Data Mining in Business
Introduction
Data Mining is the process of extracting usable data from a vast set of raw data. Data mining is also known as Knowledge Discovery in data. Data mining significantly focuses on Business Intelligence. Data mining plays a significant role in the business field; it is often used to analyze the trends and patterns in the business field to determine the current position of a business, gauge the sales, and predict the future direction of a market. There are numerous data mining techniques. Several industries and business companies dealing with extensive data have deployed knowledge of data mining to boost brand names and increase sales. Some of the large companies that have been successful in implementing the knowledge of data mining include Amazon, Capital One, Netflix, and American Express, among others. For data mining success, some data mining techniques are deployed; these techniques include clustering, regression, classification, and association. Therefore, this context examines the Health Care organization as one of the business organizations that require the knowledge of data mining to solve various problems.
The health care industry avails a massive volume of data concerning the patient’s data and their illness, medication, treatment schedule, information of various physicians, and so forth. Therefore, there is a growing demand in health care organizations to transform this voluminous amount of data into value-added data by discovering trends, patterns, and relationships that co-exist, thus aiding in decision-making. It is important to note that the dataset employed in the healthcare industry somewhat differs from the global dataset used in the standard scientific data mining process. Research depicts that the application of data mining in healthcare organization help in cutting or reducing more than 30 % of the total spending in the healthcare organization
Why the problem is interesting
Most health institutions face challenges associated with inferior information technology, discharge, being certain about the correct medication for a patient, dealing with the patient’s most basic complaints, storing and retrieving health records, and so forth. What makes these problems so amusing is the way the big data is exponentially growing. Moreover, it becomes cumbersome and complicated because the data collected takes various forms; hence integrating and harnessing into meaningful information requires demanding skills from data analysts. Due to the advancing technology and deployment of data mining in the health industry, physicians can determine the kind of disease, optimize medication and payment, and carry out some statistical operations concerning the admission, discharge, and trends of patients prone to a particular condition among others. Data mining in the health field entails discovering knowledge and techniques such as classification, neural networks, logistic regression, and regression trees to determine the health status of a patient by taking into account the availed medical and demographic parameters.
General approach
Some of the essential strategies deployed in bolstering data mining in health care comprise various data mining techniques such as decision trees, cluster analysis, neural networks, and specific time series networks that aid in facilitating the acquisition of the appropriate information. For instance, electronic health records are a vital approach applied in hospitals. It is essential to consider the utilization or the application of the data mining techniques to facilitate the maximization of efficiency and the quality of the health care organization. Classification technique aid in classifying data in different classes. The clustering technique is deployed to identify data that are alike, while the regression method helps analyze the relationship between the variables. Data mining techniques are essential in acquiring the appropriate type of data besides facilitating the comprehension of the health care benefit to the patients.
Data plan to use
Data plans are data quotas that are offered by telecommunication or data hosting companies. Data plans, in other terms, refers to what you can buy so that one can access and provide some services on the internet. Therefore, data plans take into account everything and anything that can be done online through the use of various devices or gadgets that can access the internet. Most healthcare systems in the current world aim to increase the efficiency of their services by availing their services online. Some online services include online booking of appointments, consultation, online doctor, and payment of Insurance funds. Some of the well-known online medical platforms include SteadyMD.com, DoctorOnDemand, Sherpaa, among others. These services advertise not only the health care institution but also aids in making some extra profit for the institution. The health systems should create holistic views of patient’s data, enhance health results, improve communication, and personalize treatment.
How to get the data
Effective data mining largely depends on legit data collection, warehousing, and computer processing. The process of data mining follows some systematic steps: first, data is collected and loaded in the warehouse. Data is collected by various forms of data collection in healthcare. This includes the pre-existing statistics and records which are is going to be analyzed. The data may also be collected by various research methods such as observation, questionnaires, and interviewing multiple health personnel. Secondly, the collected data is then stored in the cloud with a dedicated server for storage. Thirdly, the data analysts and IT personnel deploy various methods of analyzing and organizing the stored. Thirdly, data is sorted out by many data mining software based on the user’s results. Lastly, the end-users access the data through a written or visual format. It is vital to note that each step’s success depends entirely on the data analyst and managers. This is because a misstep risks the entire process and can jeopardize the success of the organization. Therefore, it is urged for the data analyst and data collectors to be keen with their data collection lest they jumble everything, hence risking the lives of the patients in the healthcare.
Problem
The principal business problem under consideration, in this case, is difficulties in the sorting of the large volume of data. In this regard, the organization is required to deploy a specific thinking strategy that would facilitate data sorting. Hence the hospital industry has a large volume of information and is now transferring from a paper-based system to Electronic Health Records. This process is tedious because it needs appropriate tools that will help identify the problem so that it can be solved in the right manner.
This problem is interesting to me because it provides an essential as well as well-planned outlook concerning the applicability of the BI tools while at the same time giving an overview of the strategy that could be used to handle fundamental changes. Therefore, the primary focus was to comprehend how the tools are utilized to obtain appropriate required information from the source data.
Approach
Approaches aimed at analyzing the existing data with the aid of BI tools such as the Power BI and the Tableau. The approach adopted has to meet this need. Therefore, the analysis approach assists in data collection through the utilization of the data collection technique, which will help in identifying the appropriate changes as far as data collection is concerned. Moreover, the approaches employed are typically pure since the analysis of the data is likely to be achieved or done quickly and then presented in many forms such as charts and figures that provide a concise and clear presentation of the information.
