by June Kaminski, RN MSN PhD(c)
Citation: Kaminski, J. (2025). Editorial. Theory applied to informatics: Continuous Quality Improvement. Canadian Journal of Nursing Informatics, 20(2). https://cjni.net/journal/?p=14783
In today’s complex, data-driven health care environment, the integration of theory into informatics is not merely an academic exercise, it is a critical strategy for sustainable change, better outcomes, and system-wide improvement. Among the most powerful theoretical frameworks applied in health informatics is Continuous Quality Improvement (CQI). Credited to the early work of Walter Shewhart who pioneered quality control theory in the early 20th century, and W. Edwards Deming who applied Shewhart’s work to manufacturing processes, as well as others who refined the theory to the current understanding of quality improvement that is continually evolving (Covetus, 2020; Endalamaw et al., 2024).
Rooted in systems thinking, human factors, and organizational learning, CQI provides a structured yet flexible methodology to evaluate and improve the processes, tools, and technologies that underpin health care (National Learning Consortium, 2013). As health systems increasingly rely on digital infrastructure, the application of CQI theory in informatics ensures that innovation is not only implemented but continuously refined for safety, efficiency, and equity.
CQI is a management philosophy and methodological approach that emphasizes dynamic, iterative, data-driven improvements in processes. Initially developed in industrial settings, CQI has been adapted in health care to foster system-level thinking, user engagement, and performance accountability. The modern application of CQI in health care is supported by interdisciplinary collaboration, digital feedback loops, and a learning health system mindset (Friedman et al., 2017; Groulx et al., 2024).
In informatics, CQI theory informs how electronic health records (EHRs), clinical decision support (CDS) tools, and health data analytics systems are designed, implemented, and evaluated. It promotes a culture where users, from frontline clinicians to IT developers, engage in cycles of testing, reflection, and revision. Rather than “set-and-forget” deployments, informatics systems are seen as evolving assets that must align with changing clinical needs and patient safety priorities.
CQI’s Methodological Foundations
Several contemporary methodologies underpin strategies for CQI’s application in health informatics (Gutierrez, 2023; National Learning Consortium, 2013; O’Donnell & Gupta, 2023). Each of these methods can guide the CGI process, depending on what the desired outcome relates to. In some situations, more than one can be applied (such as LEAN and Six Sigma) to provide a wider lens and guide to direct the CGI initiative.
Complex Adaptive Systems (CAS)
CAS theory positions health care as a dynamic network of interactions where small changes can have unpredictable effects. Informatics interventions must be flexible and designed for continuous learning in real-world conditions (Braithwaite et al., 2018).
LEAN Theory
This approach looks at improving value and improving flow while reducing waste within organizations. “Lean defines seven types of waste, i.e., transport, inventory, motion, waiting, overproduction, over-processing, and defects. The goal is to reduce the amount of non-value-added activities, thereby increasing the amount of time and effort spent on value-added tasks” (O’Donnell & Gupta, 2023, p. 1). Key features of LEAN include value stream mapping, Just-in-Time (JIT) production, and continuous flow to minimize interruptions and delays (Gutierrez, 2023).
Plan-Do-Study-Act (PDSA)
This model is central to CQI, enabling incremental cycles of change that are rapidly tested and scaled (Taylor et al., 2014). This model, also known as “the Deming or Shewhart cycle, offers a systematic approach to problem-solving and solution implementation and is the most common continuous quality improvement methodology” (Hoare, 2023, p. 4). This is particularly suited to digital tools, where rapid prototyping and iteration can enhance adoption and functionality.
Six Sigma
This methods focus on reducing errors and variability, following five DMAIC phases: “define, measure, analyze, improve, and control” (O’Donnell & Gupta, 2023, p. 1) and “is a data-informed continuous quality improvement methodology aimed at reducing defects, enhancing product quality, and optimizing processes” (Hoare, 2023, p. 5). This is a data-driven method that targets near-perfect quality levels “by systematically identifying and eliminating the root causes of errors, defects, and deviations” (Gutierrez, 2023, p. 5).
