Leveraging Big Data in Clinical Systems – Benefits, Challenges, and Mitigation Strategies

Leveraging Big Data in Clinical Systems – Benefits, Challenges, and Mitigation Strategies

Potential Benefit of Big Data in Clinical Systems

One of the benefits of big data for clinical systems is based on the effectiveness of predictive analysis. The surgical intensive care unit (SICU) can benefit from predictive analytics. For instance, from EHRs, it is possible to apply a machine learning method to develop algorithms that indicate that the patient has a high probability of developing other associated complications, such as sepsis, as highlighted by Wang et al. (2019). As such, these models help diagnose conditions or diseases in their early stages, which makes treatment possible, thus reducing the incidence of diseases and deaths. It can also make patient treatment more personalized, including ensuring that patients most at risk receive more attention and one-on-one care. Consequently, big data is essential in enhancing patient satisfaction, resource management, and quality of healthcare solutions.

Potential Challenge or Risk of Big Data in Clinical Systems

The integration of big data into clinical systems also has its challenges, as discussed under data management challenges, data privacy, and data security. The first argument for this is that healthcare organizations generate and retain a comparatively large volume of sensitive data. For example, in a SICU, patient data may be exposed, which can lead to a severe violation of privacy and misuse of information (Seh et al., 2020). Some of the consequences of such a break include betraying the patient’s trust, lawsuits, and the cost of dealing with the consequences of such a break.

Strategy to Mitigate Challenges

To mitigate the risks associated with the use of Big Data, adequate actions are required to minimize cyber threats. From the study done by Suleski et al. (2023), a few of the effective measures that may be adopted include the use of secure encryptions and Multi-Factor Authentication (MFA) systems. For instance, all the information circulating in the hospital may be encrypted to ensure that only the right people get access while in transit. Even if the unauthorized person manages to obtain the login credentials, MFA can serve as an additional precaution to keep them safe. Healthcare workers should also undergo periodic training in data security measures because human negligence remains a major source of threats. Considering these threats, it is safe to say that by creating a culture of safety and being alert to the threats, big data can be safely adopted to enhance the patient’s health.

References

Seh, A. H., Zarour, M., Alenezi, M., Sarkar, A. K., Agrawal, A., Kumar, R., & Khan, R. A. (2020). Healthcare data breaches: Insights and implications. Healthcare, 8(2), 133. NCBI. https://doi.org/10.3390/healthcare8020133

Suleski, T., Ahmed, M., Yang, W., & Wang, E. (2023). A Review of Multi-factor Authentication in the Internet of Healthcare Things. Digital Health, 9(1), 205520762311771-205520762311771. https://doi.org/10.1177/20552076231177144

Wang, Y., Kung, L., & Byrd, T. A. (2019). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126(1), 3–13.

ORDER A PLAGIARISM-FREE PAPER HERE

We’ll write everything from scratch

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.

Leveraging Big Data in Clinical Systems – Benefits, Challenges, and Mitigation Strategies

Propose at least one strategy you have experienced, observed, or researched that may effectively mitigate the challenges or risks of using the big data you described. Be specific and provide examples.