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Big Data in the Healthcare Industry


As the volume and variety of electronic data increase, the big data phenomenon is expanding quickly. This is brought on by the widespread use of smart gadgets, such computers and smartphones, which produce and broadcast copious quantities of data. New methods of data collecting, storage, and processing made possible by big data have also led to previously unimaginable potential for analysis and reasoning (Lee, 2017; Maciejewski, 2017).

The requirement for record-keeping necessitates the use of enormous volumes of data, and the healthcare industry is no exception to this trend (Raghupathi & Raghupathi, 2014). This information has the power to drastically alter patient outcomes and raise public health. The quickest and most economical approach to accomplish these aims is via extracting this information from big data (BDV, 2016). Beyond the healthcare sector, big data offers opportunities that have an influence on technology, economics, and society at large. These opportunities have the potential to change business paradigms. But it’s difficult to efficiently extract information from massive data; success depends on having a good awareness of these difficulties. In order to acquire a thorough understanding of current developments in the field and maybe identify opportunities for development, this essay will conduct a SWOT analysis of big data in the healthcare sector.

SWOT Analysis


In health economics, data is essential to decision-making, especially when allocating resources. Open datasets provide a wealth of historical data that may be utilized to build economic models that are relevant to a variety of populations. Big data in the healthcare sector is widely used in policy-making in the UK. It often consults the NHS Economic Evaluation Database and the Cost-Effectiveness Analysis Registry for economic evaluations (Al Kadour, Al Marridi, & Al-Badriyeh, 2018).

Big data is essential for tracking patients’ lifestyle choices. Data insights from the time a patient seeks therapy through the course of treatment they take may be recorded by the Internet of Things (IoT) to offer insights into their health. Aggregated data may be used to enable care services be tailored to patients’ requirements, improving treatment results, according to Joiya et al. (2017). Open datasets also make it possible for stakeholders outside of the research community to access and analyze data, generating novel suggestions and viewpoints that will help advance openness in the healthcare industry.

Clinicians may improve the effectiveness of clinical decision-making by using individual-level data from big data repositories to understand the efficacy of certain medications for particular groups. Big data may also be used by policymakers to inform evidence-based decisions that advance society as a whole. For instance, the EQ-5D utility ratings connected to specific health issues are computed using temporal trade-off approaches and a generic population sample. According to Collins (2016, p. 105) this method is generally recognized as the NICE-recommended standard for calculating quality-adjusted life expectancy.


Big data presents a number of difficulties for the healthcare sector. The data in the healthcare sector is multifaceted and extensively segmented, as it is in other sectors (Dias, Santos & Portela, 2020). According to Mehta et al. (2020), real-time production and synchronization across various data sources are essential for minimizing gaps and inaccurate information. A fundamental challenge in data collecting, however, is combining data from many sources into unique forms. Furthermore, data redundancy caused by storage across several sources, as noted by Mehta et al. (2020), makes it very difficult to aggregate data while distinguishing important information from redundant data.

Due to the enormous variety of formats and data sources in the healthcare industry, there is also a large danger of receiving insights that are of low quality. This necessitates an information extraction approach, which is a technological challenge, that can recognize relevant information and present it in a way that is suitable for study. For data to be used meaningfully, it must be cleaned up and normalized by eliminating extraneous information (Mehta et al., 2020).


According to Combi and Pozzi (2019), big data’s future has limitless possibilities given the state of technology. Large datasets that may interact with one another to conduct complex analysis are now possible thanks to the incorporation of artificial intelligence. Big data and broad trial registries have made it feasible to match medications with comparable effects for increased effectiveness. By enhancing the capacity for decision-making, the use of big data may also result in a considerable decrease in medical mistakes.

Cirillo and Valencia (2019) suggest that huge datasets are progressing the area of individualized genomic mapping in accordance with this. People may take preventative actions to be healthy if they are aware of their propensity for certain illnesses.


The use of big data in the healthcare sector is not without risk. The security and privacy of health-related data is the main difficulty confronting big data in healthcare, according to a research by Alexandru, Radu, and Bizon (2018). Healthcare organizations need to give data security priority due to the sensitivity of healthcare data and the rising frequency of security breaches.

The industry’s growing reliance on big data poses a further challenge. Mismanagement of this data may cause tension and worry as the quantity of information accessible to individuals increases. Knowing one’s risk of illnesses, particularly ones for which there is no therapy, might create unheard-of worry. In addition, the use of this knowledge presents ethical and moral concerns, and there is a chance that insurers would abuse data by using big data applications to forecast healthcare expenses. When consumers are uncomfortable with how their data will be utilized, a backlash in health economics may occur (Collins, 2016).

Chart: SWOT analysis

Chart: SWOT analysis


Big data’s introduction has revolutionized the healthcare sector. The interaction between sector stakeholders has evolved as a result of the extensive usage of data. When making judgments on public health and other relevant policies, policymakers have access to real-life experiences that they may draw from. Medical professionals then rely on enormous datasets to support evidence-based procedures, which leads to better patient outcomes and higher standards of care. Stakeholders in the industry should concentrate on maximizing their strengths while simultaneously correcting their shortcomings and minimizing possible risks in order to take advantage of the possibilities that are there.


Al Kadour, A., Al Marridi, W., & Al-Badriyeh, D. (2018). Pharmacoeconomics evaluations of oral anticancer agents: systematic review of characteristics, methodological trends, and reporting quality. Value in Health Regional Issues16, 46-60.

Alexandru, A., Radu, I.-M., & Bizon, M. (2018). Big Data in Healthcare – Opportunities and Challenges. Informatica Economica, 47–58. 10.12948/issn14531305/22.2.2018.05

BDV. (2016). Big Data Technologies in Healthcare. Big Data Technologies in Healthcare Needs, Opportunities and Challenges, 31. Retrieved from Data Technologies in Healthcare.pdf

Cirillo, D., & Valencia, A. (2019). Big data analytics for personalisedpersonalised medicine. Current opinion in biotechnology58, 161-167.

Collins, B. (2016). Big Data and Health Economics: Strengths, Weaknesses, Opportunities and Threats. PharmacoEconomics, 34(2), 101–106. 10.1007/s40273-015-0306-7

Combi, C., & Pozzi, G. (2019). Clinical information systems and artificial intelligence: recent research trends. Yearbook of medical informatics28(01), 083-094.

Joyia, G. J., Liaqat, R. M., Farooq, A., & Rehman, S. (2017). Internet of medical things (IoMT): Applications, benefits and future challenges in healthcare domain. J. Commun.12(4), 240-247.

Lee, I. (2017). Big data: Dimensions, evolution, impacts, and challenges. Business Horizons, 60(3), 293–303.

Maciejewski, M. (2017). To do more , better , faster, and more cheaply : using big data in public administration.

Mehta, N., Pandit, A., & Kulkarni, M. (2020). Elements of Healthcare Big Data Analytics. In Big Data Analytics in Healthcare. 31672-3_2

Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2(1), 3.


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