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Big Data In Healthcare Assignment Sample

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Big Data In Healthcare Assignment Sample


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The pilot study is about the big data in healthcare for securing lives of common people. Big data is important in present scenario in order to handle the situation of increasing number of patient. The principal components of healthcare systems are health facilities, health professionals, and in order to provide support to these two systems, a financing institute is also one of the major components. Moreover, there are several departments in the healthcare industry that need to be dealt with according to the seriousness of the situation. Traditionally, there are varieties of software available that tend to manage the data of the respective industry; however, this software fails to maintain the complexity of the data.

1. Methods applied in the pilot study

1.1 Data collection method

The current pilot study has been evaluated as the "secondary data collection method". Moreover, this method uses the data that already exists in the present environment related to any specific topic. According to the illustration of Davidson et al. (2019), the secondary method for collecting data is suitable for each kind of research whether the research is conducted on a large scale or a small scale. The concerned method for data collection procedure uses data that already exists. Furthermore, these data are collected as well as combined in order to provide effective results to the research being processed.
Secondary research for any specific study adopts the availability of the previously performed research or collected data from the existing report. According to the opinion of Dufour and Richard (2019), the data that has been collected before this distinct research is mainly considered for further studies and research. Generally, sources of these data are already existing journals and published research material. Along with that, those data and documents are also preferred that are available at libraries and different websites.
A secondary method of data collection that is being used in this pilot study also retrieves information from Government as well as non-government departments. Together with this, data is also retrieved from the existing surveys for this specific research. In this context, surveys are an admirable and astonishing way to collect information (Corti, 2018). Though the information that is collected is previously designed and structured, surveys are an effective way for giving the relevant information that is related to this pilot study. Surveys provide a phenomenal outcome for any particular research work as it consists of real information based on the perceptions of an individual.
In this pilot study, a secondary method is exempted for collecting data and analyzing for the specific research work. The entire study consists of several data that need to be implemented in the present research framework. According to the observation of Sherif (2018), all the data and information are used that are previously collected for already performed research papers. In this context, it can be stated that the second method is cost-effective as it needs less investment of money as compared to the primary method of data collection. The fact categorizes the situation of investing fewer amounts of money and at the same time receiving efficient coverage for the pilot study and specific research work.
All these relevant factors that elaborate the use of secondary data for this pilot study, imperatively suggest that it is the effective and efficient method for proving the adequate outcome (Ruggiano and Perry, 2019). Cost-effectiveness is the compelling circumstance that grants the permission for selecting a secondary method in order to collect data for the pilot study.

1.2 Data Analysis

In this specific research, data that are being analyzed are collected via "secondary data analysis". Researchers proceeding with the relevant pilot study are inspecting, transforming, modelling the data, and improvising the decision-making procedure. According to the statement of Akcam et al. (2019), the secondary analysis uses the data that are previously performed for the relevant research work in order to discover the answers. The pilot study that is being executed has several related questions that the researchers need to identify and answer all correctly.
Furthermore, the data that are being used in this pilot study need to be dealt with in such a way that all the answers are provided from the previously performed research work. The huge advantage of secondary analysis for the evaluation of data is that it is cost-effective. According to the illustration of Edwards et al. (2021), less investment of money is required for analyzing the data in order to generate answers for the current pilot study. Along with this, effective and efficient adoption of data is being implemented in this specific research work. The researchers of this particular pilot study do not need to indulge in giving their full effort because smart work provides excellent work.
Data analysis that is being performed for this specific research is secondarily based as it has several advantages over primary analysis of data. Along with the cost-effectiveness of the secondary analysis, this also provides time consumption for the researchers (Edge et al. 2019). Furthermore, with time consumption, less effort is required by the researchers because they can use the already existing data without enough changes. The fact states for more relevant profits especially for researchers as they do not need to spend their worthy time in preparing the data. The data is already available; they just need to centralize their focus on analyzing them for getting an effective and efficient outcome.
According to the secondary analysis of data for the specific research that is being implemented in the respective pilot study, it needs to be analyzed without wasting time on preparing the data. According to the opinion of Tarrant and Hughes (2021), secondary analysis of data is viable in order to use the method magnificently. The data that is being implemented in the pilot study is collected from both Governmental and non-Governmental sites. Furthermore, these data are required to be manifested in such a way that it is reformed in order to provide an effective answer for the current study.
Intensified use of secondary methods for analysis provides the researchers with various facilities. Together with this, the researchers need to analyze the answer and frame it in such a manner that it facilitates the current pilot study (Maietta et al. 2021). Researchers do not need to waste their time on the creation of data; rather, they just need to analyze it and this can probably save their money, effort as well as their costly time.

