Big Data, Cloud Computing, Analytics and Health Market - Essay Example

Big Data, Cloud Computing, Analytics and Health Market Table of Contents Table of Contents Introduction 3 Big Data, Analytics and Decisions 3 Big data 3 Cloud computing 4 Analytics 4 Sources of inefficiencies in the health market 4 Payment systems 4 Communication and coordination costs 5 Role of big data, IT and analytics in helping eliminate the inefficiencies of health care 6 Coordination and communication cost reduction 7 Preventions, early predictions and diagnosis of ailments 7 Speeding up discovery and innovation 7 Automating diagnosis and error reduction 8 Cost reductions 8 Evolution of the health sector in response to the innovative technologies 8 Conclusion 9 Reference List 10 Introduction Health care is an important sector that experiences significant spending in almost all countries of the world. For example, in the US, around $3 trillion are spent yearly on health care. Yet, the sector is plagued with inefficiencies such as, high medical bills, high operational costs of medical service providers and poor quality of service (Dranove, Forman, Goldfarb and Greenstein, 2012; United Health Group, 2009). To tackle these problems, the health care sector has already switched to maintaining electronic databases of patients, service providers and medical companies in order to increase transparency in operations and minimise excessive expenditures (Chen, 2011). However, with developments in data recording and declining prices of IT products (Bresnahan, Brynjolfsson and Hitt, 2002), the health care sector collects huge volumes of data every day, much more than conventional data management and analytical applications can handle (Groves, Kayyali, Knott and Kuiken, 2013). In light of this situation, new technologies such as, big data, cloud computing and advanced analytics, are introduced in this document and their potential to transform the health care sector is discussed in detail. Big Data, Analytics and Decisions Big data The capability to produce and collect information has increased tremendously in recent times, owing mainly to advancements in data recording and storage instruments; and digitisation of information (Manyika, Chui, Brown, Bughin, Dobbs, Roxburgh and Byers, 2011). Even on a personal scale, earlier we used floppy disks that could store up to 1.44 megabytes of information, but today there are portable hard disks available that can store up to 3 terabytes of information, more than 2 million times the capacity of a floppy disk (1 terabyte equals 1048576 megabytes). And yet, we often fall short of space with these hard disks. As per Winston Hide of Harvard School of Public Health, information of scientific importance, collected in the last five years, has superseded the volume of information ever collected before that (Harvard Public Health, 2012). “Big data” refers to a giant chunk of data, which can be collected, shared, stored and analysed. Big data is so vast that it often renders existing storage, retrieval and analysis technologies of management overburdened and ineffective (Harvard Public Health, 2012). Nonetheless, big data is also extremely important as it can be considered a treasure trove of first-hand, unexplored and exhaustive information for stakeholders about their points of interest (Salehnejad, 2013). Cloud computing Cloud computing refers to a form of handy, demand-based access to data through computer equipments, such as, servers, data networks, software and the internet. Cloud computing enables users to store data with third parties and retrieve online. The prime features of cloud computing are anytime access to information, compatibility with multiple modes of access, such as, computers and hand-held devices, easy real-time sharing of data with any party in any corner of the world, incremental storage capacity with respect to requirements and increased transparency through monitoring of data and data users. Service providers may offer users a range of flexible options such as, using third party cloud computing software and hardware support system, personal cloud computing service on third party hardware support system, or dedicated personal cloud computing service and hardware support system. Consequently, cloud computing can offer public, private or mixed networks to the users (Mell and Grance, 2011; Salehnejad, 2013). Analytics Analytics refers to the mechanism of assessing and critically analysing the collected data to formulate solutions for existing issues in the company as well as potential issues that may arise in the future. The characteristics of analytics are that the problems being considered must be clearly defined and scientific, statistical and analytical tools must be applied to translate data into meaningful information. As an outcome of analytics, concrete, workable action plans or ideas must be developed, that are effective in mitigating the problems considered. Aside from non-human tools, analytics also involves extensive input from executives, in terms of complex problem-solving and critical decision-making abilities (Cooper, 2012; Salehnejad, 2013). Sources of inefficiencies in the health market Payment systems Some of the leading inefficiencies in health care stem from payment systems of health care. In the US, the largest medical insurance providers are Medicare and Medicaid, both of which are extended by the government. These two insurance programs reimburse medical service providers (such as, doctors, nurses and surgeons) solely on the basis of the list of services extended to the patients. Although the system sounds logical, it does not take into account several of these services such as, over-expensive drugs and costly surgeries, which may not even be necessary under the treatment and care actually required by the patient (Binder, 2013). Moreover, such a system does not provide incentives to service providers for improving the quality of service extended, or for inexpensive, simple medical treatments that constitute of preventive care (Miller, 2007). Lastly, services extended by inept providers may even lead to treatment failure and cause heightened risks for the patient. In other words, the payment system of US health care does not relate reimbursements to medical service providers in relation to