Total Quality Management (TQM)
TQM provides a holistic organizational approach to ensuring quality that aims to “create a culture of quality by integrating quality practices into daily operations, decision-making processes, and customer interactions” (Gutierrez, 2023, p. 5). Important features of this approach include a client focus, an emphasis on continual training and education, and process improvement (Gutierrez, 2023). This method is a good choice when the goal is to evolve a continuous improvement culture within an organization.
These strategic methodologies can be singly adopted or combined to ensure that CQI efforts in health informatics are not only technically robust but socially meaningful and clinically relevant.
Applying CQI to Health Information Systems
Consider a health system that identifies low uptake of its EHR-integrated sepsis alert system. A CQI-informed response would begin by engaging clinicians to identify usability barriers, perhaps excessive false alerts or poor timing in the workflow. Data analytics could uncover alert fatigue patterns, while focus groups yield qualitative insights.
Using the PDSA cycle, the system could be revised to trigger alerts based on updated clinical criteria or incorporate user feedback into its interface design. These changes could be tested in one hospital unit, evaluated for impact on clinician response time and patient outcomes, and then either revised or scaled up.
What makes this CQI-based approach unique is its commitment to continuous feedback, frontline leadership, and real-time data use. This model transforms health informatics from static infrastructure to a living, evolving system of care improvement.
Nursing Informatics and CQI Leadership
Nurse informaticians are particularly well-equipped to lead CQI efforts in informatics, given their understanding of clinical workflows, client-centered care, and system usability. Their role in bridging the divide between clinical teams and technical experts is essential in building health IT tools that truly work in practice (Sensmeier, 2021).
For example, during implementation of a mobile health app for chronic disease management, nurse informaticians may lead usability testing, track patient engagement metrics, and revise content based on patient feedback. They also play a key role in coaching peers on digital literacy and data interpretation, helping foster a culture of continuous learning and improvement (Law et al., 2017).
Looking even deeper, all nurses can play an important role in CQI endeavours since nurses play a vital and multifaceted role in enhancing the quality of care. They identify opportunities to improve existing processes and protocols, develop strategies to deliver high-quality care, and collect and analyze patient outcome data to pinpoint areas needing improvement. Nurses actively participate in interdisciplinary meetings, collaborate with providers on proposed changes, assist in implementing new protocols, and help update training materials. They also document progress toward established goals, evaluate outcomes using metrics such as patient satisfaction scores or readmission rates, and provide feedback to leadership on both successes and areas requiring further attention (Yip, 2022).
Beyond these responsibilities, nurses uphold the integrity of CQI programs by ensuring that other staff understand the rationale behind proposed changes and how these changes benefit patient care. Acting as liaisons between administration and frontline clinicians, they advocate for improvements that serve both patients and staff. Drawing on their clinical experiences, nurses offer valuable insights into the real-world impact of changes, ultimately helping to shape practices that lead to better patient outcomes. Through their active engagement in CQI initiatives, nurses can contribute significantly to the continuous enhancement of care within their institutions.
Challenges and Opportunities
As with any organizational undertaking, there are always challenges that must be addressed. CQI in informatics faces challenges such as data fragmentation, insufficient interoperability, and digital burnout among clinicians. Additionally, there are risks of inequity when systems are not designed with diverse populations in mind, since what works for one group may fail others if not adapted appropriately (Bouckley et al., 2025).
“Continuous quality improvement initiatives face various cultural, strategic, technical, and structural barriers. Cultural dimension barriers involve resistance to change (e.g., not accepting online technology), lack of quality-focused culture, staff reporting apprehensiveness, and fear of blame or punishment” (Endalamaw et al., 2024, p. 5).
However, new tools and frameworks are strengthening CQI’s role in digital transformation. Dashboards with near real-time indicators, patient-reported outcomes integrated into EHRs, and AI-powered analytics are making it easier to monitor system performance and make timely improvements. When grounded in CQI, these technologies can shift from being passive record-keepers to active quality enhancers.
Public health informatics also benefits from CQI approaches. During the COVID-19 pandemic, CQI principles helped rapidly improve contact tracing apps, vaccine registry dashboards, and testing portals, showcasing how iterative design can support urgent and evolving health needs (Wang et al., 2020).
