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. 2020 Sep 11:8:544.
doi: 10.3389/fped.2020.00544. eCollection 2020.

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

Chang Lu  1 , Simran K Ghoman  2   3 , Maria Cutumisu  1   2   4 , Georg M Schmölzer  2   3
Affiliations

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

Chang Lu et al. Front Pediatr. .

Abstract

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 (HCPs) remains largely unexplored. Objectives: This study explores HCPs' attitudes toward 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 to August 2019 with 2-month (June-October 2019) and 5-month (September 2019-January 2020) follow-up at a tertiary perinatal center in Edmonton, Canada. Of 300 available neonatal HCPs, 50 participated. Participants completed a demographic survey, a pretest, two practice scenarios using the RETAIN neonatal resuscitation digital simulation, a posttest, and an attitudinal survey (100% response rate). Participants repeated the posttest scenario in 2 months (86% response rate) and completed another posttest scenario using a low-fidelity, tabletop simulator (80% response rate) 5 months after the initial study intervention. Participants' survey responses were collected to measure attitudes toward digital simulation and technology. Knowledge was assessed at baseline (pretest), acquisition (posttest), retention (2-month posttest), and transfer (5-month posttest). 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.

Keywords: digital simulator; education; medical education; resuscitation; serious games; simulation; table-top simulator; training.

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Figures

Figure 1
Figure 1
The binary (A) and 5-item Likert (B) survey items.
Figure 2
Figure 2
Optimal number of clusters (k) decided by the Elbow method (A) and the Silhouette method (B).
Figure 3
Figure 3
Cluster dendrogram on the cluster membership for each participant.
Figure 4
Figure 4
The estimated marginal means of performance scores by clusters.
See this image and copyright information in PMC

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