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Data Analytics Assignment: Tools For Healthcare System


Task: Write a data analytics assignment exploring the tools and techniques utilized for the improvement of data analytics in healthcare system.


The aim of the report on data analytics assignment is to highlight the data analytics in the healthcare industry, with the help of certain tools and techniques that have evolved over the time in the improvement of the data analytics in healthcare. The complexity of the data of healthcare is on a rise due to the late availability of essential data. There are various rare diseases that have been brought into notice with the technical advancement in mobile applications and technologies. The productivity and efficiency of the nurses and doctors are needed to be improved. This leads to the emergence of the technologies and tools of data analytics in the healthcare system. These techniques can lead to the advancement of the healthcare industries and provide solutions to the challenges that are faced by the usage of those techniques. There are techniques which are never enough in managing this issue. However, with the help of the report on data analytics assignment, all the matters are taken care of. The merits, demerits of the traditional techniques are critically discussed.

Review of select literature
Information technology and Information System in Healthcare industry
The healthcare industry is taken as the most essential industries in the field of Information technology. It is taken as the practice that benefits the performance of the healthcare through the usage of information and data in an efficient way in the healthcare sector. Therefore, by the experts it was said in this data analytics assignment that for a proper understanding of the relationship between healthcare and Information technology, the usage of technologies in the healthcare sectors is needed to understood (Mehta & Pandit, 2018).

During the mid-'80s, a big transformation was brought about in the healthcare industry by the Information technology, when the usage of microcomputers took place. They had a small shape at that time. However, at that time they were very powerful and fast. This point mentioned in this section of data analytics assignment facilitated the development of the clinical applications to allow different settings for medical care. The emergence of the challenges merged with the integration of data by the professionals (Bala & Venkatesh 2017). However, it was granted by the surveys, that the improvement of the healthcare sector has been majorly due to the advent of Information Technology. Some constraints associated with the usage of information technology were highlighted by them. Hence, it was found out that the implementation of Information technology comprises of retention and long-term training of the professionals that are skilled (Manogaran, Lopez, Thota et al., 2017)

Data Mining and healthcare analytics
The concept of data mining defined herein data analytics assignment refers to the process by which the gathering, analysis, and storage of data takes place, for the purpose of production of high quality and useful knowledge and information. It also comprises of gathering, filtering and preparing data to be put into use, and processing of the data in order to provide support to data analytics and the modeling that is predicted (Mehta, Pandit & Kulkarni, 2020).

Data analytics and the Healthcare sectors
Storage of data and Management
Once the data is collected, the most important thing is to be taken into consideration is dealing with the management of data and knowing where the storage of data will take place. The traditional methods that are used for the storage and retrieval of data do not put much into the sufficiency nowadays. The classification and transformation of data takes place even before making the information ready to function and use (Tresp et al., 2016).

Role of patients in Analytics
In this section of data analytics assignment, the importance of individuals in the improvement of the analytics of healthcare, by way of analysing the knowing the personal and small data and educating themselves for the collaboration with the data analytics of healthcare in achieving the highest level of accuracy and efficiency has been discussed. A term, 'Citizen Science' has been identified. Here, the individuals who are educated and the non-professionals are considered skilled to the extent to support and conduct the system of healthcare analytics (Alani et al., 2018).

Decision Support System (DSS) and Predictions of Healthcare
Prediction of healthcare is considered a very essential method that focuses on the reduction of future costs. The predictive techniques take the medical history of the patient and make the evaluations of all the risks that are associated with it and predict a treatment that the patient can have in the future (Kankanhalli et al.,2016).

Research Methodology
The main aim of the study developed within this data analytics assignment is to review several papers that help in the encouragement of the various professional bodies like medical staffs, doctors, patient and other staffs attached to the chain of healthcare in the adoption and utilization of the technologies for providing assistance to the healthcare analytics and improvement of the process of decision making in the daily scenario (Bala & Venkatesh 2017). The methodology of research on data analytics assignment follows three steps, which include hunting of the primary studies and the ones which are related to it, relevant evaluation, and appraisal of the data and the final one is the extraction of data. hunting for the primary resources for finding the articles was needed for specifying the key terms. The relevant papers were studies by way of a proper survey, to get hold of adequate information about the data analytics in the health care sector. After the determination of the key terms within this data analytics assignment, the relevant papers were filtered to ensure the relevance of the data that is been extracted (Wang, Kung & Byrd 2018). The articles and papers that have been eliminated had criteria of exclusion in accordance with many factors, which are as follows:

  • Not focusing on the utilization of the technology for the improvement of healthcare analytics
  • Not providing methods of application and evidences of experiments.
  • Publishing in languages other than English
  • The papers being old and irrelevant, the application of which today’s world is not possible.
  • Non-availability of the articles.

