Information and Technology Integrated into Healthcare

Technology has rapidly become integrated into a modern healthcare industry, which led to increased clinical efficiency and improved patient outcomes. Medical organizations worldwide face a number of concerns and challenges, including the shortage of nursing staff, an increase in the aging population and life expectancy, as well as ethical dilemmas in facilitating care. Technologies, especially the ones focusing on collecting and analyzing medical information, are now more crucial than ever. They ensure that hospitals can serve a larger load of patients and provide them with the care of the highest quality and safety. The purpose of this essay is to explore a nurse’s role in championing the use of various technologies and data to make the practice of care delivery safe and efficient.

There are multiple reasons as to why technology and information play such an important role in healthcare nowadays. Some examples of these applications may include electronic physician orders, patient-specific information to support clinical decision-making, computerized sign-out and hand-off, automated drug dispensing tools, and so on. Some of the opportunities technology provides to healthcare organizations include “reducing human errors, improving clinical outcomes, facilitating care coordination, improving practice inefficiencies, and tracking data over time” (Alotaibi & Federico, 2017, p. 1173).

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The primary benefit of electronic health records is the ability clinicians have to store and retrieve data in the right format, making it easier to communicate it to colleagues (Rajkomar et al., 2018). The improvement in patient safety is achieved through the integration of medication alerts, reminders, and more accurate diagnostic testing, all of which are dependent on technology. Computerized health information systems can ensure the care a patient receives is evidence-based and efficient as they can evaluate the efficacy of certain treatments and their adherence to existing guidelines.

Nurses are often the connecting branch between a patient and their healthcare provider. Thus, it is exceptionally important for nursing staff to be knowledgeable about the integration of technology into medical practice. In order to do that, they have to go through IT training in various categories. The realm of informatics is changing rather rapidly. It is crucial for medical executives to empower nurses to obtain education via short- and long-term courses focusing on specialized application of information and technology tools (Pepito & Rozzano, 2018).

In this day and age, a nurse’s role entails meticulous synchronization of clinical and technical information in order to coordinate care properly. They have to ensure the data is exchanged seamlessly for the treatments provided to be effective and the workflow efficient. In addition, nurses can use technology directly to make health-related decisions. For example, they can utilize computerized command centers to receive the latest detailed and applicable health insights on a patient, which would, in turn, affect what patients or procedures are prioritized.

In order to utilize information technology properly, it is crucial to understand the relationship between data integrity and quality. High-quality data should be valid and accurate, which means that to get quality, there should be integrity first. However, information integrity is not the only determinant of quality. For example, medical staff can have accurate data on a patient, but it would still be considered low quality if it were incomplete or inconsistent. The primary challenge the nursing team faces is ensuring that the data collected and stored via electronic health records meets the quality standards.

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References

Alotaibi, Y. K., & Federico, F. (2017). The impact of health information technology on patient safety. Saudi Medical Journal, 38(12), 1173–1180. Web.

Pepito, J. A., & Rozzano, L. (2018). Can nurses remain relevant in a technologically advanced future?. International Journal of Nursing Sciences, 6(1), 106-110. Web.

Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G. E., Irvine, J., Le, Q., Litsch, K., … Dean, J. (2018). Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine, 1(1), 18-27. Web.

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