IT Assignment 1: Big Data Technology in Healthcare Analysis

This report provides a comprehensive analysis of big data technology within healthcare organizations. It begins with an overview of the background and the aim of leveraging big data to address challenges in the healthcare industry. The report delves into the meaning of big data, highlighting its key characteristics of volume, variety, and velocity, and explores its potential significance, including improved outcomes, fraud detection, and personalized medicine. It also examines various innovation tools and techniques like JTBD, outcome expectation, and digital prototyping, demonstrating how big data can foster innovation in the healthcare sector. The conclusion emphasizes the transformative potential of big data in healthcare, making this report a valuable resource for understanding the technology's impact and future possibilities. The report also discusses the significance of big data in healthcare, including its impact on early disease detection, population health management, and fraud detection. Furthermore, the report analyzes the role of big data in healthcare innovation, highlighting tools like JTBD, outcome expectation, and digital prototyping.

Running head: IT ASSIGNMENT 1
Big Data Technology in Healthcare Organizations
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Table of Content
Contents......................................................................................................................................................2
Executive summary...................................................................................................................................3
Introduction...............................................................................................................................................4
Background............................................................................................................................................4
Aim.........................................................................................................................................................4
Scope.......................................................................................................................................................5
Big data and healthcare industry.............................................................................................................5
The meaning of big data........................................................................................................................5
The potential significance of big data...................................................................................................6
Big data and innovation in health care................................................................................................7
Job to be done JTBD.........................................................................................................................8
Outcome expectation.........................................................................................................................8
Filter idea...........................................................................................................................................8
The ideal final result..........................................................................................................................9
Random stimulus...............................................................................................................................9
Digital prototyping............................................................................................................................9
Inno360...............................................................................................................................................9
Viima..................................................................................................................................................9
Conclusion................................................................................................................................................10
Recommendation.....................................................................................................................................10

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Executive summary
The impacts of big data is felt in many fields and the technology provide solutions to
numerous organizations worldwide. The new types of data acquired in healthcare sector should
be a wakeup call that a more advanced data management technique is required. Additionally,
technological innovations has enabled a large scale as well as a more diverse data collection and
data management strategies. The health data originate from a diverse sources some of which
include data from medication performance, records of patients, electronic healthcare records and
treatment outcomes to mention a few. Big data technology has a potential of creating a profound
value in the industry including improving outcomes as it lowers the values. It also enhances
flexibility as well as effectiveness in healthcare services.
Big data technology has been found to be having many values in the industry. Some of
these include managing a significant population, identifying a disease at an early stage when
handling it is still easy allow for prediction of outcomes and enable spotting frauds in a more
efficient manner for appropriate actions. Overall, the analysis concluded that big data can
significantly transform healthcare industry; its potentials are great.

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Introduction
Background
The big data technology has resulted significant ramifications in many industries and has
proven to be a novel solution to a substantial problems in the current world. It is arguable that
healthcare industry is the sector where the transformative impacts of the technology has been felt
most. The industry has been in need for transformation for a long time (Raghupathi W. &
Raghupathi V. 2014). And the big data technology has availed a novel opportunity for
transformation in the industry. The industry has been always facing demanding consumers.
Customers need better services and engagement as well as effective care solutions. Additionally,
the healthcare industry has long been facing compliance and legislative pressures in managing
their resources along with improvement of their care delivery at a reduced cost to mention a few
(Srinivasan & Arunasalam, 2013). There have also been problems in improvement of the access
to healthcare services as well as risk management strategies in the healthcare industry.
Aim
Massive healthcare data are generated on a daily basis from various sources, some of
which include laboratories, prescriptions, and CRM systems among other numerous sources.
Some of these data include electronic health records HER data, treatment outcomes, data from
sensors and wearable, data for medicine performance proteomic and genomic data, data for
medicine performance, individual preference data as well as behaviors on health seeking and
many others. In this rationale, this document aim to examine the relevance of big data
technology in the healthcare industries for handling the aforementioned problems.

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Scope
The scope of this article is limited to healthcare industry. The technology in healthcare
industries is essential and it is one of the integral part of data processing (Groves, Kayyali, Knott
& Kuiken, 2016). The issue on big data management is more acute in the healthcare industry
because failures and mistakes may even result in loss of life. Thus the innovation of this
technology is a novel value within the industry.
Healthcare industry and big data
The meaning of big data
The big data technology has no definition that is widely accepted, however, there are
three main characteristics of big data that are generally accepted including variety, volume and
velocity (Zhang, Qiu, Tsai, Hassan & Alamri, 2015). Variety is the first key feature of the big
data. Data from healthcare industries come in various forms today, some of which were
mentioned earlier in this document. However, a lot of these information are not utilized in the
healthcare organizations to bring about change in healthcare services and improve customer
experience. The big data technology promise efficiency in relating and analyzing row data for
answering operational questions in the industry.
The second feature of big data is volume. A huge amount data lags the efficiency of
traditional systems of data storage, data management as well as data retrieval. Big data needs
data storage and management solutions that are expandable and flexible (Sun & Reddy, 2013).
The third key feature include velocity. The traditional data management solutions and
infrastructure are not capable of handling a huge amount of data that is differently formatted and
constantly refreshed in real time. With big data technology, it is possible to store and manage
data in a more quick and flexible manner.

