Assignment Task: 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. (2025). Project management for the advanced practice nurse (3rd ed.). Springer Publishing.
- Chapter 4, "Planning: Project Management-Phase 2" (pp. 85-130)
- Chapter 2, "Foundational Project Management Theories that Support Decision-Making" (pp. 22-54)
- American Nurses Association. (2015). Nursing informaticsLinks 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)
- Cato, K. D. (2020). Transforming clinical data into wisdom: Artificial intelligence implications for nurse leaders. Nursing Management, 51(11), 24-30.
Required Media:
- Analytics Guy. (2020, August 25). Developing understanding using the DIKW pyramid [Video]. YouTube.
- Massachusetts DESE. (2020, February 25). Introduction to implementation science [Video]. YouTube.
- Project Manager. (2018, July 2). Risk Analysis How to Analyze Risks on Your Project - Project Management Training. [Video]. YouTube.
Optional Resources:
- IRL - Research and Science Course. (2019, August 30). What is implementation science? [Video]. YouTube.
- Sustainability Science Education. (2019, August 23). What is systems thinking? [Video]. YouTube.
To Prepare:
- 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.
Analyze informatics frameworks and models that are applicable to healthcare organizations and nursing practice. What are the key principles and best practices that you can leverage from these frameworks to support your practice? Need Assignment Help?
Read a selection of your colleagues' responses and respond to at least two of your colleagues on two different days. Expand upon your colleague's posting or offer an alternative perspective.
Respond To This Discussion Post
Charline
Change Theories, Systems Thinking, Implementation Science
In healthcare organizations, especially hospital settings, informatics frameworks help structure the development, adoption, and evaluation of health information technologies (HIT). These models guide nurse leaders and stakeholders in aligning digital tools with clinical workflows, safety standards, and patient outcomes.
The DIKW framework (Data-Information-Knowledge-Wisdom) remains fundamental in nursing informatics. It illustrates how raw data becomes actionable wisdom through contextualization and application, supporting decision-making and clinical judgment. Key principles in hospitals are to improve documentation structure to ensure high-quality data input, implement clinical decision support systems (CDSS) that convert information into meaningful knowledge, and train staff to interpret and apply data for safe patient care (Cato, 2020).
TAM (Technology Acceptance Model) explains how users adopt technology based on perceived usefulness and ease of use. In hospitals, this is crucial for nurses' adoption of EHRs, barcode scanning, and mobile health apps. Best Practices involve nurses in HIT design to ensure systems meet workflow needs, conduct usability testing, provide firsthand training, and monitor and evaluate ongoing user satisfaction (Rahimi et al., 2018).
The SLC framework (Systems Life Cycle) outlines structured phases for HIT projects: planning, analysis, design, implementation, and evaluation. It ensures that informatics tools are aligned with both technical and clinical needs. Applications in hospitals are to perform stakeholder analysis and workflow mapping before implementation, use of pilot-test systems in selected units, and use of iterative feedback for optimization and sustainability (Sipes, 2025).
The American Nurses Association (ANA) defines informatics practice standards that parallel the nursing process. These include assessment, diagnosis, outcomes identification, planning, and evaluation-all tailored for technology-driven environments. Hospital integration would include using Standard 1 (Assessment) to evaluate existing EHR functionality, applying Standard 4 (Planning) for strategic rollout of clinical tools, and leveraging outcomes metrics (Standard 3) to assess success (ANA, 2022).
Nursing and health informatics frameworks such as DIKW, TAM, SLC, and ANA Standards help guide strategic implementation and evaluation of health technologies in hospital settings. By applying these models, healthcare leaders can improve care quality, boost technology adoption, and foster data-driven nursing practices.
References:
- American Nurses Association. (2022). Nursing Informatics: Scope and standards of practice (3rd ed.). ANA.
- Cato, K. D. (2020). Transforming clinical data into wisdom: Artificial intelligence implications for nurse leaders. Nursing Management, 51(11), 24-30.
- Rahimi, B., Nadri, H., Lotfnezhad Afshar, H., &Timpka, T. (2018). A systematic review of the technology acceptance model in health informatics. Applied Clinical Informatics, 9(3), 604-634.
- Sipes, C. (2025). Project management for the advanced practice nurse (3rd ed.). Springer Publishing.