Response 1
Data gathering provides some possible advantages for the e-health industry. The provision of health-related Big Data will also be a delicate problem for various institutions: a positive effect on medical and medical activities. These institutions face many changes that could impact the healthcare system's survival (Pastorino et al., 2019). By 2025, life expectancy is expected to rise more, leading to more people living longer, but not always in a stable and active climate. This will place more pressure on the costs and productivity of healthcare.
Big data will offer new insights into disease risk factors. The patient is more directly interested, and data from electronic healthcare apps or smart devices will be imported. This information can be processed in real-time and easily altered habits that can mitigate health hazards, minimize adverse reactions to the atmosphere or improve health results. Big data will also help classify high-risk and cost-effective patients and intervene promptly. Effective methods to handle these data will also make it possible for reliable drugs to detect the heterogeneity of patient reactions to therapies and customize healthcare to the individual's unique needs. These things could potentially result in a decrease in inefficiency and an increase in healthcare system cost control.
However, because of the personal value of the included knowledge, the use of Big Data in healthcare raises new ethical and legal problems (Pastorino et al., 2019). The possibility of violating anonymity, personal autonomy, and influence on government requests for accountability, confidence, and justice by using big data are ethical and legal challenges. The critical challenges in technological and infrastructural terms that challenge large-capacity healthcare have been data fragmentation, data security, computational flows in data collection, and the absence of sufficient data-store infrastructure.
Big data continues to revolutionize healthcare by providing pathways and solutions for improving people's health, improving healthcare systems' quality and performance.
References
Pastorino, R., De Vito, C., Migliara, G., Glocker, K., Binenbaum, I., Ricciardi, W., & Boccia, S. (2019). Benefits and challenges of Big Data in healthcare: an overview of the European initiatives. European journal of public health, 29(Supplement_3), 23-27.
Response 2
In Healthcare industry, big data includes collection of all the clinical data of the patient. Once this complex data has been collected, it analyzed and interpret by traditional methods of data processing. Machine learning algorithms and data scientists usually process the big data. The aim behind e-Healthcare industry is to use all this collected information for producing actionable insights such as to prevent epidemics, cure disease, cut down cost and provide quality care.
Here we will be discussing about knowledge Discovery and Information Interpretation in e-Healthcare industry:
The Healthcare industry has various processes, including diagnosis, treatment, and prevention of diseases, injuries, and impairments in human beings. This industry is transforming at a great pace, and it is rich in data generated from a patient’s medical records, personal information, benchmarking findings, and administrative reports. These healthcare data are essential to the industry because they are a source of knowledge and valuable information required in the clinical practice. Large volumes of data in the healthcare industry helps in the prediction of various diseases and assist doctors in diagnosis and making clinical decisions. Through the use of Internet of Things (IoT) devices, doctors obtain data that enable them to monitor personal health of their clients, model the spread of disease, and come up with measures to contain the outbreak of that disease (Dash et al., 2019). The IoT devices that generate large amounts of healthcare data include biosensors, health-tracking wearable devices, and devices used to monitor vital signs. The integration of these devices with electronic medical records and personal health records provide data that can be interpreted to understand a patient’s health status (Jothi et al., 2015).
However, there are significant challenges in knowledge discovery and information interpretation in big data analytics. The major challenge lies in the interpretation patterns of information after analysis. The use of Internet of Things devices generates large volumes of data that require the use of Machine Learning and Artificial intelligence to interpret. However, there has been a challenge of a simple representation of knowledge that has been extracted from big data (Ayani et al., 2019). It is challenging to develop and apply the interpreted knowledge if it is not novel. Besides, there is a need for multidisciplinary expert teams to identify invalid patterns and accredit the knowledge extracted. Interdisciplinary research on knowledge discovery in databases has emerged in this decade. In health care, pattern recognition has been linked with expertise. Data mining, as automated pattern recognition, is a set of methods applied to KDD that attempts to uncover patterns that are difficult to detect with traditional statistical methods. This usually involves the detection of "outliers", pattern recognition over large data sets, classification, or clustering using statistical modeling. Medical data has a lot of information buried within it that will reveal patterns relating to successes and failures in clinical operations. For example, instead of the user asking for a report of patients with congestive heart failure, the provider can ask for patterns leading to a lower hospital admission rates for these patients.
References
Ayani, S., Moulaei, K., Khanehsari, S. D., Jahanbakhsh, M., & Sadeghi, F. (2019). A Systematic Review of Big Data Potential to Make Synergies between Sciences for Achieving Sustainable Health: Challenges and Solutions. Applied Medical Informatics., 41(2), 53-64. Retrieved from: https://ami.info.umfcluj.ro/index.php/AMI/article/download/642/638
Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0217-0
Jothi, N., Rashid, N. A., & Husain, W. (2015). Data Mining in Healthcare – A Review. Procedia Computer Science, 72, 306–313. https://doi.org/10.1016/j.procs.2015.12.145
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