NURS 6051 Discussion: Big Data Risks and Rewards SAMPLE 1
“The lack of data standardization can also make it challenging for a CNE to assess how the organization or a particular unit is performing and to make well-informed decisions about what to change” (Thew, 2018). “Englelbright says that by breaking down data silos, big data will also facilitate a balanced approach to assessing organizational and nursing performance” (Thew, 2018).
As we have discussed and learned during this course, nursing informatics and big data are beneficial to our professions and patients, but all of these benefits come with a number of obstacles.
What are the chances that we’ll be discussing ‘Big Data’ and its advantages and disadvantages this week, just a week after Hackensack Meridian Health, New Jersey’s largest hospital system, was hit by a ransomware cyber-attack? Personal and financial information, including healthcare insurance information, was stolen despite the fact that no patient medical records were reported stolen. Hackensack Meridian was forced to pay an undisclosed sum in ransom to regain access to its systems (Eddy, 2019). Because I had lived in New York City for many years, I was aware of Hackensack’s extensive hospital system. Even so, I had no idea it had 17 facilities ranging from acute care centers to nursing homes and rehabilitation centers. “As a result of the attack, hospitals had to reschedule non-emergency surgeries, and doctors and nurses had to provide care without access to electronic data” (The Associated Press, 2019a). Cyber-attacks on healthcare facilities are far more common than we realize. On October 2, 2019, a ransomware attack targeted an Alabama hospital system. According to reports, during the cyber-attack, the hospitals involved stopped accepting new patients. “According to Tuscaloosa News spokesman Brad Fisher, the attackers were funded by the medical system” (The Associated Press, 2019b). A quick Google search revealed more than a dozen recent cyber-attacks on healthcare facilities in which patient personal, financial, healthcare insurance, and healthcare records were stolen.
During these cyber-attacks, the same computers and systems meant to assist our patients were locked and highjacked for ransom. Although the reports mentioned no patient health record was exposed, the investigation at Hackensack is still ongoing. These cyber-attacks have exposed the vulnerability of a system we usually do not associate with cyber-crimes. When think of data breach, banks, government offices, and credit bureaus such as Trans-Union and Equifax come to our minds, not Hackensack Meridian, Mount Sinai, or Swedish Health.
Big data threat is not limited to cyber-attacks, but also internal data mishandling. “One-quarter of all the cases [of healthcare data breaches] were caused by unauthorized access or disclosure – more than twice the amount that was caused by external hackers” (Brooks & Jiang, 2018). Sometimes the data is mistakenly shared with the wrong recipients by hospitals, doctors, pharmacies, and even health insurance companies as not all facilities have strict regulations.
When I think about privacy in healthcare, I initially think of patient privacy and the Health Insurance Portability and Accountability Act (HIPPA). We put all that data and not always know who has access to it. How do we know this data is truly kept private when so many agencies, organizations, and analytic companies have access to it? Who keeps track of what is shared, how it is used, and what is used for? We live ina society where personal data and our digital footprint is worth billions of dollars to companies that want to influence us. How do we ensure patient data does not fall in the hands of them?
When you wake in the morning, you may reach for your cell phone to reply to a few text or email messages that you missed overnight. On your drive to work, you may stop to refuel your car. Upon your arrival, you might swipe a key card at the door to gain entrance to the facility. And before finally reaching your workstation, you may stop by the cafeteria to purchase a coffee.
From the moment you wake, you are in fact a data-generation machine. Each use of your phone, every transaction you make using a debit or credit card, even your entrance to your place of work, creates data. It begs the question: How much data do you generate each day? Many studies have been conducted on this, and the numbers are staggering: Estimates suggest that nearly 1 million bytes of data are generated every second for every person on earth.
Also Read: Assignment: NURS 6051 Knowledgeable Nurse
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NURS 6051 Discussion: Big Data Risks and Rewards SAMPLE 2
NURS 6051 Discussion: Big Data Risks and Rewards SAMPLE 3
Big data refers to a large and complex set of data that, when examined as a wholly integrated data, yields essential information than a small unintegrated collection of data. In the contemporary world, big data is increasingly becoming more prevalent, impacting nursing in various ways. Big data offers a nursing system a considerable opportunity to advance the vision of promoting human health and well-being. Although big data analytics is riddled with challenges, it is useful in decision-making in clinical systems.
Big data is a promising breakthrough in health care decision making. Big data analytics in the context of nursing enable an organization to analyze large volumes and velocity of data from various nursing networks to aid in evidence-based decision making and action (Macieira et al., 2017). It allows the integration of clinical information that provides health care insights to help nurses meet patients’ needs and improve the quality of healthcare. Moreover, big data can be used to understand the impact of nursing care and to expand the responsibility to meet continuous emerging needs.
