+44 203 318 3300 +61 2 9052 0853

Use of Big Data in Improving the Quality of Healthcare Assignment Sample

Pages Pages: 14

Words Words: 3537

Use of Big Data in Improving the Quality of Healthcare Assignment

Get free samples written by our Top-Notch subject experts for taking Assignment helper.

Introduction - Use of Big Data in Improving the Quality of Healthcare

Big data (BD) is information, which contains a huge variety of information. It is primarily utilised in predictive modelling and projects of machine learning. However, nowadays, big data has been utilised in the healthcare sector for reducing cost of care measurement, managing the population of patients who are at high risk, and providing best clinical support. Besides, big data assists to accelerate innovation by reducing time to derive a pristine product, such as prescription meds, and others in healthcare. Hence, possibility of securing lives becomes easier alongside shaping strategic planning for additional services beforehand. Additionally, considering breaches of sensitive information of patients and other security incidents can be retorted with bid data analytics, which helps to secure lives. This study is going to do a literature review on the utilisation of big data within the healthcare as a recommending factor to secure lives. In order to achieve the research topic, this study will formulate a specific research question and provide a rationale for selecting that particular research question. Additionally, to answer the formulated research question, it will review the existing resources on this similar topic and consider a systematic review approach.

Background

Big data (BD) is a huge volume of information, which can do wonders. Many private and public companies store analyse and generate big data to improve their provided services. Prediction of outbreaks, alleviate preventable diseases, reducing time of patients’ diagnosis and cost of healthcare delivery, and others are possible with big data. These in turn, assist to secure lives in today’s uncertain and vulnerable situations. This type of data needs appropriate analysis and management for deriving meaningful information. Catalyst (2018) explained that big data within healthcare mentions abundant health information amassed from various sources such as genomic sequencing, pharmaceutical research, medical devices, and medical imaging, wearable and payer records.

Use of Big Data in Improving the Quality of Healthcare

Figure 1: Big data sources within healthcare

(Source: Catalyst, 2018)

Agrawal and Prabakaran (2020) explained that big data becomes an essential part of the future generation regarding technological developments. It allows people to get new ideas through the huge amount of information that is made by modern life. One example of use of BD within the healthcare sector is about development of consumer devices; it leads to remarkable advances to collate clinical data. Hence, possibility of securing lives is widened by ensuing precision medicines and accurate treatments. Additionally, with the help of big data, it has become possible to consistently gather patient crucial and analyse them within real life. Information has suggested that UK has developed a national strategy, resulting in the UK Biobankas well as 100,000 Genomes projects (National Healthcare Services, 2019).

Research questions

  • Big Data in Healthcare: lesson learnt and recommendation in securing lives

Rationale of selected topic

In this study, the chosen topic is the use of BD to improve the health care quality. By considering this topic, this study has formulated the research as mentioned above question. Regarding formulation, this particular research question is that the study can answer every single topic of big data within the healthcare sector based on this question. This RQ can help to discern use of big data to secure lives alongside maintaining information security and privacy in healthcare. Jagadeeswari et al. (2018) opined that patients’ lives largely rely on data generated and collected. It further emphasized that use of Big Data assists to diagnose at initial stage, and thus, better healthcare can be offered to patients, which enhances their lives’ security. Moreover, treatment through big data analysis can improve chances of saving patient lives by monitoring patients’ vitals and other essentials (Kumar and Singh, 2018). Detection and prevention of frauds can also be facilitated with use of BD in healthcare.

The reason for selecting this particular topic is that nowadays, big data is used in every sector, however, in regards to the healthcare sector, the number of studies is negligible, and that is why many healthcare organisations still do not have a clear insight about BD and its benefits within the healthcare sector. Therefore, this systematic review will consider the existing information on this topic and highlights the role of big data recommending lives’ security. This study has considered systematic review, and for methodology, it has selected a qualitative approach because qualitative approach helps to get a better understanding of thoughtful reporting. 

