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Knowledge Discovery and Data Mining has often been employed to determine the functions of various industries. However, recent times have seen an increase in the use of Knowledge Discovery and Data Mining (KDD) in the healthcare industry. This is a consequence of its successes in the industrial sector. KDD has several advantages which can be used to further healthcare. KDD is an extension of the prevailing need to engage better science in primary care.
Practice-Based Research Networks (PBRNs) make up the scientific tools that can be used to enhance the efficiencies of the efforts extended in health care interventions. Science-based frameworks reinforce accuracy and efficiency in health care interventions. The article Supporting better science in primary care: a description of practice-based research networks (PBRNs) in 2011 refers to the changing dynamics of healthcare intervention processes. The conventional healthcare intervention processes are not as efficient as they are before and therefore the need for the engagement of science-based components (Peterson, Lipman, Lange, Cohen, & Durako, 2012). [“Write my essay for me?” Get help here.]
The article states that PBRNs project an increased research capacity and growth in experience (Peterson, Lipman, Lange, Cohen, & Durako, 2012). PBRNs have a myriad advantage in healthcare practices. Firstly, PBRNs offer increased capacities for the examination of questions that are important to the clinical practices. PBRNs ensure there is a possibility of acquiring a larger and diverse reserve of data, which may be used to influence the choices of the physicians in the administration of the health care interventions. The networks encourage a diverse membership that eases the process of acquisition of data that may be employed to enhance the efficiencies of the engaged interventions. [Need an essay writing service? Find help here.]
Studies also show that PBRNs enhance the dissemination of research results (Peterson, Lipman, Lange, Cohen, & Durako, 2012). Given the large number of members involved in the networks, information is transferred over a shorter distance, and that enhances the speed with which the results are transmitted. The networks further promote the quality of the data that is disseminated to the members. It also ensures that all the aspects of the health care interventions have been included during the transmission of information appertaining to the intervention.
The PBRNs further enhance the investigative initiatives undertaken by the members (Peterson, Lipman, Lange, Cohen, & Durako, 2012). An increased number of individuals engage in the given investigations, and that determines the most appropriate results. Assessment of results in PBRNs enables the identification of components that may have been overlooked initially. It further encourages the engagement of the best metrics which ensure that the investigative processes result in the optimum results. Essentially, PBRNs have the potential to change the entire dynamics of healthcare practices.
Knowledge Discovery and Data Mining (KDD) is the extraction of useful and valid information from a large data reserve. KDD entails large volumes of data and the use of a myriad data resource. KDD employs several databases, statistics, and machine learning. These components can be used to promote the health care informatics industry. KDD can be employed in the various healthcare processes ranging from the decision-making initiatives to the healthcare intervention process.
KDD can be used to promote the health care processes in four different segments. These include clinical medicine, public health, health care text mining, and healthcare policy and planning (Raheemi, 2014). In clinical medicine, KDD can be used to enhance the storage of patient of data. It also enhances the retrieval processes of information appertaining to the subject patient. This, in turn, enhances the efficiency of the intervention processes that are given to the patient. The availability of data on the patient sickness trends eases the decision-making process on patient intervention initiatives. KDD enables hospitals to act as centers of patient information. This eases the process through which information on the patients can be retrieved.
KDD further has databases and accumulates statistics in large volumes of patient information, which can be employed in public health (Raheemi, 2014). Such information can be used to study the trends and patterns of diseases in the society. It further eases the process through which statistics drawn from the large databases can be calculated and determined. Through programming, offered by KDD, public health researchers will be able to determine the most appropriate measurement mechanisms that can be used to boost the accuracy of the calculations on disease patterns. Artificial intelligence in KDD eases the detection of epidemics before they pose a threat to the sustenance of the public.
It is also significant to note that KDD eases the health care policy and planning initiatives (Raheemi, 2014). The KDD framework enables the determination of the best resources and the correct amounts to engage in the mitigation of diseases. With the engagement of artificial intelligence, health care planners will be able to solve the resource allocation problem. Artificial intelligence enables the assessment of those communities that need to be given resources more than others do, and that eases planning. Likewise, the large volume databases enable the healthcare policy makers to determine healthcare trends. This empowers them to make the best decisions that protect the community against diseases.
Likewise, KDD may be used to ease health care text mining (Raheemi, 2014). The large databases can hold information in large volumes, which may be in turn be employed by healthcare researchers to prime their research. Literature information stored in the databases eases the process by which researchers access information. It also mitigates the costs associated with such research. This is because the researcher will no longer be compelled to move from one region to another while seeking to collect information. It is also vital to note that the statistics addressed in KDD enhance the speed with which a researcher can determine their results. Artificial intelligence in KDD enables the researchers to calculate statistics that reinforces the accuracy of the conclusions drawn in the research. [“Write my essay for me?” Get help here.]
Knowledge Discovery and Data Mining (KDD) and practice-based research networks (PBRNs) have the potential to enhance the efficiencies of the healthcare practices. KDD positively influence health care informatics given the large reserve of data and the presence of artificial intelligence which can be used in decision and policy making processes. The PBRNs ease the health care intervention processes by easing information transmission and the investigative initiatives.
Peterson, K. A., Lipman, P. D., Lange, C. J., Cohen, R. A., & Durako, S. (2012). Supporting better science in primary care: a description of practice-based research networks (PBRNs) in 2011. J Am Board Fam Med, 25(5), 565-571.
Raheemi, B. (2014, April 30). Data mining and knowledge discovery in healthcare and medicine. Retrieved October 29, 2016, from IEEE OTTAWA