Big data allows clinical systems to realize informatics benefits, including improved quality and accuracy of clinical decision and instant access to vital health records and information. Additionally, big data is a potential source for managerial benefits which allow health care organization to monitor and monitor the firm’s resources and evaluate the operation and support strategic business decisions (Wang, Kung & Byrd, 2018). Furthermore, big data analytics gives the clinical system the capability to generate accurate data and make predictions based on new observations. Predictive analytics play a crucial role in the clinical order of reducing uncertainty and preventing readmissions
Lack of substantial experience in big data analytics is one of the most significant challenges impeding the realization of maximum big data benefits. Evidence indicates that only a few percentages of health care organizations have the capability to conduct rigorous big data analytics to aid in the decision-making process (Wang et al., 2018). The lack of understanding by clinical officers on the value of big data analytic in the clinical system compounds this challenge. Therefore, the clinical system can leverage big data analytics as a means of transforming nursing in the era of informatics.
References NURS 6051 Discussion: Big Data Risks and Rewards
Macieira, T. G., Smith, M. B., Davis, N., Yao, Y., Wilkie, D. J., Lopez, K. D., & Keenan, G. (2018). Evidence of progress in making nursing practice visible using standardized nursing data: A systematic review. AMIA Annual Symposium Proceedings Archive, 1205–1214. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977718/
Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3-13. doi:10.1016/j.techfore.2015.12.019
Thank you for your articulate post. As you explained big data can be used to understand the impact of nursing care and expand the responsibility to meet continuous emerging needs. Big data can be used in a variety of industries. The top benefits of using big data in healthcare include advancing patient care, improving operational efficiency, finding cures for diseases (Business Wire, 2018). An example of improving operational efficiency would be running reports on readmissions of COPD exacerbations. With this data, a company can examine the admission rates while analyzing staff efficiency. Predictive analytics can be used on the COPD patient to understand key factors in readmissions.
As nurses, we understand that healthcare is continually changing. The same principals can be applied to big data. One of the biggest problems with big data is that “it grows constantly, and organizations often fail to capture the opportunities and extract actionable data” (Joshi, 2018). Because of this, we may miss opportunities to best serve our patients.
Business Wire. (2018). Top Benefits of Big Data in the Healthcare Industry. Retrieved from https://www.businesswire.com/news/home/20180207005640/en/Top-Benefits-Big-Data-Healthcare-Industry-Quantzig
Joshi, N. (2018). Problems with Big Data that we failed to notice. Retrieved from https://www.allerin.com/blog/problems-with-big-data-that-we-failed-to-notice
NURS 6051 Discussion: Big Data Risks and Rewards SAMPLE 4
Cybercrime in healthcare indeed puts a patient’s health and privacy at risk. “It is one of the most targeted sectors globally; 81% of 223 organizations surveyed, and >110 million patients in the US had their data compromised in 2015 alone” (Martin, Martin, Hankin, Darzi, & Kinross, 2017, para. 3). We often think about cybercrime in terms of a virus or spyware stealing information, or money, however, cybercriminals are always coming up with new schemes. For example, in 2016 The Hollywood Presbyterian Medical Center’s entire computer system was essentially hijacked for ransom, “shut down its network for ten days, preventing staff from accessing medical records or using medical equipment until the hospital paid the ransom” (Martin et al., 2017, para. 6).
The increased use of nursing informaticists in the healthcare system is a step in the right direction for hospitals as they continue to fight cybercrime while expanding the use of big data. The article, Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations, finds that while hospital systems are investing a large number of finances into big data, much of its capabilities are still underutilized (Wang, Kung, & Byrd, 2018). As hospital systems increase the usage and find new applications for big data, opportunities for cybercrime will increase, and informaticists will have to be ever vigilant in their protection of patient privacy.
Wang, Y., Kung, L., & Byrd, T. A. (2018, January 2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. ScienceDirect, 126(), 3-13. http://dx.doi.org/https://doi.org/10.1016/j.techfore.2015.12.019
I do agree with your statement about the positives of using a common language within our charting process. This idea is also true regarding electronic health records (EHRs) in general. If health systems were able to use compatible EHRs in their hospitals and clinics, the amount of data that could be mined would be massive. How nice it would be to have the ability to pull records nationally from multiple large health system data banks at once.
One issue with this approach would be the financial expense of either upgrading existing EHRs for interoperability or buying into a different EHR platform altogether. According to Sines and Griffin (2017), many smaller facilities may not have the funds needed to acquire these “extra” functionalities and only operate base model software. Any nurse that has worked at multiple hospitals has undoubtedly noticed differing layouts and models of the same EHR being utilized. I understand the ability to customize the look and charting environment is a selling point, but I am curious as to the interoperability functions of a base model system versus one more luxurious. Ultimately, a national goal with using the EHR is complete connectivity and the linking of data collection and handling (Wilson & Khansa, 2018).
Sines, C. C., & Griffin, G. R. (2017). Potential Effects of the Electronic Health Record on the Small Physician Practice: A Delphi Study. Perspectives in health information management, 14(Spring), 1f.
Wilson, K., & Khansa, L. (2018). Migrating to electronic health record systems: A comparative study between the United States and the United Kingdom. Health Policy, 122(11), 1232-1239. https://doi.org/10.1016/j.healthpol.2018.08.013