On the other hand, home visits made by home care nurses can lead to a 50% reduction in cases of unplanned hospital admissions. This would also result in 50% reduction in bed day rates for patients with chronic diseases (Department of Health & Children, n.d.).
Efficiency of Current Automated Trigger Systems
Automated trigger systems are designed to detect changes in patients bodies that could lead to an attack and issue an early warning. These trigger systems are effective and proactive in helping detect and manage possible attacks than an on-demand system (Tamblyn et al., 2008 Depending on how the system is automated, an early warning is either issued to the patient, or to a nurse who will respond to them it according to the set guidelines, contact the patient and counsel them on the best course of action (Finkelstein, Khare & Vora, 2003).
Managing chronic diseases is within reach only if interest is spurred within all parties. This is a war that cannot be won without considerable input. If the trends go unchecked remain uncontrolled, future generations stand to suffer even more consequences from these diseases.
Automated systems have been designed to help in detection, management, and treatment of chronic diseases. Their capacity is however greatly underutilized. More work needs to be done if we are to tame chronic diseases.
CDC,. (2009). The Power of Prevention: Chronic disease . . . the public health challenge of the 21 st century (p. 8).
Department of Health & Children,. Tackling Chronic Disease: A Policy Framework for the Management of Chronic Diseases (p. 16). Hawkins House Dublin 2.
Finkelstein, J., Khare, R., & Vora, D. (2003). Home automated telemanagement (HAT) system to facilitate self-care of patients with chronic diseases. Journal Of Systemics, Cybernetics And Informatics, 1(3), 82.
Tamblyn, R., Huang, A., Taylor, L., Kawasumi, Y., Bartlett, G., & Grad, R. et al. (2008). A Randomized Trial of the Effectiveness of On-demand versus Computer-triggered Drug Decision Support in Primary Care. Journal Of The American Medical Informatics Association, 15(4), 430-438. http://dx.doi.org/10.1197/jamia.m2606
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