Big Data Risks and Rewards Discussion

Part A

Big Data Risks and Rewards discussion

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.

Review the Resources and reflect on the web article Big Data Means Big Potential, Challenges for Nurse Execs. https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-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.

Resources for Part A & B

Rutherford, M. A. (2008). Standardized nursing language: What does it mean for nursing practice? Online Journal of Issues in Nursing, 13(1), 1–12. doi:10.3912/OJIN.Vol13No01PPT05.

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

Topaz, M. (2013). The hitchhiker’s guide to nursing theory: Using the Data-Knowledge-Information-Wisdom framework to guide informatics research. Online Journal of Nursing Informatics, 17(3).

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.

PART B

Impact of Standardized Nursing Terminology

Among the Resources in this module is the Rutherford (2008) article Standardized Nursing Language: What Does It Mean for Nursing Practice? In this article, the author recounts a visit to a local hospital to view the recent implementation of a new coding system.

During the visit, one of the nurses commented to her, “We document our care using standardized nursing languages but we don’t fully understand why we do” (Rutherford, 2008, para. 1).

How would you respond to a comment such as this one?

To Prepare:

Review the concepts of informatics as presented in the Resources, particularly Rutherford, M. (2008) Standardized Nursing Language: What Does It Mean for Nursing Practice?
Reflect on the role of a nurse leader as a knowledge worker.
Consider how knowledge may be informed by data that is collected/accessed.
The Assignment:

Thanks for sharing such an interesting post!. Big data is a term that is typically used in the medical field to refer to the enormous amounts of data that have been generated as a result of the widespread use of various types of digital technology that gathers information on patients and assists in the administration of the operations of hospitals. This term is derived from the phrase “big data,” which was coined to describe the massive amounts of data that have been generated. However, according to McGonigle and Mastrian (2022), “Hospitals and medical centers have more to gain from big data analytics than perhaps any other industry. But as data sets continue to grow, healthcare facilities are discovering that success in data analytics has more to do with storage methods than with analysis software or techniques.” In other words, data management is complex.

On the article Healthcare Big Data and the Promise of Value-Based Care (2018) explains about the applications in healthcare industries, “Consumer products like the Fitbit activity tracker and the Apple Watch keep tabs on the physical activity levels of individuals and can also report on specific health-related trends” These wearable gadgets are used in my hospital to encourage employees to be healthy. While the employee’s personal iPhone health or wellness app may share data into their Virgin Pulse App to measure physical activity, they can build challenges to keep employees interested.

References

Healthcare Big Data and the Promise of Value-Based Care. (2018, January 1). nejm catalyst. Retrieved December 26, 2022, from https://catalyst.nejm.org/doi/full/10.1056/CAT.18.0290Links to an external site.

McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett Learning

In a 2- to 3-page paper, address the following:

Explain how you would inform this nurse (and others) of the importance of standardized nursing terminologies.
Describe the benefits and challenges of implementing standardized nursing terminologies in nursing practice. Be specific and provide examples.
Be sure to support your paper with peer-reviewed research on standardized nursing terminologies that you consulted from
Don’t forget a concluding paragraph
Reference page must include 3 of the resources provided

“BIGDATA” generally refers to data that is very large and complex and needs complex processing methods. The three Vs. describe big data: volume, velocity, and variety (McGonigle & Mastrian, 2022). Big data is expected to significantly impact healthcare, advancing treatment innovation, drug discovery, customized medicine, and optimal patient care while lowering healthcare costs and improving patient outcomes. To support their significant data initiatives, the private and public sectors have invested billions of dollars in data collection efforts (McGonigle & Mastrian, 2022). Healthcare-related big data can originate from various sources, including internal sources such as electronic health records and external sources like Testing, Radiology, pharmacies, laboratories, and government agencies (McGonigle & Mastrian, 2022).

