NURS 6051 BIG DATA RISKS AND REWARDS
Sample Answer for NURS 6051 BIG DATA RISKS AND REWARDS Included After Question
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.
As the volume of data increases, information professionals have looked for ways to use big data—large, complex sets of data that require specialized approaches to use effectively. Big data has the potential for significant rewards—and significant risks—to healthcare. In this Discussion, you will consider these risks and rewards.
RESOURCES
Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.
WEEKLY RESOURCES
To Prepare:
- Review the Resources and reflect on the web article Big Data Means Big Potential, Challenges for Nurse Execs.
- Reflect on your own experience with complex health information access and management and consider potential challenges and risks you may have experienced or observed.
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.
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.
*Note: Throughout this program, your fellow students are referred to as colleagues.
A Sample Answer For the Assignment: NURS 6051 BIG DATA RISKS AND REWARDS
Title: NURS 6051 BIG DATA RISKS AND REWARDS
As technology advances, so does the ability to obtain and analyze large sets of data from numerous differing sources. The result of this voluminous information is called big data. Big data is the name given to the vast amount of datasets within the organization that are difficult to manage due to their lack of structure (McGonigle & Mastrian, 2018). The universal move from paper charting to the electronic health record (EHR) has allowed more efficient access to all aspects of current healthcare documentation along with older data via backup and storage media.
The daily use of an EHR provides a continual data set that can easily be probed and assimilated to produce information that can then be used to influence positive patient outcomes. Trends in the documentation found to be useful during the monitoring and management of patient care can be examined and used to direct change in future policies and procedures. Due to the universal language of most charting modules within a health system, the data can easily be searched and mined for a specific metric. An issue arises when a query wishes to retrieve charting details from an unstructured area, such as narrative charting entries.
As long as EHRs allow custom narrative entries, the ability to pull organized system-wide search results will be time and labor-intensive. The unformatted information must then be manually viewed, read, and sorted. Lack of integration is a prime example of how big data mining can be overwhelming and cumbersome within a clinical system (Wang et al., 2018).
One strategy used to mitigate the challenge of big data is using a checkbox flowsheet method of universal charting. The structured format of this technique provides organized, easily accessible, and easily interpreted results to the informaticist (Glassman, 2017). Although using the narrative approach can be more efficient at times by grouping together multiple assessment categories in one location, the information
could be invisible and, therefore, unavailable for the requested project at hand.
References
Glassman, K. S. (2017). Using data in nursing practice. American Nurse Today, 12(11), 45–47. Retrieved from https://www.americannursetoday.com/wp-content/uploads/2017/11/ant11-Data-1030.pdf
McGonigle, D., & Mastrian, K. (2018). Nursing Informatics and the Foundation of Knowledge (4th ed.). Jones and Bartlett Learning.
Wang, Y., Kung, L., & Byrd, T. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3-13. https://doi.org/10.1016/j.techfore.2015.12.019
A Sample Answer 2 For the Assignment: NURS 6051 BIG DATA RISKS AND REWARDS
Title: NURS 6051 BIG DATA RISKS AND REWARDS
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.
As the volume of data increases, information professionals have looked for ways to use big data—large, complex sets of data that require specialized approaches to use effectively. Big data has the potential for significant rewards—and significant risks—to healthcare. In this Discussion, you will consider these risks and rewards.
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A Sample Answer 3 For the Assignment: NURS 6051 BIG DATA RISKS AND REWARDS
Title: NURS 6051 BIG DATA RISKS AND REWARDS
When nurses enter data, they document essential information for the health team’s understanding of the patient. The importance of this information is critical to understanding and treatment methods. This data and the value of technology are meaningful use to a clinical system (Glassman, 2018). Technologies can be beneficial to gathering and analyzing patient data in a clinical setting. According to Glassman (2018), nurses must engage themselves in their feedback on big data processes and technology support. Nurses having a voice and being at the table to use good data for improved outcomes is key to making effective positive changes (Thew, 2016). Nurse leaders are interested in how to use big data for advocacy in best practice management.
