Data Mining in Health Care Data Mining

Data Mining in Health Care

Data mining has been used both intensively and extensively in many organizations.in the healthcare industry data mining is increasingly becoming popular if not essential. Data mining applications are beneficial to all parties that are involved in the healthcare industry including care providers, HealthCare organizations, patients, insurers and researchers (Kirby, Flick,.&Kerstingt, 2010).

Benefits of using data mining in health care

Care providers can make use of data analysis in identifying effective treatments and the best practices. This can be achieved through making comparison of causes, symptoms and adverse effects. Data mining can also be used in making analysis of the cause of action that will be effective for a specific group of patients. This can also be used in the identification of best clinical practices hence help in the development of guidelines and standards of care. To patients data mining is useful in that they can receive better and more affordable healthcare services. This is particularly the case when healthcare managers use the data mining applications in the identification and tracking of chronic diseases and design appropriate interventions and reduce the number of admissions made in hospitals as well as claims being made. Healthcare organizations use data mining in order to make better decisions when it comes to their patients. For example data mining provides information that guides patient's interactions by determining the preferences of a patient, pattern of usage and the current and future needs of the patients all which aid in the improvement of the satisfaction of patients. There is a lot of financial pressure with healthcare organizations therefore data mining can be of great influence when it comes to revenues, costs and the operating efficiency at the same time maintaining high-quality care. Finally data mining is useful to insurers as it helps to detect fraud and abuse through the establishment of norms and identification of unusual claims patterns (Kusserow, 2010).

Types of data warehousing

There are different types of data warehouse including enterprise data warehouse, operational data store and data mart. Enterprise data warehouse provide a control base for decision support in the entire enterprise. Operational data store is where data is refreshed in real time and used for routine activities. Data mart is a sub-part of data warehouse that is in support of a particular design for specific lines of business. Data warehousing tools are included in a software package that can be divided into different categories which are data extraction, table management, query management and data integrity (Conjecture corporation, 2003).In the resource article the data mining tool that has been used and is being spoken about is machine learning. Machine learning involves the directing of a program to pore through huge databases. The machine learns profiles of patients whose data is in the database .with this program doctors can enter new patients data profiles. Machine learning impacts data mining in that the program allows for storage of a lot of information in the data bases which can be used to make future references for patients and hence help in dealing with some of the chronic diseases.

Data mining through machine learning

Data mining through machine learning can be applied in healthcare and can be quite effective. Machine learning can be used for diagnosis of diseases in future due to the patient's profiles that are stored in the data bases. Once patients profiles are stored this information can be used for studies and symptoms, treatments and interventions got from this profiles. Therefore data mining can be used through machine learning as the information in these data bass can be used in future as they are stored safely and only retrieved when there is need. Machine learning can therefore enter the data mining loop in real time constantly comparing each and every new case with other cases of infectious outbreaks and looking out for patterns which might help in prevention measures.

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