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Unsupervised Machine Learning Algorithms Examine Healthcare Providers' Perceptions and Longitudinal Performance in a Digital Neonatal Resuscitation Simulator

机译:无监督的机器学习算法检查数字新生儿复苏模拟器中的医疗保健提供者的看法和纵向性能

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Background Frequent simulation-based education is recommended to improve health outcomes during neonatal resuscitation, but is often inaccessible due to time, resource, and personnel requirements. Digital simulation presents a potential alternative, however its effectiveness and reception by healthcare professionals (HCP) remains largely unexplored. Objectives This study explores HCPs’ attitudes towards a digital simulator, technology, and mindset to elucidate their effects on neonatal resuscitation performance in simulation-based assessments. Methods The study was conducted from April-August 2019, with 2-month (June-October 2019) and 5-month (September 2019-January 2020) follow-up at a tertiary perinatal centre in Edmonton, Canada. Of 300 available neonatal HCPs, 50 participated. Participants completed a demographic survey, pre-test, two practice scenarios using the RETAIN neonatal resuscitation digital simulation, post-test, and attitudinal survey (100% response rate). Participants repeated the post-test scenario in two-months (86% response rate) and completed another post-test scenario using a low-fidelity table-top simulator (80% response rate) five-months after the initial study intervention. Participants’ survey responses were collected to measure attitudes towards digital simulation, technology, and mindset. Knowledge was assessed at baseline (pre-test), acquisition (post-test), retention (2-month post-test), and transfer (5-month post-test). Results Fifty neonatal HCPs participated in this study (44 females and 6 males; 27 nurses, 3 nurse practitioners, 14 respiratory therapists, and 6 doctors). Most participants reported technology in medical education as useful and beneficial. Three attitudinal clusters were identified by a hierarchical clustering algorithm based on survey responses. Although participants exhibited diverse attitudinal paths, they all improved neonatal resuscitation performance after using the digital simulator and successfully transferred their knowledge to a new medium. Conclusions Digital simulation improved HCPs’ neonatal resuscitation performance. Medical education may benefit by incorporating technology during simulation training.
机译:背景技术建议使用基于仿真的教育,以改善新生儿复苏期间的健康结果,但由于时间,资源和人员要求往往无法进入。数字仿真提出了潜在的替代方案,但医疗保健专业人员(HCP)的有效性和接待仍然很大程度上是未开发的。目的这项研究探讨了HCPS对数字模拟器,技术和心态的态度,以阐明其对基于模拟评估中新生儿复苏性能的影响。方法研究该研究于2019年4月至8月进行,2个月(2019年6月)和5个月(2019年1月20日2020年)在加拿大埃德蒙顿的第三级围产期中心随访。 300个可用的新生儿HCP,50名参加。与会者使用保留新生儿复苏数字模拟,测试后和态度调查(100%回复率),完成了人口调查,预先测试,两种实践方案。参与者在两个月内重复测试后场景(86%的响应率),并在初始研究干预后五个月使用低保真桌面模拟器(80%的回复率)完成了另一个测试场景。收集了参与者的调查答复,以衡量数字模拟,技术和心态的态度。知识在基线(预测试),收购(测试后),保留(测试后2个月)和转移(测试后5个月)。结果五十个新生儿HCP参加了本研究(44名女性和6名男性; 27名护士,3名护士,14名呼吸治疗师和6名医生)。大多数参与者报告了医学教育的技术,作为有用和有益。基于调查响应的分层聚类算法鉴定了三个态度簇。虽然参与者表现出不同的态度路径,但它们在使用数字模拟器后,所有改善新生儿复苏绩效,并成功将他们的知识转移到新媒体。结论数字模拟改进了HCPS新生儿复苏性能。通过在模拟培训期间纳入技术,医学教育可能会受益。

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