The proper processing of medical information is a priority for present-day healthcare institutions. However, this task seems to be complex and, therefore, challenging for them. In order to successfully perform it, hospitals need to apply big data to the field, thereby ensuring the correspondence to the technological needs of the time and efficiently processing all patients’ information. The application of new technologies in healthcare appears beneficial for all institutions but needs to be examined to secure their appropriate use.
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The researchers have already attempted to define the term and its suitability for the field. They claim that it covers all the processes related to data collection and storage, and this perspective allows them to assess the efficiency of its use (Collins 101). At present, the theory behind the notion gives an idea of strengths and weaknesses corresponding to the application of big data within hospitals. Hence, the strengths relate to the creation of opportunities to link the systems, use the real-world evidence, and identify safety issues (Collins 103). Weaknesses, in turn, are the costs of the processes and the sole focus on people who are digitally connected (Collins 104). Their consideration allows assessing the overall productivity of the method.
Other sources highlight the importance of the application of big data in terms of limited public budgets and the aging population. They consider the usefulness of this approach for policymakers while providing the information on its general benefits (Bodas-Sagi and Labeaga 47). In this way, the authors suggest that the application of big data will lead to saving costs in the long run (Bodas-Sagi and Labeaga 51). Such a positive outcome is complemented by the creation of health economic outcomes research (HEOR) practices (Chen et al.). They are vital for assessing the effectiveness of the application of big data in the healthcare field. In this way, the consideration of the economic aspect of new technologies adds to the current information on the topic.
The previous studies mentioned above allowed the researchers to provide extensive information on the state of big data and its theoretical application throughout the country in the future. Nevertheless, there is a knowledge gap in the field regarding its practical use, and it requires further research. Such a study will allow defining the actual weaknesses of the systems and eliminating them afterward. Hence, the research questions are: What are the weaknesses of the application of big data within the present-day medical institutions? What approaches can be used for their elimination?
The study will be based on specific methods allowing to receive the information in a hospital setting. The first research question will be addressed by the analysis of statistics and other data gathered in the selected healthcare facilities. The second research question will be answered through the survey conducted among medical specialists who apply big data in their work. The results of the two methods will provide a practical view of the matter and suggest solutions to the revealed problems.
Thus, conducting research on the application of big data with the use of information from the hospitals is beneficial in terms of defining the current state of their technological development. It will contribute to more efficient use of patients’ records and, consequently, promote the expansion of the systems. This outcome, in turn, will allow the healthcare facilities to provide equal amounts of services to people and ensure their distribution to all population groups.
Bodas-Sagi, Diego J., and José M. Labeaga. “Big Data and Health Economics: Opportunities, Challenges and Risks.” International Journal of Interactive Multimedia & Artificial Intelligence, vol. 4, no. 7, 2018, pp. 47-52.
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Collins, Brendan. “Big Data and Health Economics: Strengths, Weaknesses, Opportunities and Threats.” Pharmacoeconomics, vol. 34, no. 2, 2016, pp. 101-106.
Chen, Yixi, et al. “Precision Health Economics and Outcomes Research to Support Precision Medicine: Big Data Meets Patient Heterogeneity on the Road to Value.” Journal of Personalized Medicine, vol. 6, no. 4, 2016.