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.
RESOURCES
Required Readings
McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett Learning.
Chapter 22, “Data Mining as a Research Tool” (pp. 537-558)
Chapter 24, “Bioinformatics, Biomedical Informatics, and Computational Biology” (pp. 581-588)
Glassman, K. S. (2017). Using data in nursing practiceLinks to an external site.. American Nurse Today, 12(11), 45–47. Retrieved from https://www.americannursetoday.com/wp-content/uploads/2017/11/ant11-Data-1030.pdf
Thew, J. (2016, April 19). Big data means big potential, challenges for nurse execsLinks to an external site.. Retrieved from https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs
Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizationsLinks to an external site.. Technological Forecasting and Social Change, 126(1), 3–13.
Walden University, LLC. (Executive Producer). (2012). Data, information, knowledge and wisdom continuumLinks to an external site. [Multimedia file]. Baltimore, MD: Author. Retrieved from http://cdn-media.waldenu.edu/2dett4d/Walden/NURS/6051/03/mm/continuum/index.html
Walden University, LLC. (Producer). (2018). Health Informatics and Population Health: Analyzing Data for Clinical Success [Video file]. Baltimore, MD: Author.
Vinay Shanthagiri. (2014). Big Data in Health InformaticsLinks to an external site. [Video file]. Retrieved from https://www.youtube.com/watch?v=4W6zGmH_pOw

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 by offering one or more additional mitigation strategies or further insight into your colleagues’ assessment of big data opportunities and risks.
1st Response
A potential benefit of using big data systems in the healthcare system is the ability to identify patterns and trends that may not be evident through traditional assessments. Through analyzing data like electronic health records (EHRs), researchers can identify correlations between patient characteristics and outcomes, as well as other factors that contribute to diseases or conditions (Ngiam & Khor, 2019). This information can help develop more effective treatments for patients.
However, one potential challenge of using big data in clinical settings is the potential for errors or bias in the data. For example, data collected from electronic health records may be incomplete or inaccurate, leading to incorrect conclusions or treatment decisions (Lee & Yoon, 2017). For instance, many patients will either omit, not recall, or potentially lie about practices and health history which may create incomplete data sets. Additionally, the use of big data can raise privacy concerns for patients, as their personal health information is being collected and analyzed.
In order to mitigate these challenges one strategy could be to implement quality control measures like regular data validation and cleaning. This is not practical for floor nurses but could be allocated to nurse informaticists or other appropriately trained IT employees. Another strategy could be additional communication training for nurses or other healthcare workers to ensure a more complete patient history. For challenges such as privacy concerns, data encryption, and secure storage practices can be put in place (Abouelmehdi et al., 2017).
References
Abouelmehdi, K., Beni-Hssane, A., Khaloufi, H., & Saadi, M. (2017). Big data security and privacy in healthcare: A review. Procedia Computer Science, 113, 73-80.
Ngiam, K. Y., & Khor, W. (2019). Big data and machine learning algorithms for health-care delivery. The Lancet Oncology, 20(5), e262-e273.
Lee, C. H., & Yoon, H. J. (2017). Medical big data: Promise and challenges. Kidney research and clinical practice, 36(1), 3.
2nd Response
In a world where everyone uses data for most of their daily activities, it is only natural that healthcare would become a data-rich industry. The term "big data" refers to large amounts of data being collected and analyzed in order to develop trends in care and improve patient outcomes ("What is big healthcare data," 2020). The advantages of big data and healthcare are limitless. All medical professionals have access to the data entry system. Still, the nursing staff is likely to be the primary users, responsible for entering a large portion of the patient information that is entered into the system from the time the patient arrives to the time they are discharged. Some of the advantages of using big data include the data that nurses enter into the electronic medical system, which is then used to provide better patient care.
Other advantages include the development of policies and processes, improved patient outcomes, and the ability to improve employee education and care trends. The data system can reduce hospital readmissions by trending risk factors and collaborating costs, clinical settings, and operational data to monitor productivity and outcomes (W. Raghupathi & V. Raghupathi, 2014). With all the advantages listed, there are some drawbacks, such as standardized vocabulary issues, which prevent the system from incorporating nursing abbreviations into the charting systems. There is also the risk of sensitive information being stolen or hacked, which was a problem at one facility I heard about in 2016. In 2016, Desert Valley Hospital and Chino Medical Center, both owned by Prime Healthcare, were targeted by a cyber hacker who demanded a ransom for medical data from their systems. Fortunately, the problem was resolved before the patient information was released, but this was a terrifying incident for all the staff members that were working (Monegain, 2016).
Nursing terminology in daily nursing care cannot be incorporated into an electronic medical charting system (Macieira et al.,2017). This nursing language promotes better communication among nurses, as well as between care facilities and other hospital units. When big data is compromised, everyone gets into a thinking mode. I have always wondered how electronic medical records could be secure. Access to these records is provided not only to primary care providers, but also to all consulting physicians, nurses, technicians, labs, and radiology, who all have remote access to them. The risk comes not only from outside hackers but also from employees within the facility who access the wrong chart or gain unauthorized access.
