Big Data Risks and Rewards
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.
ORDER A PLAGIARISM-FREE BIG DATA PAPER NOW
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 4
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 4
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.
Data is collected everywhere whether we realize it or not, especially in health care. According to Dash (2019), “Big data represents large amounts of data that is unmanageable using traditional software or Internet based platforms (para 5). “Applied to health care, big data will use specific health data of a population (or of a particular individual) and potentially help to prevent epidemics, cure disease, cut down costs, etc” (Lebied, 2018, para 2). Big data analytics is why we are able to use preventative medicine. Health care data has been analyzed over the years from patient to patient to predict intervention based on patient’s individual symptoms. Using big data in the clinical system can help health care providers gain insight on patients from previous visits or looking at data collected on a monitoring device worn at home by the patient such as a holter monitor or even an AICD that transmits data if a patient goes into an irregular heart rhythm. A benefit of big data and the clinical system is that the information collected and analyzed can be used to our advantage and help us predict things before they happen. For example septic patients. An algorithm was created for patient’s who meet criteria based on their presenting vital signs and symptoms to initiate measures to combat sepsis. Studies analyzing big data have been conducted in sepsis patients so that now we know how to treat it sooner and help save lives.
Many challenges arise with big data as part of a clinical system. Although all health care facilities in the U.S. had to convert to government approved electronic health records, “the availability of hundreds of EHR products certified by the government, each with different clinical terminologies, technical specifications, and functional capabilities has led to difficulties in the interoperability and sharing of data” (Dash, 2019, para 33). Sure everyone is using electronic health records or should be but they are not all using the same one so the data can not cross over program to program. So patients health records from other facilities can not be seen. One strategy I have used is called Care Everywhere for Epic users. This platform allows patients to give providers permission to look up records from other facilities that use Epic and are able to provide continuity of care. “Today, organizations using Care Everywhere exchange two million patient records per day with Epic and non-Epic systems. Epic is the only major EHR vendor that has 100% of its health system customers interoperable with each other, other EHR’s, government organizations and other national networks” (Epic, 2017, para 3). Through research I learned that Epic has come up with even newer technology of sharing patients information with providers that can not interoperate with Epic and Care Everywhere, now called Share Everywhere. “Through Share Everywhere, patients will be able to use their smartphones to direct a view of their Epic chart in minutes to any clinician, anywhere in the world. Because the patient determines who gets access, the patient’s privacy is protected and in addition, Epic records all access” (Epic, 217, para 4).
Reference
Dash, S. (2019). Big Data in Healthcare: Management, Analysis, and future prospects. Retrieved from https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0217-0
Epic Outcomes. (2017). Retrieved from https://www.epic.com/epic/post/epic-announces-worldwide-interoperability-share-everywhere
Lebied, M. (2018). 12 Examples of Big Data Analytics in Healthcare That Can Save People. Retrieved from https://www.datapine.com/blog/big-data-examples-in-healthcare/
| 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 |
|---|
According to Wang, Kung, and Byrd (2018) big data analytics offers many potential benefits, however, it has some unintentional downfalls. For instance, data suggesting a positive opportunity for one unit, may mean a deleterious one for another. “Having good data is the key to making effective changes” (Thew, 2016). Recently, an analysis on armbands versus patient stickers being scanned for point-of-care testing showed an overwhelming amount of users, being certified nursing assistants (CNA) and nurses, scanning patient stickers rather than armbands at a Levell II Trauma Center in Winchester, Virginia. This can and has lead to patient data being uploaded into the wrong patient’s chart. As such, a software upgrade to the iSTAT machines was performed, no longer allowing stickers to be scanned, only patient armbands. As we know as nurses and caregivers, the proper way to perform point-of-care testing is to confirm name and date of birth with the patient, followed by scanning their armband. The sticker scanning is an assumed shortcut, and as such should be eliminated completely. However, in the operating room, specifically open-heart procedures, it is standard to run blood gases and ACT’s regularly. It is also standard for the patient’s arms to be tucked by their sides and thus, nearly impossible to access perioperatively. What was a great idea and advocated for patient safety on one unit has become a negative patient safety issue on another. The solution in this institution, which still poses problems, is for the preop nurse to print and additional patient armband to follow the patient’s chart through the operating room to be scanned intraoperatively, and be disposed of post-op. Although this is the quickest and likely easiest solution, it is probably not the most efficient or safest. One solution could be for the point-of-care testing machines (iSTAT) to have a specific operating room code that allows for sticker scanning. With the evolving use of big data analytics, it is essential that new methods are used to analyze, compute and visualize these ever-changing complex and voluminous data sets (Maciera et al., 2017).
Maciera, T. G. R., 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. AMIA Annual Symposium Proceedings. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977718/
Thew, J. (2016). Big data means big potential, challenges for nurse execs. Retrieved fromhttps://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 organizations. Technological Forecasting and Social Change, 126(1), 3–13. doi:10.1016/j.techfore.2015.12.019.