Data mining comprises different approaches that enable corporations to make informed decisions based on company productivity issues. Collecting data and converting it into knowledge provides an enabling environment for company managers to identify the threats that hinder productivity in the workplace. Data analysis techniques offer healthcare practitioners an opportunity to examine the meaning of different processes across various population groups. Thus, the continued use of data mining in healthcare allows medical practitioners to evaluate the effectiveness of treatment methods, manage customer relationships, and detect medical fraud.
Treatment methods should have a significant impact on individuals’ perspectives because of their ability to identify specific tactics that enhance the relations of medical professionals and their patients. Analyzing the effectiveness of medical standards provides an opportunity to develop viable solutions that improve healthcare outcomes in the country and beyond (Ţăranu, 2016). Developing this level of understanding creates an enabling environment for doctors to connect with their patients and eliminate any challenges that may affect their relationship. Patient experience in the healthcare system plays a significant role in defining the nature of outcomes that can be recorded at any given time (Pika, Wynn, Budiono, ter Hofstede, van der Aalst, & Reijers, 2019). Using data mining techniques to acquire information and convert it into knowledge equips healthcare facilities with an advantage to accomplish different objectives.
The continued use of data mining in healthcare allows medical practitioners to evaluate the effectiveness of treatment methods, manage customer relationships, and detect medical fraud. Data mining can identify the problems affecting the quality of outcomes in the healthcare context and encourage medical practitioners to resolve the issues using specific approaches that improve the relationship with patients. The technological approach improves healthcare outcomes by eliminating challenges that hinder facilities’ ability to respond to different patient needs.
Pika, A., Wynn, M. T., Budiono, S., ter Hofstede, A. H., van der Aalst, W. M., & Reijers, H. A. (2019, September). Towards privacy-preserving process mining in healthcare. In International Conference on Business Process Management (pp. 483-495). Springer, Cham.
Ţăranu, I. (2016). Data mining in healthcare: decision making and precision. Database Systems Journal, 6(4), 33-40.
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