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The company is a medical reimbursement company that deals with patients’ personal information from social security, to medical history and banking information. As technology in the healthcare field continues to expand, we have begun to use more big data to store all our patient personal health records and our employees’ personal records. Maintenance and processing of various and high volume data have created the “Big Data” challenge. As Gartner (2015) said: “Big Data is high-volume, -velocity and -variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making” [6]. Under the Health Insurance Portability and Accountable Act (HIPAA) laws, the healthcare industry is privy and held to a very high standard to protect and keep all patients’ personal information private, safe and secured; Digitalization and accumulation large healthcare data provides great potential for healthcare data. The key to the best patient-centric, evidence-base and accountability care is delivered through big data in healthcare (Yang, Li, Wang, Chen, Wu, Wang, Pan and Mulder 2015). There are still many healthcare challenges that need to be addressed to help enhance healthcare services. With cloud computing in healthcare, the company is one step closer to lowering their operational cost and eliminating the use of hardware and software to store data. With all the company’s data stored on networks, servers and applications it makes it easier for the data to be accessible any part of the world. Some of the advantages of cloud computing includes availability, performance and load balancing (Malekabadi, Javan & Akbari 2015). When all of the company’s data is stored on the networks and computed, it is taken by Iron Mountain and stored in a remote location. With the continued growth of innovative technology cloud computing will continue to

1. Aggregate data can be used to detect patterns or similarities from numerous groups of patients which help healthcare providers determine plan of care or prevention of illnesses and better management of disease processes. Aggregate data can help patients and physicians keep track of vital signs and blood sugar on a daily basis so if there is a need for a different dose or type of medication, it is caught and corrected early. Aggregate data can also be used in research as specific information can easily be drawn from a large data bank for analytics and research purposes. Disadvantages of aggregate data include some patients not being able to interpret the information gathered and sometimes platforms for which patients may use to access their information may not be user friendly which in turn limits patient’s participation in their

With the growing ability of organizations to capture and analyze large batches of data, there are ever-increasing possibilities for the development of healthcare studies utilizing Big Data’s promise of advancement in data processing. These tools can benefit the field of healthcare by treating data as an asset to be managed, as well as providing new insights into genomic development of large populations that were previously not fathomable. The health care industry is one sector of the economy where data analysis presents great opportunities for improvement in the quality of services provided, but with these possibilities come great challenges in collecting, utilizing, and education the next generation of data stewards, and the NIH is sure to look to global models for data quality in carrying out their new directive.

1). Decision making by healthcare professionals is based on the assimilation of data, information and knowledge to support patient care. Organizing data, information and knowledge for the processing by computers is accomplished through the use of information technology and information structures (Newbold, 2008). The first level is data which “…are recorded (captured and stored) symbols and signal readings” (Liew, 2007, Definitions). Data is bits of information though to just have data is not meaningful to decision making. The second level is information which is organized, interpreted and communicated data between machines or humans. “Characteristics of quality information are: complete and clear in its descriptions, accurate, measurable, preferably by measurable objective means such as numbers, variable by independent observers, promptly entered, rapidly and easily available when needed, objective, rather than subjective, comprehensive, including all necessary information, appropriate to each user’s needs, clear and unambiguous, reliable, easy and convenient form to interpret, classify, store, retrieve and update” (Theoretical issues, 1998, Concepts). Knowledge is the third level of the model and is the collection of information that is obtained from several sources to produce a concept used to achieve a basis for logical decision-making. The information needs to be

            For many years medical records have been amassed and stored electronically in databases that have grown, have been linked, and have been extended to include different types of medical information from many sources.  The fact that we can now use one database to procure personal information from sources such as hospitals, HMOs, and drug companies has led to countless benefits. Any type of medical information relevant to a given treatment can theoretically become available through a seamlessly accessed electronic network.  If an elderly person arrived in the emergency room with symptoms of a stroke, an emergency room doctor could check the patient's current medications before treating them to prevent harmful drug interactions.  Similarly, an extreme skier who travels the world and has broken his leg in Alaska would have the peace of mind to know that a doctor could find and access critical information such as

1) Big Data in Health Care via EHR: - Big data in healthcare comprises of 4 major components: - a) Volume of Data b) Variety of Data c) Velocity of Data flow and d) Veracity of Data. Big Data represents the tools, processes and procedures

