Nurses’ View towards the Use of Robotic during Pandemic COVID-19 in Indonesia: A Qualitative Study
DOI:
https://doi.org/10.3889/oamjms.2022.7645Keywords:
COVID-19, Nurse, Qualitative study, Robot, Utilization, ViewAbstract
Background: Rapid advances in artificial intelligence and robotics have alleviated difficulties for patients, hospitals, and the industry as a whole. However, the health care system is identically human-centered at its core, and many healthcare professions may not be ready to work with robots. Understanding nurses' views toward robotics can help integrate robotic technologies into future patient care.
Objectives: This study aimed to explore how nurses view using robotics during the COVID-19 pandemic.
Methods: This study used a qualitative descriptive technique to registered nurses who provide direct care to the patients with COVID-19 recruited from two hospitals in Indonesia. Purposive sampling was used to select respondents with criteria of those who had worked for at least one year and were willing to participate—the analysis used qualitative content analysis.
Results: A total of 20 female nurses with an average age of 32.8 ± 4.0 years participated in this study. The qualitative findings revealed three themes with nine sub-themes, namely the use of robotic in nursing care (sub-theme: reducing the risk of COVID-19 transmission, monitoring patients remotely, and helping in providing care), the burden of using robotic in nursing care (sub-theme: digital literacy in nursing care, culture difference in providing care, changing care practice habits, and safety concern, and attitude toward robotic in nursing care (sub-theme: negative response).
Conclusions: This study explored nurses' views on the usage of robotics during the pandemic COVID-10. It implies that a strategic plan would have many benefits and limitations, such as nursing care burden, negative attitude, and cultural awareness.
Downloads
Metrics
Plum Analytics Artifact Widget Block
References
Kujat L, John S, College F. How have robotics impacted healthcare? How has open access to fisher digital publications benefited you? Rev J Undergrad Stud Res. 2010;12:6-8.
Smith A, Anderson J. AI, robotics, and the future of jobs. Pew Res Cent. 2014;6:51.
Homma K, Matsumoto O. Development of a risk assessment assistance tool for robotic care devices. Stud Health Technol Inform. 2017;242:551-7. PMid:28873852
English SW, Barrett KM, Freeman WD, Demaerschalk BM. Telemedicine-enabled ambulances and mobile stroke units for prehospital stroke management. J Telemed Telecare. 2021. https://doi.org/10.1177/1357633X211047744 PMid:34636680 DOI: https://doi.org/10.1177/1357633X211047744
Coco K, Kangasniemi M, Rantanen T. Care personnel’s attitudes and fears toward care robots in elderly care: A comparison of data from the care personnel in Finland and Japan. J Nurs Scholarsh. 2018;50(6):634-44. https://doi.org/10.1111/jnu.12435 PMid:30354007 DOI: https://doi.org/10.1111/jnu.12435
Papadopoulos I, Koulouglioti C. The influence of culture on attitudes towards humanoid and animal-like robots: An integrative review. J Nurs Scholarsh. 2018;50(6):653-65. https://doi.org/10.1111/jnu.12422 PMid:30242796 DOI: https://doi.org/10.1111/jnu.12422
Polit DF, Beck CT, Owen SV. Is the CVI an acceptable indicator of content validity? Appraisal and recommendations. Res Nurs Health. 2007;30(4):459-67. https://doi.org/10.1002/nur.20199 PMid:17654487 DOI: https://doi.org/10.1002/nur.20199
Lincoln YS, Guba EG, Pilotta JJ. Naturalistic Inquiry. Newbury Park: Cal Sage; 1985. DOI: https://doi.org/10.1016/0147-1767(85)90062-8
Lincoln YS, Guba EG. Naturalistic Inquiry. Vol. 75. Thousand Oaks, CA: Sage; 1985.