2. Feasibility of the study

Big Data

Big data has developed a new and innovative pathway in order to manage, leverage, and analyze the data across the entire industry, especially healthcare industries. Notable and important changes have occurred in the era of healthcare because of the increased potential work. According to the observation of Pashazadeh and Navimipour (2018), big data is considered as a data container that consists of a larger and complex set of data that generally specifies the voluminous share of relevant data. Traditionally, there are varieties of software available that tend to manage the data of the respective industry; however, this software fails to maintain the complexity of the data. In order to maintain this, big data provides an effective solution eliminating all the unnecessary factors and centralizing the focus on specific data.
Volume, velocity, and variety are the three elemental factors of big data that are also considered as three Vs within big data. Along with these criteria, big data supplements its advantageous factors by increasing its management in various industries. According to the statement of Jagadeeswari et al. (2018), traditional software available in various industries is unable to cope up with the organizing nature of big data. Therefore, in order to work with it efficiently, technically advanced software is required. Furthermore, for utilizing the advantageous character of big data, it is necessary to implement algorithms related to novel fusions and “Artificial Intelligence” (AI).
Due to the increased storage of data in various industries especially in healthcare industries, the use of big data is tremendously increased. The drastic increase in its use suggests its importance in several services and social infrastructure. According to the opinion of Chen et al. (2018), big data can play a vital role by making itself user-friendly and assisting in societal development. Moreover, big data can assist with business activities as well as help in satisfying customers' demands. Along with this, big data provides numerous advantages and a few of them are product development, security from hackers, machine learning, driving innovative notions, operational efficiency as well as providing a satisfactory customer experience.

Big data in healthcare

Healthcare industries are multi-dimensional organizations that emphasize the treatment of health-related impairments and issues in humans. The principal components of healthcare systems are health facilities, health professionals, and in order to provide support to these two systems, a financing institute is also one of the major components. Moreover, there are several departments in the healthcare industry that need to be dealt with according to the seriousness of the situation (Bahri et al. 2018). Health professionals are responsible for various levels of distributed work and they need to handle each assigned work with care and concern. Records of different patients as well as their medical history with present registration, all need to be noted in a systematic form.

Figure 1: Nutshell of Big data in healthcare

According to the figure, several importances of big data in healthcare department is highlighted. Earlier, these records had been maintained in a register in a handwritten format or a typed format. However, with the increasing population, the number of patients in the healthcare industry is also increasing drastically. Moreover, this venerable method is disrupting the present scenario and new innovative technology is being implemented. According to the illustration of Mir and Dhage (2018), big data in healthcare is one of the ways that terminates all the relevant issues that occur in healthcare industries. Further, there are several applications of healthcare industries that supplement the advantage of the patient as well as healthcare industries.

Electronic health records
Big data plays a crucial role in maintaining health records electronically and this assures for creating no aberration. Maintaining the historical record of the patient electronically is effective as it suggests easy access to their records as well as giving them efficient treatment (Bote-Curiel et al. 2019). On the same hand, it also prefers the secure information system of the specific patient.

Advanced risk and disease management
Big data influences the management of disease and risk via its advanced technology system. According to the observation of Bates et al. (2018), health professionals' knowledge gets more enhanced and they can manage the risks and diseases of the patient in the most admirable way. Advanced technology supports the management system within the healthcare departments as well as enhances its efficiency in dealing with patients effectively.

