Introduction: Understanding Big Data
Discuss About The Medical Science And Healthcare Management.
Without a doubt, in the past years, the majority of business organisations have started heavily relying on data and information not only to survive in this competitive environment but also to compete with other business organisations. In fact, these data and information are believed to be the most critical asset for business organisations for the reason that they make use of these data to drive usage patterns by which they take effective decisions. Big data is a very important development in the realm of information technology. In describing Big Data, it is important to mention that worldwide data storage, management, and transaction rates have increased several times in the last 20 years (Baker, Fletcher, Garvey, & Sweazy, 2015). Big Data is a novel data management technology that is capable of including very large data sets. Big Data can significantly rapidly capture, process, and management enormous amounts of data having varied complexities and purposes. Thus, it has profound effect on scientific research, business intelligence, weather forecasting, etc. Distinguishing characteristics of Big Data are its capabilities of circumnavigating data utilization and limitation issues. It deploys highly cohesive methods for data nomenclature and architecture. It also deploys dedicated and powerful processors just for data storage and retrieval functions. While normal range of handling data sets varies from megabytes to gigabytes, Big Data can handle data sets varying in the range of terabytes and pentabytes. However, functioning of Big Data at such an enormous scale of database management may have some unwanted impact. It often becomes highly difficult to detect privacy violations or flaws in data capturing methods while operating Big Data. Also, because of latest tools and technologies such as the Internet, this world has turned into the information based age (“BID 2017 program schedule,” 2017). There is a massive amount of data available on the Internet. This report presents an analysis of big data. The primary purpose of this research is to give an overview of big data and how the organisations can use it for the betterment of their organisational tasks. This report will start with an introduction of big data. After that, a general discussion will provide big data and its associated aspects. In the start this report discusses the general concepts associated with big data however after that a detailed analysis will be provided on the impact of big data on a specific organisation. In this scenario, this report will present a detail discussion of the effects of big data on the healthcare sector (Big data and big challenges for law and legal information, In Jayasuriya, & Georgetown University, 2015).
Big Data and Its Associated Aspects
Basically, “the term big data is normally used as a marketing concept refers to data sets whose size is further than the potential of normally used enterprise tools to gather, manage and organise, and process within an acceptable elapsed time.”. In fact, the size of these enormous data sets is believed to be a continually growing target. Additionally, the size of big data is presently ranging from a few dozen terabytes to some petabytes of data in a single data set. Given the fact, this era is known as the age of information and communication technology in which everything appears in digital format, and as a result, everything comes under the domain of data. For instance, in the medical sector, an electrocardiogram is now used in a digital form which can be collected and stored as a dataset and information (attained after the processing of these data). In the same way, MRIs, CT scans and a variety of medical images are at the present digital, and these unique digital records and files are being stored and processed in the form of datasets. Hence, thousands and thousands of distinct datasets are adding up to the big data (Ebeling, 2016).
At present, with the big data, the majority of business organisations and retailers make use of data more efficiently to produce planned decisions that commence with the customer and help to develop a more thorough design process. Also, “this analytics-driven design can intensify major touch points all the way through the customer experience at the same time as improving sales beneficially” (Eberhardt, n.d.)
