Research topic of Big Data & Analytics for Healthcare specifically.

Write a minimum two-page explaining how it works and also if in your opinion it is Ethical or not.

Use APA 7 Format and list all your references.

ORDER A PLAGIARISM-FREE PAPER HERE

We’ll write everything from scratch

Big Data & Analytics in Healthcare

Healthcare has not been left behind due to the adoption of Big Data and analytics in various industries. Healthcare providers can enhance the quality of patient care while making the processes more efficient and informed. However, the application of ethical big data in the healthcare industry remains a topic of discussion. This paper focuses on utilizing big data and analytics in the healthcare sector and attempts to answer whether it is ethical.

How Big Data & Analytics Work in Healthcare

Batko and Ślęzak (2022) described Big Data in healthcare as massive data from new sources such as EHRs, medical imaging, genomics, patient monitoring devices, and social media. Analytics, therefore, refers to categorizing and making meaning out of this kind of information. Knowledge acquisition and integration are crucial in this process. EHRs hold detailed patient information, including patient history, present and previous diseases, medications, and care plans. This information is compiled across different organizations, giving a holistic view of a patient’s health. Most imaging applications such as MRI, CT scans, and X-rays generate vast volumes of data. Integrating intricate algorithms into such images aids in the early detection of diseases and treatment planning. Genomics is the process whereby the entire genome of a particular patient is sequenced to obtain as much information as possible about their diseases and treatments (Brlek et al., 2024). Smartwatches, fitness trackers, and wearable IoT devices track health data like pulse rate, physical activity, and rest, allowing for frequent check-ups and detecting health issues.

Writing Assignment Week 2

Data processing and analysis follow next. Van Calster et al. (2019) noted that with the help of historical data, predictive analytics can also predict future health status. For instance, the use of predictive models assists in identifying patients who are likely to be readmitted so that necessary actions can be initiated. The signs of diseases such as cancer or diabetes are detected at an early stage with the help of machine learning algorithms analyzing medical images or electronic health records. Some approaches applied in Natural Language Processing (NLP) include analyzing raw text data extracted from clinical notes and research articles.

Big data has numerous uses in the healthcare sector. Big data also supports the concept of precision medicine, where an individual’s unique genetic profile, behaviors, and surroundings are considered in developing treatment plans. Wearable technology offers up-to-date details, allowing for early intercession and increased positive patient outcomes. Data analysis on hospital operations, for example, patient throughput and resource consumption, enables organizations to improve efficiency and productivity and decrease costs. In addition, big data fuels medical research by giving researchers large datasets, hence speeding up treatment and drug discovery, as highlighted by Awrahman et al. (2022).

Writing Assignment Week 2

Ethical Considerations

The ethical issues associated with applying big data in the health sector are Privacy, Consent, and Security. Patients’ confidentiality is Sacro sensitive since big data deals with patients’ personal sensitive information, and any privacy violation has extreme effects (Tariq & Hackert, 2023). It is, therefore, critical for patient data to be protected by strong data encryption and access control mechanisms. The patient also must know how their data will be used and should agree to it. This means that transparency when it comes to the policies regarding data collection and usage is paramount. Healthcare is one of the industries most targeted by cybercriminals; therefore, measures should be implemented to enhance security and prevent data loss. To eliminate these risks, it is recommended that frequent auditing be carried out and necessary changes to security measures made. Furthermore, algorithms and even predictive models may be unfair due to inheriting the bias from training data. It is, therefore, crucial that diverse and inclusive data be gathered and that bias checks be run on the algorithms regularly to ensure that the system is fair.

Conclusion

Big data and analytics bring more opportunities to the healthcare industry by enhancing the quality of patient treatment, organizational efficiency, and study. Nevertheless, seeking ways to solve ethical problems, such as privacy, consent, and security in the context of big data, is crucial. Big data needs to be moral in the healthcare setting, taking the following steps to protect patient information and being transparent about the usage of such information. Thus, ethical norms could assist the industry in leveraging big data to enhance care delivery without compromising patients’ trust and privacy.

References

Awrahman, B. J., Aziz Fatah, C., & Hamaamin, M. Y. (2022). A review of the role and challenges of big data in healthcare informatics and analytics. Computational Intelligence and Neuroscience, 2022(5317760), 1–10. https://doi.org/10.1155/2022/5317760

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

Brlek, P., Bulić, L., Bračić, M., Projić, P., Škaro, V., Shah, N., Shah, P., & Primorac, D. (2024). Implementing Whole Genome Sequencing (WGS) in Clinical Practice: Advantages, Challenges, and Future Perspectives. Cells, 13(6), 504. https://doi.org/10.3390/cells13060504

Tariq, R. A., & Hackert, P. B. (2023, January 23). Patient Confidentiality. Nih.gov; StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK519540/

Van Calster, B., Wynants, L., Timmerman, D., Steyerberg, E. W., & Collins, G. S. (2019). Predictive analytics in health care: how can we know it works? Journal of the American Medical Informatics Association, 26(12), 1651–1654. https://doi.org/10.1093/jamia/ocz130