Unsupervised Machine Learning Algorithms Examine Healthcare Providers' Perceptions and Longitudinal Performance in a Digital Neonatal Resuscitation Simulator
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 health...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-09-01
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| Series: | Frontiers in Pediatrics |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fped.2020.00544/full |
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| author | Chang Lu Simran K. Ghoman Simran K. Ghoman Maria Cutumisu Maria Cutumisu Maria Cutumisu Georg M. Schmölzer Georg M. Schmölzer |
| author_facet | Chang Lu Simran K. Ghoman Simran K. Ghoman Maria Cutumisu Maria Cutumisu Maria Cutumisu Georg M. Schmölzer Georg M. Schmölzer |
| author_sort | Chang Lu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T07:01:13Z |
| format | Article |
| id | doaj.art-5b957819757f4b60bf3e4a18881c0db0 |
| institution | Directory Open Access Journal |
| issn | 2296-2360 |
| language | English |
| last_indexed | 2024-12-10T07:01:13Z |
| publishDate | 2020-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pediatrics |
| spelling | doaj.art-5b957819757f4b60bf3e4a18881c0db02022-12-22T01:58:19ZengFrontiers Media S.A.Frontiers in Pediatrics2296-23602020-09-01810.3389/fped.2020.00544565998Unsupervised Machine Learning Algorithms Examine Healthcare Providers' Perceptions and Longitudinal Performance in a Digital Neonatal Resuscitation SimulatorChang Lu0Simran K. Ghoman1Simran K. Ghoman2Maria Cutumisu3Maria Cutumisu4Maria Cutumisu5Georg M. Schmölzer6Georg M. Schmölzer7Department of Educational Psychology, Faculty of Education, Centre for Research in Applied Measurement and Evaluation, University of Alberta, Edmonton, AB, CanadaCentre for the Studies of Asphyxia and Resuscitation, Neonatal Research Unit, Royal Alexandra Hospital, Edmonton, AB, CanadaDepartment of Pediatrics, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, CanadaDepartment of Educational Psychology, Faculty of Education, Centre for Research in Applied Measurement and Evaluation, University of Alberta, Edmonton, AB, CanadaCentre for the Studies of Asphyxia and Resuscitation, Neonatal Research Unit, Royal Alexandra Hospital, Edmonton, AB, CanadaDepartment of Computing Science, Faculty of Science, University of Alberta, Edmonton, AB, CanadaCentre for the Studies of Asphyxia and Resuscitation, Neonatal Research Unit, Royal Alexandra Hospital, Edmonton, AB, CanadaDepartment of Pediatrics, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, CanadaBackground: 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.https://www.frontiersin.org/article/10.3389/fped.2020.00544/fulleducationtrainingsimulationresuscitationtable-top simulatorserious games |
| spellingShingle | Chang Lu Simran K. Ghoman Simran K. Ghoman Maria Cutumisu Maria Cutumisu Maria Cutumisu Georg M. Schmölzer Georg M. Schmölzer Unsupervised Machine Learning Algorithms Examine Healthcare Providers' Perceptions and Longitudinal Performance in a Digital Neonatal Resuscitation Simulator Frontiers in Pediatrics education training simulation resuscitation table-top simulator serious games |
| title | Unsupervised Machine Learning Algorithms Examine Healthcare Providers' Perceptions and Longitudinal Performance in a Digital Neonatal Resuscitation Simulator |
| title_full | Unsupervised Machine Learning Algorithms Examine Healthcare Providers' Perceptions and Longitudinal Performance in a Digital Neonatal Resuscitation Simulator |
| title_fullStr | Unsupervised Machine Learning Algorithms Examine Healthcare Providers' Perceptions and Longitudinal Performance in a Digital Neonatal Resuscitation Simulator |
| title_full_unstemmed | Unsupervised Machine Learning Algorithms Examine Healthcare Providers' Perceptions and Longitudinal Performance in a Digital Neonatal Resuscitation Simulator |
| title_short | Unsupervised Machine Learning Algorithms Examine Healthcare Providers' Perceptions and Longitudinal Performance in a Digital Neonatal Resuscitation Simulator |
| title_sort | unsupervised machine learning algorithms examine healthcare providers perceptions and longitudinal performance in a digital neonatal resuscitation simulator |
| topic | education training simulation resuscitation table-top simulator serious games |
| url | https://www.frontiersin.org/article/10.3389/fped.2020.00544/full |
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