The research has shown that the organisations that use big data for their business can be familiar with their customers and the way they communicate with the company and shop online much better than many of those customers can be familiar with themselves. In fact, these datasets are not only the enormous volumes of data but also they provide the organisations with excellent ways to determine and keep records of their transactions as well as other communications with suppliers, retailers, banks, utilities and service providers. In addition, at present there have emerged a number of algorithms which can be applied on these data sets to determine their customers’ behaviors, shopping patterns, usage of sales coupons and how the business organization performs transactions and certain tasks are recorded and analyzed with the purpose of getting a broad and effective depiction of who your customers are and what products you should take the chance to offer them. In their research article, (Feinleib, 2014) discusses an example in which Portland Oregon Savory Spice Shop owners Jim Brown and Anne have decided to put into practice social media-based marketing and advertising with the intention of getting “the best of big data’s” support and capabilities for launching their new boutique store. In this scenario, by making use of their Facebook ads, they have been capable of routing to catch the attention of those potential customers and groups of purchasers who almost certainly wish to purchase their high-end speciality products. It is an admitted fact that in the past few years the majority of business organisations have started utilising social networking based sites such as Facebook to advertise their products and services for the reason that these social networks provide huge amounts of data (Hurwitz, Nugent, Halper, & Kaufman, 2013). Considering these innovative aspects of social networks, they just had to invest in the ad and then Facebook algorithms and performing analysis by utilising a ton of consumer data available on Facebook to identify those people who most directly match their customer profile. “Those potential customers then get targeted ads and special announcements from the store.” Though big data provides a large number of advantages, and if it is used effectively, then it can bring some opportunities and benefits to organisations. On the other hand, significant data can also be turned into a potential source of annoyance (and even bad) when it encourages unnecessary and unwanted advertising and marketing movements, emails, phone calls; or get the wrong impression about the central theme, causing refutation of credit, erroneous charges, or in severe cases, the harmful certainty of identity theft (In Chan, In Subramanian, In Abdulrahman, M. D.-A, & IGI Global, 2017).
Impact of Big Data on Healthcare Sector
At present, the majority of organisations heavily rely on data to not only run their business tasks but also for the improvement of their organisational performance. Hence, the implementation of big data can be seen in every field and industry. However, the healthcare industry is believed to be the largest that has taken the maximum advantage of this technology (Jack, 2010).
However, there are various problems associated with big data, for instance, there can be some data security and privacy-related issues. Given the fact that big data contain large volumes of raw data and extracting useful information from these mountains of data is a challenging, costly and time-consuming task. Without a doubt, the emergency “big data” analytics have transformed the way that information is gathered, stored and processed (Langkafel, 2016). In fact, a wide variety of techniques are applied to that stored data to change it into business intelligence and make practical use of this data. Without a doubt, this is a fantastic technology however with little or no rules concerning its use. Additionally, companies and users dealing with big data are surrounded by some concerns and issues such as information ethics, data privacy and data ownership; however, up to now, these issues have not been addressed adequately. In this scenario, there is no particular research or study that differentiates between privacy and data analytics. In fact, the emergence of big data analytics has made this line even more blurred. Also, the advancements and developments in the field of big data analytics have raised a wide variety of privacy, security, and ownership concerns and issues, not only for customers, however as well as for company making use of data and analytics to deal with these customers. In spite of all the developments and improvements, data privacy and security strategies are up till now serious concerns. Moreover, any questions related to these subjects have a propensity for obtaining little or even no attention (Liebowitz & LaCugna, 2013).
Undoubtedly, massive amount of data and sophisticated analytical techniques applied to data not only make it simple for organizations to redevelop and update their services for customers, however these practices frequently disclose lots of private information related to the customers, their daily activities, their living styles together with those of their families, relatives and friends. Additionally, at the present, there exist a number of robust algorithms and programming tools that can disclose facts that, otherwise cannot be identified regarding particular person as well as promptly show a relationship among a number of components of the data puzzle and find out from time to time with excellent intelligibility the parameters, such as, record, and even health conditions of a particular person. The extent of this correlation will increase with the amount of data. In this scenario, before implementing prominent data analytics organisations should cautiously think about the possible privacy and security issues that automatically come with big data and analytics (Rajkumar, Srikanth, & Ramasubramanian, 2017). Also, there is no appropriate rule of law which defines specific measures on the use of big data. For instance, how much data will be used and for what purpose it will be used and what are the penalties for the misuse of this data.
Challenges of Big Data in Healthcare Management
Also, the majority of people do not know for what purpose their information is being collected in fact how much information is being gathered. In some cases, they are entirely unaware that stores are recording and keeping track of their purchases in due course). Moreover, the fact goes to the substantial problem to put out of sight its knowledge from its customers. Though this information is being collected for some positive activity but in many cases, this information is hacked and misused by hackers, and the customers bear considerable loss (Saadoun & Human Rights Watch (Organization), 2017).
