Data Mining in Healthcare: Predictive Analytics and Privacy Concerns

This discussion post examines the application of data mining in the healthcare sector, particularly within the Seton Healthcare Family. It highlights the use of data mining in predictive medicine, fraud detection, healthcare management, and customer relations management. The post discusses how data mining helps in measuring treatment effectiveness by comparing different treatments and establishing the most efficient treatment plans. It also explores the use of classification and regression trees in developing prediction models. The post acknowledges the benefits of data mining, such as improving patient care and standardizing treatment methods, while also addressing the privacy concerns associated with the sharing of patient data and suggests solutions such as giving patients the option to choose whether their personal information can be used for healthcare improvement and offering incentives to encourage participation.

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Use of electronic records is an everyday activity in most of the organization today. With the
increase in the amount of data, firms are now working on optimizing quality and organizational
efficiency by using data mining. In the past, firms have been using data mining for fraud
detection and determining the credit score of customers (Sundermann, Miller, Marsh, Saul,
Shutt, Pacey, & Snyder, 2019). Today, companies are looking for similar benefits from the use of
predictive analytics and data mining. The primary objective of data mining usage in the firm is to
establish useful, understandable business and processes patterns through analysis of a large set of
data. This is critical is forecasting future industry dynamics, observe trends and determine how to
react to them.
Most companies have utilized the use of data mining in improving customer satisfaction and
experience, increase product usability and safety (Pika, Wynn, Budiono, ter Hofstede, van der
Aalst, & Reijers, 2019, September). In the Seton Healthcare Family, data mining has been
widely used in predictive medicine, fraud detection, healthcare management and customer
relations management. In measuring treatment effectiveness, it involves comparing and
contrasting various causes and course, symptoms and treatment alternatives to determine the
most effective action to be taken to manage a given condition or illness (Sohail, Jiadong, Uba, &
Irshad, 2019). For instance, patients being treated with different drug regiments can be compared
to establish the most efficient treatment plan that works best to determine the treatment that
works best and saves money. Continued use of the determined treatment plan can help in the
standardization of treatment method for a given group of diseases, thus, speeding the diagnosis
and the treatment process.
Interestingly, the application of data mining has proved beneficial to most firms, including the
Seton Healthcare Family. However, it has exposed the firms to some privacy concerns. The

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process involves sharing of massive volumes of patient’s data during the mining process, which
has led to the increasing interest from patients regarding their sensitive data falling on the wrong
hands (Eickhoff, Kim, & White, 2020, January). However, data mining experts have argued that
the risk is worth taking for the benefit of the patients. Due to the risk attached to it, most people
have suggested giving the patients an option to choose on whether their personal information can
be used in improving relevant areas of healthcare. This is to be done through the provision of
incentives such as giving tax breaks to encourage them to get involved.
Data science which is an iterative and agile process has exposed companies to a scientific
methodology in getting predictive analytics and efficient application of data mining successfully.
Data mining has helped improve various processes and operations by suggesting ways in which
the procedures and procedures can be optimally conducted. The above discussion has focused on
the general discussion on the application of data mining in the Seton Healthcare Family.
Alternatively, classification and regression trees models can be used in developing prediction
models from available data. The relevant data from patients can be obtained through recursive
data partitioning and inserting a prediction model in the partition spaces created. From the
prediction observed, the trends identified can be presented on a graphical decision tree.
Classification trees are effective since they are in apposition to take finite data both in ordered in
unordered way. Which makes it suitable as compared to data mining.

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References
Eickhoff, C., Kim, Y., & White, R. W. (2020, January). Overview of the Health Search and Data
Mining (HSDM 2020) Workshop. In Proceedings of the 13th International Conference on
Web Search and Data Mining (pp. 901-902).
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 Seton Healthcare
Family. In International Conference on Business Process Management (pp. 483-495).
Springer, Cham.
Sohail, M. N., Jiadong, R., Uba, M. M., & Irshad, M. (2019). A comprehensive looks at data
mining techniques contributing to medical data growth: a survey of researcher reviews. In
Recent Developments in Intelligent Computing, Communication and Devices (pp. 21-
26). Springer, Singapore.
Sundermann, A. J., Miller, J. K., Marsh, J. W., Saul, M. I., Shutt, K. A., Pacey, M., ... & Snyder,
G. M. (2019). Automated data mining of the electronic health record for investigation of
healthcare-associated outbreaks. Infection Control & Hospital Epidemiology, 40(3), 314-
319.