Be sure that the responses to the Post of peers include 2 peer reviewed references with content that demonstrates critical analysis and synthesis of references used.
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Big Data Analytics (BDA) is a set of techniques, technologies, systems, processes, procedures, and applications for analyzing large quantities of data to aid a business in better understanding its business, market, and making timely decisions (Galetsi, Katsaliaki, & Kumar, 2019).
Healthcare has constantly been inundated with a massive quantity of complex data that comes in at a breakneck speed. Data from hospitals and healthcare providers, medical insurance, medical devices, life sciences, and health research are produced in many industries in the health sector. With the progress in technology, the use of this data for healthcare transformation is enormous. Using analytics, machine learning, and artificial intelligence over extensive data allows trends and correlations to be identified and thus offers actionable insights into improving healthcare delivery (Mehta, Pandit, & Shukla, 2019).
For instance, healthcare data management may benefit organizations in areas such as the development of effective drugs and devices for patient well-being, fraud detection in billing, and service speed, as well as society in addressing global health issues such as disease prevention, public health surveillance, and timely provision of essential medical services during emergencies. (Galetsi, Katsaliaki, & Kumar, 2019).
Potential benefit of using big data as part of a clinical system
To provide the best possible patient outcomes, nurses are critical to patient care quality and safety. Nurses need to access and analyze a large amount of data about their patients and their treatment to make educated practice choices (Glassman, 2017). However, the high volume digital flow of information being produced in healthcare complicates the equation (Wang, Kung, & Byrd, 2018).
Big data analytics is being utilized in health care to enhance efficiency and quality, resulting in better healthcare practices and patient results. For example, it would be critical to learn more about each patient, such as if they had any other illnesses or diseases (comorbidities) that might influence their results and age, gender, educational level, and so on. The information gathered can create a more comprehensive set of evidence-based recommendations and support decision-making (McGonigle & Mastrian, 2017).
Competence in informatics enables nurses to communicate, manage knowledge, reduce error, and enhance decision-making at the point of care by using information and technology (Glassman, 2017). Thus, healthcare providers seek appealing IT products that may combine organisationally reliable resources with a high level of patient experience, improve corporate performance and perhaps even create new, more profitable business models powered by data. (Wang, Kung, & Byrd, 2018).
Potential challenge or risk of using big data as part of a clinical system
The most frequently reported challenges include data management, security, and privacy. Because of the fast creation of new kinds of data and the ease with which data can be transferred and shared, data privacy has become more relevant in recent years (Galetsi, Katsaliaki, & Kumar, 2019).
Healthcare big data analytics, maybe more than other fields, is prone to integrity and privacy breaches. The utilization of private health information (PHI) is required for big data analytics in health care. Practitioners must guarantee that such data do not contain any patient-specific information and preserve confidentiality (McGonigle & Mastrian, 2017).
While most nations have laws to safeguard patients’ data from improper use, this merely requires healthcare practitioners to avoid collecting specific identifying characteristics. Even in the United States, HIPAA permits hospitals to deviate from the regulations if they have a compelling reason—which seems difficult to comprehend in healthcare big data gathering. Additionally, informed consent may be receiving less scrutiny from patients and doctors. Medical devices/implants used in healthcare emit wireless readings that may be intercepted. For instance, when a patient is driving through a weigh station, toll bridge, parking lot, or border crossing, their data may be read without their knowledge or permission (Strang & Sun, 2020).
Strategy to mitigate the challenges or risks of using big data
Encryption may be a solution to this prevalent big data privacy issue in the healthcare sector as software and hardware develop to make it quicker and cheaper. For example, as computing power increases, encryption methods will grow faster, allowing for more real-time usage. Additionally, a government-managed security clearance network may establish a link between healthcare devices/implants and an external system (Strang & Sun, 2020).
Legislation governing data protection varies by the nation since each country safeguards medical and health-related data differently. Data generated through interactions with recognized professionals, such as lawyers, physicians, professors, researchers, accountants, investment managers, and project managers, or through online consumer transactions, is governed by laws requiring informed consent and drawing on the Fair Information Practice Principles (FIPP) legislation (Strang & Sun, 2020).
Most industrialized nations have laws to safeguard individual privacy in big healthcare data, such as the HIPAA rules under the Privacy Rule of 2003 in the United States. For example, HIPAA mandates healthcare providers to erase 18 different identifiers from patient data, such as birthdates, vehicle serial numbers, picture URLs, and voice prints (Strang & Sun, 2020).