Running head: BIG DATA ANALYTICS 1 BIG DATA ANALYTICS 8 Big Data Analytics in Healthcare Name of the Student Instructor Institution Course Date The health care system is increasingly adopting the use of electronic health records. This has led to an increase in the quantity of clinical data that is available. As a result, big data has been adopted as a way of analyzing these large quantities of data. The main reason why big data technology has gained popularity is because it can be able to handle large volumes of data compared to the traditional methods(Wang et al., 2018). It also supports all kinds of data including the structured, semi-structured and unstructured. It also provides predictive model design and data mining tools and this makes the decision making process to be better. Big data framework allows for batch processing as well as stream processing of information. Batch processing makes the analysis of data within a specific period of time possible (Wang et al., 2018). On the other hand, stream processing is used for applications which need real-time feedback. Applications of big data analytics in health care leads to an improvement in the patient-based services as well as detection and control of spread of diseases. It also leads to new knowledge and intelligence as a result of the integration and analysis of data with different nature. Therefore, the use of big data analytics in the health sector has increased due to the need for improved medical services, faster analysis of information and accuracy, and cost reduction. The main role of the health care sector is to ensure that the population remains healthy. Therefore, there is need for better service delivery at all times. Big data analytics have enhanced the ability to provide the services to the patients in a number of ways. First of all, it has positively resulted to better image processing (Wang &Hajli, 2017). This has enhanced the processes of diagnosis, therapy assessment and planning. Medical images present the data that is used in all these processes. As such, big data analytics provides for an efficient way of storing the information because it requires large storage capacities in the long run. The demand for accuracy also makes big data analytics an efficient tool to use in the analysis of information related to image processing. Signal processing is another area in medicine that requires the use of big data analytics. This is because it results to production of large volumes of data which require being stored in high speeds from several monitors and different patients(Wang &Hajli, 2017). On the other hand, physiological signals also have a problem because of the spatiotemporal nature. This makes the analysis of such signals to be more meaningful when they are analyzed alongside the situat.

1. Running head: BIG DATA ANALYTICS
1
BIG DATA ANALYTICS
8
Big Data Analytics in Healthcare
Name of the Student
Instructor
Institution
Course
Date
The health care system is increasingly adopting the use of
electronic health records. This has led to an increase in the
quantity of clinical data that is available. As a result, big data
has been adopted as a way of analyzing these large quantities of
data. The main reason why big data technology has gained
popularity is because it can be able to handle large volumes of
data compared to the traditional methods(Wang et al., 2018). It
also supports all kinds of data including the structured, semi-
structured and unstructured. It also provides predictive model
design and data mining tools and this makes the decision
making process to be better. Big data framework allows for
batch processing as well as stream processing of information.
Batch processing makes the analysis of data within a specific
period of time possible (Wang et al., 2018). On the other hand,
stream processing is used for applications which need real-time
feedback. Applications of big data analytics in health care leads

2. to an improvement in the patient-based services as well as
detection and control of spread of diseases. It also leads to new
knowledge and intelligence as a result of the integration and
analysis of data with different nature. Therefore, the use of big
data analytics in the health sector has increased due to the need
for improved medical services, faster analysis of information
and accuracy, and cost reduction.
The main role of the health care sector is to ensure that the
population remains healthy. Therefore, there is need for better
service delivery at all times. Big data analytics have enhanced
the ability to provide the services to the patients in a number of
ways. First of all, it has positively resulted to better image
processing (Wang &Hajli, 2017). This has enhanced the
processes of diagnosis, therapy assessment and planning.
Medical images present the data that is used in all these
processes. As such, big data analytics provides for an efficient
way of storing the information because it requires large storage
capacities in the long run. The demand for accuracy also makes
big data analytics an efficient tool to use in the analysis of
information related to image processing.
Signal processing is another area in medicine that requires
the use of big data analytics. This is because it results to
production of large volumes of data which require being stored
in high speeds from several monitors and different
patients(Wang &Hajli, 2017). On the other hand, physiological
signals also have a problem because of the spatiotemporal
nature. This makes the analysis of such signals to be more
meaningful when they are analyzed alongside the situational
context. The current methods used in the analysis of disparate
and continuous monitoring devices are unreliable(Wang &Hajli,
2017). Therefore, the use of data analytics helps in the
improvement of the alarm systems because they provide true
patient psychological condition from a broader and a
comprehensive point of view.
Big data analytics has also led to faster processing of
information. The use of EHR leads to production of high

