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Web Article On Big Data Means Big Potential
Web Article On Big Data Means Big Potential
Order ID# 45178248544XXTG457 Plagiarism Level: 0-0.5% Writer Classification: PhD competent Style: APA/MLA/Harvard/Chicago Delivery: Minimum 3 Hours Revision: Permitted Sources: 4-6 Course Level: Masters/University College Guarantee Status: 96-99% Instructions
Web Article On Big Data Means Big Potential
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.
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.
https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs
https://www.youtube.com/watch?v=4W6zGmH_pOw
APA format and at 3 references
Then respond to two peers by offering one or more additional mitigation strategies or further insight into your colleagues’ assessment of big data opportunities and risks.
In APA format and 2 references
Peer 1
Nursing informatics is a common part of any medical structure. Specifically, the specialty involves integrating healthcare information and knowledge with technology to better patient outcomes (McGonigle & Mastrian, 2022). In most instances, clinical systems utilizing nursing informatics rely on big data systems to collect, store, and disseminate medical care information and knowledge to relevant parties. However, these said big data structures are technologically-based, making them vulnerable to different problems despite displaying multiple benefits. Therefore, the assessment discusses the potential benefits and risks of using big data in a clinical setting, including strategies to mitigate identified risks.
Data usage is part and parcel of standard nursing practice. According to Glassman (2017), nurses use data to make informed medical practice decisions; specifically, healthcare data helps nurses analyze, develop and assess patient care by determining the ideal or efficient approach to prevent and treat illnesses, resulting in improved health outcomes. The extensive use of data in daily nursing operations has contributed to big data incorporation in clinical systems. It comprises massive patient or population information volumes created by employing digital technologies to gather and store the data.
In furtherance, big data use in nursing is associated with many benefits. Wang et al. (2018) describe how big data analytics in healthcare operations like nursing supports high-quality patient care through evidence-based practice promotion; the analytics allows for caregivers to discover associations from massive medical records, enabling a detailed identification of care patterns which ensures sufficient evidence is obtained to backup any medical intervention chosen to manage a specified health condition. Moreover, it contributes to interprofessional collaboration within a given healthcare organization by improving communication among varying healthcare practitioners and staff members. Other benefits constitute avoiding unnecessary medical costs incurred by the healthcare organization, such as information technology (IT) expenditure, quick transfer of information among exiting medical IT systems, shortening diagnosis periods, and reducing patient travel time.
Nonetheless, there are certain challenges affiliated with big data use in nursing. One of the top challenges of using big data is showcased during evaluating and synthesizing patient or population data; the steps are usually conducted manually, resulting in high demand for time and labor power (Thew, 2016). In particular, the big data structures are built in silos, leading to a difference in data systems amongst existing units. Thus, any nursing practitioner intending to use the information may find the data standardization lack a great challenge, primarily when examining how a healthcare organization performs to facilitate informed decision-making, attributing to the need to analyze the information manually.
The identified challenge can be mitigated by breaking down the traditional structured big data silos. According to Thew (2016), breaking down said silos will offer a balanced approach to evaluating nursing or organizational performances by eliminating the need for manual operations. Additionally, it ensures any individual analyzing the performances has access to real-time data, making the evaluations accurate and relevant. Hence, the analysis will be conducted by a few people within a short time.
Peer 2
The collection of data by nurses never stops. Information is created by combining and analyzing individual data, which is then synthesized to give it meaning and create knowledge (McGonigle & Mastrian, 2017). Massive volumes of data, or “big data,” can be retrieved and continuously evaluated with the inclusion of EMRs and cloud storage (Benke & Benke, 2018). Technology-advanced computer processing systems must be used to examine huge data in order to retrieve, sort, analyze, and synthesize meaningful information. (2018) (Benke & Benke). As processing systems advance and artificial intelligence is added, the delivery of healthcare is changing (Benke & Benke, 2018). Through the use and distribution of the acquired information, nurses are placed at the forefront of this shift.
RUBRIC
Excellent Quality 95-100%
Introduction 45-41 points
The background and significance of the problem and a clear statement of the research purpose is provided. The search history is mentioned.
Literature Support 91-84 points
The background and significance of the problem and a clear statement of the research purpose is provided. The search history is mentioned.
Methodology 58-53 points
Content is well-organized with headings for each slide and bulleted lists to group related material as needed. Use of font, color, graphics, effects, etc. to enhance readability and presentation content is excellent. Length requirements of 10 slides/pages or less is met.
Average Score 50-85%
40-38 points More depth/detail for the background and significance is needed, or the research detail is not clear. No search history information is provided.
83-76 points Review of relevant theoretical literature is evident, but there is little integration of studies into concepts related to problem. Review is partially focused and organized. Supporting and opposing research are included. Summary of information presented is included. Conclusion may not contain a biblical integration.
52-49 points Content is somewhat organized, but no structure is apparent. The use of font, color, graphics, effects, etc. is occasionally detracting to the presentation content. Length requirements may not be met.
Poor Quality 0-45%
37-1 points The background and/or significance are missing. No search history information is provided.
75-1 points Review of relevant theoretical literature is evident, but there is no integration of studies into concepts related to problem. Review is partially focused and organized. Supporting and opposing research are not included in the summary of information presented. Conclusion does not contain a biblical integration.
48-1 points There is no clear or logical organizational structure. No logical sequence is apparent. The use of font, color, graphics, effects etc. is often detracting to the presentation content. Length requirements may not be met
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Web Article On Big Data Means Big Potential