The lack of data standardization and understanding of what to do with all the big data is an obvious barrier. As overwhelming as this subject is, I empathize with the nurse leader’s role in how to make changes from a large influx of data. To untap the value of big data, big data analytics and data mining may offer some solutions for healthcare organizations. Data management systems will help compartmentalize data suitable for big data that comes with healthcare data entry. A potential benefit for leaders is nurse managers using data analytics to view consolidated daily reports concerning patient safety concerns Wang et al. (2018). Data mining are tools to convert data into valuable knowledge. McGonigle & Mastrian, 2022 find that “Data mining includes tools for visualizing relations in the data and mechanizes the process of discovering predictive information in massive databases” (p.537). Nurse managers would be interested in how data mining technology could benefit the interests of their departments and patient outcomes. A nurse manager in a medical unit may be interested in fall prevention methods. Lee et al. (2011) further describe not all falls can be unavoidable, but reducing injuries and avoiding future falls align with desirable goals that healthcare providers and organizations could use from incident reporting system data. Incident data documentation reported by nurses would serve as data to establish fall prevention measures, guidelines, policies, and interventions. Big data could benefit this clinical area of interest through its abilities in data collection measures, methods, and analysis.
Some challenges of using big data are the need for data standardization and the failure of how data can interact (Thew, 2016). In the example of incidents of falls, accurate fall prediction models may use data from the incident reporting systems. Data that is hard to code or document may use the free text option, which can be a challenging variable. Misinterpretation can also add to the risks and challenges of big data.
A researched mitigation strategy to combat resistance to using big data is accepting and assuming the risk. Since fall prevention is a hot topic, I believe big data is a risk worth investing in. The link between evidence-based nursing knowledge and big data can intertwine in the improvement efforts in fall prevention programs. Stevens et al. (2017) described how “improving case management and implementation strategies that promote patient adherence to evidence-based strategies is crucial to successfully reducing falls” (p.77). The argument of why a nurse manager would advocate for specific methods concerning fall prevention measures would have supportive data rather than resorting to a person-to-person debate. In efforts to improve and understand healthcare to a greater degree, meaningful data is necessary (McGonigle & Mastrian, 2022). By using big data, there is an opportunity for improvements in several aspects of healthcare.
Big data has had a global impact on the healthcare industry and coined a new way of adding to the productivity of our lives and task. “The potential of Big Data in healthcare relies on the ability to detect patterns and to turn high volumes of data into actionable knowledge for precision medicine and decision makers”(Pastorino et al., 2019).
Many potential come from using big data as part of the clinical system. One potential benefit is using the big data clinical system as a scheduler, specifically Ultimate Kronos, to create flexible employee scheduling. Employees can access Kronos from work, home, or mobile devices. Kronos is highly interactive with its users allowing staff to see their pay stubs and benefits, request time off, and make various requests through this system. Staff can also upload essential or requested documents to this software, such as a social security card showing one name has been changed due to marriage. This big data saves time and prevents the staff from physically walking or driving to Human Resources. Using Kronos prevents the saturation of nurses on the same shift or day, as they can see where the holes are in the schedule. It organizes the workflow for a seamless workday. “The flexibility, customization, and modularity of Kronos make it an attractive system to use in any high-throughput genomics analysis endeavor”(Taghiyar et al., 2017).
One challenge or risk of using big data as part of a clinical system is when a system’s failure to operate occurs. When this happens, employees and hospital staff cannot view the schedule. Unit holes need to remain, leaving nurses to work short.
One solution or strategy to mitigate this challenge is printing off schedules a week ahead of time to accommodate any system failure and prevent workflow disruption. Employees can also be encouraged to print out their schedules if the system goes down.
Healthcare data management and analysis used to be time-consuming and expensive. Thanks to technology, the healthcare industry has recently made great strides to keep up with the flow of big data in the industry. Big data analytics in healthcare leverages health-related information about a person or community to comprehend a patient, organization, or community ((Illinois, 2021).
A Sample Answer 4 For the Assignment: NURS 6051 BIG DATA RISKS AND REWARDS
Title: NURS 6051 BIG DATA RISKS AND REWARDS
Addressing big data risks requires a comprehensive approach that involves implementing various measures to mitigate potential threats and ensure data security and privacy. Establishing strong data governance practices is essential for managing big data risks (Favaretto et al., 2020). This includes defining clear data policies, procedures, and standards that govern data collection, storage, access, and usage. Data governance frameworks help ensure accountability, compliance, and risk management throughout the data lifecycle. Robust security measures are crucial to protect data from unauthorized access, breaches, or cyber-attacks. This includes employing encryption techniques, implementing firewalls and intrusion detection systems, conducting regular security audits, and adopting secure coding practices. Data should be protected at rest and in transit to prevent unauthorized disclosure or tampering (Luan et al., 2020). With the growing concerns around privacy, organizations must prioritize protecting individuals’ personal information. This involves compliance with relevant data protection laws and regulations, such as the General Data Protection Regulation (GDPR). Implementing privacy-enhancing technologies, anonymization techniques, and obtaining informed consent from data subjects are crucial aspects of privacy protection.