The extraction of data came after the specification and identification of various related papers. These papers utilized to develop this data analytics assignment have guided us to get access to various tools that are used by the professionals, making a thorough investigation of the usefulness of the tools in today's world, the solutions that are derived after the usage of such tools, the role played by the patients in providing assistance data analytics of healthcare, and lastly improvement of the decision making process of the medical care (Al Mayahi, Al-Badi & Tarhini, 2018).

Healthcare Data Analytics tools and platforms
The review of literature done in the data analytics assignment has come forward with various techniques and tools that would lead to the improvement of the data analytics in the field of healthcare. They support the predictive, prescriptive and descriptive data analytics in healthcare. The tools are mentioned in the next section of data analytics assignment:

  • Advanced-Data Visualization (ADV): ADV, makes a difference from various other standard line charts and Bar Charts, as it has the capacity to sell its visualisation for a million for points of data. It also has the capacity to handle various types of data. The usage of ADV is quite easy and the analysts also get the support in the exploration of data on a wider basis. With the usage of ADV, the quality-related problems can be reduced, which have the possibility to occur while retrieval of medical data for further analysis (Bala & Venkatesh 2017).
  • Presto: it is an SQL query engine distributed in nature. It is used for the purpose of analysis of huge data that is collected every single day. There is no substitute for the sectors of Healthcare to get hold of a product that can be used for handling large data coming into the system (Hiller 2016).
  • Hive: The tool described in the data analytics assignment is considered very essential programs that is developed for the purpose of handling huge data. However, it does not have the capacity to process and analyse the data with the speed with which Presto does (Sahu et al., 2020).
  • Vertica: This program has quite similarity with Presto. This program is less expensive in comparison to the various techniques and tools. It does not involve any costly architecture to associate huge data amounts with it. It has a characteristic of scalability; irrespective of how big is the data (Krishnan, 2016).
  • Key indicators of performance: This tool mentioned in the data analytics assignment refers to the strategy that helps in the evacuation of the execution of the strategic vision of the company. It helps in the improvement of the medical healthcare quality for the patients who are prone to conditions where the usage if KPI is essential (Shahbaz et al., 2019).
  • Online Analytics Processing (OLAP): it helps in the improvement of the system of healthcare by way of performance of statistical calculation with a high speed, through multidimensional and hierarchical organized data (Kamble et al 2019).
  • Online transaction Processing (OLTP): it has a lot of similarities with the OLAP. The designation of OLTP is done for the purpose of processing the operations of patients like registration of patients, documents of the hospital, and review of the results (Reddy & Kumar, 2016).
  • Hadoop Distributed File System (HDFS): the HDFS system helps in the enhancement of data analytics of Healthcare by way of segregation of huge data into small ones and distribution among the various other systems (Bala & Venkatesh 2017).
  • Casandra File System (CFS): CFS is also considered a distributed system somewhat similar to HDFS. Though it is a system that is designated for the performance of the analytic operations without a failure (Kulkarni et al.,2020).
  • Map Reducing system: the task of Map reducing system is breaking the task further into subtasks and extracting its output. As noted in the data analytics assignment, it helps in enabling most common calculations related to operations that would be performed in an efficient manner in large amounts (Bala & Venkatesh 2017).
  • Complex Event Processing (CEP): it is a recent program that has been introduced in the system of healthcare. It indicates an event of a change of state. The complex processing of events can be tracked with the help of this tool (Bala & Venkatesh 2017).

