Effect Of Data Mining and Knowledge Discovery in Databases In Nursing

Knowledge in expert nursing holds an essential part to both cost effective and high quality patient care in healthcare. According to (Goodwin et al., 2003), nurses experiences two chances to develop essential clinical practice knowledge which include utilizing data mining techniques for developing nursing knowledge and explicating the expert nurses knowledge. As nurses identifies better standardized and structured nursing data, the clinical information systems which gather those data will offer a data mining analysis foundation which contain the ability to develop nursing knowledge, with regard to the association between nursing interventions, data, as well as patient outcomes.

The ability to store and collect data has developed at a drastic rate in all disciplines including healthcare field. The move toward evidence-based practice and research outcomes presents important challenges and opportunities to extract significant information from huge volumes of clinical data to change it into best accessible knowledge to lead nursing practice. Data mining which is a step in databases knowledge discovery process is defined as a technique of unearthing information from huge data sets. Founded on statistical analysis, machine learning, and artificial intelligence technologies, data mining can evaluate huge volumes of data and offer interesting and useful information regarding relationships and patterns that exist in the data which may otherwise be missed. As experts of domain, nurse researchers are in suitable position to utilize this proven technology to change the information which is accessible in current data repositories into understandable and useful knowledge to direct nursing practice and for dynamic interdisciplinary research and collaboration (Berger & Berger, 2004).

Discovery of knowledge in database is the nontrivial mining of implicit, potentially useful and previously unknown information from raw data. Knowledge discovery employs machine learning and data mining methods which have evolved via an interaction in artificial intelligence, statistics, computer science as well as other associated fields. Data mining is an influential technique which can help in developing knowledge directly from data of clinical practice for evidence-based and decision-support nursing practices. Data mining assist in data exploration, reduction and formulation of hypothesis to establish new information and patterns in data which surpass processing limitation of human information processing.

According to Lucero and Bakken (2013), electronic databases assembled by health-care organization will be essential sources of information for assessing the practice-founded interventions effects. The utilization of electronic clinical databases can offer chances to identify new comprehensive evidence from clinical practices and to speed-up generation of knowledge. Discovering effectual patterns of practice via the utilization of electronic databases will offer experiential evidence of what medical interventions constitute high-quality, efficient, safe care for patient on danger for problematic situations such as pressure ulcers.

The discovery of knowledge through organizing framework of informatics for comparative effectiveness research is one of techniques to the informatics and outcome integration paradigms with intention of guiding comparative effectiveness research knowledge discovery. This approach according to Lucero and Bakken (2013) guides in the research development on what is applicable in clinical practices, offer a framework for research outcome and discovery of knowledge which engages researchers and clinicians across multiple disciplines, and support the clinical results research diversity. This framework proposes that knowledge discovering the moderate impacts of practice-based interventions is an iterative and interactive process informed by skillful practice-founded knowledge at all stages.

 

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