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DATA ANALYTICS 2
Data Analytics in Health Organizations
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Data Analytics in Health Organizations
There is a general agreement among data analytics experts that using big data analytics can improve the quality of health care that leading health facilities provide. Data analytics have helped ensure an increase in care delivery and better disease diagnosis, which has led to a reduction in administration costs.
In deciding the best data analytics approach, healthcare facilities have to ensure that they use comprehensive and quality training data in their system. The more data is collected, the higher the chances that algorithmic systems will generate more accurate models (Lee & Shin., 2019). At the same time, organizations should be aware that obtaining quality data is an intensive exercise that requires extensive research.
Secondly, the organizations must ensure that they employ effective methods to eliminate bias in the system data analysis and algorithms. There are concerns that health systems are overlying on algorithms biased across ages, races, and other physical features (Khalil et al., 2020). The more these claims keep emerging, the fewer patients will believe in their prediction, thus lowering their confidentiality. The use of techniques such as accredited machine learning algorithms can g help in the elimination of bias.
The third recommendation is that, regardless of the strategy these organizations would use, they should ensure that the tools used are quality and there are high levels of maintaining the patient’s privacy. Suggestions such as decentralization of data to ensure minimum damages should be embraced in case of any breach (Mavriki & Karyda., 2020). Before deploying any analytic data system, they should be tested by accredited personnel to ensure that they meet the set market standards. More importantly, they should ensure that they look at any emerging issues, laws, and market practices to incorporate them in their data analytic designs. Cumulatively, these organizations must appreciate that data analytics is not a one-day event but a perfect process that requires evidence-backed techniques.
References
Khalil, A., Ahmed, S. G., Khattak, A. M., & Al-Qirim, N. (2020). Investigating bias in facial analysis systems: A systematic review.
IEEE Access,
8, 130751-130761.
https://doi.org/10.1109/ACCESS.2020.3006051
Lee, I., & Shin, Y. J. (2020). Machine learning for enterprises: Applications, algorithm selection, and challenges.
Business Horizons,
63(2), 157-170.
https://doi.org/10.1016/j.bushor.2019.10.005
Mavriki, P., & Karyda, M. (2020). Big Data Analytics in Healthcare Applications: Privacy Implications for Individuals and Groups and Mitigation Strategies. In
Information Systems: 17th European, Mediterranean, and Middle Eastern Conference, EMCIS 2020, Dubai, United Arab Emirates, November 25–26, 2020, Proceedings 17 (pp. 526-540). Springer International Publishing.
https://doi.org/10.1007/978-3-030-63396-7_35
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