Big Data, Cloud Computing, Analytics, and Health Markets-2015 - Literature review Example

number BIG DATA, CLOUD COMPUTING, ANALYTICS AND HEALTH MARKETS Introduction The statistical evidence on the introduction of Information Technology in the 1990s has proved that no major increment was registered in terms of productivity and efficiency in the production process. Reliable evidence has shown that earlier investment in Information Technology was not properly backed by talents, skills as well as adoption of an organizational culture that could allow firms capitalize on the full potential of IT. This paper focuses on the evolution of the use of big data in organizations in their quest for better storage and transmission of resources materials and information. The health care industry is one of the quickly changing industry owing to the ever increasing population that is increasing the demand for better health care services. As a result, companies in the health industry have to consider the introduction of the big data analytics so as to promote quick relay of information as well be in a position to offer better services. According to Minelli, Chambers and Dhiraj (2013, 0112), maximum benefits were only registered among firms that incorporated skills and talents in their application of IT. According to Aluya (2014, 67-71), the reinforcement of organizational changes by firms can only be achieved through the adoption of computers and big data so that an organization can attain some level of success. Organizational change is paramount for success to be realized through investment in big data as this will prevent losses. Essentially, big data as a subject has been paramount over the last few years. Big data itself has contributed to significant organization changes and it has thus been considered as highly crucial even by the World Economic Forum (Galbraith, 2014, 2). Before the introduction of computerized systems and big data, managers used to depend on intuition, their past experiences as well as personal judgment in order to make the necessary decisions. This on most cases led into wrong decisions that affected the firms negatively. Connaughton (2014, 109-129) contends that the introduction of new methods such as prediction markets, big data, A-B testing and analytics has greatly assisted in lowering the cases of personal decisions that were initially made by the managers. The general application of big data with analytics has been attached to great potential in the healthcare care. The pairing of big data with cloud based systems ensures a cost effective means of delivering healthcare cloud services. The Big Data Revolution Big Data refers to data sets that are of great sizes beyond the ability of generally used software tools for incorporating, capturing, curating, merging and processing of data within a tolerable elapsed duration. The big data is composed of movement of dozens of terabytes to many petabytes of data at any given instance. It is a set of techniques that as well as a form of technologies that need new forms of integration so as to expose large and concealed values obtained from large databases that are complex and big in measure. In this regard, firms and organizations have been making use of large databases as well as analytics in which transactions are kept within data warehouses and then analyzed using data-mining algorithms (Galbraith, 2014). Various changes have occurred regarding data, its storage, and analysis. In the recent past, only structured data, mainly, in rows and columns, was stored, but today unstructured data from various sources is stored (Galbraith, 2014). According to Chui (2011, 122-127) big data has been attributed to high volume as well as high variety information assets that need well versed forms of processing so as to facilitate decision making, insight discovery and process optimization. The main aim of big data analytics has been to assist big firms to make more informed decisions in while with their long-term goals and objectives. Catlett (2013, 36) argues that Big data has enabled key players like data scientists and predictive modelers analyze large volumes of transactional data that could otherwise go unnoticed by convectional business intelligence programs. Big data can be associated with semi-structured as well as unstructured data. Some firms also consider structured forms of data as valid components of big data analytics. The use of big data analytics has depicted many advantages to healthcare organizations. The systems has enabled them detect diseases at their earlier stages at a time when they could be treated effectively. Healthcare fraud is detected easily through effective management of specific individual and population health. Zadrozny and Kodali (2013, 39-47) confirms that the use of big data assist in addressing a number of questions that tend to arise within the healthcare industry. Wang (2015, 107) defines cloud computing refers to a form of computing that depends on the sharing of computing resources as opposed to using local servers or the use of personal devices in the handling of various applications. The term cloud is used as s phrase to symbolize internet. So term cloud computing simply means internet-based computing in which a number of services that include servers and applications are delivered to the computers of firms through the use of the internet. This form of computing can be closely associated with the grid computing system in which unused processing cycles of a chain of computers in a network can be harnessed in solving problems that may be too intensive for a single machine. Williamson (2015, 56-64) outlines the many benefits of cloud computing to the organization such as offering of self-service provisioning in which the end users of the systems can spin up computing resources for any form of data work in the organization. It allows companies to scale up their computing needs that tend to increase or to scale down as the demand lowers. It finally allows firms to only pay for what they have used since the computing resources can easily be measured at a granular level. Impact on the Firm / Organization Bresnick (2014, 107-112) asserts that the big data analytics is composed of 6cs system that is made up of connection involving sensor and networks; cloud made up of