Burnard P. Teaching the analysis of textual data: An experiential approach. Nurse Educ Today. 1996;16(4):278-81. https://doi.org/10.1016/s0260-6917(96)80115-8 PMid:8936234 DOI: https://doi.org/10.1016/S0260-6917(96)80115-8
Elo S, Kyngäs H. The qualitative content analysis process. J Adv Nurs. 2008;62(1):107-15. https://doi.org/10.1111/j.1365-2648.2007.04569.x PMid:18352969 DOI: https://doi.org/10.1111/j.1365-2648.2007.04569.x
Best J, Day L, Ingram L, Musgrave B, Rushing H, Schooley B. Comparison of robotic vs. standard surgical procedure on postoperative nursing care of women undergoing total abdominal hysterectomy. Medsurg Nurs. 2014;23(6):414-21. PMid:26281645
Iavazzo C, Gkegkes ID. Enhanced recovery programme in robotic hysterectomy. Br J Nurs. 2015;24(16):S4-8. https://doi.org/10.12968/bjon.2015.24.Sup16.S4 PMid:26355452 DOI: https://doi.org/10.12968/bjon.2015.24.Sup16.S4
Aymerich-Franch L. Why it is time to stop ostracizing social robots. Nat Mach Intell. 2020;2(7):364. https://doi.org/10.1038/s42256-020-0202-5 DOI: https://doi.org/10.1038/s42256-020-0202-5
Demaitre E. Coronavirus Response Growing from Robotics Companies. Vol. 3; 2020.
Cresswell K, Cunningham-Burley S, Sheikh A. Health care robotics: Qualitative exploration of key challenges and future directions. J Med Internet Res. 2018;20(7):e10410. Available from: http://www.jmir.org/2018/7/e10410. [Last accessed on 2018 Mar 15]. DOI: https://doi.org/10.2196/10410
Ricks DJ, Colton MB. Trends and considerations in robot-assisted autism therapy. In: 2010 IEEE International Conference on Robotics and Automation. 2010. p. 4354-9. DOI: https://doi.org/10.1109/ROBOT.2010.5509327
Wikström AC, Cederborg AC, Johanson M. The meaning of technology in an intensive care unit-an interview study. Intensive Crit Care Nurs. 2007;23(4):187-95. PMid:17467992 DOI: https://doi.org/10.1016/j.iccn.2007.03.003
Aarts J, Gorman P. IT in health care: Sociotechnical approaches “To Err is System”. Int J Med Inform. 2007;76 Suppl 1:S1-3. https://doi.org/10.1016/S1386-5056(07)00078-0 PMid:17466251 DOI: https://doi.org/10.1016/S1386-5056(07)00078-0
Coombs C. Will COVID-19 be the tipping point for the intelligent automation of work? A review of the debate and implications for research. Int J Inf Manage. 2020;55:102182. https://doi.org/10.1016/j.ijinfomgt.2020.102182 PMid:32836639 DOI: https://doi.org/10.1016/j.ijinfomgt.2020.102182
White PJ, Marston H, Shore L, Turner R. Learning from COVID-19: Design, Age-friendly Technology, Hacking and Mental Models [Version 1; Peer Review: Awaiting Peer Review]; 2020. DOI: https://doi.org/10.35241/emeraldopenres.13599.1
Otter JA, Yezli S, Perl TM, Barbut F, French GL. The role of “no-touch” automated room disinfection systems in infection prevention and control. J Hosp Infect. 2013;83(1):1-13. https://doi.org/10.1016/j.jhin.2012.10.002 PMid:23195691 DOI: https://doi.org/10.1016/j.jhin.2012.10.002
Marra AR, Schweizer ML, Edmond MB. No-touch disinfection methods to decrease multidrug-resistant organism infections: A systematic review and meta-analysis. Infect Control Hosp Epidemiol. 2018;39(1):20-31. https://doi.org/10.1017/ice.2017.226 PMid:29144223 DOI: https://doi.org/10.1017/ice.2017.226
Goh PS, Sandars J. A vision of the use of technology in medical education after the COVID-19 pandemic. MedEdPublish. 2020;9(1):49. http://doi.org/10.15694/mep.2020.000049.1 DOI: https://doi.org/10.15694/mep.2020.000049.1
Downloads
Published
How to Cite
License
Copyright (c) 2022 Taryudi Taryudi, Linlin Lindayani, Heni Purnama, Astri Mutiar (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
http://creativecommons.org/licenses/by-nc/4.0