It’s a value care for U.S patient population. Back to the digital health care prediction, Dr. Brown indicated that the vast amount of untapped data could have a great impact on health that exists outside of medical systems. However, there were challenges when it came to collecting healthcare data. First, the unstructured big data which presents in medical literature. How do we know which one to read? Second, a data associated with a single patient in an electronic medical record (EMR). An electronic medical record came with a structured data and an unstructured data. There was a question about the HIPPA regulation, and Dr. Brown assured that IBM adhered to the regulation completely. All these pooled data were placed in the Watson Cloud that aggregated the data together to perform different analytics. It’s all automated system. Then, IBM acquired Truvan that tracked insurance and reimbursement data and enabled to see overall

After I read “The Challenge: Moyen Sante Medical Sente, Chapter 7,” I learned how big data and data warehouses had caused a positive impact on health informatics. These impacts have reduced hospital readmissions, which has been a big setback for hospitals and healthcare organizations. The analytics system has caused health care systems to transform their health care system as they improve their information technology. Moreover, this may help advance the awareness of practitioner’s psychological decisions making especially towards data sources. As Ana and Ima showed a sign of agreement regarding the improve of their MSMC healthcare system in the future, Sarah provided a short video that intended to help answer the questions they were seek, such

The ability of companies, governments and healthcare to harness data is important to find hidden information about how things are working in their organization. These organizations can then decide how thing should work and how things might work better depending on management decisions. Companies and governments use big data to track and analyze processes and customers for profit and efficiency. In healthcare big data is analyzed to understand how effective treatments and facilities are and make necessary changes. The bigger the data set the more necessary it is to use proper analytic techniques, types of analytic techniques are descriptive, predictive and prescriptive.

The Clinical Decision Support Systems have challenges to overcome such as information technology must be design a effective system which notify certain outlier medical information data input. This requires a very through collaboration between the Information Technology Department and the medical facility. Once the specified notifications are designed the medical facility will perform a test run to confirm accuracies and promptness of the required notifications. The next challenge with Clinical Decision

Clinical decision-support systems (CDSS) apply best-known medical knowledge to patient data for the purpose of generating case-specific decision-support advice. CDSS forms the cornerstone of health informatics research and practice. It is an embedded concept in almost all major clinical information systems and plays an instrumental role in helping health care achieve its ultimate goal: providing high quality patient care while, at the same time, assuring patient safety and reducing costs. This computer based systems designed to impact clinician decision making about individual patients at the point in time that these decisions are made. If used properly, CDSS have the potential to change the way medicine has been taught and

Big data is an interesting concept, in which people use data to analyze trends, patterns, and associations and make use of these revelations to predict outcomes. You are using data every day that is being recorded to identify people’s desires and requests, and more specifically your desires and requests. Big data is used in retail, government, healthcare, car companies, and education, basically everywhere. Big data can allow for great advancements and prevention in all aspects of life, more specifically in healthcare. Big data is important to healthcare, because it can allow professionals to identify who has a greater risk of a disease and thus allows early detection and prevention. It allows tracking which medicine is more effective than the other. It allows for healthcare providers to have better records and accuracy in each and every patient. Big data is important to healthcare and here is why.

As we know, for delivering good qualitative service in healthcare industry, data plays an important role. So it’s necessary to understand the fact that the big data must be used in a right way to make health service industries successful. For managing and analysing the big data it’s important to have a good knowledge about the healthcare data complexity, framework, technologies for “big data analytics in healthcare industries”.

Systems for questioning and mining Big Data are essentially not quite the same as conventional factual examination on little specimens. Huge Data is frequently loud, powerful, heterogeneous, between related and deceitful. In any case, even boisterous Big Data could be more significant than little specimens in light of the fact that general measurements acquired from continuous examples and connection investigation normally overwhelm person changes and regularly unveil more dependable concealed examples and information. Mining requires coordinated, cleaned, reliable, and productively available information, revelatory question and mining interfaces, versatile mining calculations, and huge information processing situations. At the same time, information mining itself can likewise be utilized to enhance the quality and reliability of the information, comprehend its semantics, and give canny questioning capacities. The estimation of Big Data examination in medicinal services, to take only one sample application area, must be acknowledged in the event that it can be connected vigorously under these troublesome conditions. On the other side, learning created from information can help in rectifying mistakes and evacuating vagueness. Scaling complex inquiry preparing methods to terabytes while empowering intelligent reaction times is a noteworthy open exploration issue today. An issue with current Big Data examination is the absence of coordination between database frameworks, which

Big data is an extensive collection of structured and unstructured data. It is a modern day technology which is applied to store, manage and analyze data that are not possible to manage, store and analyze by using the commonly used software or tools. Since all of our daily tasks are overtaken by the modern technologies and all the businesses and organizations are using internet system to operate, the production of data has increased significantly in past