Application of Technology in Nursing
Ali, H. B., & Li, H. (2015). Developing a fall-prevention system for nursing homes. Proceedings
of the Human Factors and Ergonomics Society Annual Meeting, 59(1), 601-605. doi:10.1177/1541931215591132.
In this article, the authors examine the current fall prevention systems in nursing homes, the challenges associated with these current systems, and how technology can be used to alleviate these challenges. The article describes the benefits and challenges of the systems currently used, such as chair/bed sensors, clip alarms, and emergency lighting systems. A literature review was carried out to determine that these systems may not effectively prevent falls (Ali & Li, 2015). A systematic study was carried out with interviews with the administrative and nursing staff, field observations, and job analysis. Employees were asked about daily routines, the reaction of the calling, and disconnection systems. Observations took 120 hours between shifts at four nursing homes in New York State.
Genovese, V., Mannini, A., Guaitolini, M., & Sabatini, A. (2018). Wearable inertial sensing for
ICT management of fall detection, fall prevention, and assessment in the elderly. Technologies, 6(4), 91. doi:10.3390/technologies604009
This article examines a patient care monitoring device called a portable inertial measurement device and how it can be used to quantify a patient’s risk of falling and improve fall response time (Genovese, Mannini, Guaitolini & Sabatini, 2018). The WIMU fall detector is a device worn around the patient’s waist during activities and a six-minute walk test to determine the risk of falling. In addition to patient and gait stability data, the sensor uses algorithms and an air pressure sensor.
Ren, L., & Peng, Y. (2019). Research of fall detection and fall prevention technologies: A
systematic review. IEEE Access, 7, 77702-77722. Retrieved from https://ieeexplore.ieee.org/document/8736227
This article is a systematic overview of various fall detection and prevention technologies. Systematic review articles were selected using databases such as PubMed and PubMed Central. More than 1,000 articles were collected, and 150 were selected for review. Four categories of fall detection and prevention systems were examined. The categories are accelerometers, smartphones, image processing systems, and multi-sensors. Several fall detection systems based on an accelerometer have been studied. Those with an algorithm that included speed, impact, and posture had the greatest sensitivity, and the best method for detecting falls based on the accelerometer. Smartphone-based systems use a sensor and a detection mechanism.
Yap, T. L., Kennerly, S. M., & Ly, K. (2019). Pressure injury prevention: Outcomes and
challenges to use of resident monitoring technology in a nursing home. Journal of Wound, Ostomy, and Continence Nursing: Official Publication of the Wound, Ostomy and Continence Nurses Society, 46(3), 207-213. Retrieved from https://oce-ovid-com.library.capella.edu/ article/00152192-201905000-00008/HTML
In this study, before and after the test, the benefits and challenges of using surveillance technologies in a project were examined to avoid the prevalence of pressure ulcers in nursing homes. The study looked at usability, employee awareness, and compliance with monitoring technology (Yap,2019). The study used the validated nursing culture assessment tool to determine changes in the nursing culture related to the implementation of sensors. Residents used sensors in front of the chest, and sensor data was transmitted wirelessly to a central control station to improve compliance and diaper replacement for residents. The reset information is displayed on the nursing station’s LCD screen with the resident’s color code.
Shah, V., Dileep, A., Dickens, C., Groo, V., Welland, B., Field, J., . . . Boyd, A. D. (2016). Patient-Centered Tablet Application for Improving Medication Adherence after a Drug-Eluting Stent. Frontiers in Public Health, 4. doi:10.3389/fpubh.2016.00272
Many authors contributed to this article by presenting their research and findings on this subject. All authors were part of a project supported by the Department of Science of Biomedical Information and Health (Shah et al., 2016). The aim of the study was to determine whether the patient’s knowledge about his therapy and medications was expanded. The article describes how patients were classified for educational purposes using a patient-centered tablet app.
Nelson, R., & Staggers, N. (2018). Health informatics an interprofessional approach. St. Louis,
MO: Elsevier.
The purpose of this book is to introduce the reader to the specialty in health information technology. The authors provide “readers with a comprehensive understanding of health information technology, its practices and related research on health information technology issues” (p. Xvii) (Nelson & Staggers, 2018). The authors ask study questions to offer readers a better understanding of health informatics and its role in health. Ramona Nelson has a doctorate in education from the University of Pittsburgh and a postdoctoral scholarship from the University of Utah. Nancy Staggers, a pioneer in health informatics, earned her doctorate from the University of Maryland School of Nursing with a focus on the computer.
Woods, H. B. (2012). Know your RO from your AE? Learning styles in practice. Health
Information & Libraries Journal, 29(2), 172-176.
In this article, Woods explains the difference between behavioral theories and theories of experimental learning (Woods,2012). Kolb’s theory is more successful because of the next learning cycle, which begins with engaging in an experience, reflecting on the experience, drawing conclusions about the experience, and finally testing its practices.
References
Ali, H. B., & Li, H. (2015). Developing a fall-prevention system for nursing homes. Proceedings
of the Human Factors and Ergonomics Society Annual Meeting, 59(1), 601-605. doi:10.1177/1541931215591132.
Genovese, V., Mannini, A., Guaitolini, M., & Sabatini, A. (2018). Wearable inertial sensing for
ICT management of fall detection, fall prevention, and assessment in the elderly. Technologies, 6(4), 91. doi:10.3390/technologies604009
Nelson, R., & Staggers, N. (2018). Health informatics an interprofessional approach. St. Louis,
MO: Elsevier.
Ren, L., & Peng, Y. (2019). Research of fall detection and fall prevention technologies: A
systematic review. IEEE Access, 7, 77702-77722. Retrieved from https://ieeexplore.ieee.org/document/8736227
Shah, V., Dileep, A., Dickens, C., Groo, V., Welland, B., Field, J., . . . Boyd, A. D. (2016). Patient-Centered Tablet Application for Improving Medication Adherence after a Drug-Eluting Stent. Frontiers in Public Health, 4. doi:10.3389/fpubh.2016.00272
Woods, H. B. (2012). Know your RO from your AE? Learning styles in practice. Health
Information & Libraries Journal, 29(2), 172-176.
Yap, T. L., Kennerly, S. M., & Ly, K. (2019). Pressure injury prevention: Outcomes and
challenges to use of resident monitoring technology in a nursing home. Journal of Wound, Ostomy, and Continence Nursing: Official Publication of the Wound, Ostomy and Continence Nurses Society, 46(3), 207-213. Retrieved from https://oce-ovid-com.library.capella.edu/ article/00152192-201905000-00008/HTML