Risks and Rewards of Big Data

Rewards of Big Data:

1. Big data and analytics can provide valuable insights and support healthcare and clinical decisions in several ways, as follows:

• Identifying patterns and trends: big data identify patterns and trends in patient data that may not be apparent to healthcare providers (Lohr, 2014). For example, analyzing large datasets can reveal common risk factors, genetic predispositions, or environmental factors contributing to specific health conditions. This information can help healthcare providers make more informed decisions about treatment plans, disease management, and prevention strategies (Lohr, 2014).
• Personalizing treatment plans: big data enables personalized medicine by analyzing patient data to identify genetic markers and drug interactions. It allows healthcare providers to tailor treatment plans to individual patients, optimizing outcomes and reducing the risk of adverse events (Lohr, 2014).
• Improving patient outcomes: big data predict patient outcomes, enabling healthcare providers to identify patients at high risk of complications and intervene early. For example, analyzing patient data can allow healthcare providers to implement interventions to reduce readmission rates (Lohr, 2014).

2. Big data can play an essential role in driving healthcare innovations and research in several ways:

• Enabling precision medicine: big data provides insights into patient data at the molecular level, allowing for the development of personalized treatment plans based on an individual’s genetics, environment, and lifestyle factors (Lohr, 2014).
• Enhancing clinical trials: big data optimizes clinical trials by identifying patients most likely to benefit from a new treatment, reducing the cost and time required for drug development (Lohr, 2014).
• Improving healthcare operations: big data can help healthcare organizations optimize their scheduling and help hospitals reduce wait times, and improve patient satisfaction.
• Supporting public health initiatives: big data can monitor disease outbreaks, predict the spread of infectious diseases, and initiate public health policies and interventions to improve population health (Lohr, 2014).
• Medical research: big data can be used to find new disease risk factors, develop more effective treatments, and improve patient outcomes (Lohr, 2014).

3. Big data can help in increasing operational efficiency in healthcare, which can lead to cost savings as follows:

• Improving resource allocation: By analyzing data on patient flow, resource utilization, and operational efficiency, big data can help healthcare organizations optimize resource allocation and reduce waste (Davenport, 2014).
• Reducing readmissions: big data can help identify patients at high risk of readmission, allowing healthcare providers to intervene early and prevent readmissions (Davenport, 2014).
• Enhancing preventive care: big data can help identify patients at high risk of developing chronic conditions, allowing healthcare providers to provide preventative care and reduce the need for costly treatments later (Davenport, 2014).
• Reducing medical errors: big data can help identify patterns and trends in medical mistakes, allowing healthcare organizations to implement targeted interventions and reduce medical errors (Davenport, 2014).
• Fraud detection: big data can identify fraudulent activity patterns in healthcare billing and claims (Davenport, 2014).
• Streamlining administrative processes: big data can help automate administrative functions such as patient flow, claims processing, billing, and coding, reducing the administrative burden by improving efficiency (Davenport, 2014).
• Optimizing staff schedules: big data analyzing patient flow and staffing needs can help healthcare organizations optimize staff schedules, reducing overstaffing and understaffing and improving efficiency (Davenport, 2014).
• Improving inventory and supply chain management: big data can help healthcare organizations track inventory levels and usage, allowing them to optimize inventory levels and reduce waste. This can lead to cost savings and improved efficiency (Davenport, 2014).

4. Big data can help improve customer experience in healthcare in several ways:

• Remote monitoring: big data can monitor patient health remotely, allowing healthcare providers to track patient progress and intervene if necessary, improving patient outcomes and reducing the need for in-person visits (McGonigle & Mastrian, 2022).
• Telemedicine: Big data can support telemedicine, allowing patients to access healthcare services remotely and improving access to care for patients who may not have access to traditional healthcare services (McGonigle & Mastrian, 2022).
• Patient engagement: big data can engage patients and empower them to take an active role in their healthcare by providing personalized health information, feedback on treatment progress, and educational resources (McGonigle & Mastrian, 2022).

Challenges Associated with Big Data:

According to Sivarajah, Kamal, Irani, & Weerakkody, (2017). Big data presents several challenges that organizations must address to harness its potential benefits effectively. Here are some of the critical challenges associated with big data:

Volume: Big data refers to large and complex data sets that can be difficult to manage and process (Sivarajah et al.,2017)

Velocity: The speed at which data is generated can be challenging for organizations that must process and analyze data in real-time or near-real-time (Sivarajah et al.,2017).

