Knowledge Discovery and Information Interpretation
When healthcare is the case, big data involves collecting, analyzing, and leveraging consumers, patient, physical, and clinical data, too complex to comprehend via the usual process of data processing. Therefore the processing of big data is done by machine learning algorithms as well as data scientists. Healthcare big data has gained momentum over the past few years. This can be attributed to or rather a response to the digitalization of information affecting healthcare and the rise value-based care that has propelled the industry to employ data analytics to develop informed and strategized business decisions.
The healthcare business has numerous activities, that is its responsibility. Activities such as patient treatment, disease, and pathogen diagnosis and disease or injury prevention depend on the industry in a bit to improve human life. As a result, vast data quantity is generated and involved. This kind of data and information results from the medical records of patients, their personal and relevant information, as well as administrative reports and sometimes benchmarking findings. Thus there is a bit to secure and safeguard such information as to its very vital and crucial to the industry as it’s the main knowledge sources and general the data or information is required to be able to process the universal practices involved in the industry (Healthgrades, 2020).
There is a relationship between huge sums of data and disease prediction. This is because gigantic volumes of data within the healthcare industry play the role of aiding in the prediction of numerous and consequently enable the medical practitioners in the making of clinical decisions and diagnosis (Healthgrades, 2020). Additionally, Doctors and other verified medical practitioners can take advantage of the availability of the Internet of things as a source of obtaining data that can aid in the monitoring of the proceedings of a patient’s health control spread of diseases and be in a position to create and come up with protocols that when followed can help mitigate diseases outbreaks.
Also, the Internet of Things can generate vast amounts of data when integrated with the healthcare facilities. Some of the devices that have a high potential of generating huge data amounts include biosensors, devices that vital monitor signs, and devices that can be worn by patients or other individuals to track the health of the people wearing them. Besides, integrating IoT in healthcare will ease giving data necessary for understanding the health status of patients.
Unfortunately, the discovery of knowledge and interpretation of information in big data analytics is faced by numerous challenges. Like any other industry, challenges are encountered, which has a negative outcome to the whole integrity of knowledge discovery and data interpretation. The greatest challenge encountered is big data sorting and information prioritizing. This is because the data generated daily is vast and large to the extent that it sometimes becomes very difficult to determine what data is more urgent, crucial and what kind of insights are useful (Sadeghi et al., 2019). What follows is the use of Artificial intelligence and machines learning to process this kind of data with exceptional nimbleness.
Subsequently, make sure that the right access to big data and insights as well as analysis is channeled to the individuals who need it is another huge challenge. The data collected ought to be availed to the right individuals considering that big data in healthcare is collected and generated from various sources.
Additionally, challenges resulting in heterogeneous (Missing claim data) exists. Every healthcare organization compounds the data, further filling claims with data from other Hospital Information System making the data more complex, making working with such data hard. Also, IoTs generate gigantic sums of data that humans cannot interpret, thus the necessity for Artificial intelligence and machine learning to construe (Sadeghi et al., 2019). As a result of the challenges resulting from healthcare data, the health systems ought to adopt the necessary technology with the capability of collecting, storing, and analyzing the generated data or information to have more practical perceptions.
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. Retrieved September 3, 2020, from https://ami.info.umfcluj.ro/index.php/AMI/article/download/642/638
Healthgrades. (2020). What is patient engagement? | Evariant: The leading healthcare CRM solution. Healthgrades | Evariant. Retrieved September 3, 2020, from https://www.evariant.com/faq/what-is-healthcare-big-data#:~:text