NURS-6051 Week 5 Walden: Discussion BIG DATA RISKS AND REWARDS
BY DAY 3 OF WEEK 5
Post a description of at least one potential benefit of using big data as part of a clinical system and explain why. Then, describe at least one potential challenge or risk of using big data as part of a clinical system and explain why. Propose at least one strategy you have experienced, observed, or researched that may effectively mitigate the challenges or risks of using big data you described. Be specific and provide examples.
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BY DAY 6 OF WEEK 5
Respond to at least two of your colleagues* on two different days, by offering one or more additional mitigation strategies or further insight into your colleagues’ assessment of big data opportunities and risks.
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Main Post
Discussion on Big Data: Risks and Rewards Simplified
In our tech-savvy world, we’re all constantly connected through computers, cell phones, and social media. We even use these gadgets at work, especially in healthcare. As nurses, we help connect patients to massive data systems. It means we live in a world filled with valuable information, also known as big data. This data can have both good and not-so-good effects.
The Benefits of Using Big Data in Healthcare
Big data has lots of advantages in healthcare. When we log into electronic health records (EHR), we enter valuable info that can lead to better healthcare practices. It can improve patient care, safety, and make our nursing jobs easier. Big data helps with more accurate diagnoses and treatment, which results in higher quality care. It also allows us to analyze trends and patterns in patient data, leading to better care.
Big data helps us detect diseases early by looking at signs and symptoms. It also helps identify lifestyle factors that increase disease risks, so we can advise patients on staying healthy. We can monitor the health of populations no matter where they are and adjust treatment plans quickly. Additionally, we can analyze financial, operational, and clinical data in real-time to use our resources better.
Challenges and Risks of Big Data
But there are challenges when dealing with big data. One big issue is the difficulty of fully implementing standardized nursing technology (SNT). If we can solve this problem, it would help us analyze data more effectively. SNTs make it easier to retrieve and analyze data through nurses’ clinical reasoning. They can also make nursing interventions more visible. Not implementing SNTs is a significant challenge for healthcare systems.
Another challenge is the lack of data standardization. When data isn’t standardized, healthcare systems struggle to assess how well organizations are performing and make informed decisions. Breaking down data silos can help improve nursing performance. There’s also the risk of data breaches, both from cyberattacks and internal mishandling. A quarter of healthcare data breaches result from unauthorized access, and hackers are responsible for more breaches than internal mishandling.
Proposed Strategy
To address these challenges, I suggest implementing strict measures through laws or regulations on healthcare data privacy. For example, we can follow the Health Insurance Portability and Accountability Act (HIPPA) to protect patient data. By doing this, we can ensure that patient data is safeguarded, making healthcare services better and more secure.
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References
Fox, M., & Vaidyanathan, G. (2016). IMPACTS OF HEALTHCARE BIG DATA: A FRAMEWORK WITH LEGAL AND ETHICAL INSIGHTS. Issues in Information Systems, 17(3).
Macieira, T. G., Smith, M. B., Davis, N., Yao, Y., Wilkie, D. J., Lopez, K. D., & Keenan, G. (2017). Evidence of progress in making nursing practice visible using standardized nursing data: a systematic review. In AMIA Annual Symposium Proceedings (Vol. 2017, p. 1205). American Medical Informatics Association.
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health information science and systems, 2(1), 1-10.
Thew, J. (2016, April 19). Big data means big potential, challenges for nurses execs. Retrieved from https://www.healthleadersmedia.com/nursing/big-data-means-bigpotential-challenges-nurse-execsLinks to an external site.
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.
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Collapse SubdiscussionOluyemi Adeagbo Hi Sheilah,
In a society with fast-growing technological needs, the big data concept quickly becomes a society. You have pointed out the interrelation of phones, computers, and social media platforms, necessitating big data technology to ease data collection and assimilation. The healthcare space highly benefits from big data integration, for example, through protocol development, diagnosis insight, and heightened patient safety mechanisms (Pruinelli, 2021). Despite the benefits of big data, potential hazards exist, such as cybercriminals’ potential hacking of information (Founds, 2018). Losing private and confidential data related to health data puts patients and institutions at risk. Big data should get protected to guard patients and healthcare professionals from the threats mentioned above. I also suggest that more healthcare institutions should invest in big data for them to partake of its advantages.
