please give feedback, please add 2 references
Laura Diaz
RE: Discussion – Week 5
COLLAPSE
Big data in healthcare refers to consumer, patient, physical, and clinical data sets that are too large or complicated to be processed in the conventional way. Machine learning algorithms and data scientists are often relied upon instead to process huge data (Mehta et al., 2019). From the emergence of new diseases to the pursuit of maximum operating efficiency, the clinical sector faces a wide range of obstacles. These problems in healthcare can benefit greatly from the insights of big data analytics. Health care providers are aided by electronic health records in their quest to collect patient demographic and medical information such test results, clinical data, diagnoses, and ailments. As part of their business intelligence strategies, healthcare organizations use big data to look back at patient admission rates and assess employee productivity. With the aid of predictive analytics, healthcare providers can reduce healthcare costs while simultaneously improving the quality of their services. Improvements in financial and administrative performance and fewer readmissions are two further ways in which big data aids in the fight against drug mistakes. With the help of Big Data, businesses may streamline their operations and cut costs. By analyzing their Big Data, hospitals and other medical facilities can find ways to save money on things like patient admissions, diagnostics, and internal operations.
Data security, health data collecting, sharing, and utilization all present significant difficulties for big data. The ability to analyze large amounts of data using state-of-the-art methods allows for improved data storage and more rational decision-making. Privacy, security, standards, and governance are all important concerns. Since nanotechnology plays a significant role in the drug delivery process of cancer treatment, information like nano particle therapy on cancer patients might also be combined in big data to provide an overview and optimal treatment for cancer. Companies frequently lack even the most fundamental understanding of big data, including its definition, its potential benefits, and the technical requirements for supporting it (Tzanou, 2020). A big data adoption initiative is destined to failure if its goals are not well defined from the outset. It’s possible for businesses to squander a lot of time and money on things they don’t understand. The acceptance of big data, which represents a significant shift for an organization, should begin at the very top and work its way down. More training and workshops should be organized by IT and informatics departments to increase big data awareness and knowledge at all levels.
The introduction and utilization of brand-spanking-new big-data-solutions require close supervision if we’re going to do more than just pay lip service to big data. Top management should exercise caution, though, because too much control might have negative consequences. Big data is beginning to revolutionize the industry just as it has transformed many others, but much remains to be done. The industry is embracing a number of cutting-edge technologies that will propel it into the future and allow it to operate more efficiently and effectively (Krishnan, 2020). These technologies include the ability to predict daily patient income so that staffing levels can be adjusted accordingly and the use of electronic health records. It is also useful for bolstering data security and reducing fraud.
References
Krishnan, K. (2020). Building the big data application. Building Big Data Applications, 175-197. https://doi.org/10.1016/b978-0-12-815746-6.00010-7
Mehta, N., Pandit, A.,