1. Organisation for Economic Co-operation and Development. Strengthening health information infrastructure for health care quality governance: good practices, new opportunities and data privacy protection challenges. Paris, France: Organisation for Economic Co-operation and Development;2013.
2. Institute of Medicine Health professions education: a bridge to quality. Washington (DC): National Academies Press;2003.
8. Choi M, Park JH, Lee HS. Assessment of the need to integrate academic electronic medical records into the undergraduate clinical practicum: a focus group interview. Comput Inform Nurs. 2016; 34(6):259–65.
https://doi.org/10.1097/cin.0000000000000244
.
Article
9. Mollart L, Newell R, Geale SK, Noble D, Norton C, O’Brien AP. Introduction of patient electronic medical records (EMR) into undergraduate nursing education: an integrated literature review. Nurse Educ Today. 2020; 94:104517.
https://doi.org/10.1016/j.nedt.2020.104517
.
Article
10. Abrahamson K, Anderson JG, Borycki EM, Kushniruk AW, Malovec S, Espejo A, et al. The impact of university provided nurse electronic medical record training on health care organizations: an exploratory simulation approach. Stud Health Technol Inform. 2015; 208:1–6.
https://doi.org/10.3233/978-1-61499-488-6-1
.
Article
11. Jung SY, Hwang H, Lee K, Lee D, Yoo S, Lim K, et al. User perspectives on barriers and facilitators to the implementation of electronic health records in behavioral hospitals: qualitative study. JMIR Form Res. 2021; 5(4):e18764.
https://doi.org/10.2196/18764
.
Article
14. Sittner BJ, Aebersold ML, Paige JB, Graham LL, Schram AP, Decker SI, et al. INACSL standards of best practice for simulation: past, present, and future. Nurs Educ Perspect. 2015; 36(5):294–8.
https://doi.org/10.5480/15-1670
.
Article
15. Choi M, Lee H, Park JH. Effects of using mobile device-based academic electronic medical records for clinical practicum by undergraduate nursing students: a quasi-experimental study. Nurse Educ Today. 2018; 61:112–9.
https://doi.org/10.1016/j.nedt.2017.11.018
.
Article
16. Whang JH, Kim DH. An empirical study on the critical factors for successful m-learning implementation. J Inf Technol Appl Manag. 2005; 12(3):57–80.
17. Vijayalakshmi P, Math SB. Response and attitudes of undergraduate nursing students towards computers in health care. Can J Nurs Inf. 2013; 8(3):1–8.
21. Yang F, Wang Y, Yang C, Zhou MH, Shu J, Fu B, et al. Improving clinical judgment by simulation: a randomized trial and validation of the Lasater clinical judgment rubric in Chinese. BMC Med Educ. 2019; 19(1):20.
https://doi.org/10.1186/s12909-019-1454-9
.
Article
22. Kuiper R, O’Donnell SM, Pesut DJ, Turrise SL. 2017 The essentials of clinical reasoning for nurses: using the outcome-present state test model for reflective practice. Indianapolis (IN): Sigma Theta Tau International;2017.
25. Kleib M, Chauvette A, Furlong K, Nagle L, Slater L, Mc-Closkey R. Approaches for defining and assessing nursing informatics competencies: a scoping review. JBI Evid Synth. 2021; 19(4):794–841.
https://doi.org/10.11124/jbies-20-00100
.
Article
26. Vana KD, Silva GE. Evaluating the use of a simulated electronic health record and online drug reference in a case study to enhance nursing students’ understanding of pharmacologic concepts and resources. Nurse Educ. 2014; 39(4):160–5.
https://doi.org/10.1097/nne.0000000000000046
.
Article
27. Samadbeik M, Fatehi F, Braunstein M, Barry B, Saremian M, Kalhor F, et al. Education and training on electronic medical records (EMRs) for health care professionals and students: a scoping review. Int J Med Inform. 2020; 142:104238.
https://doi.org/10.1016/j.ijmedinf.2020.104238
.
Article
28. Behrends M, Paulmann V, Koop C, Foadi N, Mikuteit M, Steffens S. Interdisciplinary teaching of digital competencies for undergraduate medical students: experiences of a teaching project by medical informatics and medicine. Stud Health Technol Inform. 2021; 281:891–5.
https://doi.org/10.3233/shti210307
.
Article
29. Udelsman B, Chien I, Ouchi K, Brizzi K, Tulsky JA, Lindvall C. Needle in a haystack: natural language processing to identify serious illness. J Palliat Med. 2019; 22(2):179–82.
https://doi.org/10.1089/jpm.2018.0294
.
Article