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All the more indistinguishably organized information are accessible for examinations
than at any other time. For instance, Medicare claims data accessible to specialists and what the
Affordable Care Act alludes to as "qualified elements" for examination from the Centers
Medicare and Medicaid Services. Examining these data with cases information from private back
up plans can offer critical advantages in the medical industry (Viceconti, Hunter & Hose, 2015).
Be that as it may, these efforts should not be characterized as utilizing the potential of big data.
This is in the sense that is only when the medical data are combined with other data which are
differently formatted and rapidly analyzed that the aforementioned key characteristics of big data
are achieved and the potential of big data is leveraged.
The potential significance of big data
Health care organizations can access an opportunity to realize multitudinous benefits by
combining and effective utilization of big data. Some of the significant benefits include
identifying health problems at an early stage when they can be effectively handled, management
of specific population or individual health as well as spotting frauds in healthcare in a more
effective and efficient manner (Hansen, Miron-Shatz, Lau & Paton, 2014).
Additionally, big data analytics enable health professionals to address numerous
questions regarding medical care. Big data innovation can also allow for prediction of some
developments and outcomes based on the use of large amount of the historical data for example
patients who are likely not to benefit from certain medical treatments, the length of stay for
certain patients, the patients who are at risk for a given medical complication and hospital
acquired illness among others.
According to Zaslavsky, Perera & Georgakopoulos (2013), big data have a potential of
creating 300 billion dollars annual value in the health care industry. The authors add that the

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largest portion, approximately three quarter would be generated through lowering medical
expenses. Big data technology has shown its clinical and economic value in various instances. In
the first occasion, the delivery of personal medications has been shown for patients suffering
from cancer among other diseases.
Secondly, the use of systems that support decision making has been improved through
automated analysis of x-rays, magnetic resonance imaging and computer tomography scan as
well as mining medical literature for tailoring medications to a patient’s risk profile. In the third
occasion, the reliance on data generated by patients has been shown using mobile devices for
tailoring medications and diagnostic along with educative messages for supporting patients’
behavior (Costa, 2014; Manogaran et al. 2017). As an example, many healthcare organizations
have started certain mobile healthcare initiatives targeting specific providers and patients by use
of a rapid collection and analysis of the data generated by patients.
The fourth occasion where the big data technology has demonstrated its significant in the
healthcare industry is in relation to big data based population health analysis. The big data based
population health analysis has demonstrated patterns that might have been missed on the off
chance that the smaller consignments of a uniformly formatted data would be analyzed instead.
Finally, the big data technology has generated more than 4 billion dollars by replacing the
traditional ways of fraud detection (Roski, Bo-Linn & Andrews, 2014; Belle et al. 2015).
Big data and innovation in health care
Innovation landscape have become open and is getting collaborative with new stakeholders
in the industry especially in sectors that are enabled by internet technology as well as advances in
data science enable collection and analysis of data. In the ever changing landscape, it is required
that researchers, healthcare professionals, policymakers and payers, patients, regulators as well

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as public consider a richer and a large amount of evidence for making decisions within the
sustainable and supportive health data ecosystem (Archenaa & Anita, 2015). The following
innovation tools and techniques suggest the relevance of big data technology in the healthcare
industry.
Job to be done JTBD
The job to be done JTBD technique has a lot to do with finding customers’ true needs.
According to Kaivo-oja (2011), customers prefer hiring solutions for completing their job to be
done instead of buying products or services. This technique can move innovation further than
just improving the current solution. With big data, health care organizations can gain new
insights as well as ideas on the innovative ways of improving the needs of patients.
Outcome expectation
This technique expound that customers are given what they want. They are construed as the
expectations that entail getting a given job done (Yuan & Woodman, 2010). A job may have
several outcomes encompassing the desired as well as the undesired outcomes. However, it has
been proven that when customers are given more than what they expect and less of what they do
not expect, the solutions become of high value thus creating customer satisfaction. This use of
big data in health organizations enable outcome expectations; this can be achieved through
analysis of the massive previous data.
Filter idea
This tool involve selecting the very best ideas from a bunch of ideas. The tool was designed
to provide lots of ideas in a systematic manner. It is essential to select the best idea from the
generated ideas. As such, filtering become of great importance. Coupled with big data
technology, this strategy can be of great importance in the healthcare industry.