Dataset
In this case, the business organization will provide the data that is used in this analysis. This data is in the form of excel.
years
number of discharges
Column1
2001
634019
2002
620082
2003
663516
2004
717,024
2005
745581
2006
768544
2007
775613
2008
786843
2009
789576
Evaluation of the Application of Data Mining in the Health Care Industry
Hospitals or healthcare, in general, provide large volumes of data that are typically complex. This data comes from extensive data and might be information about the patients, hospital resources, disease diagnosis, electronic patient health records, and medical devices. The processing of this more massive data is essential for the acquisition of the knowledge of extraction that further help in reducing the cost while at the same time ensuring a well-informed decision-making process (Viega, 2020). Therefore, there are many data mining applications in healthcare; some of these applications include:
Treatment Effectiveness
Data mining is applied to assess the efficiency of medical treatments. Additionally, it further assists delivery or in the analysis of various actions to identify or prove the action that turns out to be useful (Viega, 2020). This is achieved through the comparison techniques where the particular symptoms are compared. For instance, an appropriate comparison of the causes and symptoms.
Healthcare Management
Moreover, data mining techniques are applied to the development of appropriate applications that are further used to track particular long-lasting and severe health care conditions or diseases. Additionally, the apps may also help the patients who are more likely or have a higher disease risk to interpret their information and ensure coordination or collaboration with the doctor. This helps reduce the rate of hospital admissions. In such a case, data mining is applied to aid in measuring the massive amounts of data as well as statistics that focus on searching the patterns of whatever has to be analyzed(Song, 2018).
Customer Relation management
Customer relationship management focuses on obtaining and comprehending the data as time goes; this further provides detailed and further comprehension of the business organizations and how to manage them effectively. With this method in place, the internal administration of the hospital is adequately developed.
Fraud and Abuse
Data mining help in identifying and detecting various fraud as well as abuse. This then facilitates the identification of the unusual patterns as well as claims by medics or the physicians. Besides this, Data mining further helps to detect inappropriate prescriptions, referrals, or fraudulent insurance as well as improper medical claims.
Medical Device Industry
The medical device industry is quite essential for the healthcare industry. The health care is entitled to deploy a low-cost sensor which will aid in the development of a better healthcare application; particularly it will help in ensuring an adequate, safe and constant supply of the required information to patients while at the same time providing sufficient and proper monitoring of vital signs of the patients (Song, 2018).
Pharmaceutical Industry
Data mining is an advanced technology that has proved to be beneficial to the health care system. For instance, the pharmaceutical industry can maintain track of their industry and, at the same time, produce/create new products and services. Data mining generally assists in understanding the processes such as this and then helps streamline the appropriate outliers in the organization.
Molecular Biology
Data mining is applied to facilitate the comprehension of the Molecular Biology concept. Data mining tools have also been applied to determine the way that Data mining may assist in understanding Biology through the use of various techniques. The ideas incorporate the little interaction with data at the microscopic level (Song, 2018). Therefore, this implies that data mining tools and the techniques might offer a better snap view of remarkable that facilitate the existence of the cellular point.
Analysis and evaluation of the Advantages of Data Mining in Health Care
Data mining has contributed significantly to patient feasibility while also providing a clear view of what is to be expected in the industry. Facilitated making of informed decisions because the medics can access the patient’s information, which means that they can come up with sound decisions.
Data mining facilitates the comprehension of the data and, therefore, can understand how to collect, analyze, and propose the changes that have to be made.
Lastly, Data mining facilitates the comprehension of the intricacies that are typically incorporated and involved in the formulation of the correct information, hence determining the data that ought to be utilized (Wang, Hu, & Zhu, 2008). Hospitals can use data mining to collect data appropriate to the patients.
The table showing the number of discharges between 2001 to 2009
The pie chart showing the discharge of patients from 2001 to 2019
Conclusion
This paper has examined the application of Data mining to solve a business problem. The paper identified healthcare organizations to be among organizations that have business problems that require data mining. In this regard, therefore, the article has explained how data mining is applied in a healthcare organization to solve various issues, the paper further examined some of the advantages of data mining as far as healthcare organizations are concerned. The paper concludes by acknowledging that data mining is essential in the healthcare system because it helps in acquiring appropriate information that facilitates well-informed decision-making processes s within the healthcare organization that would help reduce human effort and increase diagnostic accuracy. Moreover, the data mining tools further help in reducing the cost as well as a time constraint. Therefore, exploring data becomes essential with the use of data mining tools.
References
Song, G. (2018). Application of data mining technology in the CRM of the pharmaceutical industry. 2018 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS). doi:10.1109/icitbs.2018.00023
Viega, M. T. (2020). Heart disease prediction system using data mining classification techniques: Naïve Bayes, KNN, and decision tree. International Journal of Advanced Trends in Computer Science and Engineering, 9(3), 3028-3035. doi:10.30534/ijatcase/2020/82932020
Wang, J., Hu, X., & Zhu, D. (2008). Applications of data mining in the healthcare industry. Encyclopedia of Healthcare Information Systems, 68-73. doi:10.4018/978-1-59904-889-5.ch010