References
Favaretto, M., De Clercq, E., Schneble, C. O., & Elger, B. S. (2020). What is your definition of Big Data? Researchers’ understanding of the phenomenon of the decade. Plos One, 15(2). https://doi.org/10.1371/journal.pone.0228987Links to an external site.
Luan, H., Geczy, P., Lai, H., Gobert, J., Yang, S. J. H., Ogata, H., Baltes, J., Guerra, R., Li, P., & Tsai, C.-C. (2020). Challenges and future directions of big data and artificial intelligence in education. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.580820Links to an external site.
Rubric
| NURS_5051_Module03_Week05_Discussion_Rubric | ||||||
| Criteria | Ratings | Pts | ||||
| This criterion is linked to a Learning OutcomeMain Posting |
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50 pts | ||||
| This criterion is linked to a Learning OutcomeMain Post: Timeliness |
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10 pts | ||||
| This criterion is linked to a Learning OutcomeFirst Response |
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18 pts | ||||
| This criterion is linked to a Learning OutcomeSecond Response |
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17 pts | ||||
| This criterion is linked to a Learning OutcomeParticipation |
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5 pts | ||||
| Total Points: 100 | ||||||
NURS 6051 BIG DATA RISKS AND REWARDS Grading Rubric
| Performance Category | 100% or highest level of performance
100% 16 points |
Very good or high level of performance
88% 14 points |
Acceptable level of performance
81% 13 points |
Inadequate demonstration of expectations
68% 11 points |
Deficient level of performance
56% 9 points
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Failing level
of performance 55% or less 0 points |
| Total Points Possible= 50 | 16 Points | 14 Points | 13 Points | 11 Points | 9 Points | 0 Points |
| Scholarliness
Demonstrates achievement of scholarly inquiry for professional and academic topics. |
Presentation of information was exceptional and included all of the following elements:
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Presentation of information was good, but was superficial in places and included all of the following elements:
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Presentation of information was minimally demonstrated in all of the following elements:
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Presentation of information is unsatisfactory in one of the following elements:
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Presentation of information is unsatisfactory in two of the following elements:
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Presentation of information is unsatisfactory in three or more of the following elements
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| 16 Points | 14 Points | 13 Points | 11 Points | 9 Points | 0 Points | |
| Application of Course Knowledge
Demonstrate the ability to analyze and apply principles, knowledge and information learned in the outside readings and relate them to real-life professional situations |
Presentation of information was exceptional and included all of the following elements:
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Presentation of information was good, but was superficial in places and included all of the following elements:
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Presentation of information was minimally demonstrated in the all of the following elements:
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Presentation of information is unsatisfactory in one of the following elements:
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Presentation of information is unsatisfactory in two of the following elements:
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Presentation of information is unsatisfactory in three of the following elements
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| 10 Points | 9 Points | 6 Points | 0 Points | |||
| Interactive Dialogue
Initial post should be a minimum of 300 words (references do not count toward word count) The peer and instructor responses must be a minimum of 150 words each (references do not count toward word count) Responses are substantive and relate to the topic. |
Demonstrated all of the following:
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Demonstrated 3 of the following:
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Demonstrated 2 of the following:
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Demonstrated 1 or less of the following:
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| 8 Points | 7 Points | 6 Points | 5 Points | 4 Points | 0 Points | |
| Grammar, Syntax, APA
Points deducted for improper grammar, syntax and APA style of writing. The source of information is the APA Manual 6th Edition Error is defined to be a unique APA error. Same type of error is only counted as one error. |
The following was present:
AND
AND
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The following was present:
AND/OR
AND/OR
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The following was present:
AND/OR
AND/OR
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The following was present:
AND/OR
AND/OR
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The following was present:
AND/OR
AND/OR
AND/OR
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The following was present:
AND/OR
AND/OR
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| 0 Points Deducted | 5 Points Lost | |||||
| Participation
Requirements |
Demonstrated the following:
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Failed to demonstrate the following:
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| 0 Points Lost | 5 Points Lost | |||||
| Due Date Requirements | Demonstrated all of the following:
A minimum of one peer and one instructor responses are to be posted within the course no later than Sunday, 11:59 pm MT. |
Demonstrates one or less of the following.
A minimum of one peer and one instructor responses are to be posted within the course no later than Sunday, 11:59 pm MT. |