References
Founds, S. (2018). Systems biology for nursing in the era of big data and precision health. Nursing Outlook, 66(3), 283-292. https://doi.org/10.1016/j.outlook.2017.11.006Links to an external site.
Pruinelli, L. (2021). Nursing and data: Powering nursing leaders for big data science. Revista Brasileira de Enfermagem, 74(4). https://doi.org/10.1590/0034-7167.2021740401Links to an external site.
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Collapse SubdiscussionAndrea M Allen Response 1
Hi Sheila,
Thanks for the read. In response to information getting to ill intending people, some healthcare facilities are really logging behind in technology and needs to get on board as businesses do by investing in protecting patients information especially since the health information hostage situations a few years ago and still ongoing, where hackers held patients information for ransom by using blackmail. As I recall, a few years ago (2020) during the Pandemic, All computers at my job were out of service. We had only started using EMAR for patients medication administration so everything else was done by pen and paper. Instead of succumbing to the demands of hackers, the hospital bought new computers. I guess in this case logging behind paid off but had the hospital advanced in technology and had the right security in placed, the issue could have been avoided. Some security features they could use includes Cryptographic Technologies (encoding and decoding to implement strong user authentication), Authentication (which is based on more than one criteria of unique account ID’s generally assigned by a system administrator), and Kerberos Organization Authentication (practical management of secret keys). In time hackers may find ways around those too but in the meantime, these programs will safeguard patients information until a better form of protection is generated.
Best,
National Research Council (US) committee on Maintaining Privacy and Security in Health Care Applications of the National Infrastructure. Washington (DC): National Academies Press (US); 1987.4 Technical Approaches to Protecting Electronic Health Information www.ncbi.nlm.nih.gov
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Collapse SubdiscussionMleh Porter Hello Sheila,
Thank you for your post. I completely agree that big data can benefit the clinical/healthcare system. It can help provide more accurate diagnoses, better treatments, and more efficient care. It is also helpful in patient safety, as it allows health professionals to monitor and manage patients’ health better. In addition, it can provide valuable insight into health trends and patterns, which can be used to improve the quality of care (Shilo et al., 2020). Big data can truly make a positive impact on the healthcare system.
As you have stated, the lack of standardized nursing technology is a significant challenge and can make it difficult to analyze data. Data standardization is essential for assessing organizational performance and making informed decisions. Without standardization, organizations may be unable to compare performance accurately across different areas and departments and may be unable to identify trends and areas that need to be improved. Senthilkumar et al. (2018) have stated that data standardization can help reduce the time and effort required to analyze data and reduce the possibility of errors and misinterpretations. Without data standardization, organizations risk making decisions based on inaccurate or incomplete information, leading to poor decision-making and costly mistakes. Moreover, the risk of data leaks to cybercriminals or internal mishandling is a real danger that needs to be taken seriously. It is essential to have robust systems to protect data and prevent damaging breaches. Thank you for highlighting the challenges and risks of big data.
References
Senthilkumar, S. A., Rai, B. K., Meshram, A. A., Gunasekaran, A., & Chandrakumarmangalam, S. (2018). Big data in healthcare management: A review of literature. American Journal of Theoretical and Applied Business, 4(2), 57-69. https://doi.org/10.11648/j.ajtab.20180402.14Links to an external site.
Shilo, S., Rossman, H., & Segal, E. (2020). Axes of a revolution: Challenges and promises of big data in healthcare. Nature Medicine, 26(1), 29-38. https://doi.org/10.1038/s41591-019-0727-5Links to an external site.
