NURS 5051/NURS6051 Transforming Nursing and Healthcare

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.

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.

Respond to post with two scholarly references

Week 5: Informatics: Main Discussion

Benefit of Using Big Data

Working in a psychiatric hospital, it was surprising to learn that big data has been used to compare different types of brain matter to measure neuroanatomical deficit to evaluate for major depressive dis (MDD) and Alzheimer’s disease (AD). According to Kochunov et al. (2021), an elevation of Regional Vulnerability Index-major depressive dis (RVI-MDD) and Regional Vulnerability Index-Alzheimer’s disease (RVI-AD) can be used as an assessment tool to diagnose MDD and AD. The RVI is used to compare the white matter microstructure, the cortical gray matter thickness, and subcortical structural volumes of the brain. The similarity of patterns for MDD and AD were derived from a large-scale meta-analytic studies (Kochunov et al.). Per Kochunov et al. (2021), neuropsychiatric illness assessments have specificity and dis specific deficit patterns that are useful biomarkers for this population.

ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) is an amazing worldwide collaboration. Its purpose is to perform a “…large-scale neuroimaging genetic study…” (Kochunov et al., 2020). It is a worldwide group (WG) that studies the human brain. According to Kochunov et al. (2020), 12 WGs are using magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS) measures with genetic and epigenetic data, and electroencephalography (EEG) to produce analysis about the human brain. The psychiatric dis s that are included in this collaboration include schizophrenia, bipolar, major depressive dis , attention deficit hyperactivity dis , obsessive compulsive dis , and autism spectrum dis . There are many other dis s of the brain that are included in the studies.

Having the proper diagnosis helps psychiatrists and nurse practitioners treat psychiatric dis s with different medications. Each dis or combination of dis s require different medications. According to Stern et al. (2018), precision medication for psychiatric dis s is in the beginning stage of development. Neuroimaging, EEG, and other methods used to diagnosis psychiatric diagnosis are ways to determine the best medication to give patients. Big data can be utilized to determine the best medication for each patient.

Challenges of Using Big Data

One of the challenges of using big data in the psychiatric field is that the data that phenotyping varies in different centers and leads to limited clinical and limited scales used for assessments (Kochunov et al., 2020). ENIGMA is a data-driven approach that is complementary “…to well-designed, hypothesis-driven, smaller scale prospective single-center or multi-center studies with in-depth phenotyping” (Kochunov et al., 2020).

Mitigating the Use of Big Data

Although the use of big data has implications of being misused or the ability of data bases being breached, using data to treat psychiatric dis s is important. Many psychiatric patients try different medications before finding one that effectively treat the patient’s symptoms. By using the neuroimaging tools and/or the EGNIMA to aid in defining the psychiatric dis , the health care provider will have the tools to give patients precision medication.

References

Almost, J., Wolff, A. C., Stewart, P. A., McCormick, L. G., Strachan, D., & D’Souza, C. (2016). Managing and mitigating conflict in healthcare teams: An integrative review. Journal of Advanced Nursing (John Wiley & Sons, Inc.), 72(7), 1490-1505. https://doi-org.ezp.waldenulibrary.org/10.1111/jan.12903

Kochunov, P., Ryan, M. C., Yang, Q., Hatch, K. S., Zhu, A., Thomopoulos, S. I., Jahanshad, N. Schmaal, L., Thompson, P. M., Chen, S., Du, X., Adhikan, B. M., Bruce, H., Hare, S., Goldwaser, E. L., Kyarta, M. D., Nichols, T. E., & Hong, L. E. (2021). Comparison of regional brain deficit patterns in common psychiatric and neurological dis s as revealed by big data. Neuroimaged: Clinical, 29. https://doi-org.ezp.waldenulibrary.org/10.1016/j.nici.2021.102574