Smart staffing
With the use of advanced technology such as big data, healthcare is establishing an effective and efficient team of staff members. Moreover, these members are responsible for providing the patients with excellent methods for their treatment procedure (Mehta and Pandit, 2018). With the assistance of big data tools, the activities of staff are streamlined and this directly boosts their smart performance.

Prevention of unnecessary visits to healthcare departments
Analytics within big data assists in saving energy, money, and time as well as consequently prevents the patients from unneeded visits to the relevant departments. According to the opinion of Kaur et al. (2018), patients receive treatment at home through online consultation with doctors and nurses. Furthermore, advice is provided to the patient without any complications and dilemmas.

With the advent of smart phones, wearable, wireless devices, and online video conferences, facts about telemedicine are being spread in a wide-open area. According to the opinion of Nazir et al. (2020), big data supports telemedicine facilities by initiating the use of advanced technology. Telemedicine facilitates the patients because of easy delivery of medicines and effective consultation with online healthcare professionals.

In curing cancer
Medical researchers have found that big data is successful in curing cancer as per the observation in changing trends. Effective and efficient treatment that is being provided to the patient through the use of big data supports the departments as well.

Ensures strategic planning
Big data assures strategic planning in the healthcare departments and satisfies the patient's requirements by providing effective treatment facilities. Managers of healthcare departments analyze for various demographic groups present and identify their effective treatment procedure.

3. Results

3.1 Theme 1: Issues related to big data in healthcare

With several advantages, there are issues related to big data in healthcare that are a severe concern. Lack of governance procedures is one of the obstacles in healthcare that arises due to the inappropriate use of big data in the concerned department. According to the illustration of Khan et al. (2019), the data that are being used in the big data are required to be correct, precise, and clean in order to assure a complete analysis of the patient. However, these are not attained on a serious note and this creates a huge problem in the healthcare department. Each issue related to the concerned department needs to tackle the situation and handle it appropriately.

Figure 2: Challenges of Big Data

According to the figure, various challenges of big data in healthcare is estimated that needs to be eliminated for smooth running procedure of the system. Big data enables electronic records of the patient yet, in case not dealt with in a proper way, the scenario can be extremely serious. Furthermore, this can give drastic results by not accomplishing the requirements of the patient. From time-to-time treatment is necessary in order to make the patient cure of the respective disease they are suffering from. Electronic systems keep the records of the patient with safety and security however retrieving the information of an individual patient lies down to a problem of getting the information leaked or frequently misused.

3.2 Theme 2: Impact of issues related to big data in healthcare

The advantages of big data in healthcare departments affect it positively while there are a few drawbacks of big data that impacts the healthcare departments negatively. Furthermore, there are ways related to the execution of negative impacts that affect the patients’ ongoing treatment. According to the statement of Kumar and Singh (2018), big data is impacting the treatment facilities of the patient and some cases just lead to drastic conditions of the professionals as well as the patient. Healthcare industries while using sensitive data need to be extremely cautious in maintaining the information system of the departments.
Leakage of patients' data or departments' data can result in a negative impact. Data security is highly mandatory in healthcare departments because it contains such information that cannot be disclosed without any previous authorization. In this context, it can be stated that the healthcare department needs to be cautious in dealing with prioritized information or data about an individual patient or any department. According to the observation of Ristevski and Chen (2018), information within the department needs to be dealt with in a highly organized and systematic manner. Data analytics and big data are effective enough in dealing with the members of the healthcare departments, however, intimate knowledge about the technology is required by each individual.
Due to the increased population and changing lifestyles, the number of patients in healthcare departments is increasing tremendously. Furthermore, with this increase, advancement in technology is also mandatory and this can be achieved by enhancing as well as upgrading the knowledge about innovative technology such as big data. According to the observation of Bote-Curiel et al. (2019), installation of this innovative and advanced technology cannot be advantageous unless proper training is not provided to the employees of the healthcare departments. Moreover, there are several issues related to big data in the healthcare department, and all need to be eradicated for smooth dealing with the advanced technology. The technology is beneficial yet precautions need to be taken in order to violate criminal offenses.