health progress of the patient (Binder, 2013). Another problem under the scope of payment systems is the lack of transparency by medical service providers in billing transactions. More often than not, patients are handed over very high medical bills with little or no unbundling of practitioner fees and other medical charges. This leaves a loophole in billing, that can be used to dupe patients in paying exorbitant money with little justification, often causing insurance providers to severely limit the amount of reimbursement extended (Binder, 2013). Communication and coordination costs Another major source of inefficiency in health care is communication and coordination costs. As per Brian Fung (2012), major causes of unnecessary costs incurred by patients are medical services that are not consistent with the ailments diagnosed, very high service charges of hospitals due to overstaffing and maintenance of overtly elaborate facilities and inept care of patients by service providers. Figure: Major causes of unnecessary costs in health care (Source: Fung, 2012). One of the major factors to contribute to these causes is usage of substandard drugs. These drugs provide high incentives to doctors and practitioners who recommend them, but in reality, are not effective in treating the ailments of patients. Some of these drugs are more expensive than conventional, cost-effective brands. Moreover, many of the drugs are counterfeits, which are not approved by national health boards (World Health Organisation, 2014). Other factors that contribute to the causes stated above include the lack of adequate expertise; sincerity and compassion among service provider personnel such as, nurses and compounders; lack of coordination between doctors and nurses; and deployment of failed or unethical strategies by hospital management and private service providers. Lack of proper communication and coordination between doctors, nurses, management and hospital staff results in high medical bills payable by the patient; inefficient and incomplete health care extended by service providers; and poor service quality. Role of big data, IT and analytics in helping eliminate the inefficiencies of health care Conventionally, the application of big data, IT and analytics in the health care market has so far been limited. Few of the main causes for this setback are attitude of the personnel to stick to conventional business practices and opposition to implementation of newer technologies that reduce or replace human labour. Medical service providers are traditionally used to judge patient problems and also, to decide on the route of medication without any help from external contributors, such as, big data, IT systems, cloud computing and analytical tools. Many service providers also back out as they would require significant initial investments, over and above their present expenditures. Lastly, implementing the said technologies would encompass a massive overhaul of human resources and initiation of new intensive training programs in order to orient business operations in line with these technologies. However, once applied, big data, IT systems, cloud computing and analytical tools together have tremendous potential of transforming the health care market in a number of ways. Some of these ways are discussed briefly in the section below (Groves, Kayyali, Knott and Kuiken, 2013). Coordination and communication cost reduction A major benefit of using big data, IT systems, cloud computing and analytical tools is that it enables access of relevant information from a huge reserve of data in real time, simultaneously with multiple remote users. This means that both the past and present history of a patient can not only be shared between doctors, nurses and hospital administration, but also between different doctors or hospitals, when the patient is transferred from one to another. This facilitates an effective coordination between medical service providers, ensuring continuity in the course of treatment of the patient. It also helps in delivering a uniform quality of service. All these benefits translate to lower coordination and communication costs (Groves, Kayyali, Knott and Kuiken, 2013). Preventions, early predictions and diagnosis of ailments Big data, IT systems, cloud computing and analytical tools facilitate the involvement and collaboration of a number of entities, most importantly, the doctor and the patient. Involvement of the patient, together with an exhaustive database about the patient’s past history and critical analytical tools, enables in identifying key symptoms or predicting latent or potential ailments and medical conditions of the patient, thereby helping in taking preventive measures immediately (Groves, Kayyali, Knott and Kuiken, 2013). Speeding up discovery and innovation Widespread usage of big data, IT systems, cloud computing and analytical tools has spurred a chain of new developments, discoveries and innovations. One of the avenues that are experiencing the most innovation is the interaction between medical service providers and patients. Innovations here encompass interfaces enabling remote interaction between doctors and patients; software systems that help in studying and analysing patient history in more details; and cost management tools for patients to manage their health expenditures (Groves, Kayyali, Knott and Kuiken, 2013). Automating diagnosis and error reduction Since big data, IT systems, cloud computing and analytical tools involve collection of current and previous patient data and comparison of large volumes of data to produce relevant observations using analytical tools, the intervention of humans is largely minimised and most of the task is computerised. This effectively reduces the scope of error in human judgement and also, enables identifying trends and symptoms that can evade the human eye. Furthermore, human error is further minimised due to data sharing between internal and external parties, which are involved in extending medical service to the patients (Groves, Kayyali, Knott and Kuiken, 2013). Cost reductions Perhaps, the biggest benefit of using big data, IT systems, cloud computing and analytical tools is that it helps in reducing costs dramatically by enabling medical service providers to assess the patient’s condition