3. dimensional data which requires both long time to compute as
well as accuracy. The use of simple tools leads to reduced
accuracy of the overall data (Manogaran et al., 2017).
Therefore, big data analytics allows for effective analysis of the
information including classification of the right information to
patient history as well as ensuring an uncorrupted medical
record and hence resulting to effective treatment.
Available techniques to use in big data analytics such as
filtering and wrapping also make analysis of data easier. The
use of filtering methods leads to limiting of the number of
features that are included in the analysis. Wrapper methods
allow for the selection of the selection of features by evaluating
the metrics such as cross-validation accuracy(Manogaran et al.,
2017). Therefore, big data analytics make data analysis to be
simpler and more accurate. Medication errors are caused by the
human factors such as mixing of patient names. Therefore these
errors may lead to negative impacts on the patients. As a way of
improving on medication accuracy, the health sector uses big
data analytics(Manogaran et al., 2017). This is because it aims
at eliminating the medical mistakes which the employees are not
able to avoid on their own. Big data makes it possible to avoid
giving patients the wrong medication and hence treat them
better.
Cost is one of the major problems experienced in health
care. Costs range from storage as well as information finding.
The cost of human genome sequencing has been reducing
because of the adoption of big data analytics. This is because it
has made it possible for the development of high throughput
sequencing technology (Wang, Kung & Byrd, 2018). Adopting
big data analytics also helps to decrease the hospital costs and
wait times. This is because using the available information; it is
possible to approximate the number of patients who are
expected to visit the hospital at a particular time. It helps
monitor patients at all times and hence avoid hospitalization.
This can be done through the use of sensor devices which are
applied on the patients so that they are monitored concerning

4. their state and be able to help them.
To be able to reduce the costs in health care, it is
important to identify the high cost patients. This can be done
through identifying the case managers to give them effective
care. However, the process of identifying the high-cost patients
is expensive(Wang, Kung & Byrd, 2018). Therefore, it becomes
necessary to identify the issues that affect the process. The first
one is the determination of the approach that is used in
predicting the high-cost patients. The second issue is the
measurement sources which can be adopted and ensure that
there is an improvement on predictions. These are aspects such
as behavioral health and the socio-economic factors. The third
issue is the determination of how the predictions can be made
actionable. Lastly is to account for the cases of outcomes in
predictive models which often come from low-risk groups.
Therefore, all the information that is required as well as the
analysis that needs to be conducted can be most effective using
tools such as those found in big data analytics. As such, health
care requires adapting to the new developments of this
technology to lower the costs.
The cost of healthcare can also be reduced by predicting
the frequency of high cost hospital readmissions. It is necessary
that health care facilities use algorithms that can predict the
likelihood of readmission. However, the values produced by
predictive algorithms tend to be much similar. As such, there
are four areas of the predictive algorithms that may require
differentiators; the patient tailored intervention, patient
monitoring after discharge, precise interventions to the patients,
and monitoring some specific patterns after discharge (Groves
et al., 2016). In this way, it is possible to identify whether the
patients have complications after discharge and hence be able to
reduce the false rate of readmissions.
The cost medical services can also be reduced by estimating the
risks of complications that are experienced when the patient is
presented to the hospital for the first time. This is a useful area
because it leads to proper management of resources in the

5. hospital. The number of patients in a hospital requires that right
information is fed to the system. However, most importantly is
to have a clear calculation of the risks that are probable as well
as the data on the number of patients in and out of the hospital
(Kruse et al., 2016). In most cases, this information is
complicated because it involves data from various sources. As a
result, applying big data analytics can ensure that this
information is well classified to avoid complications with
admissions and management of the hospital activities.
In conclusion, developments and adoption of big data analytics
in the health care sector is due to the need to improve medical
services, faster analysis as well as reducing the cost. Faster
analysis is enabled by the use of tools which support analysis of
different forms of data. It is also because big data analytics
enables data of high capacity to be stored. Accurate information
is obtained from accurate analysis hence reducing medical
errors. It also helps in providing the right information hence the
management of aspects such as readmissions and costs.
References
Groves, P., Kayyali, B., Knott, D., &Kuiken, S. V. (2016).
The'bigdata'revolution in healthcare: Accelerating value and
innovation.
Kruse, C. S., Goswamy, R., Raval, Y. J., &Marawi, S. (2016).
Challenges and opportunities of big data in health care: a
systematic review. JMIR medical informatics, 4(4), e38.
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.
Wang, Y., &Hajli, N. (2017).Exploring the path to big data
analytics success in healthcare. Journal of Business
Research, 70, 287-299.
Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics:
Understanding its capabilities and potential benefits for
healthcare organizations. Technological Forecasting and Social

6. Change, 126, 3-13.
Wang, Y., Kung, L., Wang, W. Y. C., &Cegielski, C. G. (2018).
An integrated big data analytics-enabled transformation model:
Application to health care. Information & Management, 55(1),
64-79.