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 |
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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 |
format | Article |
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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&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 & 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&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 & 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? A feasibility pilot study chatbot in beta version to assist OHCA bystanders</title><author>Otero-Agra, Martín ; Jorge-Soto, Cristina ; Cosido-Cobos, Óscar J. ; Blanco-Prieto, Jorge ; Alfaya-Fernández, Cristian ; García-Ordóñez, Enrique ; Barcala-Furelos, Roberto</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c352t-77fd4cc962571e0d09aff72ed08d5595858ef6fa345b1f89b988136e99ceac0a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Cardiopulmonary resuscitation</topic><topic>Chest</topic><topic>Colleges & universities</topic><topic>Compression</topic><topic>CPR</topic><topic>Deep learning</topic><topic>Emergency medical care</topic><topic>Emergency services</topic><topic>Feasibility studies</topic><topic>Heart</topic><topic>Participation</topic><topic>Simulation</topic><topic>University students</topic><topic>Verbal communication</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><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><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Immunology Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>The American journal of emergency medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Otero-Agra, Martín</au><au>Jorge-Soto, Cristina</au><au>Cosido-Cobos, Óscar J.</au><au>Blanco-Prieto, Jorge</au><au>Alfaya-Fernández, Cristian</au><au>García-Ordóñez, Enrique</au><au>Barcala-Furelos, Roberto</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Can a voice assistant help bystanders save lives? 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&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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