Discussion: 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.
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.
Rubric Detail
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Name: NURS_5051_Module03_Week05_Discussion_Rubric
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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_Week05_Discussion_Rubric
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Big Data Risks and Rewards
Student’s Name
Institutional Affiliation
Course
Professor’s Name
Date
Big Data Risks and Rewards
With the rapid pace of change and complexity in health care, now amplified by the pandemic, the caregivers need support. That is, they need timely, insight-driven, and evidence-based data to foster quick decision making at all points of care. Efficient big data management, analysis, and interpretation can change the game and open new avenues for modern health care by providing advanced patient care (Murdoch & Detsky, 2013). For instance, data collected from a first AI-enabled early warning patient monitoring systems, mobile connectivity, clinical decision support algorithm can be analyzed to help health care systems identify the patient’s needs sooner and respond faster (Bates, Saria, Ohno-Machado, Shah & Escobar, 2014). It has been identified that the use of early warning systems resulted in a reduction of 35% in severe cases of cardiac arrest(Bates et al., 2014).
Data standardization is one of the challenges facing Big Data in health. That is, data is stored in a manner that is not interoperable with all applications and technologies. As data is rarely standardized, limited interrogability poses a big challenge. This leaves big data to face issues related to acquiring and cleaning data into a standard format for analysis and global sharing of data (Kruse, Goswamy, Raval & Marawi, 2016). Big data will have to deal with different terminologies, demographic data, standards, and even language barriers with the shift to data globalization. For instance, the patient’s incorrect or incomplete demographic data can be very detrimental to patient matching and identification. This can create the risk of providers to associate two different patients with the same record mistakenly.
Clinical managers should work together to re-engineer and standardize the Master Patient Index(MPI) record. This can be done by developing an extensive organization-wide data cleaning process of the MPI, especially on areas duplicating patient record discrepancies due to default or blank entries (Randall, Ferrante, Boyd & Semmens, 2013). This improves the quality of data. That is, an efficient data-driven clinical decision support.
References
Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123-1131.
Kruse, C. S., Goswamy, R., Raval, Y. J., & Marawi, S. (2016). Challenges and opportunities of big data in health care: a systematic review. JMIR medical informatics, 4(4), e38.
Murdoch, T. B., & Detsky, A. S. (2013). The inevitable application of big data to health care. Jama, 309(13), 1351-1352.
Randall, S. M., Ferrante, A. M., Boyd, J. H., & Semmens, J. B. (2013). The effect of data cleaning on record linkage quality. BMC medical informatics and decision making, 13(1), 64.
Rn, Jennifer Thew. “Big Data Means Big Potential, Challenges for Nurse Execs.” BIG DATA MEANS BIG POTENTIAL, CHALLENGES FOR NURSE EXECS, 2016, www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs.