Systematic literature review

Concept of big data

BD contains a large volume of information, which is unmanageable by utilising traditional software. Currently, the word big data is very popular throughout the world. Almost every research sector, from academic to industry, analyses and generates big data regarding different purposes. Dash et al. (2019) elaborated that it is a challenging task to manage a large quantity of data and since big data cannot be managed with traditional software. Thus, technologically advanced software and applications are required, which can use cost-effective and fast computational power regarding those tasks. Implementation of AI algorithms would be important for making sense of the huge amount of data. In this context, Pastorinoet al. (2019) explained that organisations could accomplish automated decision-making by implementing machine learning. Conversely, in the absence of proper hardware and software support, big data can be hazy. That is why people need to develop appropriate techniques for handling an endless sea of information as well as smart web applications regarding effective analysis so that they can gain workable insights.

Big data in the healthcare industry

Nowadays, Big Data has an integral role within the healthcare environment because information is growing increasingly. Since the cost of treatment increases over time, it has made a huge burden on middle-class families because they cannot afford the treatment cost. That is why doors are opened regarding optimisation within the healthcare sector. Suter-Crazzolara (2018) mentioned that key components of the healthcare system include health facilities, health professionals, and financing institutions. Health professionals are responsible for different types of indentation at every level, including medical history of patients, clinical data, and private medical data. Ramesh and Santhi (2020) stated that electronic health records (EHR) had made advantages to handling data related to modern data. BD that incorporates medical records of patients and hospital records, results of medical examinations, and others, help to shape treatments for individuals, and allow personnel to avoid outbreaks and possible health crises (Dash et al. 2019). Hence, effectiveness of treatments can be compared to secure lives, as Big Data Analytics aggravates healthcare data.

Use of Big Data in Improving the Quality of Healthcare

Figure 2: Big Data analytics workflow in Healthcare sector

(Source: Dash et al. 2019)

Big data within biomedical research

Dash et al. (2019) elaborated that biological systems, including human cells, show physical and molecular events about complex interplay. Biomedical experiments gather information about simpler and smaller components to understand interdependencies about different components of these complex systems. Thus, this needs various simplified experiments for generating a broad map of provided interest in biological phenomena. This process indicates that when the healthcare sector has more data, they have a better understanding of biological processes. Yang et al. (2019) propounded that healthcare big data storage system accelerates security with chances to save lives. For instance, in emergency situations, staffs require medical records of patients to latter’s life, however, ambulance personnel often do not have access to encrypted medical records. It obstructs emergency rescue of patients’ lives, which can be addressed with BD systems.

Based on this idea, modern technology has evolved at an extraordinary pace. For example, healthcare organisations can imagine a significant amount of information generated by integrating effective technologies such as NGS (next-generation sequencing) and GWAS (Genome-wide association studies) for decoding human genetics. Additionally, Galetsi, Katsaliaki and Kumar (2020), with the help of big data, healthcare providers cannot only study single gene transcriptions, and they can study expression of every gene and the whole transcriptome of an organism under the study of transcriptomics. Hence, it helps to enhance possibility of securing lives by providing finer care with personalised treatments.

Benefits of big data within healthcare

BD is a sensitive problem regarding institutions of European Union. Presence of BD in relation to health has a huge positive impact on healthcare and medical functions. It has huge potential for yielding new ideas into risk factors, which lead to diseases. Pastorino et al. (2019) stated that Big Data within Healthcare has several benefits, such as big data can increase earlier diagnosis as well as quality and effectiveness of treatments by discovering disease intervention and early signals. It, indeed, enhances possibility of securing lives alongside alleviating chances of chronic illness by providing early treatments and predicts outbreaks. Additionally, big data can reduce the probability of the opposite. Next benefit is that it can widen the possibilities regarding disease prevention by identifying different risk factors regarding diseases. Another perspective of securing medical records with regard to patients’ information can be secured with use of big data analytics. In this context, Lee and Yoon (2017) explained that Big Data within the Healthcare sector is responsible for ensuring patient safety and improving pharmacovigilance by the capability of formationbetter-informedclinical decisions based on directly provided information towards the patients. This scholar also elaborated that big data within healthcare can produce the outcomes as it has potential for analysing and utilising in real-time towards behaviour changes, which can reduce health-related risks, reduce environmental exposure towards harmful chemicals, and optimise health outcomes.