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Collapse SubdiscussionMenard Tchatchou-Tchoubia Discussion Response Big Data Risks and Rewards
Response Post # 2
Menard Tchatchou
Walden University
NURS 6051 N
Dr. Lynne Taylor
12/30/2022
Hi Sheila, what a thoughtful post! I enjoyed going through. A significant big data risk is data manipulation to produce intended results (Katkade, Sanders & Zou, 2018). For example, medical practitioners in their research sway data to support their selfish findings and conclusion. The data is forced to support specific theories for obvious economic or political gains. Different organizations and government agencies have played significant roles in data manipulation during the recent pandemic to convey certain messages. Most end up benefiting the primary stakeholders in the pharmaceutical industry. Therefore, data manipulation is a big data risk that requires a permanent solution to avoid duping the masses who depend on healthcare organizations. Safety in healthcare organizations should be a priority, especially when dealing with big data.
I also believe that data privacy is a primary risk that affects big data in healthcare. However, data protection is more of an organizational initiative than an oversight authority duty. Healthcare organizations use big data and other software to formulate data. Therefore, they should purchase or develop applications that ensure data protection in different spheres. For instance, when handling patient health records and medical history, healthcare professionals should not allow any personnel to access the big data. Besides, security protocol measures and paths to track those who accessed it should be implemented.
Secondly, professional training should focus on data protection and privacy ethics. Medical practitioners use patient data for research without patients’ consent; it breaches privacy. It indicates inadequate clinical ethics training that guides crucial issues like data protection. Healthcare practitioners without ethics engage in data violation activities for many patients. Therefore, training institutions should focus on wholesome training and producing ethical professionals who hardly violate big data in their practice field. Implementing robust data governance policies and practices mitigates the challenges or risks of using big data (Shahid, Rappon & Berta, 2019).
References
Katkade, V. B., Sanders, K. N., & Zou, K. H. (2018). Real world data: an opportunity to supplement existing evidence for the use of long-established medicines in health care decision making. Journal of multidisciplinary healthcare, 11, 295. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6033114/Links to an external site.Links to an external site.
Shahid, N., Rappon, T., & Berta, W. (2019). Applications of artificial neural networks in health care organizational decision-making: A scoping review. PloS one, 14(2), e0212356. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0212356Links to an external site.
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Collapse SubdiscussionMaxine A Lewis Hi Shelia, big data is certainly a benefit to health care. Patient waiting days instead of minutes or hours for test results (decreasing the anxiety of waiting), having a tele visit with ones primary physician at any convenient location or attending staff meetings and completing yearly mandatory competence without leaving home. Big data is evolving so rapidly it’s definition is not consistent. Pastorino et.al cites that “Big Data having become ubiquitous, there is no universal definition until now on the use of this term” (Pastorino et.al., 2019). One of the terminology refers to big data as “datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze” (Pastorino et.al., 2019). There is a growing consensus that ‘Big’ is no longer the defining parameter, but rather how ‘smart’ the data are. Big Data has enormous potential for improving health, but its true value is realized only when it is used to drive decision making. Efficient processes for analyzing and transforming large amounts of data into meaningful insights are required (Pastorino et.al., 2019).
Presently I am at work responding to this post and as I look around me data is everywhere from the monitors, ekgs, smart pump (no mental calculations), accuchek and to biometric access in administering medications: Healthcare has evolved indeed but what happens in the event there is a cyberattack who among us know how to revert back to paper charting? I saw the effect of a cyber-attack 3 years ago. It was amusing for us the more seasoned nurses and secretaries (most of which have recently retired), to see the expression on our co-workers’ and physician’s faces when told everything from admission to discharge orders must be handwritten. Some did not know the difference between a regular prescription from a narcotic prescription.
