Pontevedra School of Nursing, University of Vigo, Joaquín Costa, 41, 36004 Pontevedra, Spain; REMOSS Research Group, Faculty of Physical Activity and Educational Science, University of Vigo, Campus a Xunqueira, s/n, 36005 Pontevedra. Spain.
SICRUS Research Group, Santiago Health Research Institute. Choupana 15706, Santiago de Compostela, Spain; CLINURSID Research Group, Faculty of Nursing of Santiago, University of Santiago de Compostela, Praza do Obradoiro, 0, 15705, Spain.
Am J Emerg Med. 2022 Nov;61:169-174. doi: 10.1016/j.ajem.2022.09.013. Epub 2022 Sep 16.
Evaluating the usefulness of a chat bot as an assistant during CPR care by laypersons.
Twenty-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.
91% 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).
The 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.
评估聊天机器人作为非专业人士进行心肺复苏护理助手的有效性。
21 名大学毕业生和对基本生命支持一无所知的大学生参与了这项准实验性模拟试验。设计了一个版本的 beta 聊天机器人，旨在指导需要帮助护理心搏骤停患者的潜在旁观者。通过问答（Q&A）流程图，聊天机器人使用语音识别技术将用户的音频转换为文本。转换后，它会生成答案，通过机器和深度学习算法提供必要的帮助。使用 Laerdal Little Anne 人体模型进行了模拟测试。参与者启动聊天机器人，引导他们识别心搏骤停事件。识别心搏骤停后，聊天机器人指示开始进行 2 分钟的胸外按压。通过检查表评估心搏骤停识别序列，使用 Laerdal 指导员应用程序收集心肺复苏术的质量。
91%的参与者能够正确完成整个序列。所有参与者检查了现场的安全性，并确保拨打 112。62%的人将手放在正确的按压点上。整个过程需要 158 秒的媒体时间（IQR：146-189）。33%的参与者实现了高质量的心肺复苏术，其中 QCPR 的中位数为 60%（IQR：9-86）。压缩深度中位数为 42 毫米（IQR：33-53），压缩率中位数为 100 次/分钟（IQR：97-100）。
对于没有先前培训的人来说，使用语音助手进行院外心搏骤停识别序列可能会很有用。聊天机器人能够指导所有参与者拨打 112 并进行连续的胸外按压。对于基本生命支持方面一无所知的潜在旁观者的第一个聊天机器人版本需要进一步开发，以减少响应时间，并更有效地反馈胸外按压。
