WEEK 5 DISCUSSION QUESTIONS 5
Week5 Discussion questions
Week5 Discussion questions
Inthe modern era, technology has revolutionized the healthcare sector.Patient-driven technologies have enhanced how nursing practitionersmake decisions in the clinical setting. Therefore, this has resultedin improved patient care quality. Furthermore, patient drivenprediction technologies have enabled ill people to take part in theirown care hence, promoting good health and minimizing various risks.
Patient-centeredcare encourages individuals to take an active role in recovering fromillnesses and medical conditions. According to Jiang, Boxwala,El-Kareh, Kim, and Ohno-Machado (2012), adaptive technologies thatempower patients to monitor their conditions bring a deep sense ofconfidence that is vital in the process of recovering from thedisease. Therefore, patient-driven adaptive technologies promote goodhealth since they enable ill people to monitor their state of health.Subsequently, they can contribute towards clinical decision making.As such, nursing practitioners can use the information they obtainfrom patients to supplement their professional evidence-based andtheoretical knowledge to arrive at conclusive clinical decisions(Jiang, Boxwala, El-Kareh, Kim & Ohno-Machado, 2012). Suchdecisions are critical in providing adequate patient care.
Concerningminimizing risks, patient-driven technologies have played a pivotalrole in reducing various health risks in the clinical setting. Jiang,Boxwala, El-Kareh, Kim. and Ohno-Machado (2012) indicated thatpredictive models of patient-driven technologies enable ill peopleand nurses to observe different patterns of medicinal treatment thatcould pose a health risk. For instance, for some diseases andconditions, certain drugs are known to have serious side effects thatput the lives of patients at risk. Usually, the risks come from themetabolic reactions that such medications cause. For this reason,patient-driven technologies are useful in minimizing risks that couldjeopardize the lives of those affected. Besides, such risks have adamaging impact on the image of the nursing profession.
Thehealth care system of 2030 would be a highly efficient one in whichcutting-edge technology will be the driving force behind improvedpatient attention. Advancements in technology would further improvehow health care providers make decisions that promote good wellbeingand patients’ recovery from diseases. According to Gubbi,Buyya, Marusic, and Palaniswami (2013),the speed and convenience of technology at that time wouldrevolutionize health care in ways few people could have expected.Furthermore, patients would interact with their families easily andhealth care systems would operate in real time.
Thehealth care system of 2030 would look different from the one thatexists today. One technological innovation that would be availablefor patients is surveillance devices (Gubbi,Buyya, Marusic, & Palaniswami, 2013).Patients would have the ability to watch closely all aspects oftreatment in real time. According to Gubbi,Buyya, Marusic, and Palaniswami (2013),this contrasts the current health care setting in which patients arenot able to observe all aspects of the care they receive. Some of thecare happens in the background. Surveillance gadgets would haveimproved functionalities of Smartphones that are currently notavailable. As such, improved surveillance technologies for patientswould enable them to offer their personal opinions on what they feel.Such feedback would then enable healthcare providers to make betterdecisions. Surveillance technologies would also facilitate healthcare systems to perform major operations effectively due to real-timefeedback from patients.
Anothertechnological improvement that could be available to patients isrobots that use artificial intelligence techniques of providing care.Gubbi,Buyya, Marusic, and Palaniswami (2013)note that patients would use remote control systems to direct robotsto perform certain actions at the clinical setting. For instance,robots will have the capability to administer appropriate medicationsto patients in instances where nurses may be unavailable due to external factors.
Gubbi,J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet ofThings (IoT): A vision,
architecturalelements, and future directions. FutureGeneration Computer Systems, 29(7),1645-1660.
Jiang,X., Boxwala, A., El-Kareh, R., Kim, J. & Ohno-Machado, L. (2012).Apatient-driven adaptiveprediction technique to improve personalized risk estimation forclinical decision support.Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3392846/