Variety: Big data can come in various forms, such as structured, semi-structured, and unstructured, and can be generated from diverse sources (Sivarajah et al.,2017).

Veracity: Big data can contain inaccuracies, inconsistencies, and errors that can impact the quality of insights (Sivarajah et al.,2017).

Privacy and Security: Big data can contain sensitive information about individuals, which can be used to profile and discriminate against them (Sivarajah et al.,2017).

Skilled Workforce: Big data requires a skilled workforce with expertise in data mining, data analytics, data science, and big data technologies (Sivarajah et al.,2017).

Strategies to overcome the Challenges. Mitigating the challenges associated with big data requires combining technical, organizational, and cultural systems. Here are some strategies that organizations can use to mitigate the challenges of big data in health care:

Develop transparent data governance policies: Healthcare organizations should develop clear policies and guidelines for data collection, storage, analysis, and sharing to ensure data is used ethically and securely (Bughin & Manyika, 2018).

Invest in data management infrastructure: To effectively manage and analyze big data, healthcare organizations must invest in the necessary infrastructure, such as data warehouses and analytics tools (Bughin & Manyika, 2018).

Use cloud-based solutions: Cloud-based solutions can help organizations store and process large volumes of data without investing in expensive hardware and software. Cloud providers also offer scalable solutions that can grow as data volumes increase (Bughin & Manyika, 2018).

Implement data quality and accuracy checks: To ensure that data is accurate and reliable, healthcare organizations should implement quality checks throughout the data collection and analysis (Bughin & Manyika, 2018).

Adopt data analytics tools: Adopting data analytics tools can help organizations process and analyze large volumes of data quickly and efficiently. These tools can also provide insights to help organizations make data-driven decisions (Bughin & Manyika, 2018).

Train and develop a skilled workforce: Investing in training and development programs can help organizations develop a skilled workforce with expertise in data analytics, data science, and big data technologies. This can help organizations leverage big data effectively and address the skills gap (Bughin & Manyika, 2018).

Prioritize data privacy and security: Organizations should implement measures to protect sensitive data, prevent data breaches, and comply with relevant data protection regulations. This can be achieved through data encryption, access controls, and monitoring tools (Bughin & Manyika, 2018).

Foster a data-driven culture: Organizations should foster a data-driven culture that encourages collaboration, experimentation, and innovation. This involves educating employees about the benefits of big data and providing access to data and analytics tools (Bughin & Manyika, 2018).

Ensure patient privacy: Healthcare organizations must ensure that patient privacy is protected throughout the data collection and analysis, including obtaining informed consent and de-identifying data when necessary (Bughin & Manyika, 2018).

Foster collaboration and communication: Effective management of big data in healthcare requires collaboration and communication between stakeholders, including healthcare providers, researchers, and patients (Bughin & Manyika, 2018).

In summary, by mitigating the challenges associated with big data, organizations can gain a competitive advantage in the market and improve patient outcomes by reducing healthcare costs.

References:

Bhughin, J., & Chui, M., & Manyika, J. (2018). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Quarterly, 1-13.

Davenport, T. H. (2014). Big data at work: Dispelling the myths, uncovering the opportunities. Harvard Business Review Press.

Dumbill, E. (2012). Making sense of big data. O’Reilly Media, Inc.

Kudyba, S., & Hoptroff, R. (2016). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 4(1),3. https://doi.org/10.1186/s13755-016-0017-yLinks to an external site.

Kshetri, N. (2014). Big data’s impact on privacy, security, and consumer welfare. Telecommunications Policy, 38(4), 1134-1145.

Kuo, M. H., & Sahama, T. (2018). Big data for healthcare: A review. Journal of biomedical informatics, (87), 1-10.

Lohr, S. (2014, August 18). For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights. The New York Times. https://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html

Links to an external site..

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.

McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett Learning.

Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of significant data challenges and analytical methods. Journal of Business Research, (70), 263-286.

You must proofread your paper. But do not strictly rely on your computer’s spell-checker and grammar-checker; failure to do so indicates a lack of effort on your part and you can expect your grade to suffer accordingly. Papers with numerous misspelled words and grammatical mistakes will be penalized. Read over your paper – in silence and then aloud – before handing it in and make corrections as necessary. Often it is advantageous to have a friend proofread your paper for obvious errors. Handwritten corrections are preferable to uncorrected mistakes.