3.3 Theme 3: Factors associated with issues related to big data in healthcare

Excessive visit to healthcare departments by the patients needs to be controlled and this can be managed efficiently by the use of big data. According to the illustration of Lv and Qiao (2020), big data plays an important role in smoothing the process of treatment that is being provided to the patient. Though profit-making is not the priority of the healthcare departments, the departments need to pay attention to the resources that it requires, installation of innovative techniques, and enhanced infrastructure in order to provide the patient with effective treatment services.
The availability of advanced technology does not emphasize on full security of the service that is being provided to the patient. Rather it suggests that knowledge about each field is mandatory for effectively accessing the technology. Various factors are available in big data that result in an advantage for the concerned healthcare departments. The thing that is required by each individual is to be cautious and aware of fraud and criminal activities that are taking place in day-to-day life.

4. Ethical consideration

In the current pilot study, various ethics have been used in order to get an effective result. Several ethics are associated with the use of big data in healthcare and drastically it plays an important role in the research work. The data are collected from several journals and magazines that are relevant to the current study. According to the opinion of Dash et al. (2019), the entire process involved in this pilot study marmalade the ethical problems and associated potential value. Resources for this specific study are collected from articles, magazines, and journals and these entirely support the researchers.
Ethical consideration is considered as the values and principles that need to be recommended in order to solve human affairs. Furthermore, these considerations work in such a manner that it eliminates harmful effects on individuals or society (Shilo et al. 2020). Along with this, ethical considerations also block the path that leads an individual to participate in miss-behavioural conduct. Therefore, preserving ethics are necessary while conducting the research is of prime importance. Researchers are responsible for managing as well as assuring the rights of the candidates involved in the research work.
Various information and data are retrieved from Google Scholar and ProQuest for the current pilot study. Along with these considerations, ethical consideration assures the validity of the current study, variable methods used in the study, confidentiality, consent of partners involved in the study, transparency, integrity as well as accessibility for compatible information. According to the statement of Senthilkumar et al. (2018), each consideration is important in order to succeed in the respective research work without any failure. Researchers are responsible for implementing all the considerations and working positively to achieve victory in the relevant research. Health professionals are responsible for various levels of distributed work and they need to handle each assigned work with care and concern. Records of different patients as well as their medical history with present registration, all need to be noted in a systematic form.

