Discussion – Week 4 Big Data Analytics
Discussion – Week 4 Big Data Analytics
Discussion – Week 4 Big Data Analytics
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. 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. Learning Resources Note: To access this week’s required library resources, please click on the link to the Course Readings List, found in the Course Materials section of your Syllabus. Required Readings McGonigle, D., & Mastrian, K. G. (2017). Discussion – Week 4 Big Data Analytics
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Nursing informatics and the foundation of knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning. Chapter 25, “The Art of Caring in Technology-Laden Environments” (pp. 525–535) Chapter 26, “Nursing Informatics and the Foundation of Knowledge” (pp. 537–551) American Nurses Association. (2018). Inclusion of recognized terminologies supporting nursing practice within electronic health records and other health information technology solutions. Retrieved from https://www.nursingworld.org/practice-policy/nursing-excellence/official-position-statements/id/Inclusion-of-Recognized-Terminologies-Supporting-Nursing-Practice-within-Electronic-Health-Records/ Macieria, 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, 2017, 1205–1214. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977718/ Office of the National Coordinator for Health Information Technology. (2017). Standard nursing terminologies: A landscape analysis. Retrieved from https://www.healthit.gov/sites/default/files/snt_final_05302017.pdf 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. Note: You will access this article from the Walden Library databases. Thew, J. (2016, April 19). Big data means big potential, challenges for nurse execs. Retrieved from 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). Note: You will access this article from the Walden Library databases. 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. Note: You will access this article from the Walden Library databases. Required Media Laureate Education (Executive Producer). (2012). Data, information, knowledge and wisdom continuum [Multimedia file]. Baltimore, MD: Author. Retrieved from http://mym.cdn.laureate-media.com/2dett4d/Walden/NURS/6051/03/mm/continuum/index.html Laureate Education (Producer). (2018). Health Informatics and Population Health: Analyzing Data for Clinical Success [Video file]. Baltimore, MD: Author.
Potential Benefit of Big Data in Healthcare Clinical System
Big data analytics is fast becoming a promising field in the provision of insights into data sets of large magnitude while simultaneously minimizing healthcare costs. Thew (2016) points out that for big data to be utilized in influencing meaningful outcomes within the nursing field, nurse executives need to take up their role as architects and visionaries of data.One of the potential benefit of using big data is dexcribed in the article by Thew, the local hospital mentioned using Meditech, a software program that allows physicians remote access to patients’ charts. By using Meditech, it is less time consuming and easier in comparison to making medical rounds. Meditech allows physicians to view a patient’s lab tests, CT scans, ultrasounds, notes on the patients from all attending or attended physicians, among other vital information, Stware such as Meditech is beneficial to a clinical sytem as it primarily saves time through remote patient assessment by physicians.
Potential Risk of Big Data Use in a Clinical System
One example of a potential challenge of using big data is in accessing and capturing data that is correctly formatted, accurate, complete and clean for utilization in multiple systems. This is because data comes from different sources and some of the sources lack impeccable governance (Bresnick, 2017). A recent study by Valikodath, Newman, Lee, et al., (2017) depicted one such challenge. The researchers conducted a study in an ophthalmology clinic where the patient-reported data from 23.5% of EHR was analyzed. The study revealed that when patients reported three or more ocular problem symptoms, the EHR data did not reconcile the same. This was an example of poor reconciliation of data despite the accurate presentation of information by patients. Asri, Mousannif, Moatassim, et al., (2015) assert that poor usability of EHR, convoluted workflows and a failure to completely understand the importance of capturing big data well, can add to the quality issues that compromise data throughout the entire lifecycle.Discussion – Week 4 Big Data Analytics
Strategy to Mitigate the Challenge
To ensure that data sourced by a healthcare facility is reliable, clean, and accurate, quality assurance measures need tobe put in place. More specifically policies that enforce the validation of data should be enforced in a healhtcare facility. Additionally, data on patients should be comprehensive which means that patient records should be complete with their care events as well as in the information that is of relevance regarding individual patients. Comprehensiveness will require for a healthcare faciiltiy to have accurate information on the patient’s every encounter with the system over time. To achieve this, a patient seeking medical assistance in a new healthcare facility will be required to inform on whether they have had other hospital visits. The information availed by the patient will be counter-checked in the state and federal healthcare system for clarification and reconciliation. Doing so will ensure that a patient receives the right treatment and avoid medical errors.
References
Asri, H., Mousannif, H., Al Moatassime, H., & Noel, T. (2015, June). Big data in healthcare: Challenges and opportunities. In 2015 International Conference on Cloud Technologies and Applications (CloudTech) (pp. 1-7). IEEE.
Bresnick, J. (2017). Top 10 Challenges of Big Data Analytics in Healthcare. Health IT Analytics. Available online at: https://healthitanalytics. com/news/top-10-challenges-of-big-data-analytics-in-healthcare (AccessedJun20, 2018).
Thew, J. (2016, April 19). Big data means big potential, challenges for nurse execs. Retrieved from https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs
Valikodath, N. G., Newman-Casey, P. A., Lee, P. P., Musch, D. C., Niziol, L. M., & Woodward, M. A. (2017). Agreement of ocular symptom reporting between patient-reported outcomes and medical records. JAMA ophthalmology, 135(3), 225-231.
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Discussion – Week 4 Big Data Analytics
