Discussion: Big Data Risks and Rewards NURS 5051

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Discussion: Big Data Risks and Rewards NURS 5051

Main Discussion.

Big Data

Big data are extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. These large and complex data sets are voluminous and traditional data processing software often cannot manage them. Different types of data are used in healthcare, some of which are; electronic health records, clinical trial data, health surveys. Some of these the data obtained in healthcare settings fall under the big data category. Data can also be obtained from medical monitory devices like the electrocardiogram and life support machines. Healthcare data are usually voluminous since they are continuous data and are challenging to manage.

The potential benefits of using big data in healthcare is the ability of clinicians to have a huge archive of patient’s records in the electronic health records system to enable them to retrieve information about past and present medical history about patient diagnosis, laboratory results and general clinical data required to provide adequate medical care to patients. Big data also helps in reducing medication errors and is used in safety surveillance of drugs. Big data are used in increasing the efficacy of healthcare delivery, reduction of health cost and improving patient outcome. performing risk assessments for chronic diseases is more convenient with the collection of medical data through electronic health records (EHR). (Chen, M., Hao, Y., Hwang, K., & Wang, L., 2019).

Challenges of Managing Big Data

There are various challenges in managing big data, but the most severe challenge is the security and privacy of the data. Healthcare data contains very sensitive information which makes it a prime target for identity hackers. When things of this nature happen, the information obtained can be used for blackmail and other types of fraud. This breach in confidentiality has far reaching consequences. The fear of litigation and breach of privacy discourages providers from sharing patient health data, even when they are de-identified (Adibuzzaman, M., DeLaurentis, P., Hill, J., & Benneyworth, B. D. 2018). Healthcare organizations are subjected to huge fines and disciplinary sanctions for any type of security breech in electronic health record systems. For example, a colleague of mine who lost her job a few years back because she logged into the health record of a family member and shared the information with another family member breeching confidentiality in the process.

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Another challenge is that some of these data do not fully capture temporal and process information. In these cases, clinical data captured remotely at various sites even within the same organization are not well integrated. For example, an EHR is primarily used for documenting patient care and was designed to facilitate insurance company billing, and pharmacy records were designed for inventory management, These systems were not developed to capture the temporal and process information which is indispensable for understanding disease progression, therapeutic effectiveness and patient outcomes. Therefore, they are not flexible in their use.

Mitigation of challenges or risks

One of the ways of mitigating the challenges of risk of big data is to install an effective cyber security software which protects the electronic health record system from hacking. It is important to implement strict data regulation and control mechanism in all healthcare organizations to prevent breech in security and protect patient privacy [Wang, Y., Kung, L & Byrd, T. A.,2018]

References

Adibuzzaman, M., DeLaurentis, P., Hill, J., & Benneyworth, B. D. (2018). Big data in healthcare – the promises, challenges and opportunities from a research perspective: A case study with a model database. AMIA … Annual Symposium proceedings. AMIA Symposium, 2017, 384–392.

Chen, M., Hao, Y., Hwang, K., & Wang, L., (2019). “Disease Prediction by Machine Learning Over Big Data from Healthcare Communities,” in IEEE Access, vol. 5, pp. 8869-8879, 2017, doi: 10.1109/ACCESS.2017.2694446.

Wang, Y., Kung, L & Byrd, T. A., (2018). Big data analytics: understanding its capabilities and potential benefits for healthcare organizations. Technological Forcasting & social change ,126, 3-13. doi; 10.1016/j.techfore.2015.12.019

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.

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.

Submission and Grading Information

Grading Criteria

To access your rubric:

Week 5 Discussion Rubric

Post by Day 3 and Respond by Day 6 of Week 5

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To participate in this Discussion:

Week 5 Discussion

Next Module

To go to the next module:

Module 4

RE: Discussion – Week 4

COLLAPSE

Big Data: Benefits and Challenges

As emerging technology and healthcare research continues to increase, there is a growing collection of data in numerous datasets that can be analyzed and categorized for multiple healthcare innovations. According to Mastrian, K. & McGonigle, D. (2018), big data is defined as “voluminous amounts of datasets that are difficult to process using typical data processing; huge amounts of semistructured and unstructured that are unwieldy to manage within relational databases” (pp. 558). In order to effectively analyze big data, the practice of data mining is used. According to Mastrian, K. & McGonigle, D. (2018), “data mining focuses on producing a solution that generates useful forecasting through a four-phase process: problem identification, exploration of data, pattern discovery, and knowledge deployment” (pp. 478, para. 4). Overall, the ultimate goal of data mining in healthcare is to analyze various big data databases and draw conclusions based on patterns perceived. However, the use of big data to draw conclusions in the healthcare system has both benefits and challenges.