At present, a large number of business organisations make use of regression models to identify some useful trends in their business data. Without a doubt, regression analysis is a form of business analytics. In this scenario, regression analysis allows the business organisation to use the value of some variable(s) which is known or they can control to forecast the benefit of another variable. Also, it can be acknowledged as a metric for which a firm can optimise (almost certainly obtain as far above the ground as possible). In simple words, a regression analysis method can refer to the equation for a line that goes onto a dispersed area. In this scenario, each value relates to an object of one variable’s response provided the condition of another variable. However, it requires the business management to learn basic concepts of reasonably sophisticated linear algebra especially the partial derivatives (Sathe & Hiwale, 2017). There are many software, applications which provide excellent support for regression analysis. Some of the well-known software applications can be MS Excel or SAS, or R, or a wide variety of other statistical analysis applications and tools. In a business, regression analysis can be acknowledged as the relationship between two variables. For instance, what will be the effect on variable B if the value of variable A changes to some extent? This scenario can be understood with another example, in which a business wants to invest in e-commerce but what will be the return on investment (Simon, 2013). In this example, investment will be variable A and return on investment will be variable B. In another example, a business can use this regression analysis to determine if the some employees are increased then what will be the he effect on business performance (Jeff)
There is a number of considerations that are made in the process of Database management within the usual healthcare field, which encompasses the collection of data from numerous patients and the record keeping process that is crucial as each and every detail counts. The mistake or even slight misinformation could be fatal for patients, and could cost the hospital clients, as well as nurses who might end up losing their jobs. As such, database construction is not only crucial but also one of the greatest skill requiring processes in the field. Informatics and information technology specialists, as well as assistance of nursing technicians are utterly important in this field (Singh, 2017).
The various databases are created for several purposes besides information management. One of the core competences resulting from such a system is marketing. A majority of patients mostly prefers institutions with an accurate and elaborate database. Besides, efficiency is another main advantage derived from the use of these systems. Majority of the databases is made to increase the speed and capability of the organization. Database systems are developed through a number of issues and a number of strategies that have been employed over a long period. These are mostly known as the dimensions of database construction (Wang, Li, & Perrizo, 2015).
Several dimensions are used in the development of database systems for not only nursing but virtually all fields. The core field that will have to be assessed is the data transformation dimension, which is basically the main field in database construction. Database construction involves the development of a field, or a transformational item that will directly and automatically convert raw information or data into information that can be stored. For this part of the system, an already developed system for this purpose, say digital computers, or the usual computer system, and the development of servers to store data will be used. The server will store the information whereas the computers, connected to the servers will be used for as the access interfaces for the users (Wehmeier & Baumann, n.d.).
Besides the transformation of data, developing a dimension or criterion for information storage is vital and crucial, for instance, deciding who can access the information and who can alter the content in the information. The basic overall consideration that will be used in the development of this data management system is the basic and common use of fact tables. According to (Williamson, 2014) basic use of data management tables and dimensional factual information is crucial in the development of database dimension systems. These systems basically involve the entry of information that has been crosschecked by a supervisor for certainty. The use of factual tables is the simplest data entry method used in most database systems, which allows even the least technologically aware employee to use the system after the use of basic training.
The final dimension that will have to be considered in this case is the basic consideration of context. What information will be contained in the information system and what will not? Automatically, the information used will be of a digital and alphanumerical nature. This is judged through the consideration of patient information that has to be put in words and not only digits or amounts the client in question has consumed. Besides patients’ information, employee information, such as their experience and their applicability to the patients’ conditions can be used in assigning different nurses to different patients depending on their familiarity with the conditions in question. According to (Zimmermann-Rittereiser & Schaper, n.d.), general nursing informatics encompasses the inclusion and use of various dimensions from nurses’ information, to patients’ conditions and information, their respective usage of hospital facilities, such as beds, electronics and other similar facilities. In addition to that, practically any nurse can use consideration the software developed for other purposes such as medical records, issuing of medicine, and for considering which of the patients require more and thorough attention.
One of the easiest ways to develop a sound information system is the basic cloud computing processes. One of the greatest advantages attributable to cloud computing is scalability. A hospital can store and manage the quantity of information it needs to store in the cloud without having to purchase additional software and hardware. In the case where a hospital needs to store massive data, cloud computing facilitates this through its unlimited database. The network load dictates the quantity of information that the hospital can hold. This also implies manageable service cost for the organization. With cloud computing, organizations need to pay only for what they use (Simon, 2013).
