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.
Benefit of Using Big Data in Clinical Systems
One significant benefit of using big data in clinical systems is improved patient outcomes through predictive analytics. By analyzing vast amounts of patient data, including electronic health records (EHRs), genetic information, and real-time monitoring, clinicians can identify patterns that predict disease onset, progression, and response to treatment. For example, big data analytics can help detect early warning signs of sepsis in hospitalized patients by analyzing vital signs, lab results, and historical data, allowing for early intervention and improved survival rates (Hassan et al., 2021).
Challenge or Risk of Using Big Data in Clinical Systems
A major challenge of using big data in healthcare is data privacy and security concerns. Since big data involves collecting and storing sensitive patient information, it is at risk of cyberattacks, data breaches, and unauthorized access. A notable example is the 2015 cyberattack on Anthem Inc., which exposed the health records of nearly 80 million individuals (McCoy & Perlis, 2018). Such breaches not only compromise patient confidentiality but also erode trust in healthcare institutions.
Mitigation Strategy
To mitigate security risks, implementing robust encryption and multi-factor authentication (MFA) measures is essential. Encryption ensures that patient data remains secure during storage and transmission, making it unreadable to unauthorized individuals. MFA adds an extra layer of protection by requiring multiple forms of verification before granting access to sensitive data. Additionally, regular cybersecurity training for healthcare staff can help prevent phishing attacks and insider threats. For example, organizations such as the Mayo Clinic have successfully integrated advanced cybersecurity protocols and continuous security monitoring to protect patient data while utilizing big data analytics to enhance patient care (Sittig & Singh, 2020).
References
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Hassan, M. M., Islam, M. T., & Kwak, K. S. (2021). Big data analytics for healthcare: Challenges and opportunities. Healthcare Informatics Research, 27(2), 96-109.
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McCoy, T. H., & Perlis, R. H. (2018). Big data and health care: Using analytics to identify and manage high-risk and high-cost patients. New England Journal of Medicine, 379(3), 273-282.
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Sittig, D. F., & Singh, H. (2020). A socio-technical approach to preventing cybersecurity threats in healthcare. Journal of the American Medical Informatics Association, 27(4), 639-647.