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Unsupervised Machine Learning Algorithms Examine Healthcare Providers' Perceptions and Longitudinal Performance in a Digital Neonatal Resuscitation Simulator
- Source :
- Frontiers in Pediatrics, Vol 8 (2020), Frontiers in Pediatrics
- Publication Year :
- 2020
- Publisher :
- Frontiers Media SA, 2020.
-
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.
- Subjects :
- Resuscitation
serious games
media_common.quotation_subject
resuscitation
education
Mindset
030204 cardiovascular system & hematology
Pediatrics
digital simulator
03 medical and health sciences
0302 clinical medicine
table-top simulator
030225 pediatrics
Perception
Intervention (counseling)
Medicine
Simulation
Digital simulator
Original Research
media_common
Response rate (survey)
training
business.industry
lcsh:RJ1-570
lcsh:Pediatrics
simulation
Pediatrics, Perinatology and Child Health
Unsupervised learning
medical education
business
Algorithm
Neonatal resuscitation
Subjects
Details
- ISSN :
- 22962360
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- Frontiers in Pediatrics
- Accession number :
- edsair.doi.dedup.....65cf246d97d0badb59f8c79b68faca78
- Full Text :
- https://doi.org/10.3389/fped.2020.00544