correctly and chart an appropriate course of medication; eliminating the compulsion of all involved entities to be physically present at the same place for an interaction; and replacing permanently stationed staff with a simple IT infrastructural backbone that connects people remotely (Groves, Kayyali, Knott and Kuiken, 2013; Wikler, Basch and Kutler, 2012). Evolution of the health sector in response to the innovative technologies In response to the new and innovative technologies, such as, big data, cloud computing and advanced analytical tools, companies and institutions in the health care sector, such as, Cardinal Health, are already adopting them and gaining tremendous advantages, thereby transforming the health care sector in an unprecedented fashion (Carte, Schwarzkopf, Shaft and Zmud, 2005; Robertson, 2012). For example, the United States Department of Health and Human Services (HHS) has allocated over $500 million for creating an information exchange platform, where registered medical services providers can upload and download information on patients in a standardised format, creating a comprehensive medical history that can be used in the future (Groves, Kayyali, Knott and Kuiken, 2013). In a similar micro-effort, Kaiser Permanente integrated all its locations and has implemented a digital database of all its patients, which can be accessed from all sites run by the company. As a consequence, on-site interaction between doctors and patients dropped by 26%, whereas electronic communication jumped by 8% (Groves, Kayyali, Knott and Kuiken, 2013). Using big data, Sanofi analysed its insulin product against the backdrop of natural insulin to note that its product betters natural insulin by over 15% and also, offsets the requirement of expensive treatments. As a result, Sanofi’s product has been a critical success and is used widely in its home country of Germany (Groves, Kayyali, Knott and Kuiken, 2013). In yet another development, AstraZeneca and HealthCore have tied up to use their big data databases and analyse them to establish affordable and commercially viable medication solutions to both severe and common ailments (Groves, Kayyali, Knott and Kuiken, 2013). The above developments confirm that the health care sector is undergoing a tremendous transformation to emerge as an inclusive, collated environment, where the medical service providers would constantly have knowledge of the past and current medical records of all patients and involve the patients in formulating the most effective treatment procedures (Computing Research Association, 2011). Conclusion The document effectively discusses new technologies such as, big data, cloud computing and advanced analytics and realizes that these technologies together can help analyse very high volumes of complex data and identify meaningful trends, at a minimum operating cost and simple IT infrastructure, even though initial set-up costs can be high. Through critical analysis of the sources of inefficiencies in the health market, the document has, thus, concluded that these new technologies can effectively handle health care problems, such as, inefficient payment systems and high communication and coordination costs. They can contribute greatly towards coordination and communication cost reduction; preventions, early predictions and diagnosis of ailments; speeding up discovery and innovation; automating diagnosis and error reduction; and operational cost reductions. Reference List Binder, L., 2013. The Five Biggest Problems In Health Care Today. [online] Available at: [Accessed 6 February 2014]. Bresnahan, T.F., Brynjolfsson, E. and Hitt, L.M., 2002. INFORMATION TECHNOLOGY, WORKPLACE ORGANIZATION, AND THE DEMAND FOR SKILLED LABOR: FIRM-LEVEL EVIDENCE. The Quarterly Journal of Economics, pp. 339-376. Carte, T.A., Schwarzkopf, A.B., Shaft, T.M. and Zmud, R.W., 2005. Advanced Business Intelligence at Cardinal Health. MIS Quarterly Executive, 4(4), pp 413-424. Chen, H., 2011. Smart Health and Wellbeing. IEEE Computer Society, 26(5), pp. 78-90. Computing Research Association, 2011. SMART HEALTH AND WELLBEING. [pdf] Computing Research Association. Available at: [Accessed 6 February 2014]. Cooper, A., 2012. CETIS Analytics Series Volume 1, No 5: What is Analytics? Definition and Essential Characteristics. [pdf] CETIS. Available at: [Accessed 6 February 2014]. Dranove, D., Forman, C., Goldfarb, A. and Greenstein, S., 2012. The Trillion Dollar Conundrum: Complementarities and Health Information Technology. [pdf] Massachusetts Institute of Technology. Available at: [Accessed 6 February 2014]. Fung, B., 2012. How the U.S. Health-Care System Wastes $750 Billion Annually. [online] Available at: [Accessed 6 February 2014]. Groves, P., Kayyali, B., Knott, D. and Kuiken, S.V., 2013. The ‘big data’ revolution in healthcare. [pdf] McKinsey & Company. Available at: [Accessed 6 February 2014]. Harvard Public Health, 2012. The Promise of Big Data. [online] Available at: [Accessed 6 February 2014]. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. and Byers, A.H., 2011. Big data: The next frontier for innovation, competition, and productivity. [pdf] McKinsey & Company. Available at: [Accessed 6 February 2014]. Mell, P. and Grance, T., 2011. The NIST Definition of Cloud Computing. [pdf] National Institute of Standards and Technology. Available at: [Accessed 6 February 2014]. Miller, H.D., 2007. Creating Payment Systems to Accelerate Value-Driven Health Care: Issues and Options for Policy Reform. [online] Available at: [Accessed 6 February 2014]. Robertson, J., 2012. The Health-Care Industry Turns to Big Data. [online] Available at: [Accessed 6 February 2014]. Salehnejad, R., 2013. Data-Centered Organizations Big Data, Analytics & Decisions. working paper, Manchester Business School. United Health Group, 2009. Health Care Cost Containment. How Technology Can Cut Red Tape and Simplify Health Care Administration. [pdf] United Health Group. Available at: [Accessed 6 February 2014]. Wikler, E., Basch, P. and Kutler, D., 2012. Reducing Health Care Administrative Costs. [pdf] Center for American Progress. Available at: [Accessed 6 February 2014]. World Health Organisation, 2014. Chapter 4 | More health for the money. [pdf] World Health Organisation. Available at: [Accessed 6 February 2014]. Read More