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Data mining as a term can be defined in many different ways, based on the aspect and the environment in the context, where it is used. Data mining is referred to as the critical analysis of large sets of evidence with the intention of discovering the patterns to use them in the prediction and forecast of the probability of future occurrences. The analysis is known to be categorized into three major parts, all of which are different from each other, including descriptive analysis, predictive analysis and prescriptive analysis. This process plays an important role in the healthcare industry, enabling the health systems to use the data obtained in a systematic way and ascertain the potential inefficiencies in the field and also assist in determining the practices that may help to improve the care offered to individuals, while, at the same time, cut the costs involved in the same. Data mining in healthcare, however, is supposed to be guided by rules and regulations in order to enable the proper handling of information, as explained by this paper. 

The expertise in healthcare, together with the technology involved, is supposed to be integrated together, make sense of the system and, finally, standardize the measurements in the system. Individuals in the healthcare management are expected to aggregate financial, clinical, patient satisfaction and other related data in the field into the Enterprise Data Warehouse (Hopper, 2016). This is considered as the most important foundational basis for the system, to ensure the smooth functioning, while, at the same time, the best services to the patients in need. To ensure this is achieved, there is a need for standardization and systemization of the healthcare system (Langkafel, 2015). Any organization in the world can, therefore, improve clinical effectiveness, reduce the waste involved in the work processes and also improve the level of safety offered to the patients by the system.

Data mining in health care applies the use of risk model, as a rule which is based on the physician scoring, severity score and other factors that have an impact on a particular patient in the census. The data concerning patients is ran through regression analysis and assigned a risk-score. The risk score is to be used by the medical healthcare systems to decide the kinds of care-paths that the patients should take after being discharged (Bramer, 2013). This enables the health system to establish and give the best appropriate follow-up care to the patients as per the healthcare policy. Despite the frequent tests of these models and their requirement of committed cross-functional teams, the clients are satisfied with the model and the results of the process.

Another rule in healthcare data mining is the systematic approach to healthcare analytics. The law provides that if the analysts are spending much of their time searching for data in many different places, doing a retype to the data and then creating final reports out of them rather than providing interpretations of data in the ways that give the decision makers detailed information, then the analysts are supposed to change and upgrade the way the system works (Langkafel, 2015). Medical data is critical and should, therefore, be easy to access for the good of the patient and reduce incidences of delay to administrational treatments. The rule also recommends that the data should first be unlocked and put into the healthcare enterprise data warehouse, a system that enables the analysts to eliminate the manual process that is involved in collecting, analyzing and distribution of medical data. This way, the analysts will have a chance to save more time and also devoted their time to discover patterns in the data which can be used in the relevance of the treating process to the patients (Reddy & Aggarwal, 2015).

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The best practice system is also a rule in health care data mining. This involves the standardization of the knowledge by systematically applying the methods based on best practices to deliver healthcare to individuals. Researchers establish different findings every year, involving the best clinical practices, although it takes additional years for them to be incorporated into practice. The rule suggests that having a strong base of knowledge and a good team is the best practice, since it gives organizations the ability to put the latest medical evidence into practice with ease and speed, thereby providing the best service and care to the patients and the public, in general (Hopper, 2016). According to the research by Bramer, the application of the best practices in the medical healthcare, the data mining process becomes simplified and also cost efficient. This is made possible with the help of the best digital patient and data record, including the Electronic Medical Records which are critical and very effective in terms of the patient’s safety and quality service providence (Bramer, 2013). Efficient medical records provide unnecessary orders and diagnostics to the patients, which reduce the expenses both to the patients and the medical facility at the same time.

Another rule that should be commonly applied in the healthcare system is the adoption system. The control in this scheme involves pushing change management through organizational structures that are new. The rule requires introducing and implementing team structures which are aimed at enabling consistency and, at the same time, a wide adoption of the best practice in the healthcare organization (Langkafel, 2015). The rule often applies when the system lacks systematic guidelines for approaches and priorities and helps in classifying groups of analytics capabilities, while, at the same time, providing the sequences that can be used to adopt analytics in the health care providence to the patients. Data mining heavily depends on this rule, as any successful and sustainable analytics strategies require building foundational elements following the existing model (Han & Kamber, 2006).

 
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A systematic approach to the healthcare providence is another basic rule in data mining that is applied by medical experts in the world today to ensure that healthcare to the patients is being perfectly distributed. This rule applies more when a longer time frame than usual is taken during the process of putting the latest medical evidence into use (Hopper, 2016). In the modern world, the period between medical knowledge discovery and the adoption by the vast majority of the public, is measured regarding years. Since the patients’ health and welfare are the main focus in the medical care, it is essential that the time frame is changed to accommodate any relevant changes in the patients’ data and statistics, in order to enable the healthcare providers to have a clear history and records of their patients. The rule gives a basic suggestion that weak content, clinical systems lead to hindrances to the new clinical approaches, as a result of faultiness in clinical data about the patients and any other related information in the field of healthcare.

Conclusion

Data mining provides a clear insight to the healthcare providers, and enables them to administer the right kind of treatment to the patients in the facilities. Also, through data mining, there are possibilities of improving the healthcare provision, since the analysts can predict the future based on the past, thereby getting to know the best practices used in the healthcare provision. If data mining does not apply the use of all the described rules in the essay, there are possibilities that the system will remain stagnated for a long period of time. From the essay, it is, therefore, essential for organizations to implement these rules in order to keep the records standards on the patients updated at any given period. This makes it possible for the organizations to keep up with the required health care standards to the patients. 

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