Big Data Risks and Rewards - Free Paper Sample

Introduction

Big data is a critical database in healthcare where the information amassed can be used to improve treatment and manage diseases. Recently, big data has become an inseparable component of health care in most countries because of its vast amount of data, an increase in the cost of healthcare, and the need to increase the satisfaction of patients. Big data yields numerous benefits, for instance, it helps reduce recency bias and optimizes healthcare workflow. Nevertheless, the vast volume of big data poses a series of challenges in data storage, mining, sharing. Some of the main risks of big data related to confidentiality, security, and sharing of the data to improve health care. The purpose of this paper is to outline the mentioned rewards and risks of big data in healthcare.

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Healthcare Workflow

Big data reduces recency bias and optimizes healthcare workflows. Recency bias happens when the recent events outweigh the past events relating to a situation, and it may result in wrong conclusions (Fatt & Ramadas, 2018). Lately, big data incorporates real-time information that analyzes data immediately it is produced or used to minimize recency bias and its effects. Healthcare centers have various departments, limited staff, and a varying influx of patients with dissimilar needs, rendering healthcare service very dynamic. Big data optimizes workflow by providing a reference point for patients’ information to the involved departments and professionals to smoothen coordination (Pramanik & Pal, 2018).

However, big data also poses the risks of breach of confidentiality, security, and difficulty in sharing data. First, there is a risk of misappropriation of patients’ health information, especially genetic data that may affect their personal lives like insurance coverage and employment (Salas-Vega et al. 2016). Confidentiality is correlative with data security, the risk of big data being accessed by unauthorized third parties like hackers. Second, sharing big data is a challenge because of limited standardization, interoperability, barriers of information like lack of equipment, and insufficient integration of the data (Hong et al. 2018).

I have observed that the integration of big data with Artificial Intelligence (AI) may effectively mitigate the challenges and risks of big data. AI can analyze vast data, and enhance existent software to decimate cyber-attacks. Moreover, AI algorithms are necessary to make big data logical for sharing and application in healthcare (Dash et al. 2019). I recommend the integration of AI into big data to address the challenges of data security, confidentiality, and sharing.

Conclusion

In conclusion, big data yields numerous rewards to healthcare. Some of these are it reduces recency bias and optimizes healthcare workflows. However, big data is also associated with challenges of confidentiality and security of patient data and sharing the data for the application. To address these challenges, I recommend the incorporation of AI to the acquisition, analysis, and sharing of health care big data.

References

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis, and future prospects. Journal of Big Data, 6(1), 54.
https://doi.org/10.1186/s40537-019-0217-0

Fatt, Q. K., & Ramadas, A. (2018). The usefulness and challenges of big data in healthcare. J. Healthc. Commun, 3(2), 21. DOI: 10.4172/2472-1654.100131

Hong, L., Luo, M., Wang, R., Lu, P., Lu, W., & Lu, L. (2018). Big data in health care: Applications and challenges. Data and Information Management, 2(3), 175-197.
https://doi.org/10.2478/dim-2018-0014

Pramanik, P. K. D., Pal, S., & Mukhopadhyay, M. (2019). Healthcare big data: A comprehensive overview. In Intelligent Systems for Healthcare Management and Delivery (pp. 72-100). IGI Global. DOI: 10.4018/978-1-5225-7071-4.ch004

Salas-Vega, S., Haimann, A., & Mossialos, E. (2015). Big data and health care: challenges and opportunities for coordinated policy development in the EU. Health Systems & Reform, 1(4), 285-300. https://doi.org/10.1080/23288604.2015.1091538.