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Summary: Data Mining In The Healthcare Industry

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The healthcare industry is gradually adopting the use of advanced analytics these days. Most healthcare practitioners and other stakeholders agree that the use of advanced analytics gives the necessary information for healthcare facilities to make improvements in their operations (Dinov, 2016). For instance, the use of advanced analytics can help a small-time clinic or hospital improve their quality of services and reduce expenditures. Such improvements help a healthcare facility carry out value-based programs. Ideally, advanced analytics in the medical field helps analyze healthcare data, make discoveries in different areas, reveal the best solutions for problems and assess the effectiveness s of processes that have already been implemented.
Data mining in the healthcare industry is usually the initial step of coming up with predictive analytics, which is a process called data discovery. In many ways, the practice of data mining is similar to predictive analytics since the two concepts use a mathematical approach to break down and analyze data. Crockett and Eliason (2016) explain that data mining in healthcare helps know all useful …show more content…

The method entails the extraction of significant data and information from existing medical achieves, to help understand various patterns of events. According to Wang, Kung and Byrd (2016), predictive analytics in the medical field help doctors and nurses understand how things have been happening and give a predictive future of possible outcomes. Even so, predictive analytics does not provide the exact happening of the future in medicine. An example of the use of predictive analytics is a situation where data helps prevent unnecessary visits to the emergency room. A woman with mental health issues will not have to visit different local emergency rooms and waste money just because there are no shared medical

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ISM 511 ENTERPRISE ARCHITECTURE INSTRUCTOR: DR.KEITH MORNEAU BY: ANIL PATTEMSETTY Introduction: Analytics is the organized use of data and related business understanding through applied analytical regulations. These analytics are used for taking decision in planning, management, measurement and learning (The value of analytics in healthcare, page 2). Healthcare analytics consisting of techniques, tools that help healthcare organization to check their performance and guide decision making. Healthcare Analytical Adoption Model: Healthcare organization (HCO) are mainly effective at descriptive analytics which involves collecting data from their organization and converting

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Intermountain Healthcare has encircled analytics to improve operations in order to achieve better health care outcomes and make a big difference in patients’ lives. though it is cumbersome challenging for the physicans and nurses but still they took it as challange in order to navigate it! though the use of computer programs are used in order to analyse the patient and examines the data which requires protocols for treatment.but later HELP was the first EHR system in united states which came into existence advantages : saving million bucks in procuring and also in its supply chain!

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Health informatics describes the use and sharing of information within the healthcare industry with contributions from computer science, mathematics, and psychology. It deals with the resources, devices, and methods required for optimizing the acquisition, storage, retrieval, and use of information in health and biomedicine. Health informatics tools include not only computers but also clinical guidelines, formal medical terminologies, and information and communication systems. Medical informatics, nursing informatics, public health informatics, pharmacy informatics, and translational bioinformatics are sub disciplines that inform health informatics from different disciplinary perspectives. The processes and people of concern or study are the main variables.

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The search for meaningful patterns in data has always been at the forefront for curiosity. From scientists examining the infinite blackness of space to find a set of laws in an otherwise chaotic universe, to children exploring their small ecosystem in search of reasons as to why Mother always responds positively to laughter, analytics is deeply ingrained in all of society. This phenomenon of humanity's recent obsession of metrics have now invaded the work environment. For companies that have accepted this recent shift, every action that an organization does is calculated and methodical.

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The US spends 17% of DGP on healthcare or about $3.6 trillion a year (Ozcan, 2022). Emergency medicine spending has taken up 5% of these costs while increasing 51% over the past two years (Ozcan, 2022). Many of these costs are due to the fact patients cannot access primary care or they are unsure of whether to wait out their symtpoms or visit urgent care. Since the healthcare industry is notoriously inefficient, the Big Tech companies have seen this as an opportunity to use technological innovations to advance the field of medicine. However, entering this field has proved to be far more difficult than expected for certain companies due to the necessity of protecting data privacy, despite the opportunity presented by the notoriously inefficient

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Healthcare is undergoing major changes that are making health organizations to face an unavoidable question. How to increase the performance? How to lower the costs? Analytics showed them a solution. By using analytics, health care groups can evaluate the effectiveness and efficiency of a company and find opportunities, which would not have been possible if not for analytics.

