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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.
Skijnai Summerall
Module 3 Week 5 Discussion- Initial Post
Benefits of Using Big Data in Clinical Systems
One potential benefit of using big data as part of a clinical system is the ability to analyze large amounts of health information to identify patterns and trends that can inform decision-making and improve patient outcomes. For example, by analyzing data from electronic health records, a healthcare provider may be able to identify risk factors for certain diseases in specific patient populations, allowing for targeted interventions and preventative measures. To achieve this goal, it is necessary to implement systems that will be able to learn quickly about the data generated by people within clinical care and everyday life. This will enable data-driven decision making, receiving better personalized predictions about prognosis and responses to treatments (Batko & Ślęzak, 2022).
Challenges and Risks of Using Big Data in Clinical Systems
The invasion of patient privacy is considered as a growing concern in the domain of big data analytics due to the emergence of advanced persistent threats and targeted attacks against information systems (Abouelmehdi et al., 2018).
One potential challenge of using big data in a clinical system is ensuring the privacy and security of patient information. As the amount of data collected and stored increases, there is a greater risk of data breaches and unauthorized access to sensitive health information. Because of the centralization of much health care information, the data are highly vulnerable to attacks. This can not only violate patient confidentiality but also erode trust in the healthcare system. Extreme care should be taken to protect patient privacy, and privacy concerns pose limitations in linking external data to individual insured data, which may improve consumer health-related experience and personalize service and care (Hong et al., 2018).
Mitigating the Challenges and Risks
One strategy to mitigate the risks of using big data in a clinical system is to implement robust data security measures, such as encryption, access controls, and regular audits of data systems. Healthcare organizations also implement security measures and approaches to protect their big data, associated hardware and software, and both clinical and administrative information from internal and external risks.
Additionally, healthcare organizations can ensure compliance with regulations such as the Health Insurance Portability and Accountability Act (HIPAA) to protect patient privacy. Regular training for staff on data security best practices can also help prevent breaches and unauthorized access to patient information. By prioritizing data security and privacy, healthcare organizations can harness the power of big data while maintaining patient trust and confidentiality.
As a healthcare professional, I have experienced challenges and risks associated with complex health information access and management. One common challenge is the fragmentation of health information across different systems and platforms, making it difficult to access a comprehensive view of a patient’s medical history. This can lead to gaps in information and potential errors in decision-making.
Another challenge is ensuring the accuracy and reliability of health information, especially when data is entered manually or transferred between systems. Inaccurate or incomplete information can impact patient care and outcomes, leading to potential risks such as medication errors or misdiagnosis.
Furthermore, maintaining the security and privacy of health information is a critical concern. With the increasing use of electronic health records and digital communication, there is a heightened risk of data breaches and unauthorized access to sensitive patient information. Protecting patient confidentiality and complying with regulations such as HIPAA requires robust security measures and ongoing vigilance.
References:
Abouelmehdi, K., Beni-Hessane, A., & Khaloufi, H. (2018). Big healthcare data: preserving security and privacy. Journal of Big Data, 5(1). https://doi.org/10.1186/s40537-017-0110-7Links to an external site.
Links to an external site.
Batko, K., & Ślęzak, A. (2022). The use of Big Data Analytics in healthcare. Journal of Big Data, 9(1). https://doi.org/10.1186/s40537-021-00553-4Links to an external site.
Hong, L., Luo, M., Wang, R., Lu, P., Lu, W., & Lu, L. (2018). Big data in health care: applications and challenges. Data and Information Management, 2(3), 175–197. https://doi.org/10.2478/dim-2018-0014Links to an external site.
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