One benefit of big data analysis in the clinical system is the conclusions that can be drawn relating to improved healthcare outcomes for patients. According to Mastrian, K. & McGonigle, D. (2018) regarding big data, “in healthcare it is being used to improve efficiency and quality, resulting in better healthcare practices and improved patient outcomes” (pp. 478, para. 2). Additionally, by improving healthcare practices and efficiency, big data analysis can contribute to decreased healthcare costs and improved treatment options for patients. Also, the use of analyzing big data to observe patterns or trends can potentially decrease episodes of delayed patient diagnosis and treatment for chronic diseases. For example, in the healthcare setting, big data analysis of multiple patients’ intake assessments, including their demographics, healthcare practices, and health literacy, can be examined for trends and patterns in overall risks for developing comorbidities. This is just one example of how big data analysis benefits the healthcare system.

Although the use of big data has benefits, there are also challenges associated with using big data. Many big data databases are not organized and categorized the same which leads to increased confusion for the data analyst or nurse informaticist when performing analysis methods. According to Thew, J. (2016), “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” (para. 10). Additionally, the lack of data standardization makes the analysis of big data time consuming and labor intensive.

Another risk of utilizing big data is the concern for reduced patient confidentiality related to health information and treatments provided. According to Tse, D., Tong, C., Ly, T., Tam, K., & Chow, C. (2018), “at data collection stage, seeking consent from patients has been another challenge, in the view that data owners may preserve their rights and ability to take the greatest control of the confidentiality of data they provided” (pp. 1632, para 4). Patient confidentiality plays a significant role in how patient data can be utilized and, ultimately analyzed. Overall, the risk of reduced patient privacy when accessing big data has led to many barriers when data analysts/nurse informaticists try to perform their jobs and collect information that could improve patient outcomes.

Although there are challenges to big data, there are strategies to utilize to mitigate these challenges. One important risk that I researched is the potential for decreased patient confidentiality and privacy. Many strategies are employed to protect patient privacy when utilizing big data analytics. One strategy that is already in effect is the implementation of patient privacy laws. Additionally, when medical facilities utilize big databases to store patient information, these databases must be secure to reduce the risk of hacking mechanisms.

Another strategy that I would recommend is the use of consents. These consents would allow patients to provide their permission for their medical information to be utilized by data analysts and nurse informaticists to collect, analyze, and draw beneficial knowledge to improve patient outcomes. This consent process could be similar to the consent process used for clinical trials. Although this consent process could potentially pose limitations or delays in big data analyzation, it would best protect patient privacy. I believe, that many patients will allow their medical information to be analyzed after they are informed of the potential benefits for patient outcomes and efficiencies.

As described above, the use of big data has benefits and challenges. I believe the benefits can be achieved as long as there are strategies in place to mitigate the challenges. Big data has unlimited uses and provides medical information that can save lives. However, patient privacy needs to be protected. Ultimately, big data needs to be analyzed consciously and morally.

Reference List

Mastrian, K. & McGonigle, D. (2018). Glossary. In D. McGonigle & K.G. Mastrian (Eds.),

Nursing informatics and the foundation of knowledge (4th ed., pp. 21-34). Jones and

Bartlett Learning.

Mastrian, K. & McGonigle, D. (2018). Data mining as a research tool. In D. McGonigle & K.G.

Mastrian (Eds.), Nursing informatics and the foundation of knowledge (4th ed., pp. 21-

34). Jones and Bartlett Learning.

Thew, J. (2016), Big data means big potential, challenges for nurse execs. Retrieved from

https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-

nurse-execs

Tse, D., Tong, C., Ly, T., Tam, K., & Chow, C. (2018). The challenges of big data governance in

healthcare. Retrieved from https://ieeexplore-ieee-

org.ezp.waldenulibrary.org/stamp/stamp.jsp?tp=&arnumber=8456108&tag=1

Module 4: Technologies Supporting Applied Practice and Optimal Patient Outcomes (Weeks 6-8)

Laureate Education (Producer). (2018). Informatics Tools and Technologies [Video file]. Baltimore, MD: Author.