Limitations of big data

Every system comes up with both negative and positive sides, and in the case of big data, the scenario is the same. Besides numerous benefits of Big Data in Health care, it is also associated with some limitations or challenges. For example, Pastorinoet al. (2019) explained that one of the key challenges with the collaboration of these systems is that ethical issues come up with Big Data. In other words, legal and ethical limitations include risk for compromising personal autonomy, fairness, trust, and privacy during using BD in the Healthcare sector. By agreeing to this, Benhlima (2018) stated that regarding the majority of healthcare professionals, data security is a key issue because continuous security violations and hacking have been noticed nowadays. Thus, this information is required to be handled continuously so that any kind of privacy or data breach issues do not occur. The scholar also mentioned that data capture had become a big obstacle regarding healthcare sectors because of a lack of efficient data governance processes. For utilising data more effectively, they need to be precise and clean so that data can be utilised throughout healthcare systems.

Application of Big Data within Healthcare

Keeps patients healthy

In order to keep patients healthy as well as avoid illness, the healthcare sector uses Big Data. For example, patients use several gadgets where they can track their levels of physical activity as well as current health-related trends.

Expands diagnostic service

Additionally, for expanding the diagnostic services of patients, healthcare sectors use big data. For example, patients can use mobile applications, which can advise patients about their medical status. [Referred to Appendix]

Results and analysis

The previous section of this paper has provided the literature review on the selected topic. The information from the previous section shows that Big Data has been used in healthcare for different purposes, for example, EHR, real-time altering, increasing patient engagement, utilising health-related data regarding informed strategic planning, reducing fraud, and enhancing security. Catalyst (2018) explained that with the help of Big Data, healthcare sectors effectively engage with patients. For example, nowadays, a significant number of patients use smart devices that can effectively record their heart rates as well as sleeping habits permanently. It allows them to monitor patients’ vitals and other essentials to provide necessary treatments to secure their lives even in emergencies. This crucial information can be used together with other data for identifying possible health risks of patients.

Additionally, information from the previous section shows that EHR is the most commonly used Big Data application in medicine. Agrawal and Prabakaran (2020) explained that patients have a digital record that includes medical history, laboratory tests, demographics, and allergies through BD, which help to offer immediate treatments, and hence, lives’ security is accelerated. These records are divided by a secured IS (information systems), and these are available regarding providers in both the private and public sectors. 

Information from literature review also shows that professionals can access the entire medical industry of any patient because of big data. Chen et al. (2017) elaborated that machine learning (ML) with big data is effective regarding prediction of severe disease outbreaks within a disease-frequent community. By agreeing to the previous statement, Mooney and Pejaver (2018) stated that ML is the umbrella term regarding techniques, which fits models algorithmically through adopting data patterns. Use of BD Analytics is also noticed to provide treatments at emergencies through encrypted medical records (Yang et al. 2019). On one hand, it improves security of lives; while on the other hand, it allows data security of patients’ essential information. Alongside benefits, some key challenges are also noticed in this context. One of the major limitations is related to data security because, in recent times, data hacking has been significantly increased. Therefore, the healthcare sector needs to make sure that their patients' personal information is safe and secure.

Conclusion

This study has aimed to discuss whether big data utilisation has significantly improved healthcare or not. The information has suggested that with the help of big data, healthcare sectors significantly improve their services and currently, they enhance patient engagement, enhance security, and reduce fraud. However, big data also has a risk of a data breach if proper data security is not considered. Thus, it can be concluded that healthcare sectors can improve their services with the help of Big Data if they consider proper security approaches in future.

Reference list

Agrawal, R. and Prabakaran, S., 2020. Big data in digital healthcare: lessons learnt and recommendations for general practice. Heredity, 124(4), 525-534.

Benhlima, L., 2018. Big data management for healthcare systems: architecture, requirements, and implementation. Advances in bioinformatics. 1-10

Catalyst, N.E.J.M., 2018.Healthcare big data and the promise of value-based care. NEJM Catalyst, 4(1).

Chen, M., Hao, Y., Hwang, K., Wang, L. and Wang, L, 2017.Disease prediction by machine learning over big data from healthcare communities. Ieee Access, 5, 8869-8879.

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), 1-25.

