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Data Analytics and System Performance in Healthcare

People daily use technologies to check health conditions and predict potential problems which might arise in the future. For instance, according to chapter 9, written by Spath and Kelly (2017), data collection and analysis are crucial in understanding future changes in the human body. Moms can easily understand when their child has a high temperature, and an older man may spend less time detecting a high level of insulin in the body. In chapter 10, the authors described the effect of data analysis on the general performance of the healthcare industry. This chapter is more oriented toward understanding modern trends, planning future development, and improving management. Consequently, the essay will cover key elements of data analysis, their impact on the patient’s care, and how they affect me as a medical worker.

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One of the key elements of data analytics is a quality measure which is also known as a metric. Some findings might have gaps and misunderstandings, and metric helps medical workers improve the test outcomes. Choosing the right data collection method is also a crucial step that requires a systematic preparation process and helps predict future changes in a short period (Ziemssen et al., 2016). In chapter 10, Spath and Kelly (2017) explain the use of predictive and prescriptive analytics, which helps health care workers evaluate performance. While the predictive technique helps identify potential changes, the prescriptive optimizes such aspects of the industry, such as the financial department and HR management.

In both chapters, the influence on patients is defined deeply. For example, customers of medical buildings receive many benefits when the industry does not stop developing and improving services. Moreover, there is a strategy called “clinical value compass” that states that patients’ well-being is one of the main priorities of medical workers (Spath and Kelly, 2017). Consequently, with the development of data collection methods, patients feel a significant positive influence on how they are treated. Therefore, every person who needs medical assistance receives a specific treatment according to any kind of data type (Topol, 2019). The main performance indicators are interval data, categorical data, and qualitative data (Spath and Kelly, 2017). Both chapters are oriented toward patients and their positive feedback, as they are the main supporters of the industry. As a medical worker, I find methods of data collection and improving general performance as crucial parts of my education, as the medical sphere should always develop and discover new ways of treatment. Different types of information collection may help me find the right approaches for every patient and create specific treatment plans to ensure that I receive a favorable outcome.

To conclude everything that has been stated so far, the well-being of patients is the major priority for every medical worker. By finding the right approaches, every person may receive better treatment. However, to make sure that every aspect of the illness is deeply analyzed, healthcare employees should understand basic types of data collection and methods that increase the industry’s performance. Therefore, knowledge may push the development of innovation and increase the level of understanding of patients’ needs.


Spath, P., & Kelly, D. (2017). Applying quality management in healthcare: A systems approach. Hardbound.

Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 24, 44-56. Web.

Ziemssen, T., Kern, R., & Thomas, K. (2016). Multiple sclerosis: clinical profiling and data collection as a prerequisite for personalized medicine approach. BMC Neurology, 16(124). Web.

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