Reference List

Akcam, B.K., Guney, S. and Cresswell, A.M., 2019. Research design and major issues in developing dynamic theories by secondary analysis of qualitative data. Systems, 7(3), p.40.
Bahri, S., Zoghlami, N., Abed, M. and Tavares, J.M.R., 2018. Big data for healthcare: A survey. IEEE access, 7, pp.7397-7408.
Bates, D.W., Heitmueller, A., Kakad, M. and Saria, S., 2018. Why policymakers should care about “big data” in healthcare. Health Policy and Technology, 7(2), pp.211-216.
Bote-Curiel, L., Munoz-Romero, S., Gerrero-Curieses, A. and Rojo-Álvarez, J.L., 2019. Deep learning and big data in healthcare: A double review for critical beginners. Applied Sciences, 9(11), p.2331.
Chen, M., Yang, J., Hu, L., Hossain, M.S. and Muhammad, G., 2018. Urban healthcare big data system based on crowdsourced and cloud-based air quality indicators. IEEE Communications Magazine, 56(11), pp.14-20.
Corti, L., 2018. Data collection in secondary analysis. The SAGE handbook of qualitative data collection, pp.164-181.
Dash, S., Shakyawar, S.K., Sharma, M. and Kaushik, S., 2019. Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1), pp.1-25.
Davidson, E., Edwards, R., Jamieson, L. and Weller, S., 2019. Big data, qualitative style: a breadth-and-depth method for working with large amounts of secondary qualitative data. Quality & Quantity, 53(1), pp.363-376.
Dufour, I.F. and Richard, M.C., 2019. Theorizing from secondary qualitative data: A comparison of two data analysis methods. Cogent Education, 6(1), p.1690265.
Edge, C., Stockley, R., Walsh, M., Decodts, F., Perryman, B., Lenneskog, M., Lee, T.K. and Black, G., 2019. Using qualitative data to produce an animated clinical engagement tool: secondary health-care experiences of people in prison. The Lancet, 394, p.S37.
Edwards, R., Davidson, E., Jamieson, L. and Weller, S., 2021. Theory and the breadth-and-depth method of analysing large amounts of qualitative data: a research note. Quality & Quantity, 55(4), pp.1275-1280.
Jagadeeswari, V., Subramaniyaswamy, V., Logesh, R. and Vijayakumar, V., 2018. A study on medical Internet of Things and Big Data in personalized healthcare system. Health information science and systems, 6(1), pp.1-20.
Kaur, P., Sharma, M. and Mittal, M., 2018. Big data and machine learning based secure healthcare framework. Procedia computer science, 132, pp.1049-1059.
Khan, S., Khan, A., Maqsood, M., Aadil, F. and Ghazanfar, M.A., 2019. Optimized gabor feature extraction for mass classification using cuckoo search for big data e-healthcare. Journal of Grid Computing, 17(2), pp.239-254.
Kumar, S. and Singh, M., 2018. Big data analytics for healthcare industry: impact, applications, and tools. Big data mining and analytics, 2(1), pp.48-57.
Lv, Z. and Qiao, L., 2020. Analysis of healthcare big data. Future Generation Computer Systems, 109, pp.103-110.
Maietta, R., Mihas, P., Swartout, K., Petruzzelli, J. and Hamilton, A.B., 2021. Sort and Sift, Think and Shift: Let the Data Be Your Guide an Applied Approach to Working With, Learning From, and Privileging Qualitative Data. Qualitative Report, 26(6).
Mehta, N. and Pandit, A., 2018. Concurrence of big data analytics and healthcare: A systematic review. International journal of medical informatics, 114, pp.57-65.
Mir, A. and Dhage, S.N., 2018, August. Diabetes disease prediction using machine learning on big data of healthcare. In 2018 fourth international conference on computing communication control and automation (ICCUBEA) (pp. 1-6). Piscataway: IEEE.
Nazir, S., Khan, S., Khan, H.U., Ali, S., García-Magariño, I., Atan, R.B. and Nawaz, M., 2020. A comprehensive analysis of healthcare big data management, analytics and scientific programming. IEEE Access, 8, pp.95714-95733.
Pashazadeh, A. and Navimipour, N.J., 2018. Big data handling mechanisms in the healthcare applications: A comprehensive and systematic literature review. Journal of biomedical informatics, 82, pp.47-62.
Ristevski, B. and Chen, M., 2018. Big data analytics in medicine and healthcare. Journal of integrative bioinformatics, 15(3).
Ruggiano, N. and Perry, T.E., 2019. Conducting secondary analysis of qualitative data: Should we, can we, and how. Qualitative Social Work, 18(1), pp.81-97.
Senthilkumar, S.A., Rai, B.K., Meshram, A.A., Gunasekaran, A. and Chandrakumarmangalam, S., 2018. Big data in healthcare management: a review of literature. American Journal of Theoretical and Applied Business, 4(2), pp.57-69.
Sherif, V., 2018, May. Evaluating preexisting qualitative research data for secondary analysis. In Forum: qualitative social research (Vol. 19, No. 2, pp. 26-42). Berlin: Freie Universität Berlin.
Shilo, S., Rossman, H. and Segal, E., 2020. Axes of a revolution: challenges and promises of big data in healthcare. Nature medicine, 26(1), pp.29-38.
Tarrant, A. and Hughes, K., 2021. Qualitative datafire-use and secondary analysis: Researching in and about a crisis. Qualitative and Digital Research in Times of Crisis: Methods, Reflexivity, and Ethics, p.156.

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