Knowledge Discovery and Data Mining

Knowledge Discovery and selective information MiningMelchor AbejonWith the increasing use of electronic wellness records (EHR), entropy collection in many Health Cargon Organizations (HCOs) has improved and accumulated at a remarkable pace. EHRs have enabled HCOs to generate and collect a extensive amount of information and teaching from their daily encounter of patients. And this is when Health Informatics (HI) comes into play- to develop and employ computational theories, tools and proficiencys that sack up assist in extracting useful information and knowledge from these volumes of data, and use this knowledge to uncover useful patterns and to create models that keep enhance decision- make and processes in the healthcare and HI industry. This process is called the Knowledge Discovery and Data Mining (KDDM). The purpose of this paper is to shew an overview on why KDDM is a necessity in the healthcare and HI industry, and also to discuss how the aforementioned technique conti nues to improve the healthcare and HI industry.Benefits of KDDM in the Healthcare IndustryThe abundance of data and information in healthcare had made KDDM a necessity. According to Taranu (2015), with the suppuration rate of data accumulation in HCOs, there is a need for expert analysis of these vast aesculapian data. The ability to use these data in order to extract useful information is a key factor for many health institutions to establish quality healthcare. Listed below are some of the benefits of KDDM in the healthcare industryIncrease in the accuracy of diagnoses. The use of prognostic algorithms can help healthcare providers such as the physicians to make diagnosis more than accurate and decide for the appropriate treatment for their patients.Prediction of patient population risk. KDDM enables the creation of programs and risk models that can be employed to recognize and detect defective patients and chronic diseases. Because of this, healthcare providers are able to d esign the right clinical intervention for their patients.Reduction in the rate of hospital admissions and readmissions. The creation of algorithm and predictive analytics can enable the identification of patients who are at high risk for hospital admissions, thus enabling providers to design more efficient clinical interventions to break up treat their patients.Enhancement of clinical decision support. KDDM enables the comparison of symptoms, causes, treatments and analysis of effective clinical intervention for a group of patients.Prevention of diseases and promotion of general unexclusive health. The application of predictive analytics particularly in genomics can aid physicians to recognize their patients who are highly at risk of certain diseases. Also, through predictive analytics, pharmaceutic companies are able to develop drugs that suit the needs for specific groups of people.Better patient-related decisions and patient satisfaction. The identification of usage patterns, p references and the current and future needs of patients can give information that give assist staff in their interaction with their patients. Patients will also be happy because they will be receiving a treatment that will really work for them.Detection of medical restitution fraud and abuse. The detection of unusual claims patterns can assist insurance companies in the detection of medical insurance fraud and abuse.Benefits of KDDM in the Health Informatics IndustryAccording to Shukla, Patel and Sen (2014), HI can be subdivided into four main subfields which are the (a) clinical care, (b) administration of health services, (c) medical research, and (d) education and training and that each subfield and can be extended and improved with the application of KDDM.Clinical care. One of the applications of HI in healthcare is in the aspect of clinical decision-making through the implementation of the Clinical Decision Support System (CDSS). This computer program is designed to assist he althcare providers in making clinical decisions through (a) information retrieval, (b) alert systems, (c) reminders, (d) suggestion systems, and (e) prediction models. The application of KDDM techniques on the database will render health providers analytical tools and as well as predictive tools that go beyond what is evident from the surface of the data. For example, predictive models can assist physicians to decide whether a certain patient would be treated as inpatient or as an outpatient.Administration of health services. Being an administrator of a healthcare entity can be a very tough occupation as this position requires daily critical decision making. The quality of information that these critical decisions are based on is an essential factor for this job. The KDDM technique can help in the creation of systems that can predict disease outbreaks, and can give representation of the benefits and the costs of the different preventive measures that are effective against a disease outbreak.Medical research. The application of KDDM is very successful in the medical research. Data excavation methods can be applied on the vast medical data to extract useful patterns, predictive scoring systems and cause and effect relationships.Nelson and Staggers (2014, p.56) states KDDM can also be used to patch weaknesses in clinical data that pose a barrier to research.Education and training. E-learning is one of the rapidly growing method of learning in the healthcare and even in the HI industry. The application of KDDM in e-learning can efficiently monitor the progress in the learning process and as well as enhance the learning implement of students, administrators, and educators by recommending different learning methods, resources, and study materials. For educators, it can provide objective feedback about the course and students learning patterns. For administrators, they can learn about the users behavior, so that servers can be optimized and network traffic can be distributed. Through KDDM, the effectiveness of educational programs can be efficiently assessed.ConclusionWith the growing accumulation of data in healthcare, no wonder KDDM will continue to be an indispensable tool than can be utilized to extract knowledge and insightful patterns which are essential in the development of systems and models to improve the safety and quality of healthcare. And as the use of health information systems continue to grow, KDDM will continue to mend the weaknesses and imperfections in clinical data to make these data more usable for the benefit of the healthcare and HI industry.ReferencesCrockett, D., Johnson, R., Eliason, B. (2014). What is data mining in healthcare? Retrieved January 15, 2017, from https//www.healthcatalyst.com/wp-content/uploads/2014/06/What-is-data-mining-in-healthcare.pdfFayyad, U., Shapiro, P., and Smyth, P. (1996). From data mining to knowledge discovering in databases. 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