Respond to at least two of your colleagues* on two different days, by offering one or more additional mitigation strategies or further insight into your colleagues’ assessment of big data opportunities and risks.
Main Post:
Good day classmates,
Big Data Means Big Potential
One of the benefits of using big data in a clinical setup is articulating precise diagnosis and treatment for a disease. The presence of some biomarkers will help identify predisposing conditions. For example, mutated proteins in the body will identify that the body is predisposed to cancer. The BRCA1 and BRCA2 gene mutations identify breast cancer. Such information will help the clinic specialists to generate and analyze a patient’s health history. It is beneficial in understanding irrefutable patterns in a patient’s treatment routine. As everyone’s genetic profile is idiosyncratic, a personalized medical approach is customized to suit the patient (Ebeling, 2016).
Biomarkers can allow for genetic testing, especially in inherited diseases. This would give clinical technicians the upper hand in detecting the onset of a disease. Therefore, administering peculiar drugs further enhances prevention techniques. The biomarkers have been efficient in genetic testing of the population. One of the paramount risks of using big data is data breaches and hacking (McCue & McCoy, 2017) A clinical setup using big data requires contemporary personal identifiers, genetic makeup, and a patient’s health history. This sensitive information may lead to cyber security threats that encompass blackmail and financial fraud.
The mitigating strategies for such inconveniences include inventing blueprints specific to the clinic’s data security system. For example, security checkups, controls, and passkeys should be devised to ensure only authorized access. It would be wise to allow encoded encryption and activation codes when accessing such vast data (L’heureux et al., 2017). Additionally, enforcing a tight privacy policy should be mandatory. Many clinics have made it requisite to inform the patients how their data will be used and stored. Transparency would ease the situation in case of a breach.
References
Ebeling, M. F. (2016). Healthcare and big data. London, New York: Palgrave Macmillan.
L’heureux, A., Grolinger, K., Elyamany, H. F., & Capretz, M. A. (2017). Machine learning withbig data: Challenges and approaches. Ieee Access, 5, 7776-7797. https://doi.org/10.1109/ACCESS.2017.2696365
McCue, M. E., & McCoy, A. M. (2017). The scope of big data in one medicine: unprecedented opportunities and challenges. Frontiers in veterinary science, 4, 194. https://doi.org/10.3389/fvets.2017.00194
