Respond to two of your colleagues by offering one or more additional mitigation strategies or further insight into your colleagues’ assessment of big data opportunities and risks.
Peer 1
Module 3 Discussion
Introduction
Big data has become a significant instrument in the healthcare industry, with enormous potential to transform clinical systems. But it also comes with hazards and difficulties. This talk examines a potential advantage of big data in clinical systems along with a related danger or obstacle. We’ll also talk about a plan to lessen the danger or problem that has been identified.
Benefit
Improving clinical decision-making is one important advantage of using big data in healthcare systems. Healthcare workers may get important information that supports evidence-based decision-making by evaluating vast volumes of organized and unstructured data, including electronic health records (EHRs), patient-generated data, and medical literature. Patterns, trends, and correlations that may be difficult to find with conventional techniques can be found with the use of big data analytics. These understandings can help with illness diagnosis, therapy personalization, outcome prediction, and enhanced patient safety (Park & Park, 2022).
Challenge/Risk
Ensuring data security and privacy is a significant concern when using big data in clinical systems. Patient data is in danger due to the growing volume and complexity of healthcare data. Because health data contains personally identifiable information (PII), cybercriminals find it to be appealing. Integrating data from many sources increases the possibility of inadvertent data leaks and illegal access, which might jeopardize patient confidentiality and privacy (Booth et al., 2021). Effectively managing and analyzing unstructured data presents another challenge for data analytics in the healthcare industry. This calls for the use of sophisticated text mining, natural language processing, and image recognition techniques to extract meaningful information from sources like clinical notes, research articles, and medical images (Krylov, 2023).
Mitigation Strategy
To solve issues with data privacy and security, healthcare institutions can take a complete strategy for safe data governance and encryption. This entails putting in place stringent access controls to ensure that only authorized personnel have access to data, protecting data during transmission and storage with essential encryption techniques, deleting patient data to reduce the possibility of re-identification, and regularly auditing and monitoring data access and usage to identify any security incidents (Batko & lęzak, 2022). Healthcare businesses may greatly lower their risk of data breaches and guarantee the privacy and security of patient data by putting these precautions in place.
Conclusion
It may be possible to improve patient outcomes and clinical decision-making by utilizing big data in healthcare systems. However, to fully profit from big data analytics, concerns related to data privacy and security must be successfully addressed. Encryption techniques, frequent monitoring, and secure data governance procedures may all be used to protect patient information and keep people’s faith in the healthcare system intact.
References
Batko, K., & Ślęzak, A. (2022). The use of big data analytics in healthcare. Journal of Big Data, 9(1). https://doi.org/10.1186/s40537-021-00553-4
Booth, R., Strudwick, G., McBride, S., O’Connor, S., & López, A. L. S. (2021). How the nursing profession should adapt for a digital future. The BMJ, n1190. https://doi.org/10.1136/bmj.n1190
Krylov, A. (2023, October 31). Data analytics in healthcare: Challenges and Uses. Kodjin – Turn-key FHIR Server for Healthcare Data. https://kodjin.com/blog/data-analytics-in-healthcare-challenges-and-solutions/#:~:text=One%20of%20the%20challenges%20of,research%20articles%2C%20and%20medical%20images.
Park, J., & Park, J. (2022). Identifying the knowledge structure and trends of nursing informatics. CIN: Computers, Informatics, Nursing, 41(1), 8–17. https://doi.org/10.1097/cin.0000000000000919
Peer 2
In this new age of globalization, virtually everything we do every day is captured in many things that interest us, even how long it takes to get from here to there. Whether sending an e-mail, buying a coffee, putting fuel in our car, or even entering secure facilities, each action now also creates the digital equivalent. The amount of data is mind-boggling, yet according to various organizations’ estimates, average people produce nearly a million bytes of information daily.
Extensive data analysis has done more than its share in shaking up many industries, providing previously unprecedented richness in information and opportunities. The possibilities for harnessing big data in healthcare have yet to be discovered. He contends that it will bolster clinical decision-making, individualize treatment plans, maximize resource use, and spur revolutionary research (Ahmed et al., 2020). Through the analysis of broad-base data, healthcare services can find patterns, point out outcomes, and apply individualized solutions based on anonymously analyzed big data to maximize patient potential.
However, there are also considerable drawbacks to using big data in health care. Amid the fog of war, ensuring that data stays secure and private is essential. If patients lose trust in the system, the consequences could be disastrous (Newaz et al., 2021). Further, the specialized, vast amounts of data involved are often of dense, impenetrable character that necessitates specialized tools, skills, and facilities to realize consequent insights (Saggi et al., 2018). Moreover, ethical concerns, like informed consent and the right to use one’s data, must be addressed appropriately.
In conclusion, big data is undoubtedly of great value to the future healthcare industry and encourages innovation. However, its use must also be seriously considered ethically from all angles. Balancing the promised benefits of better patient care and outcome results with taking on these inherent risks will be essential in making the best possible use of big data’s potential for healthcare.
References
Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020). Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database, 2020, baaa010.
Newaz, A. I., Sikder, A. K., Rahman, M. A., & Uluagac, A. S. (2021). A survey on security and privacy issues in modern healthcare systems: Attacks and defences. ACM Transactions on Computing for Healthcare, 2(3), 1-44.
Saggi, M. K., & Jain, S. (2018). A survey towards integrating big data analytics to significant insights for value-creation. Information Processing & Management, 54(5), 758-790.
Both of your analyses on big data opportunities and risks in healthcare are incredibly comprehensive and insightful. You’ve identified some critical points and provided great strategies to mitigate these risks. I’d like to add to both discussions:
Peer 1:
Your focus on encryption techniques, access controls, and secure data governance is crucial for mitigating data privacy and security risks. Additionally, implementing advanced anonymization techniques could be beneficial. By de-identifying or anonymizing patient data while maintaining its usability for analysis, healthcare institutions can strike a balance between data utility and patient privacy.
Peer 2:
Ethical considerations are pivotal, and I completely agree. In addition to ethical concerns like informed consent, it might be worthwhile to explore the concept of transparency and explainability in AI-driven decision-making processes. Ensuring that the models and algorithms used in healthcare are interpretable and can explain their outcomes can enhance trust and address concerns about the ‘black box’ nature of AI systems.
The challenges you’ve both highlighted are substantial, but with a multi-faceted approach that includes technological, ethical, and procedural solutions, harnessing big data’s potential in healthcare while minimizing risks is definitely achievable.