In the data analytics assignment we have discussed techniques and tools of healthcare analytics. However, it is always difficult to make a research of this kind, since it becomes harder in the adoption of new techniques of data analytics by the private and public sectors. However, it is believed that if the key factors are brought into the light, it would guide the data analytics and data mining, and will benefit the decision making and performance of the healthcare system. Most of the studies that are being made on data analytics in the healthcare sector reflect quantitative figures, so looking at them, it can be concluded why there was a decline in the publishing of papers in the last few years. From the report on data analytics assignment, further conclusions that can be drawn are that the Asian and European countries work in a proper synchronization. They mutually benefit each other, for the purpose of serving researches related to the healthcare analytics and various other studies. This data analytics assignment proposes techniques that will be helpful in the leverage of the huge amounts of data in the healthcare sector. The determination of the diseases by the nurses and doctors will be easier. The risks that are associated with the system can also be mitigated. The decision-making process of the doctors will also be enhanced. Though, both the patients as well as the doctors will be pushed for the adoption of new techniques as well as collaboration with each other to achieve higher levels of connectivity among the patients and the medical staff for the updating of the system.

List of References
Al Mayahi, S., Al-Badi, A., & Tarhini, A. (2018, August). Exploring the potential benefits of big data analytics in providing smart healthcare. In International Conference for Emerging Technologies in Computing (pp. 247-258). Data analytics assignment Springer, Cham.

Alani, A. A., Ahmed, F. D., Majid, M. A., & Ahmad, M. S. (2018). Big Data Analytics for Healthcare Organizations a Case Study of the Iraqi Healthcare Sector. Advanced Science Letters, 24(10), 7783-7789.

Bala, H., & Venkatesh, V. (2017). Employees’ reactions to IT-enabled process innovations in the age of data analytics in healthcare. Business Process Management Journal.

Hiller, J. S. (2016). Healthy Predictions: Questions for Data Analytics in Health Care. Am. Bus. LJ, 53, 251.

Kamble, S. S., Gunasekaran, A., Goswami, M., & Manda, J. (2019). A systematic perspective on the applications of big data analytics in healthcare management. International Journal of Healthcare Management, 12(3), 226-240.

Kankanhalli, A., Hahn, J., Tan, S., & Gao, G. (2016). Big data and analytics in healthcare: introduction to the special section. Information Systems Frontiers, 18(2), 233-235.

Krishnan, S. M. (2016, March). Application of analytics to big data in healthcare. In 2016 32nd Southern Biomedical Engineering Conference (SBEC) (pp. 156-157). IEEE.

Kulkarni, A. J., Siarry, P., Singh, P. K., Abraham, A., Zhang, M., Zomaya, A., & Baki, F. (2020). Big Data Analytics in Healthcare. Data analytics assignment Springer International Publishing.

Manogaran, G., Lopez, D., Thota, C., Abbas, K. M., Pyne, S., & Sundarasekar, R. (2017). Big data analytics in healthcare Internet of Things. In Innovative healthcare systems for the 21st century (pp. 263-284). Springer, Cham.

Mehta, N., & Pandit, A. (2018). Concurrence of big data analytics and healthcare: A systematic review. International journal of medical informatics, 114, 57-65.

Mehta, N., Pandit, A., & Kulkarni, M. (2020). Elements of Healthcare Big Data Analytics. In Big Data Analytics in Healthcare (pp. 23-43). Springer, Cham.

Reddy, A. R., & Kumar, P. S. (2016, February). Predictive big data analytics in healthcare. In 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT) (pp. 623-626). IEEE.

Sahu, S. N., Moharana, M., Prusti, P. C., Chakrabarty, S., Khan, F., & Pattanayak, S. K. (2020). Real-time data analytics in healthcare using the Internet of Things. In Real-Time Data Analytics for Large Scale Sensor Data (pp. 37-50). Academic Press.

Shahbaz, M., Gao, C., Zhai, L., Shahzad, F., & Hu, Y. (2019). Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change. Journal of Big Data, 6(1), 6.

Tresp, V., Overhage, J. M., Bundschus, M., Rabizadeh, S., Fasching, P. A., & Yu, S. (2016). Going digital: a survey on digitalization and large-scale data analytics in healthcare. Proceedings of the IEEE, 104(11), 2180-2206.

Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Data analytics assignment Technological Forecasting and Social Change, 126, 3-13.


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