Thompson, P. M., Jahanshad, N., Ching, C. R. K., Salminen, L. E., Thomopoulos, S. I., Bright, J., Baune, B. T., Bertolin, S., Bralton, J., Bruin, W. B., Bulow, R., Chen, J., Chye, Y. Dannlowski, U., de Kovel, C. G. F., Donohoe, G., Eyler, L. T., Faraone, S. V., Favre, P., … Zelman, V., (2020). ENIGMA and global neuroscience: A decade of large-scale studies of brain in health and disease across more than 40 countries. Translational Psychiatry, 10(1), 100. https://doi-org.ezp.waldenulibrary.org/10.1038/s41398-020-0705-1

Stern, S., Linker, S., Vadodaria, K. C., Marchetto, M. C., & Gage, F. H. (2018). Prediction of response to drug therapy in psychiatric dis s. Open Biology, 8(5). https://doi-org.ezp.waldenulibrary.org/10.1098/tsob.180031:

Respond to post with two scholarly references:

“Data exploration and resulting knowledge discovery foster proactive, knowledge-driven decision making” (McGonigle & Mastrian, 2018, p. 479). When it comes to medical care, treatment modalities occur in phases; the least invasive is generally diet-related. After observation and follow-up, if there is no improvement, medication is incorporated into the treatment plan. Finally, if that does not work, procedures or surgical intervention are initiated (Laureate Education, 2012). The challenge is that the acuity level of patients today is greater than those of the past. Patients come to us sicker with comorbidities that make it riskier to undergo invasive, traumatic procedures.

A benefit of using big data as part of a clinical system is providing patients alternative surgical treatments that will improve their quality of life. Our structural heart program integrates innovative new technologies that allow us the opportunity to care for our very sick patients. “Big data analytics…facilitate clinical information integration and provide fresh business insights to help healthcare organizations meet patients’ needs” (Wang, 2018). By participating in clinical trials, we contribute to knowledge development. Clinical involvement allows our participation in decisions about medical equipment or surgical techniques that ultimately provide better results for our patients.

A potential risk of using big data as part of a clinical system relies on incomplete or missing data. Misleading data may occur in research studies that use visual aids to discuss findings, leading to uncertainty of validity (Falk, 2018). An overabundance of big data is also a challenge. Finding pertinent information from an extensive hospital database impacts the ability to correctly find trends and patterns and apply them directly to patient care.

“Editorial Boards should provide guidance and standards on the type of data and the way these are reported in figures” (Falk, 2018). Critical analysis of data is of utmost importance when searching for treatment options. Agrawal et al. (2016) provide concise, measurable information about structural heart surgery. You will find that government registries contain invaluable information and are often reviewed during data collection. According to Weintraub (2019), purposeful use of big data would be possible if EHRs used a standardized data entry organization. It would greatly help the extraction of complete essential information. Standardization leaves less room for interpretation; accurate data can help “evaluation of quality, business, intelligence, and medical research.”

References

Agrawal, Y., Jacob, C., Konda, M.-K., Panaich, S., Kalavakunta, J., & Gupta, V. (2016). Review

of Transcatheter Aortic Valve Replacement between the Years 2012 and 2013:

Nationwide Inpatient Sample Data Analysis. Journal of the American College of

Cardiology (JACC), 67(13S), 341.

https://doi-org.ezp.waldenulibrary.org/10.1016/S0735-1097(16)30342-4

Falk, V., & Meyer, A. (2018). Is what you see all there is? European Journal of Cardio-Thoracic

Surgery, 54(5), 797–799. https://doi-org.ezp.waldenulibrary.org/10.1093/ejcts/ezy303

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

McGonigle, D., & Mastrian, K. G. (2017). Nursing informatics and the foundation of knowledge

(4th ed.). Burlington, MA: Jones & Bartlett Learning.

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.

Weintraub, W. S. (2019). Role of big data in cardiovascular research. Journal of the American

Heart Association, 8(14), e012791.

https://doi-org.ezp.waldenulibrary.org/10.1161/JAHA.119.012791