computing and data on demand; cyber consisting of model and memory; content relating to meaning and correlation; customization that involves personalization and value; and finally, community that consist of sharing and collaboration. Data has to be processed through the use of advanced tools so as to offer useful tips to the management of a given factory as well as gaining the right content. A report released by the Computer Business Review (CBR) on 04th February 2015 states that the NHS has recently increased its demand for a real Big Data policy owing to the large amount of data that it produces. The firm is considering adopting the use of its own personal data that has raised a lot of concern. Experts have argued that the plan by the NHS to combine the records of patients into a national database is likely to infringe on the privacy of the patients. The government had approved the creation of data schemes and databases of anonymized records of patients but this has not been attained due to privacy concerns (Koontz, 2013). In the United States, there has been general acceptance on the use of health data for the treatment and the improvement of the operations of the private healthcare systems. The SAP’S HANA data analytics device has been used a number of times by medical institutes in the field of research. The IBM systems have been used in several occasions in a bid to put up refined analytic information into the hands of the medical practitioners in a bid to improve the operations in the healthcare systems. This will assist in creating the right behavior as well as promoting efficiencies within the health sector. Leitner and Wall (2014, 113-117) contends that analytics is in relation to the discovery as well as communication of meaning patterns of data. This is more applicable in regions with more information. However, analytics generally relies on the simultaneous application of statistics, operations research and computer programming. It seems to favor the process of data visualization aimed at communication of insights. Firms applies the use of analytics to business data in the description, prediction as well as improvement of the performance of their business. Various branches of analytics include predictive analytics, retail analytics, enterprise decision management, web analytics, fraud analytics marketing mix modelling, price and promoting modelling. Davenport, et al. (2014, 089-092) argues that the inefficiencies that arises in the healthcare market that can be minimized by the use of big data include clinical operation inefficiencies. Research and development also presents a number of inefficiencies that are evident in the predictive modelling in which big data assists to lower attrition as well as production of leaner and faster research and development in the drugs and other devices used in the medical practice. Inefficiencies in the public health can be minimized through big data by proper analysis of disease patterns and tracking of disease outbreaks as well as transmission in bid to improve public health surveillance and quick response to emergencies. Big data can be used to turn large amounts of data into actionable information that can be used to identify specific needs, provide services, and predict and prevent crises, especially for the benefit of population. Industry Evolution Xhafa, et al. (2014, 077-112) have established that the average U.S physician will consume lose to 45 minutes each day while interacting with health plans on matters such as payment, obtaining authorizations for procedures, dealing with formalities and payment modes. The physicians must also incorporate a team of coders who are able in a position to translate clinical records into billing forms while at the same time they submit and monitor any form of reimbursements. As a result, this leads to huge amount of money as well as time being spent administration duties. There is need for the adoption of drastic measures aimed at reducing such costs. Suggestions on the use of a single-payer system will be in a position to eliminate some of the administrative expenses and this approach has not been received well by the American firms dealing in healthcare. Zikopoulos (2012, 47-56) argues that one of the most trusted means of administration cost reduction in healthcare sector is standardization. The use of this approach in other sectors such as finance and supply have shown how it can quickly assist in the reduction of such costs. The Fed was able to lower the administrative costs through the standardization of manner of communication between computers located in different banks while Walmart applied standardization rule in the making of its supplies conform with the computer standards. The introduction of the Health Insurance Portability and Accountability Act in 1996 led to the application of mandatory standards for the electronic processing of general administrative transactions. HIPAA reduced the formats used in electronic health care claims from about 400 to one. However, HIPAA still had a number of challenges such as lack of proper details, lack of rapid implementation and the fact that it allowed the payers to request for additional data from those providing them. Other proposals for effective cost reduction include restriction of access of care, reduction of complexities involved in the administrative process and the use of computerized systems in the administration of healthcare matters. In the works of Srinivasa and Bhatnagar (2012, 11-23), Big data in healthcare system refers to the electronic health data applications that are extremely large and complex and are thus difficult to manage using the traditional software or data management tools and methods. The use of big data in the healthcare system is indeed overwhelming simply because of the large volumes of data and their diversity in terms of speed and types. On his part, Duhigg (2009, 56) argues that the health care industry is one of the industries known to be generating large volumes of data due to their record keeping demands and regulatory requirements. The use of big data will assist in supporting the large number of medical and healthcare roles such as disease surveillance, population health management and clinical decision support Hassanien (2015, 112) has offered a broad depiction