We worked together for at least 2 weeks to keep things running at the end there was greater appreciation for technology. So it is especially important for health organizations to develop strategy against ciber-attack by developing training programs, awareness campaigns, and information sharing on the nature and type of cybersecurity attacks is required (Nifakos et.al., 2021).
among ue more seasoned nurses and secret
References
Nifakos, S., Chandramouli, K., Nikolaou, C. K., Papachristou, P., Koch, S., Panaousis, E., & Bonacina, S. (2021). Influence of Human Factors on Cyber Security within Healthcare Organisations: A Systematic Review. Sensors, 21(15), 5119. MDPI AG. Retrieved from http://dx.doi.org/10.3390/s21155119
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. https://doi.org/10.1093/eurpub/ckz168
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Collapse SubdiscussionBertina Boma Soh Thank you so much Maxime for your insightful contribution. You are very correct when you say that is very important for health organizations to develop a strategy against cyber- attacks by developing training programs, and awareness campaigns. With the greater number of individuals using smartphones and apps increasing gradually, and with the easily of use (Bubukayr& Almaiah, 2021). Crypto ransomware is a type of malware that locks its victim’s file for ransom using an encryption algorithm. Its popularity has risen at an alarming rate among the cyber security community due to several successful worldwide attacks (Almusaylim et Al., 2020). Criminally motivated attackers seek financial gain through money theft, data theft or business disruption. Likewise, the personally motivated, such as disgruntled current or former employees, will take money, data or a mere chance to disrupt a company’s system. However, they primarily seek retribution. Socio-political motivated attackers seek attention for their causes.
References
Bubukayr, M. A. S., & Almaiah, M. A. (2021, July). Cybersecurity concerns in smart-phones and applications: A survey. In 2021 International Conference on Information Technology (ICIT) (pp. 725-731). IEEE.
A. Almusaylim, Z., Jhanjhi, N. Z., & Alhumam, A. (2020). Detection and mitigation of RPL rank and version number attacks in the internet of things: SRPL-RP. Sensors, 20(21), 5997.
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Collapse SubdiscussionBertina Boma Soh I agree with you Sheila that “ despite the benefits of big data, potential hazards exist, such as cybercriminals’ potential hacking of information .”According to google.com, a cybercriminal is a person who conducts some form of illegal activity using computers or other digital technology such as the Internet.Cyber criminals seek to exploit human or security vulnerabilities in order to steal passwords, data or money directly (Panlogic, Cyber crime 2022).
Cybercriminals also differ greatly from threat actors in various ways, the first of which is intent. Threat actors are individuals who conduct targeted attacks, which actively pursue and compromise a target entity’s infrastructure. Cybercriminals are unlikely to focus on a single entity, but conduct operations on broad masses of victims defined only by similar platform types, online behavior, or programs used. Secondly, they differ in the way that they conduct their operations. Threat actors follow a six-step process, which includes researching targets and moving laterally inside a network. Cybercriminals, on the other hand, are unlikely to follow defined steps to get what they want from their victims.
However, that cybercriminals have also been known to adopt targeted attack methodologies in their operations.
References
Google.com. (n.d.). Google search. Retrieved January 1, 2023, from https://www.google.com/search?q=cybercriminals.
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Collapse SubdiscussionOluyemi Adeagbo Week 5 Discussion: Big Data Risks and Rewards
Data is a key driving force for change within an organization and new developments. The more information a healthcare organization has, the more it can organize itself to deliver the best healthcare services to its clients. Big data in the healthcare sector, therefore, refers to huge volumes of data generated from the adoption of digital technologies and interactions between healthcare stakeholders and healthcare systems in the collection, documentation, and retrieval of healthcare data (Wang et al., 2018). Big data can also be collected from research studies, government agencies, and laboratory results, and this has proved to be useful in managing organizational performance. One of the major rewards of using big data in the healthcare sector is a prediction of future trends and events of certain parameters which would then act as actionable information that forms the basis of evidence-based interventions (Wang et al., 2018). For example, big data may help an organization to predict future trends in the occurrence of lifestyle diseases within a particular population group. The prediction might be useful for the organization to formulate evidence-based interventions that would seek to better the quality of services delivered, focus on value-based care, and cost reduction.