References:
Brooks, C., & Jiang, X. (2018, November 16). Health care providers – not hackers- leak more of your data.MSUToday.https://msutoday.msu.edu/news/2018/health-care-providers-not-hackers-leak-more-of-your-data/
Macieira, T., 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. PubMed Central (PMC). https://www.ncbi.nlm.nih.gopmc/articles/PMC5977718/
Monegain, B. (2016, March 23). Computer science.www.dictionary.com.https://www.dictionary.com/browse/computer-scienceRaghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. PubMed Central (PMC). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4341817/
What is patient engagement? | Evariant: The leading healthcare CRM solution. (n.d.). Healthgrades Evariant.https://www.evariant.com/faq/what-is-healthcare-big-data
What is patient engagement? | Evariant: The leading healthcare CRM solution. (n.d.). Healthgrades Evariant.https://www.evariant.com/faq/what-is-healthcare-big-data
NURS_5051_Module03_Week05_Discussion_Rubric
NURS_5051_Module03_Week05_Discussion_Rubric
Criteria Ratings Pts
This criterion is linked to a Learning OutcomeMain Posting
50 to >44.0 pts
Excellent
Answers all parts of the discussion question(s) expectations with reflective critical analysis and synthesis of knowledge gained from the course readings for the module and current credible sources. … Supported by at least three current, credible sources. … Written clearly and concisely with no grammatical or spelling errors and fully adheres to current APA manual writing rules and style.
44 to >39.0 pts
Good
Responds to the discussion question(s) and is reflective with critical analysis and synthesis of knowledge gained from the course readings for the module. … At least 75% of post has exceptional depth and breadth. … Supported by at least three credible sources. … Written clearly and concisely with one or no grammatical or spelling errors and fully adheres to current APA manual writing rules and style.
39 to >34.0 pts
Fair
Responds to some of the discussion question(s). … One or two criteria are not addressed or are superficially addressed. … Is somewhat lacking reflection and critical analysis and synthesis. … Somewhat represents knowledge gained from the course readings for the module. … Post is cited with two credible sources. … Written somewhat concisely; may contain more than two spelling or grammatical errors. … Contains some APA formatting errors.
34 to >0 pts
Poor
Does not respond to the discussion question(s) adequately. … Lacks depth or superficially addresses criteria. … Lacks reflection and critical analysis and synthesis. … Does not represent knowledge gained from the course readings for the module. … Contains only one or no credible sources. … Not written clearly or concisely. … Contains more than two spelling or grammatical errors. … Does not adhere to current APA manual writing rules and style.
50 pts
This criterion is linked to a Learning OutcomeMain Post: Timeliness
10 to >0.0 pts
Excellent
Posts main post by day 3.
0 pts
Poor
Does not post by day 3.
10 pts
This criterion is linked to a Learning OutcomeFirst Response
18 to >16.0 pts
Excellent
Response exhibits synthesis, critical thinking, and application to practice settings. … Responds fully to questions posed by faculty. … Provides clear, concise opinions and ideas that are supported by at least two scholarly sources. … Demonstrates synthesis and understanding of learning objectives. … Communication is professional and respectful to colleagues. … Responses to faculty questions are fully answered, if posed. … Response is effectively written in standard, edited English.
16 to >14.0 pts
Good
Response exhibits critical thinking and application to practice settings. … Communication is professional and respectful to colleagues. … Responses to faculty questions are answered, if posed. … Provides clear, concise opinions and ideas that are supported by two or more credible sources. … Response is effectively written in standard, edited English.
14 to >12.0 pts
Fair
Response is on topic and may have some depth. … Responses posted in the discussion may lack effective professional communication. … Responses to faculty questions are somewhat answered, if posed. … Response may lack clear, concise opinions and ideas, and a few or no credible sources are cited.
12 to >0 pts
Poor
Response may not be on topic and lacks depth. … Responses posted in the discussion lack effective professional communication. … Responses to faculty questions are missing. … No credible sources are cited.
18 pts
This criterion is linked to a Learning OutcomeSecond Response
17 to >15.0 pts
Excellent
Response exhibits synthesis, critical thinking, and application to practice settings. … Responds fully to questions posed by faculty. … Provides clear, concise opinions and ideas that are supported by at least two scholarly sources. … Demonstrates synthesis and understanding of learning objectives. … Communication is professional and respectful to colleagues. … Responses to faculty questions are fully answered, if posed. … Response is effectively written in standard, edited English.
15 to >13.0 pts
Good
Response exhibits critical thinking and application to practice settings. … Communication is professional and respectful to colleagues. … Responses to faculty questions are answered, if posed. … Provides clear, concise opinions and ideas that are supported by two or more credible sources. … Response is effectively written in standard, edited English.
13 to >11.0 pts
Fair
Response is on topic and may have some depth. … Responses posted in the discussion may lack effective professional communication. … Responses to faculty questions are somewhat answered, if posed. … Response may lack clear, concise opinions and ideas, and a few or no credible sources are cited.
11 to >0 pts
Poor
Response may not be on topic and lacks depth. … Responses posted in the discussion lack effective professional communication. … Responses to faculty questions are missing. … No credible sources are cited.
17 pts
This criterion is linked to a Learning OutcomeParticipation
5 to >0.0 pts
Excellent
Meets requirements for participation by posting on three different days.
0 pts
Poor
Does not meet requirements for participation by posting on 3 different days.
5 pts
Total Points: 100
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