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Learning Objectives

Students will:

Evaluate healthcare technology trends for data and information in nursing practice and healthcare delivery
Analyze challenges and risks inherent in healthcare technology
Analyze healthcare technology benefits and risks for data safety, legislation, and patient care
Evaluate healthcare technology impact on patient outcomes, efficiencies, and data management
Analyze research on the application of clinical systems to improve outcomes and efficiencies
Due By
Assignment
Week 6, Days 1–2
Read/Watch/Listen to the Learning Resources.
Compose your initial Discussion post.
Week 6, Day 3
Post your initial Discussion post.
Begin to compose your Assignment.
Week 6, Days 4-5
Review peer Discussion posts.
Compose your peer Discussion responses.
Continue to compose your Assignment.
Week 6, Day 6
Post at least two peer Discussion responses on two different days (and not the same day as the initial post).
Week 6, Day 7
Wrap up Discussion.
Week 7, Days 1-7
Continue to compose your Assignment.
Week 8, Days 1-6
Continue to compose your Assignment.
Week 8, Day 7
Deadline to submit your Assignment.

Learning Resources

Required Readings

McGonigle, D., & Mastrian, K. G. (2017). Nursing informatics and the foundation of knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning.

Chapter 14, “The Electronic Health Record and Clinical Informatics” (pp. 267–287)
Chapter 15, “Informatics Tools to Promote Patient Safety and Quality Outcomes” (pp. 293–317)
Chapter 16, “Patient Engagement and Connected Health” (pp. 323–338)
Chapter 17, “Using Informatics to Promote Community/Population Health” (pp. 341–355)
Chapter 18, “Telenursing and Remote Access Telehealth” (pp. 359–388)

Dykes, P. C., Rozenblum, R., Dalal, A., Massaro, A., Chang, F., Clements, M., Collins, S. …Bates, D. W. (2017). Prospective evaluation of a multifaceted intervention to improve outcomes in intensive care: The Promoting Respect and Ongoing Safety Through Patient Engagement Communication and Technology Study. Critical Care Medicine, 45(8), e806–e813. doi:10.1097/CCM.0000000000002449

HealthIT.gov. (2018c). What is an electronic health record (EHR)? Retrieved from

https://www.healthit.gov/faq/what-electronic-health-record-ehr

Rao-Gupta, S., Kruger, D. Leak, L. D., Tieman, L. A., & Manworren, R. C. B. (2018). Leveraging interactive patient care technology to Improve pain management engagement. Pain Management Nursing, 19(3), 212–221.

Skiba, D. (2017). Evaluation tools to appraise social media and mobile applications. Informatics, 4(3), 32–40.

Required Media

Laureate Education (Producer). (2018). Public Health Informatics [Video file]. Baltimore, MD: Author.

Accessible player

Laureate Education (Producer). (2018). Electronic Records and Managing IT Change [Video file]. Baltimore, MD: Author.

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Rubric Detail

Select Grid View or List View to change the rubric’s layout.

Name: NURS_5051_Module03_Week04_Discussion_Rubric
Grid View
List View
Excellent Good Fair Poor
Main Posting
45 (45%) – 50 (50%)
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.
40 (40%) – 44 (44%)
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.
35 (35%) – 39 (39%)
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.
0 (0%) – 34 (34%)
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.
Main Post: Timeliness
10 (10%) – 10 (10%)
Posts main post by day 3.
0 (0%) – 0 (0%)
0 (0%) – 0 (0%)
0 (0%) – 0 (0%)
Does not post by day 3.
First Response
17 (17%) – 18 (18%)
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 (15%) – 16 (16%)
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 (13%) – 14 (14%)
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.
0 (0%) – 12 (12%)
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.
Second Response
16 (16%) – 17 (17%)
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.
14 (14%) – 15 (15%)
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.
12 (12%) – 13 (13%)
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.
0 (0%) – 11 (11%)
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.
Participation
5 (5%) – 5 (5%)
Meets requirements for participation by posting on three different days.
0 (0%) – 0 (0%)
0 (0%) – 0 (0%)
0 (0%) – 0 (0%)
Does not meet requirements for participation by posting on 3 different days.
Total Points: 100
Name: NURS_5051_Module03_Week04_Discussion_Rubric

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