fa345b1f89b988136e99ceac0a3</citedby><cites>FETCH-LOGICAL-c352t-77fd4cc962571e0d09aff72ed08d5595858ef6fa345b1f89b988136e99ceac0a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2725611831?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>315,782,786,27931,27932,64392,64394,64396,72476</link.rule.ids></links><search><creatorcontrib>Otero-Agra, Martín</creatorcontrib><creatorcontrib>Jorge-Soto, Cristina</creatorcontrib><creatorcontrib>Cosido-Cobos, Óscar J.</creatorcontrib><creatorcontrib>Blanco-Prieto, Jorge</creatorcontrib><creatorcontrib>Alfaya-Fernández, Cristian</creatorcontrib><creatorcontrib>García-Ordóñez, Enrique</creatorcontrib><creatorcontrib>Barcala-Furelos, Roberto</creatorcontrib><title>Can a voice assistant help bystanders save lives? A feasibility pilot study chatbot in beta version to assist OHCA bystanders</title><title>The American journal of emergency medicine</title><description>ObjectiveEvaluating the usefulness of a chat bot as an assistant during CPR care by laypersons.MethodsTwenty-one university graduates and university students naive in basic life support participated in this quasi-experimental simulation pilot trial. A version beta chatbot was designed to guide potential bystanders who need help in caring for cardiac arrest victims. Through a Question-Answering (Q&amp;A) flowchart, the chatbot uses Voice Recognition Techniques to transform the user's audio into text. After the transformation, it generates the answer to provide the necessary help through machine and deep learning algorithms. A simulation test with a Laerdal Little Anne manikin was performed. Participants initiated the chatbot, which guided them through the recognition of a cardiac arrest event. After recognizing the cardiac arrest, the chatbot indicated the start of chest compressions for 2 min. Evaluation of the cardiac arrest recognition sequence was done via a checklist and the quality of CPR was collected with the Laerdal Instructor App.Results91% of participants were able to perform the entire sequence correctly. All participants checked the safety of the scene and made sure to call 112. 62% place their hands on the correct compression point. A media time of 158 s (IQR: 146–189) was needed for the whole process. 33% of participants achieved high-quality CPR with a median of 60% in QCPR (IQR: 9–86). Compression depth had a median of 42 mm (IQR: 33–53) and compression rate had a median of 100 compressions/min (IQR: 97–100).ConclusionThe use of a voice assistant could be useful for people with no previous training to perform de out-of-hospital cardiac arrest recognition sequence. Chatbot was able to guide all participants to call 112 and to perform continuous chest compressions. The first version of the chatbot for potential bystanders naive in basic life support needs to be further developed to reduce response times and be more effective in giving feedback on chest compressions.</description><subject>Algorithms</subject><subject>Cardiopulmonary resuscitation</subject><subject>Chest</subject><subject>Colleges &amp; universities</subject><subject>Compression</subject><subject>CPR</subject><subject>Deep learning</subject><subject>Emergency medical care</subject><subject>Emergency services</subject><subject>Feasibility studies</subject><subject>Heart</subject><subject>Participation</subject><subject>Simulation</subject><subject>University students</subject><subject>Verbal communication</subject><issn>0735-6757</issn><issn>1532-8171</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNpdkU1LAzEQQIMoWD_-gKeAFy-7ZpJmk5ykFL9A8KLnkM1OaJbtbt2khR78726pB_E0DLx5DDxCboCVwKC6b0vX4rrkjPOSmZKBOCEzkIIXGhSckhlTQhaVkuqcXKTUMgYwl_MZ-V66njq6G6JH6lKKKbs-0xV2G1rvD0uDY6LJ7ZB2cYfpgS5oQJdiHbuY93QTuyHTlLfNnvqVy_W0xZ7WmCfrdBqHnubhV03fX5aLP94rchZcl_D6d16Sz6fHj-VL8fb-_LpcvBVeSJ4LpUIz995UXCpA1jDjQlAcG6YbKY3UUmOoghNzWUPQpjZag6jQGI_OMycuyd3RuxmHry2mbNcxeew61-OwTZYr0JWoJFcTevsPbYft2E_fTRSXFYAWMFH8SPlxSGnEYDdjXLtxb4HZQxHb2kMReyhimbFTEfEDF16BGA</recordid><startdate>20221101</startdate><enddate>20221101</enddate><creator>Otero-Agra, Martín</creator><creator>Jorge-Soto, Cristina</creator><creator>Cosido-Cobos, Óscar J.</creator><creator>Blanco-Prieto, Jorge</creator><creator>Alfaya-Fernández, Cristian</creator><creator>García-Ordóñez, Enrique</creator><creator>Barcala-Furelos, Roberto</creator><general>Elsevier Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7T5</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H94</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope></search><sort><creationdate>20221101</creationdate><title>Can a voice assistant help bystanders save lives? 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A feasibility pilot study chatbot in beta version to assist OHCA bystanders</atitle><jtitle>The American journal of emergency medicine</jtitle><date>2022-11-01</date><risdate>2022</risdate><volume>61</volume><spage>169</spage><epage>174</epage><pages>169-174</pages><issn>0735-6757</issn><eissn>1532-8171</eissn><abstract>ObjectiveEvaluating the usefulness of a chat bot as an assistant during CPR care by laypersons.MethodsTwenty-one university graduates and university students naive in basic life support participated in this quasi-experimental simulation pilot trial. A version beta chatbot was designed to guide potential bystanders who need help in caring for cardiac arrest victims. Through a Question-Answering (Q&amp;A) flowchart, the chatbot uses Voice Recognition Techniques to transform the user's audio into text. After the transformation, it generates the answer to provide the necessary help through machine and deep learning algorithms. A simulation test with a Laerdal Little Anne manikin was performed. Participants initiated the chatbot, which guided them through the recognition of a cardiac arrest event. After recognizing the cardiac arrest, the chatbot indicated the start of chest compressions for 2 min. Evaluation of the cardiac arrest recognition sequence was done via a checklist and the quality of CPR was collected with the Laerdal Instructor App.Results91% of participants were able to perform the entire sequence correctly. All participants checked the safety of the scene and made sure to call 112. 62% place their hands on the correct compression point. A media time of 158 s (IQR: 146–189) was needed for the whole process. 33% of participants achieved high-quality CPR with a median of 60% in QCPR (IQR: 9–86). Compression depth had a median of 42 mm (IQR: 33–53) and compression rate had a median of 100 compressions/min (IQR: 97–100).ConclusionThe use of a voice assistant could be useful for people with no previous training to perform de out-of-hospital cardiac arrest recognition sequence. Chatbot was able to guide all participants to call 112 and to perform continuous chest compressions. The first version of the chatbot for potential bystanders naive in basic life support needs to be further developed to reduce response times and be more effective in giving feedback on chest compressions.</abstract><cop>Philadelphia</cop><pub>Elsevier Limited</pub><doi>10.1016/j.ajem.2022.09.013</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record>
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source ScienceDirect Journals (5 years ago - present); ProQuest Central UK/Ireland
subjects Algorithms
Cardiopulmonary resuscitation
Chest
Colleges & universities
Compression
CPR
Deep learning
Emergency medical care
Emergency services
Feasibility studies
Heart
Participation
Simulation
University students
Verbal communication
title Can a voice assistant help bystanders save lives? A feasibility pilot study chatbot in beta version to assist OHCA bystanders
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