that the health data volume is predicted to increase rapidly in the coming years and changes in healthcare reimbursement models is likely to be witnessed in the near future. As a result healthcare organizations need to acquire the available tools and techniques in a bid to leverage the use of big data effectively or they may risk losing large amounts of money in terms of revenues and profits. The use of big data will allow the application of the existing analytical techniques to the patient-related health and medical data so as to achieve a deeper understanding of the results that are then used at the point of care. There is increased use of analytics by healthcare organizations so as to unlock as well as apply new insights from the information available to such organizations. According to Eaton, et al (2012, 023-102), the methods can be applied in the clinical as well as operational improvements so as to meet the challenges affecting business operations. Analytics will enable healthcare organizations to incorporate predictive analytics thus enabling them to gain a clear view of the future. This will further promote the creation of personalized healthcare that can allow detection of fraud in the systems and prediction of the behavior of patients. In evidence based medicine, Ohlhorst (2013, 89-93) argues that, big data can be applied in the combination and analysis of a wide range of structured and unstructured data, financial and operational data, clinical data as well as genomic data in order to match treatments with the various outcomes. This further assists in the prediction of patients who are at risk for given diseases as well as those who should be taken to readmission thus offering more efficient care and treatment. In genomic analytics, big data is used to execute gene sequencing in a more efficient and cost effective manner. It further make genomic analysis to be regarded as part of the regular medical care decision process. Big data assists in the pre-adjudication fraud analysis by rapidly analyzing large amounts of claim requests that reduce wastages and cases of abuse. Prajapati (2013, 78) confirms the fact that in device monitoring, big data is used to capture and analyze in real-time applications in large volumes of fast-moving data from the hospitals and the in-home devices. Patient profile analytics is undertaken through segmentation and predictive modeling so as to identify those who are likely to benefit from proactive care or changes in lifestyles. This will allow patients who are at risk of developing specific diseases to benefit from preventive care and other measures. Conclusion The use of big data and analytics will offer great benefit to healthcare service providers and patients who are the greatest consumers of health resources and are at the greatest risk for adverse outcomes. It offers specific individuals with the information they desire so that they can make informed decisions and more effectively manage their own health as well as more easily adopt and track healthier behaviors affecting their personal lives (Davenport, 2014, 012-015). Proper identification of treatments, programs and processes that do not deliver demonstrable benefits or cost too much can be attained through the use of data analytics. This further assist in the reduction of the cases of readmissions by identifying environmental or lifestyle factors that increase risk or trigger adverse events. The applications have greatly assisted in the improvement of various outcomes in the health sector through the examination of vitals obtained from at-home health monitors. The use of big data analytics has a great potential to cause transformation in the manner in which healthcare providers are able to adopt the use of sophisticated technologies so as to gain insight from their clinical as well as other data repositories thus able to make informed decisions. Widespread implementation of big data analytics is likely to be witnessed in the near future. However, other issues such as standards, governance, security and improvement of tools and technologies must be taken into consideration. The above graph indicates medical spending among different countries. It is evident that the use Big Data will greatly assist in the lowering of expenditures in the medical sectors especially in the developed countries that have fully embraced the use of big data analytics. The above graph shows the impact that big data is likely to create in an organization. This affects the five main components of an organization namely people, strategies, structures, processes and rewards. BIBLIOGRAPHYTop of Form ALUYA, JOSEPH, 2014. The Influences of Big Data Analytics Is Big Data a Disruptive Technology? Authorhouse. SRINIVASA, S., & BHATNAGAR, V., 2012. Big data analytics first international conference, BDA 2012, New Delhi, India, December 24-26, 2012: proceedings. Heiderberg, Springer. http://dx.doi.org/10.1007/978-3-642-35542-4. BRESNICK, J., 2014. Healthitanalytics. [Online] http://healthitanalytics.com/2014/10/27/89-of-execs-say-big-data-analytics-iskey-to-market-share/ [Accessed 10 February 2015]. CATLETT, C., 2013. Cloud computing and big data. Amsterdam: IOS Press. CHUI, M., 2011. Inside P&G’s digital revolution. McKinsey Quarterly, November. CONNAUGHTON, M., 2014. Innovation for Strategic Transformation in the Banking Sector. s.l. 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[Accessed 10 February 2015].Bottom of Form HASSANIEN, A. E., 2015. Big data in complex systems: challenges and opportunities. [Online] http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=935775. [Accessed 20 November 2014]. KOONTZ, L. D., 2013. Information privacy in the evolving healthcare environment. Chicago, IL, Healthcare Information and Management Systems Society. LEITNER, S., & WALL, F., 2014. Artificial economics and self-organization: agent-based approaches to economics and social systems. Cham, Springer. MINELLI, M., CHAMBERS, M., & DHIRAJ, A., 2013. Big data, big analytics: emerging business intelligence and analytic trends for todays businesses. [Online] http://site.ebrary.com/id/10643071. [Accessed 10 February 2015]. OHLHORST, F., 2013. Big data analytics: turning big data into big money. Hoboken, N.J., Wiley. PRAJAPATI, V., 2013. 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