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The ideal final result
This strategy embrace starting from the end technique. The words alone perfectly define it;
including provision of a perfect solution. As cited in Gadd (2011), the ideal final result as
derived from the theory of inventive problem solving TRIZ refers to the starting point from
which one can work backwards. As such, starting from the consumer needs, healthcare
organizations are likely to improve.
Random stimulus
This tool involve using unrelated signals to spawn new ideas. The tool helps in moving
people from their current zones. This tool provide a great way of sparking ideas thus innovation.
Digital prototyping
The digital prototyping tool provides an opportunity for health organizations to explore a
complete outcome. This technique also provide the product management team with a way of
assessing the brands operation to assess whether it will fail or not.
Inno360
This is a software for innovation management. The application specializes on creating
disruptive innovation through the search of the innovation in websites. Combined with big data
analytics, this application can help in finding bold ideas. It is also an intuitive and visually
engaging platform.
Viima
This is another innovation management application that can be used by healthcare
innovations. The application is easy to use and has an appearance that helps in keeping its users

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motivated. Utilizing the applications together with the big data can lead to a positive contribution
in the healthcare industry.
Conclusion
In summary, this document has presented an analysis on the value of big data in health
care organizations. From this analysis, it can be concluded that big data technology has provided
the solution to issues that have been long experienced in the industry including high demands
from customers, cost as well as access of healthcare services among other issues as mentioned in
the article. The big data technology can transform how healthcare organizations use sophisticated
technologies in administering medications to patients. As such, it is essential that healthcare
organization take a step towards deployment of the big data technology solutions.
Recommendation
It is recommended that healthcare organizations, when implementing the technology,
defining customers who will access the data as well as the analysis that is provided by the big
data, solutions that will result in big data for analysis, the value that the corporate wish to
generate from the big data such as the improved quality of services, improved access to services
and cost among others. This will enable the organization to move towards big data technology
for solution to the problems.

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References
Archenaa, J., & Anita, E. M. (2015). A survey of big data analytics in healthcare and
government. Procedia Computer Science, 50, 408-413.
Belle, A., Thiagarajan, R., Soroushmehr, S. M., Navidi, F., Beard, D. A., & Najarian, K. (2015).
Big data analytics in healthcare. BioMed research international, 2015.
Costa, F. F. (2014). Big data in biomedicine. Drug discovery today, 19(4), 433-440.
Gadd, K. (2011). TRIZ for engineers: enabling inventive problem solving. John wiley & sons.
Groves, P., Kayyali, B., Knott, D., & Kuiken, S. V. (2016). The'big data'revolution in healthcare:
Accelerating value and innovation.
Hansen, M. M., Miron-Shatz, T., Lau, A. Y. S., & Paton, C. (2014). Big data in science and
healthcare: a review of recent literature and perspectives. Yearbook of medical
informatics, 23(01), 21-26.
Kaivo-oja, J. (2011). Futures of innovation systems and systemic innovation systems: towards
better innovation quality with new innovation management tools. FFRC eBOOK, 8,
2011.
Manogaran, G., Thota, C., Lopez, D., Vijayakumar, V., Abbas, K. M., & Sundarsekar, R. (2017).
Big data knowledge system in healthcare. In Internet of things and big data technologies
for next generation healthcare (pp. 133-157). Springer, Cham.

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Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and
potential. Health information science and systems, 2(1), 3.
Roski, J., Bo-Linn, G. W., & Andrews, T. A. (2014). Creating value in health care through big
data: opportunities and policy implications. Health affairs, 33(7), 1115-1122.
Srinivasan, U., & Arunasalam, B. (2013). Leveraging big data analytics to reduce healthcare
costs. IT professional, 15(6), 21-28.
Sun, J., & Reddy, C. K. (2013, August). Big data analytics for healthcare. In Proceedings of the
19th ACM SIGKDD international conference on Knowledge discovery and data
mining (pp. 1525-1525). ACM.
Viceconti, M., Hunter, P., & Hose, R. (2015). Big data, big knowledge: big data for personalized
healthcare. IEEE journal of biomedical and health informatics, 19(4), 1209-1215.
Yuan, F., & Woodman, R. W. (2010). Innovative behavior in the workplace: The role of
performance and image outcome expectations. Academy of management journal, 53(2),
323-342.
Zaslavsky, A., Perera, C., & Georgakopoulos, D. (2013). Sensing as a service and big
data. arXiv preprint arXiv:1301.0159.
Zhang, Y., Qiu, M., Tsai, C. W., Hassan, M. M., & Alamri, A. (2015). Health-CPS: Healthcare
cyber-physical system assisted by cloud and big data. IEEE Systems Journal, 11(1), 88-
95.

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