Galetsi, P., Katsaliaki, K. and Kumar, S., 2020. Big data analytics in health sector: Theoretical framework, techniques and prospects. International Journal of Information Management, 50, 206-216.

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 systems6(1), pp.1-20.

Kim, J. and Groeneveld, P.W., 2017. Big data, health informatics, and the future of cardiovascular medicine.Journal of the American College of Cardiology. 69(7), 899–902

Kumar, S. and Singh, M., 2018. Big data analytics for healthcare industry: impact, applications, and tools. Big data mining and analytics2(1), pp.48-57.

Lee, C.H. and Yoon, H.J., 2017. Medical big data: promise and challenges. Kidney research and clinical practice, 36(1), 3.

Mooney, S.J. and Pejaver, V, 2018. Big data in public health: terminology, machine learning, and privacy. Annual review of public health, 39, 95-112.

National Healthcare Services, 2019.Preparing the healthcare workforce to deliver the digital future [viewed 24 November 2021]. Available from: https://topol.hee.nhs.uk/wp-content/uploads/HEE-Topol-Review-2019.pdf

Pastorino, R., De Vito, C., Migliara, G., Glocker, K., Binenbaum, I., Ricciardi, W. and Boccia, S, 2019.Benefits and challenges of Big Data in healthcare: an overview of the European initiatives. European journal of public health, 29(Supplement_3), 23-27.

Ramesh, T. and Santhi, V., 2020.Exploring big data analytics in health care. International Journal of Intelligent Networks, 1, 135-140.

Suter-Crazzolara, C., 2018.Better patient outcomes through mining of biomedical big data. Frontiers in ICT, 5, 30.

Yang, Y., Zheng, X., Guo, W., Liu, X. and Chang, V., 2019. Privacy-preserving smart IoT-based healthcare big data storage and self-adaptive access control system. Information Sciences479, pp.567-592.

Appendix

Author

Aim

Result

Dash et al. (2019)

Examine the role of Big Data in Healthcare

Revolution in healthcare is further needed to group together bioinformatics, health informatics and analytics to promote personalized and more effective treatments

Pastorinoet al. (2019)

Examine the benefits and challenges of Big Data in healthcare, in context of EU

EU Member States should agree on international technical standards, taking also into account openness that is considered as the basic paradigm for digital transformation

Suter-Crazzolara (2018)

To discuss opportunities, challenges and solutions for this health-data revolution

 It is expected that the analysis of Big Data will continue to drive better patient outcomes although some caution has the be taken in consideration 

Ramesh and Santhi (2020)

To explore Big Data analytics in healthcare

Big Data Analytics tools were addressed on top of data mining techniques in health care sector, as the health care industry is one of the leading sectors where huge revenue will be generated across globe as the numbers of patients are increasing drastically with the population

Galetsi, Katsaliaki and Kumar (2020)

To discuss the role of Big data analytics in health sector: Theoretical framework, techniques and prospects

Machine learning is the most applied technique across almost all created values and data types that offer immense potential in the healthcare predictive analytics arena to improve outcomes in many domains of research

Lee and Yoon (2017)

To identify challenges in medical big data

Medical big data analyses are complicated by many technical issues, such as missing values, curse of dimensionality, and bias control, and share the inherent limitations of observation study, namely the inability to test causality resulting from residual confounding and reverse causation

Kim and Groeneveld (2017)

To identify Big data implementation in cardiovascular medicine

Cardiology fellows should be perceptive and knowledgeable in big data and health informatics concepts, and be prepared to collaborate to achieve the ultimate goal: prevent cardiovascular disease and improve cardiovascular outcomes

Benhlima (2018)

To discuss Big Data management for healthcare system

A big data processing architecture for healthcare industry has been presented; it is capable of handling the high amount of data generated by diferent medical sources in real time

Yang et al. (2019)

To address use of big data for medical records in securing lives

With regard to big data analytics, a break-glass access method is proposed to save patients’ lives at emergencies.

Table: Summary table

Free Download Full Sample
Recently Download Samples by Customers
Our Exceptional Advantages
Complete your order here
16000+ Project Delivered
Get best price for your work

Ph.D. Writers For Best Assistance

Plagiarism Free

offer valid for limited time only*