The fact that cloud computing eliminates the need to purchase new hardware and software frequently also saves on the cost of infrastructure. The old system, whereby organizations need to purchase software and hardware relevant for storing information necessitates frequent renewal of licenses and maintenance. Much time and skill is also used in training new personnel on using these tools. However, cloud computing saves organizations of all this stress, thus facilitating delivery by saving on time and resources. Financial resources which would otherwise be used in maintaining hardware can now be used for other business-critical functions, improving the hospital’s functionality.
Cloud computing frees up the organizations of the stresses associated with data storage; including security issues and hiring of teams to cater for data management. With fewer tasks to handle within the management of an organization, it is possible for the team to cater for tasks critical to the operations of the hospital, with the knowledge that another, larger hospital, such as Google, is accountable for the storage of its information. This also saves organizations of the concerns of network outages. Organizations offering cloud storage services are known to have reliable network systems, which lower the chances of network outages – a factor that has threatened the reliability of data providers in the past (Rajkumar, Srikanth, & Ramasubramanian, 2017).
Through cloud technology, the security of the information stored is guaranteed. This is due to the fact that organizations have the option of encrypting data before storing it in the cloud. To secure information even further, cloud service providers have strict policies to mitigate security risks. Privacy policies are also formulated to the guarantee the safety of information stored by individuals. These factors, coupled by sophisticated authenticated techniques make cloud storage more reliable than traditional storage techniques, which were vulnerable to phishing attacks.
Ease of integration is the other advantage associable with cloud computing. Through this facility, users have assorted access mechanisms; hence, different individuals can access information simultaneously. Cloud computing also offers flexibility in regard to configuration. This is beneficial for organizations with several branches reliant on a central hub, as data can be manipulated appropriately without the need for the persons involved to interact physically. Organizations that require personnel to travel frequently will also benefit these individuals with access to all sorts of data upon demand. The final cloud computing advantage relevant to organizations is disaster recovery. The fact that cloud computing does not rely on hardware implies that in the occasion of disaster, data is not lost (Langkafel, 2016).
Resulting from the controversy in the issue of hacking and personal privacy, various privacy rules and laws have been set, prescribed and analyzed, which dictate the extents, levels and punishments given to people who violate other people’s privacy. Privacy issues and restrictions are meant to cover the information of a variety of people. In Information Security Protocols, these restrictions are mainly addressed to employers, clients and other institutions that prescribe the extent to which these parties can expose information pertaining to the individual to other people, or organizations. For instance, banks have been prohibited from exposing financial information of an individual at whatever costs (Jack, 2010).
Given the fact that healthcare industry has started to shift from a fee-for-service payment system to a value-based payment system, hence in this scenario, big data will undoubtedly play an all the time more critical role in how health care providers treat their patients. It is admitted fact that at present almost all the operations of organisations are based on vast volumes of data so big data or large sets of applicable information can modernise the healthcare industry. Though, big data’s industry dispersion is still much lesser than that of other sectors, for instance, consumer IT. However, a large number of well-known IT providers have started launching big data solutions which are suitable for the healthcare sector. Though, big data are used in all kinds of organisations, but its essential implementations are seen in the healthcare industry (Feinleib, 2014). There is a critical example of practical implementation of big data in this example the largest healthcare provider in Massachusetts “Partners Health Care” made use of its massive volume of health data to implement a pilot program on electronic health record usage in the post-market analysis of drugs. However, while analysing system-wide EHRs, it was found that beginning in 2001 the baseline anticipated rate of heart attack admissions to two hospitals jumped approximately 18 percent and returned to regular price in 2004 matching up with the initiation and termination of the medicine pain-reliever Vioxx. Additionally, Partners Healthcare is currently working to construct this model by making use of the latest developments in health (Baker, Fletcher, Garvey, & Sweazy, 2015).
