BACKGROUND: Nurse education and training are key to providing congenital heart disease (CHD) patients with consistent high standards of care as well as enabling career progression. One approach for improving educational experience is the use of 3D patient-specific models. OBJECTIVES: To gather pilot data to assess the feasibility of using 3D models of CHD during a training course for cardiac nurses; to evaluate the potential of 3D models in this context, from the nurses' perspective; and to identify possible improvements to optimise their use for teaching. DESIGN: A cross-sectional survey. SETTING: A national training week for cardiac nurses. PARTICIPANTS: One hundred cardiac nurses (of which 65 pediatric and 35 adult). METHODS: Nurses were shown nine CHD models within the context of a specialized course, following a lecture on the process of making the models themselves, starting from medical imaging. Participants were asked about their general learning experience, if models were more/less informative than diagrams/drawings and lesion-specific/generic models, and their overall reaction to the models. Possible differences between adult and pediatric nurses were investigated. Written feedback was subjected to content analysis and quantitative data were analyzed using nonparametric statistics. RESULTS: Generally models were well liked and nurses considered them more informative than diagrams. Nurses found that 3D models helped in the appreciation of overall anatomy (86%), spatial orientation (70%), and anatomical complexity after treatment (66%). There was no statistically significant difference between adult and pediatric nurses' responses. Thematic analysis highlighted the need for further explanation, use of labels and use of colors to highlight the lesion of interest amongst improvements for optimizing 3D models for teaching/training purposes. CONCLUSION: 3D patient-specific models are useful tools for training adult and pediatric cardiac nurses and are particularly helpful for understanding CHD anatomy after repair.
BACKGROUND: Nurse education and training are key to providing congenital heart disease (CHD) patients with consistent high standards of care as well as enabling career progression. One approach for improving educational experience is the use of 3D patient-specific models. OBJECTIVES: To gather pilot data to assess the feasibility of using 3D models of CHD during a training course for cardiac nurses; to evaluate the potential of 3D models in this context, from the nurses' perspective; and to identify possible improvements to optimise their use for teaching. DESIGN: A cross-sectional survey. SETTING: A national training week for cardiac nurses. PARTICIPANTS: One hundred cardiac nurses (of which 65 pediatric and 35 adult). METHODS: Nurses were shown nine CHD models within the context of a specialized course, following a lecture on the process of making the models themselves, starting from medical imaging. Participants were asked about their general learning experience, if models were more/less informative than diagrams/drawings and lesion-specific/generic models, and their overall reaction to the models. Possible differences between adult and pediatric nurses were investigated. Written feedback was subjected to content analysis and quantitative data were analyzed using nonparametric statistics. RESULTS: Generally models were well liked and nurses considered them more informative than diagrams. Nurses found that 3D models helped in the appreciation of overall anatomy (86%), spatial orientation (70%), and anatomical complexity after treatment (66%). There was no statistically significant difference between adult and pediatric nurses' responses. Thematic analysis highlighted the need for further explanation, use of labels and use of colors to highlight the lesion of interest amongst improvements for optimizing 3D models for teaching/training purposes. CONCLUSION: 3D patient-specific models are useful tools for training adult and pediatric cardiac nurses and are particularly helpful for understanding CHD anatomy after repair.
Authors: Abdallah El Sabbagh; Mackram F Eleid; Mohammed Al-Hijji; Nandan S Anavekar; David R Holmes; Vuyisile T Nkomo; Gustavo S Oderich; Stephen D Cassivi; Sameh M Said; Charanjit S Rihal; Jane M Matsumoto; Thomas A Foley Journal: Curr Cardiol Rep Date: 2018-05-10 Impact factor: 2.931
Authors: Ivan Lau; Yin How Wong; Chai Hong Yeong; Yang Faridah Abdul Aziz; Nor Ashikin Md Sari; Shahrul Amry Hashim; Zhonghua Sun Journal: Quant Imaging Med Surg Date: 2019-01
Authors: Giovanni Biglino; Sarah Moharem-Elgamal; Matthew Lee; Robert Tulloh; Massimo Caputo Journal: Front Pediatr Date: 2017-09-28 Impact factor: 3.569