Use a standard 10 to 12 point (10 to 12 characters per inch) typeface. Smaller or compressed type and papers with small margins or single-spacing are hard to read. It is better to let your essay run over the recommended number of pages than to try to compress it into fewer pages.

Likewise, large type, large margins, large indentations, triple-spacing, increased leading (space between lines), increased kerning (space between letters), and any other such attempts at “padding” to increase the length of a paper are unacceptable, wasteful of trees, and will not fool your professor.

The paper must be neatly formatted, double-spaced with a one-inch margin on the top, bottom, and sides of each page. When submitting hard copy, be sure to use white paper and print out using dark ink. If it is hard to read your essay, it will also be hard to follow your argument.

ADDITIONAL INSTRUCTIONS FOR THE CLASS

Discussion Questions (DQ)

Initial responses to the DQ should address all components of the questions asked, include a minimum of one scholarly source, and be at least 250 words.
Successful responses are substantive (i.e., add something new to the discussion, engage others in the discussion, well-developed idea) and include at least one scholarly source.
One or two sentence responses, simple statements of agreement or “good post,” and responses that are off-topic will not count as substantive. Substantive responses should be at least 150 words.
I encourage you to incorporate the readings from the week (as applicable) into your responses.

Weekly Participation

Your initial responses to the mandatory DQ do not count toward participation and are graded separately.
In addition to the DQ responses, you must post at least one reply to peers (or me) on three separate days, for a total of three replies.
Participation posts do not require a scholarly source/citation (unless you cite someone else’s work).
Part of your weekly participation includes viewing the weekly announcement and attesting to watching it in the comments. These announcements are made to ensure you understand everything that is due during the week.

APA Format and Writing Quality

Familiarize yourself with APA format and practice using it correctly. It is used for most writing assignments for your degree. Visit the Writing Center in the Student Success Center, under the Resources tab in LoudCloud for APA paper templates, citation examples, tips, etc. Points will be deducted for poor use of APA format or absence of APA format (if required).
Cite all sources of information! When in doubt, cite the source. Paraphrasing also requires a citation.
I highly recommend using the APA Publication Manual, 6th edition.

Use of Direct Quotes

I discourage overutilization of direct quotes in DQs and assignments at the Masters’ level and deduct points accordingly.
As Masters’ level students, it is important that you be able to critically analyze and interpret information from journal articles and other resources. Simply restating someone else’s words does not demonstrate an understanding of the content or critical analysis of the content.
It is best to paraphrase content and cite your source.

LopesWrite Policy

For assignments that need to be submitted to LopesWrite, please be sure you have received your report and Similarity Index (SI) percentage BEFORE you do a “final submit” to me.
Once you have received your report, please review it. This report will show you grammatical, punctuation, and spelling errors that can easily be fixed. Take the extra few minutes to review instead of getting counted off for these mistakes.
Review your similarities. Did you forget to cite something? Did you not paraphrase well enough? Is your paper made up of someone else’s thoughts more than your own?
Visit the Writing Center in the Student Success Center, under the Resources tab in LoudCloud for tips on improving your paper and SI score.

Late Policy

The university’s policy on late assignments is 10% penalty PER DAY LATE. This also applies to late DQ replies.
Please communicate with me if you anticipate having to submit an assignment late. I am happy to be flexible, with advance notice. We may be able to work out an extension based on extenuating circumstances.
If you do not communicate with me before submitting an assignment late, the GCU late policy will be in effect.
I do not accept assignments that are two or more weeks late unless we have worked out an extension.
As per policy, no assignments are accepted after the last day of class. Any assignment submitted after midnight on the last day of class will not be accepted for grading.

Communication

Communication is so very important. There are multiple ways to communicate with me:
Questions to Instructor Forum: This is a great place to ask course content or assignment questions. If you have a question, there is a good chance one of your peers does as well. This is a public forum for the class.
Individual Forum: This is a private forum to ask me questions or send me messages. This will be checked at least once every 24 hours.