Also, significant data can support not only health care providers, but it also provides excellent support for other industry stakeholders. One of the most important examples of this scenario is the implementation of an integrated health database by Aetna Healthcare, a national diversified healthcare benefits provider. This database is being used by the healthcare firm to enhance evidence-based medicine practices in the most affordable way. Additionally, Aetna has implemented big data in its newly coordinated CarePass and iTriage application to allow its customers to get access to an extensive collection of health resources on a centralised platform (Ebeling, 2016).
Conclusion
In the past few years, the use of data is growing for carrying out a variety of tasks. Almost all the organisations heavily rely on data they collect through different ways. In this scenario, big data is the enormous volume of data that is received from a variety of source to perform a variety of tasks on it to derive some useful facts. This report has presented a detailed analysis of different aspects associated with big data. This report has mainly focused on big data in the context of the healthcare industry. This report has discussed various advantages of these technologies by supporting them through existing literature. It is clear from the discussion that big data analytics are already being extensively utilised in some industries and particularly in the healthcare industry. The research has shown that their role will further grow in the coming years. However, the implementation of these technologies alone does not ensure the success of a healthcare firm as we have discussed many cases in which IT implementation caused deaths of many people due to errors. So in this scenario, practical knowledge and experience are required to make this implementation a success. Healthcare firms should consult literature and latest emerging trends to get insights of these technologies.
References
Baker, P., Fletcher, L., Garvey, S., & Sweazy, L. (2015). Data divination: Big data strategies.
BID 2017 program schedule. (2017). 2017 International Conference on Big Data, IoT and Data Science (BID). doi:10.1109/bid.2017.8336604
Big data and big challenges for law and legal information, In Jayasuriya, H. K., & Georgetown University. (2015). Big data, big challenges in evidence-based policymaking / H. Kumar Jayasuriya, editor; Kathryn Ritcheske, contributing editor.
Ebeling, M. F. (2016). Healthcare and big data: Digital specters and phantom objects.
Eberhardt, W. (n.d.). 9. Big Data in Healthcare: Fields of Application and Benefits of SAP Technologies. Big Data in Medical Science and Healthcare Management. doi:10.1515/9783110445749-010
Feinleib, D. (2014). Big data bootcamp: What managers need to know to profit from the big data revolution.
Hurwitz, J., Nugent, A., Halper, F., & Kaufman, M. (2013). Big data for dummies. Hoboken, NJ: Wiley.
In Chan, H. K., In Subramanian, N., In Abdulrahman, M. D.-A, & IGI Global. (2017). Supply chain management in the big data era.
Jack, B. K. (2010). Essays on developing country markets in environment and health.
Langkafel, P. (2016). Big data in medical science and healthcare management: Diagnosis, therapy, side effects.
Liebowitz, J., & LaCugna, J. (2013). Big data and business analytics. Boca Raton, FL: CRC Press.
Rajkumar, S., Srikanth, M., & Ramasubramanian, N. (2017). Health monitoring system using Raspberry PI. 2017 International Conference on Big Data, IoT and Data Science (BID). doi:10.1109/bid.2017.8336583
Saadoun, S., & Human Rights Watch (Organization). (2017). “Manna From Heaven”?: How Health and Education Pay the Price for Self-Dealing in Equatorial Guinea.
Sathe, N. P., & Hiwale, A. S. (2017). Fitness mantra: Theme for health analysis in rehabilitation process. 2017 International Conference on Big Data, IoT and Data Science (BID). doi:10.1109/bid.2017.8336585
Simon, P. (2013). Too big to ignore: The business case for big data.
Singh, S. (2017). Optimize cloud computations using edge computing. 2017 International Conference on Big Data, IoT and Data Science (BID). doi:10.1109/bid.2017.8336572
Wang, B., Li, R., & Perrizo, W. (2015). Big data analytics in bioinformatics and healthcare.
Wehmeier, A., & Baumann, T. (n.d.). 10. Big Data – More Risks than Benefits for Healthcare? Big Data in Medical Science and Healthcare Management. doi:10.1515/9783110445381-011
Williamson, J. (2014). Getting a Big Data Job For Dummies. Hoboken: Wiley.
Zimmermann-Rittereiser, M., & Schaper, H. (n.d.). 11. Big Data – An Efficiency Boost in the Healthcare Sector. Big Data in Medical Science and Healthcare Management. doi:10.1515/9783110445381-012
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