In this century, technological advancements influence all aspects of people’s life. Technology applications are changing traditional health care system methods to provide a comprehensive approach to medical treatment and decision-making processes. The health care system is rich in information and data that need to be evaluated, as medical workers have a requirement to make life-changing decisions daily.
Regarding this, information technology applications assist workers in making proper decisions during diagnosis and treatments. Here, the role of nurses is significant because they mostly choose what technology application is appropriate to use. This paper provides information about data mining technology application in the decision-making process of health care and examines the work of nurses in identifying technology for practice.
Data mining is becoming popular among medical workers due to the abundance of data they need to analyze. Health care organizations produce and collect a significant amount of information daily, so data mining makes more accessible investigations for medical workers. Data mining allows the extraction of valuable and exciting data and regularities, as raw data in health care organizations are heterogeneous and voluminous. A study on data mining found that medical workers can predict trends in the patients’ conditions and behaviors (Amin & Amir 2018). The prediction of movements can be done through data analysis from different perspectives and investigating connections and relations between information that are considered unrelated.
There are many examples of how health care organizations may use data mining. Data from organizations are stored and collected in an organized form to create hospital information systems (Amin & Amir 2018). Therefore, due to its data collection and systematic evaluation, data mining applications can help health insurers to detect abuse and fraud in organizations. Moreover, data mining is also valuable for maintaining customer relationship management as databases can provide relevant information about each patient’s treatments, answering specific patients’ specific questions (Islam et al., 2018). Health care organizations can benefit both financially and practically from using information technology applications, such as data mining.
The quality of decision-making with the use of data mining applications is better compared to traditional methods. Information technology uses big data analysis techniques to provide a comprehensive look at an assigned task. Due to a technology application, it is practically easy to access information for medical workers, as usually the needed data are organized and stored in organized forms. Moreover, data mining tools are helpful in controlling the limitations of people, such as subjectivity or error because of fatigue, and provide indicative signs for the decision-making processes (Amin & Amir 2018).
For example, Malik et al. (2018) argue that data mining technology application reveals innovative biomedical and healthcare knowledge for clinical and administrative decision making. The information application also generates scientific hypotheses from extensive experimental data, hospital databases, and scholarly literature. Therefore, there is a high quality of using information application technology.
Selecting and implementing information technology applications in health care decision-making is crucial, as medical workers will use the application daily. Information technology applications vary according to the purpose of use. For example, data mining technology has two models: predictive models and descriptive models (Sohail et al., 2019). The predictive model applies supervised learning functions to predict unknown and future values of other factors. In contrast, the descriptive model uses the unsupervised learning functions in searching patterns with descriptions of data that can be interpreted by medical workers (Sohail et al., 2019). When applying data mining applications, it is highly suggested to decide what application model is needed. Therefore, there is a need to determine which application is suitable for each type of work.
One of the main aspects of selecting and evaluating an application is the role of a nurse. A study found that an increased level of computer knowledge facilitates nurses’ more use of information technology applications (Ăberg et al., 2018).
Computer literacy of nurses determines how effective they will use applications. Additionally, by analyzing PubMed, CINAHL, and Medline databases, it is investigated that nurses are afraid of dehumanizing treatment processes through use technologies (Ăberg et al., 2018). Fear of technologies may prevent nurses from implementing information technology applications, thus undermining data evaluation in health care organizations.
On the other hand, nurses who are well trained and have increased computer experience are more likely to adapt to information technology and work with it. (Ăberg et al., 2018) suggests involving nurses in system design, creating a positive experience with the application, and as a result, making the use of information technology application effective in health care organizations. Medical workers will know how to work with applications and use them in every day work with patients and administration.
Another aspect that needs to be considered when using information technology applications is its associated costs. Health care organizations know that the higher the costs of application technology, the higher the productivity and quality of the application. Moreover, no use of such technology fails in detecting frauds and abuse in financial parts of health care organizations. Data mining has been viewed as the result of three different disciplines: database management, statistics, and computer science (Islam et al., 2018). In addition, it includes elements of artificial intelligence and machine learning, suggesting its high cost for development and realization.
One more cost associated with the use of information technology applications is not related to financial aspects but with medical workers. As was mentioned above, workers, especially nurses, are sensitive to implementing new technologies in the workplace and when interacting with patients. However, such fear of technologies by medical workers can be managed by providing practical training, information desks, and studies about the benefits of information technology applications. To organize such informative events, health care organizations need additional expenses. Moreover, organizations require information technology workers to sustain and control applications and database systems.
To conclude, the health care system requires good decision-making as its choices influence the lives of millions of people every day. Information application technologies are programmed to help medical workers make decisions related to treatments and diagnosis of patients. Due to technological development, health care organizations implement information technology applications daily. One of the examples of such an application is data mining.
Data mining is used to detect valid, new, potentially useful, and understandable relations and patterns in data. Therefore, collecting and evaluating data is faster, effective, and almost error-free using data mining. The tendency of data mining application in health care organizations suggests its quality and accessibility. Moreover, there is a need to examine the role of nurses in selecting and evaluating information technology applications. Recent studies found that nurses with decreased computer experience levels are less likely to use technologies in work.
References
Amin, M., and Amir Ali. âPerformance evaluation of supervised machine learning classifiers for predicting healthcare operational decisions.â Wavy AI Research Foundation: Lahore, Pakistan (2018).
Islam, M. S., Hasan, M. M., Wang, X., & Germack, H. D. (2018). âA systematic review on healthcare analytics: application and theoretical perspective of data mining.â Multidisciplinary Digital Publishing Institute. 6(2), 54.
Malik, M. M., Abdallah, S., & Alaâraj, M. (2018). âData mining and predictive analytics applications for the delivery of healthcare services: a systematic literature review.â Annals of Operations Research, 270(1), 287-312.
Ăberg, U., Orre, C. J., Isaksson, U., Schimmer, R., Larsson, H., & Hörnsten, Ă . (2018). âSwedish primary healthcare nursesâ perceptions of using digital eH ealth services in support of patient selfâmanagement.â Scandinavian Journal of Caring Sciences, 32(2), 961-970.
Sohail, M. N., Jiadong, R., Uba, M. M., & Irshad, M. (2019). âA comprehensive looks at data mining techniques contributing to medical data growth: a survey of researcher reviews.â Recent Developments in Intelligent Computing, Communication and Devices, 21-26