Change Theories, Systems Thinking, Implementation Science
Never have we had vast amounts of data at our fingertips like we do today. However, before we can meaningfully access and use data for interpretation, it must be transformed. To derive meaning from the data collected, you need to understand that data collection is rapidly changing and constantly evolving. The methods with which data is collected, analyzed, and used to justify, support, or lend credibility to research aims, are all important considerations for the nurse researcher. As it relates to Big Data, the methods of how data is collected, analyzed, and used for implementation is also important. While the availability of data collection certainly has its advantages, many researchers point to the concerns over Big Data.
For this Discussion, reflect on your understanding of Big Data and the implications for implementation. Consider the impact of research as it relates to collection via Big Data and consider how this impact might lead to potential barriers in implementation and practice gaps. Reflect on your experience and consider how these key issues might impact nursing practice.
Required readings:
Sipes, C. (2020). Project management for the advanced practice nurse (2nd ed.). Springer Publishing.
Chapter 4, “Planning: Project Management—Phase 2†(pp. 75–120)
Chapter 2, “Foundational Project Management Theories that Support Decision-Making†(pp. 22–25)
American Nurses Association. (2015). Nursing informatics
Links to an external site.: Scope and standards of practice (2nd ed.).
“Standard 1: Assessment†(pp. 68–69)
“Standard 2: Diagnosis, Problems and Issues Identification†(p. 70)
“Standard 3: Outcomes Identification†(p. 71)
“Standard 4: Planning†(p. 72)
Thompson, T. (2019). 6 steps to mastering the theoretical framework of a dissertation
Links to an external site.. ServiceScape. https://www.servicescape.com/blog/6-steps-to-mastering-the-theoretical-framework-of-a-dissertation
Wensing, M., & Grol, R. (2019). Knowledge translation in health: How implementation science could contribute more
Links to an external site.. BMC Medicine, 17(88). https://doi.org/10.1186/s12916-019-1322-9
Review the Learning Resources for this week, focusing specifically on the implementation science articles and web resources.
Consider the issues related to research and Big Data.
Review Lewin’s Change Theory, systems thinking, and implementation science resources provided in the media this week.
Consider the importance of these theories and frameworks to your healthcare organization or nursing practice.
Explore two additional theories or models related to change, systems, or implementation science to focus on for this discussion.
By Day 3 of Week 4
Cite specific examples of research that was completed with potential for great social impact. What are some potential barriers for implementing the research analyzed?
What potential gaps exist between evidence-based approaches and the research process?
Describe the importance and application of Implementation Science, Change Theory and Systems Thinking for healthcare organizations and nursing practice. Be specific.
Explain how theories and models provide a framework to guide projects, including your DNP Project or dissertation.
Briefly describe two additional theories or models and explain how these might be applied in research. Be specific and provide examples
ORDER A CUSTOM-WRITTEN PAPER HERE
Change Theories, Systems Thinking, Implementation Science
Big Data, as explained by Dash et al. (2019), encompasses many complex datasets that go beyond the capabilities of traditional data processing methods. The researchers note that in healthcare, big data implementation offers promising prospects for practice as it enables real-time patient monitoring and predictive analytics for early disease detection and the application of evidence-based decision-making (Hariri et al., 2019). The management, analysis, and data security guarantee are some of the critical challenges. Users such as nurses need to adapt to the evolving technology landscape and acquire data literacy skills.
Making use of big data, especially in healthcare, has the potential to revolutionize nursing practice by informing evidence-based interventions. Caution must, however, be taken because its impact has the potential to introduce implementation barriers. To elaborate on this, the large volume of data requires advanced analytics tools, and these may sometimes be out of reach for some organizations (Shilo et al., 2020). Moreover, there have been many concerns about data accuracy and privacy, and this hinders adoption. Drawing from this, bridging the practice gap requires huge investments in infrastructure, education, and interdisciplinary collaboration by organizations (Hamilton & Sodeman, 2020). This is important as it ensures that nurses effectively leverage Big Data for improved patient care. Reflecting on my practice as a healthcare professional, I have been exposed to the evolving healthcare landscape and I recognize that issues like technology integration, data management, and privacy concerns are increasingly relevant to nursing practice. I feel that adaptation to digital health tools and Big Data analytics will continue to increase because of the associated benefits such as providing improved quality of care.
References
Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of big data, 6(1), 1-25. https://doi.org/10.1186/s40537-019-0217-0
Hamilton, R. H., & Sodeman, W. A. (2020). The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources. Business Horizons, 63(1), 85-95. https://doi.org/10.1016/j.bushor.2019.10.001
Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big Data, 6(1), 1-16. https://doi.org/10.1186/s40537-019-0206-3
Shilo, S., Rossman, H., & Segal, E. (2020). Axes of a revolution: challenges and promises of big data in healthcare. Nature medicine, 26(1), 29-38. https://doi.org/10.1038/s41591-019-0727-5