Big Data Risks and Rewards Discussion Paper

Big Data Risks and Rewards Discussion Paper

Big Data Risks and Rewards Discussion Paper

Big data entails a large amount of information collected through technology used to generate meaning in the world. Various emerging technologies are deployed in healthcare settings to ascertain efficiency in care provision and assist improve patient outcomes. For example, mental health professionals use big data technology in handling patients (Dash et al., 2019). This entails the gathering, analyzing, and dissemination huge volumes of patient’s physical, and clinical data which is complex to understand. Big data allows mental health professionals to enhance efficient operations and increase their attention to patients and their mental health conditions.

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Mental health professionals utilize big data to offer information concerning diseases and their warning signs as well as administer treatment interventions. Mental health providers also use big data technology to make decisions and understand different components of co-morbidities and death cases emanating from mental health disorders (Hong et al., 2018). For instance, mental health providers gather data on opioid use disorder as well as other components of substance use disorder to make better decisions. They also leverage this data in evidence-based practice (EBP) interventions (Pastorino et al., 2019). Further, big data helps un enhancing efficiency in mental health settings as psychiatric mental health nurses use this technology in examining trends about patient admissions.

Conversely, the use of big data comes with increased challenges and data security concerns. Risks to big data include patient privacy and confidentiality, breaches to existing privacy rules, and possible access to patient health information through unauthorized access (McGonigle & Mastrian, 2021). Further, big data in health system is complex as it comprises of different and large amounts with each having its language which requires effective translation.

It is essential to develop strategies aimed at mitigating some of these challenges. Research studies show that use of data mining as a technique can help sort out the different types of complex data in healthcare, especially in mental health service provision. The use of algorithms as an effective data mining technique can help address the inherent challenges of the complex nature of healthcare data (McGonigle & Mastrian, 2021). Further, effective system upgrades and having better storage capacities are essential in managing big data challenges in healthcare settings.

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(54). DOI: 10.1186/s40537-019-0217-0

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. DOI: 10.2478/dim-2018-0014

McGonigle, D., & Mastrian, K. (2021). Nursing informatics and the foundation of knowledge.

            Jones & Bartlett Publishers.

Pastorino, R., De Vito, C., Migliara, G., Glocker, K., Binenbaum, I., Ricciardi, W., & 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. https://doi.org/10.1093/eurpub/ckz168

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To Prepare:

Review the Resources and reflect on the web article Big Data Means Big Potential, Challenges for Nurse Execs.
Reflect on your own experience with complex health information access and management and consider potential challenges and risks you may have experienced or observed.

Post a description of at least one potential benefit of using big data as part of a clinical system and explain why. Then, describe at least one potential challenge or risk of using big data as part of a clinical system and explain why. Propose at least one strategy you have experienced, observed, or researched that may effectively mitigate the challenges or risks of using big data you described. Be specific and provide examples.