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Dr. Song, Clinical Decision Support has been defined as a “process for enhancing health-related decisions and actions with pertinent, organized clinical knowledge and patient information to improve healthcare, as well as, healthcare delivery (Campbell & CPHIMS, 2013). Clinical Decision-supporting tools are utilized to manage and support patient care. Healthcare information systems and information-retrieval systems are tools that manage information. There are various programs that provide custom tailored assessments or advice based on sets of patient specific data (Musen, Middleton, & Greenes, 2014, p. 701). Decision tools may follow simple logics (such as algorithms), may be based on decision theory, cost benefit analysis, or may use numerical approaches only as an adjunct to symbolic problem solving (Musen, Middleton, & Greenes, 2014, p. 701).

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HIM professionals have also become involved with managing the use of vocabularies and clinical code sets within their organization. While traditionally this has primarily revolved around code sets for diagnosis and procedure classifications such as ICD and CPT, the evolution of the EHR and the increasing use of other clinical data such as laboratory results and drugs will increase the types of code sets that will need to be managed. Accurate data assignment, capture, analysis, and trending of healthcare operations for decision making has evolved in the HIIM field to facilitate the aggregation of data from multiple sources to enable the capture of data once so it can be repurposed many times. Capturing information that supports a more accurate measurement of healthcare quality, such as determining how adverse events (i.e., injuries caused by medical management, not the underlying disease) actually occur in hospitals is just one

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However, if no appropriate technique is developed to find great potential economic values from big healthcare data, these data might not only become meaningless but also requires a large amount of space to store and manage. Over the past two decades, the miraculous evolution of data mining technique has imposed a major impact on the revolution of human’s lifestyle by predicting behaviors and future trends on everything which can convert stored data into meaningful information. These techniques are well suitable for providing decision support in the healthcare setting. To speed up the diagnosis time and improve the diagnosis accuracy, a new system in healthcare industry should be workable to provide a much cheaper and faster way for diagnosis [1]. Clinical Decision Support System (CDSS), with various data mining techniques being applied to assist physicians in diagnosing patient diseases with similar symptoms, has received a great attention

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PKUPH started investing in BI in 2009 and now have witnessed a few benefits in healthcare management, including the control of resource usage, connection with insurance, departmental performance evaluation, and data analysis. American hospitals are also aware of the potential benefits of BI tools, such as prediction of needs, data analysis on disease treatment, and improvement of response rates [1]. Even though both of the two countries have EHRs, the adoption rate in the U.S. (more than 90%) [2] is much higher than that of in China (50%) [3].

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Implementing such a system in healthcare in general poses a threat because of the constant struggle of cuts from government payors where hospitals are still trying to do more with less. The benefit of having a business intelligence system in healthcare is it would improve patient care, effective utilization of human resources, improve processes, and cost avoidance. Due to little research that proves successful business intelligence systems many healthcare organizations have not implemented any yet. According to Foshay and Kuziemsky (2014), to help with the research gap a case study was performed on a Canadian Health Authority that addressed three questions that are 1)

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As big data things continue to grow in this modern era, today we can learn how to predict or assume anything that will happen in the future with data from the past. This studies known as Predictive Analytics. Predictive analytics combine methods from machine learning, data mining and statistics to find meaning or pattern from a huge volume of data. Tom H Davenport, a senior advisor at Deloitte Analytics has broken down three primer models on doing predictive analytics: the data, statistics, and assumptions.

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Data mining can be viewed as a result of the natural evolution of information technology. The database system industry has witnessed an evolutionary path in the development of the following functionalities

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