However, as organizations are becoming more used to electronic documentation, they have started to collect and document data about every aspect of the care continuum until information systems are flooded with large volumes of unmanageable data. In this line, accessibility to patient data in terms of interoperability, proprietary rights, and privacy is increasingly becoming the most challenging domain of big data. Access to patient data is affected by vulnerability to breaching patient privacy considerations as protected by the HIPAA act. The risk of breaching patient privacy policy often discourages providers from documenting and/or sharing patient health information effectively. According to Perlin (2016), interoperability is the ability of healthcare information systems to exchange vital health data within and across organizational boundaries, and present it in a way that is understandable to the user. Currently, interoperability is a bit compromised in many organizations (Ramadas, 2018).
I identified several strategies that would be essential in solving accessibility challenges when it comes to big data sharing. Most importantly, employing common security procedures such as encrypting sensitive data and practicing professional integrity could potentially eliminate the risk of breaching patient privacy considerations among providers while sharing big data. Additionally, Perlin (2016) identified that upgrading pre-existing information systems within health facilities will enhance the ability to share health information between providers and between health facilities efficiently. Such strategies are crucial in eliminating the risks associated with sharing and access to big data.
References
Perlin, J. B. (2016). Health information technology interoperability and use for better care and evidence. Jama,316(16), 1667-1668. doi:10.1001/JAMA.2016.12337
Ramadas, A. (2018). The usefulness and Challenges of Big Data in Healthcare. Journal of Healthcare Communications, 3(21), 1-4. doi:10.4172/2472-1654.100131
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(1), 3-13. doi: 10.1016/j.techfore.2015.12.019
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Collapse SubdiscussionMenard Tchatchou-Tchoubia Discussion Response: Big Data Risks and Rewards
Response Post # 1
Menard Tchatchou
Walden University
Dr. Lynne Taylor
NURS 6051 N
12/29/2022
Hi Oluyemi, I agree that implementing evidence-based interventions in clinical fields relies on big data. It is used to study trends, propose solutions to lifestyle diseases, and solve social problems. Notably, public health practitioners use big data to educate community members. Evidence from big data is used to convince societal members of the plausibility of certain lifestyle trends. A conscious society eats healthy foods and lives a favorable lifestyle to reduce affiliated diseases. Previously, it was hard to disseminate healthy lifestyle information due to the inadequacy of data. However, big data enhanced the efficiency of this process, and public health practitioners educated society easily (Abouelmehdi, Beni-Hessane & Khaloufi, 2018).
A possible way to mitigate big data risks is by hiring skilled data analysts or equipping medical practitioners with data analysis skills. Implementing robust data governance policies and practices mitigates the challenges or risks of using big data (Shahid, Rappon & Berta, 2019). Data manipulation or misinterpretation results from a lack of necessary skills to deduce and interpret data and inferences. Thus, it is essential for healthcare organizations to hire skilled data analysts or train all employees in data analysis skills. Big data is available to all employees in clinical fields, but its usefulness depends on the competence of those who handle it. Therefore, ensuring clinical fields have the required data handling skill set is essential to regulate the perils of big data.
Most importantly, clinical disciplines should cultivate a culture of integrity and implement quality control measures. Every organization has a culture rooted in virtues like integrity. However, some healthcare organizations do not prioritize integrity hence the increased cases of big data risks. Inculcating a culture of integrity is vital in reducing risks affiliated with big data. Also, clinicians should implement quality control measures to ensure data handling meets the required standards.
References
Abouelmehdi, K., Beni-Hessane, A., & Khaloufi, H. (2018). Big healthcare data: preserving security and privacy. Journal of big data, 5(1), 1-18. https://link.springer.com/article/10.1186/s40537-017-0110-7Links to an external site.
Shahid, N., Rappon, T., & Berta, W. (2019). Applications of artificial neural networks in health care organizational decision-making: A scoping review. PloS one, 14(2), e0212356. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0212356Links to an external site.
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Collapse SubdiscussionJodian Walford Dont wait until the last minute.
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