Big Data Risks and Rewards 

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Big Data Risks and Rewards 

 

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Big Data Risks and Rewards 

The capacity to enhance patient outcomes via personalized medicine is a possible advantage of employing big data as part of a healthcare system. Personalized medicine entails tailoring healthcare efforts, such as prevention, diagnosis, and treatment, to each patient’s unique features. Using big data, healthcare practitioners may gather and analyze vast volumes of patient data in order to detect trends and design individualized treatment regimens for each patient. This may result in more accurate diagnoses and more effective therapies, leading to improved patient outcomes.

According to Cirillo & Valencia (2019), it is possible to use big data analytics to uncover individualized treatment choices for cancer patients based on their clinical and genetic data. According to this research, cancer patients were given more personalized treatments when big data analytics were used in clinical decision-making. This led to better patient outcomes and lower healthcare expenditures (Cirillo & Valencia, 2019). Another research discovered that healthcare practitioners might use big data analytics to identify patients who are at high risk of readmission and devise personalized treatments to reduce readmissions. According to the findings of this research, healthcare practitioners were able to identify variables that related to readmissions and devise treatments to address these issues by analyzing vast volumes of patient data, resulting in a decrease in readmissions and healthcare expenditures (Flaks-Manov et al., 2020).

However, using big data as part of a clinical system also poses challenges and risks. One of the challenges is making sure that the data is accurate and of high quality. Since there is so much data to process, there is a possibility of mistakes and inconsistencies, both of which may have a detrimental effect on patient care. Patients might be harmed by inaccurate or insufficient data leading to misdiagnoses or improper treatments.

Data quality control procedures, such as data validation, may be used by healthcare organizations as a means of mitigating this challenge. These processes guarantee that data are correct, accurate, and comprehensive. The process of data validation comprises the use of data profiling methods with the goal of identifying mistakes and inconsistencies within the data and rectifying them before the data are utilized for clinical decision-making (Chi et al., 2021).

The possible issue of privacy violations and breaches of personal patient information is another potential concern associated with the use of big data as a component of a clinical system. The danger of patient data being accessed by unauthorized persons or groups is rising as a result of the proliferation of gathered and shared data. This has the potential to create ethical and legal issues, as well as damage the trust that patients have in the healthcare system.

Healthcare companies may limit this risk by using data security policies, such as access restrictions and encryption, to safeguard the privacy of their patients and prevent unauthorized access to their patient’s medical records and other personal information (Kayaalp, 2018). It is also possible for healthcare providers to engage in open and honest communication with patients about the uses that will be made of their data and the individuals who will have access to it. This can contribute to the development of trust and improve patient engagement.

To sum it up, although incorporating big data into a clinical system may have certain advantages, there are challenges and risks associated that must be considered and mitigated. Healthcare organizations may prevent these risks and leverage the potential of big data to enhance patient outcomes and lower healthcare costs by employing tactics like data quality control procedures and data security standards.

 

 

 

References

Chi, E. A., Chi, G., Tsui, C. T., Jiang, Y., Jarr, K., Kulkarni, C. V., … & Sinha, S. R. (2021). Development and validation of an artificial intelligence system to optimize clinician review of patient records. JAMA Network Open, 4(7), e2117391-e2117391.

Cirillo, D., & Valencia, A. (2019). Big data analytics for personalized medicine. Current opinion in biotechnology, 58, 161-167.

Flaks-Manov, N., Srulovici, E., Yahalom, R., Perry-Mezre, H., Balicer, R., & Shadmi, E. (2020). Preventing hospital readmissions: healthcare providers’ perspectives on “impactibility” beyond EHR 30-day readmission risk prediction. Journal of General Internal Medicine, 35, 1484-1489.

Kayaalp, M. (2018). Patient privacy in the era of big data. Balkan medical journal, 35(1), 8–17.

QUESTION

Discussion Question

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.

 

 

 

The web link provided is an article to read and reflect on. You do not have to use it but if you do, please included in the reference list and intext citation in proper APA 7 format.

 

Minimum of 3 references

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