Analysis of Big Data Applications in Healthcare Assignment Report

This assignment delves into the application of big data analytics within the healthcare sector, drawing insights from multiple research articles. The analysis begins by examining the role of physical therapy, especially in-home rehabilitation, and the challenges of monitoring patient compliance and exercise effectiveness using wearable devices. It explores the use of big data to improve healthcare quality, including health recommendation systems, and highlights the importance of data analysis in decision-making processes. The assignment also addresses the challenges and opportunities in using informatics and big data to enhance healthcare outcomes, encompassing artificial intelligence and predictive analytics. It underscores the significance of data diagnosis and the need for improved systems to support decision-making in big data analysis within the healthcare domain. The assignment emphasizes the potential of big data to improve population health and service quality, while also acknowledging the challenges faced by healthcare providers and regulatory agencies.

Running head: BIG DATA ANALYTICS 1
Big Data Analytics
Student’s Name
Institutional Affiliation

BIG DATA ANALYTICS 2
Huang, K. (2015). Exploring in-home monitoring of rehabilitation and creating an authoring tool
for physical therapists (Order No. 10181247). Available from ProQuest Dissertations &
Theses Global. (1841254429). https://proxy.cecybrary.com/login?url=https://search-
proquest-com.proxy.cecybrary.com/docview/1841254429?accountid=144789
Quotes
“Physical therapy is often part of current treatment methods for many neurological and
musculoskeletal problems, including balance disorders, spinal cord injuries, and joint mobility
disorders”.
“Because patients’ exercises are performed at home, PTs cannot supervise patients directly and
do not have observable or quantitative exercise data indicating whether patients are compliant
with the prescribed exercise frequency and whether they are performing the exercises correctly”.
“Wearable devices are portable and self-contained, making them potentially easier to set up and
configure”.
“PTs have limited time for each clinical session, and an increase in time spent deciphering data
mans a decrease in time spent with patients”.
“Pre and post exercise symptom levels are important information to PTs, whether they are
dizziness ratings or pain ratings”.
Paraphrased
Physical therapy is included in the prevailing intervention approaches for various
musculoskeletal and neurological issues and examples of the mentioned issues are joint mobility
disabilities, injuries on the spinal cord, and balance disabilities (Huang, 2015). It is important to
note that most of the workouts are executed at home and this implies that PTs do not have an

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opportunity to monitor the patients thus they lack quantitative and observable information to
show if patients engage in the workouts at prescribed frequencies.
Moreover, it is evident that most wearable gadgets are moveable and also, all-inclusive
thus, the two elements make them easy to install and configure. Such as that, wearable
technologies need little power to track body movements. Specifically, physical therapists do not
have adequate time to attend patients during the clinical sessions because they spend most of the
time distinguishing information that compromises the time needed for each session to solve
problems thus the information provided needs to be easily and quickly understandable (Huang,
2015). For this reason, dizziness levels experienced before and after exercise pose different
problems during the interpretation of results as they need to understand the exact readings.
Screenshot 1

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Sahoo, A. K., Mallik, S., Pradhan, C., Mishra, B. S. P., Barik, R. K., & Das, H. (2019).
Intelligence-Based Health Recommendation System Using Big Data Analytics. In Big Data
Analytics for Intelligent Healthcare Management (pp. 227-246). Academic Press. https://www-
sciencedirect-com.proxy.cecybrary.com/science/article/pii/B978012818146100009X
Quotes
“In today's digital world, healthcare is one of the core areas in the medical domain”.
“A healthcare system is required to analyze a large amount of patient data, which helps to derive
insights and predictions of disease”.
“In this context, health intelligent systems have become indispensable tools in decision-making
processes in the healthcare sector”.
“As people use social networks to learn about their health condition, so the HRS is very
important to derive outcomes such as recommending diagnosis, health insurance, clinical
pathway-based treatment methods, and alternative medicines based on the patient's health
profile”
“In the healthcare sector, big data analytics using a recommendation system has an important
role in terms of decision-making processes regarding the patient's health”.
Paraphrased
Healthcare is categorized as a core area in the contemporary digital world because
healthcare tends to evolve in various ways thus it has significant impacts on the treatment
methods for the patients to improve the quality of care. In most cases, healthcare systems are
actively engaged in the analysis of patients’ information that is used to offer insights and
predictions about an illness (Sahoo et al., 2019). In this case, healthcare systems establish
platforms whereby data about a patient’s lifestyle is tracked to predict the health conditions.

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Healthcare systems are also referred to as intelligent systems that are seen as
indispensable tools that facilitate the appropriate decision-making process in healthcare because
they focus on ensuring that valuable data is available at the right time for analysis (Sahoo et al.,
2019). Therefore, most people use social media for learning thus the HRS is crucial become they
acquire results about health insurance, diagnosis, intervention techniques, and other means of
treatment based on the patients profile. Moreover, big data analytics assists in facilitating
decision-making based on a patient’s health condition.
Screenshot 2
Babar, M. I., Jehanzeb, M., Ghazali, M., Jawawi, D. N., Sher, F., & Ghayyur, S. A. K. (2016,
October). Big Data Survey in Healthcare and a Proposal for Intelligent Data Diagnosis
Framework. In 2016 2nd IEEE International Conference on Computer and
Communications (ICCC) (pp. 7-12). doi: 10.1109/CompComm.2016.7924654.

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https://ieeexplore.ieee.org/document/7924654
Quotes
“Healthcare is one of the core areas in medical domain”.
“In healthcare the data exist in various forms like respiration data, blood pressure readings,
prescriptions and others”.
“The data may help in decision-making for different initiatives in order to provide better
healthcare services”.
“However, in order to make this possible there is a need to diagnose the data in a professional
way”.
“Currently, there is a lack of a system or way which may help in decision-making in big data
analysis in the form of phases”
Paraphrased
Healthcare is classified as a vital are in the medical field. Therefore, data in healthcare is
categorized into readings for hypertension, respiration, prescription data and others.
Additionally, data plays a significant role to enhance decision-making for efficient delivery of
healthcare services (Babar et al., 2016). Therefore, there is need to analyze data professionally to
achieve best results. However, there lack a method that can support decision-making in big data
analysis.

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Screenshot 3
Ahmed Otokiti, (2019) "Using informatics to improve healthcare quality", International
Journal of Health Care Quality Assurance, Vol. 32 Issue: 2, pp.425-
430,https://doi.org/10.1108/IJHCQA-03-2018-0062
“The purpose of this paper is to provide insights into contemporary challenges associated
with applying informatics and big data to healthcare quality improvement”.
“Informatics serve as a bridge between big data and its applications, which include artificial
intelligence, predictive analytics and point-of-care clinical decision making”.

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“Healthcare investment returns, measured by overall population health, healthcare operation
efficiency and quality, are currently considered to be suboptimal”.
“The challenges posed by informatics/big data span a wide spectrum from individual
patients to government/regulatory agencies and healthcare providers”.
“Informatics and big data utilization have the potential to improve population health and
service quality”.
Paraphrased
The main aim of this article is to evaluate the current challenges that result due to the use
of big data and informatics in the healthcare setting. Notably, informatics bridges the gap in big
data and its use such as predictive evaluation, artificial intelligence, and point-of-care in
decision-making (Otokiti, 2019). On the other hand, the return on investments in healthcare is
estimated through quality and operation efficiency and also, the health status of the entire
population. Apparently, the challenges caused by informatics impacts a wide range of individuals
including the government and healthcare professionals. Additionally, informatics also plays a
significant role in improving service quality and population health.

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Screenshot 4