Embedding CQI in Digital Health Culture
The true power of CQI emerges when it is not limited to individual projects but embedded into the organizational culture. This means:
- Leadership buy-in and modeling of data-driven decision making
- Training staff in quality and informatics methods, from frontline workers to executives
- Open data governance structures that empower users to view and act on performance metrics
- Inclusive design processes that center patients, families, and communities in improvement work
Embedding CQI into informatics shifts the focus from simply “using” technology to optimizing it continuously informed by those who use it every day.
Continuous Quality Improvement provides a vital theoretical and practical foundation for 21st century health informatics. As health systems grow increasingly sophisticated, the CQI mindset ensures that these systems are safe, responsive, and centered on patient needs. By applying CQI theory, informaticians, nurses, and health leaders can move beyond one-time implementations toward a culture of sustained excellence and equity. In doing so, they affirm that informatics is not just about information: it’s about continual and sustainable transformation and evolution.
References
Bakker, C. J., Wyatt, T. H., Breth, M. C., Gao, G., Janeway, L. M., Lee, M. A., Martin, C. L., & Tiase, V. L. (2023). Nurses’ roles in mHealth app development: Scoping review. JMIR Nursing, 6, e46058. https://doi.org/10.2196/46058
Bouckley, T., Peiris, D., Nambiar, D., Mishra, S., Sood, T., Purwar, P., Elshaug, A. G., Landon, B. E., Pearson, S. A., Huckel Schneider, C., & Schierhout, G. (2025). Addressing health equity during design and implementation of health system reform initiatives: A scoping review and framework. International Journal for Equity in Health, 24(1), 68. https://doi.org/10.1186/s12939-025-02436-z
Braithwaite, J., Churruca, K., Long, J. C., Ellis, L. A., & Herkes, J. (2018). When complexity science meets implementation science: A theoretical and empirical analysis of systems change. BMC Medicine, 16, 63. https://doi.org/10.1186/s12916-018-1057-z
Covetus. (2020). The history and major models of CQI and its benefits in healthcare IT industry. https://www.covetus.com/blog/the-history-major-models-of-cqi-its-benefits-in-healthcare-it-industry
Endalamaw, A., Khatri, R. B., Mengistu, T. S., Erku, D., Wolka, E., Zewdie, A., & Assefa, Y. (2024). A scoping review of continuous quality improvement in healthcare system: Conceptualization, models and tools, barriers and facilitators, and impact. BMC Health Services Research, 24(1), 487. https://doi.org/10.1186/s12913-024-10828-0
Friedman, C. P., Rubin, J. C., & Sullivan, K. J. (2017). Toward an information infrastructure for global health improvement. Yearbook of Medical Informatics, 26(1), 16–23. https://doi.org/10.15265/IY-2017-004
Groulx, A. (dir)., Chabot, C., Drolet, M., Gagnon, K., Laliberté-Gagné, J., Lévesque, M., Plouffe Malette, M. & Poirier, M-D. (2024). Position statement on continuous quality improvement (CQI). Recommendations towards exemplarity. Ministère de la Santé et des Services sociaux. https://ssaquebec.ca/nouvelles/enonce-de-position-sur-lamelioration-
continue-de-la-qualite-acq/
Gutierrez, C. (2023, Aug 14). Types of continuous improvement: LEAN, Six Sigma, Total Quality Management. Medium. https://claudiogut.medium.com/types-of-continuous-improvement-lean-six-sigma-total-quality-management-e52e57a2439d
Hoare, A. (2023, Dec 17). Harnessing continuous quality improvement methodology: A catalyst for organizational excellence. Ideagen. https://www.ideagen.com/thought-leadership/blog/harnessing-continuous-quality-improvement-methodology-a-catalyst-for-organizational-excellence
Law, M. P., Berry, A., Beehler, J., Thompson, S., Jibb, S., Hunter, D., & Pilato, K. & the CQI LDCP Team (2017). Strengthening continuous quality improvement in Ontario’s public health units: A scoping review. Public Health Ontario. https://www.publichealthontario.ca/-/media/documents/l/2017/ldcp-cqi-scoping-review.pdf?la=en
National Learning Consortium. (2013). Continuous quality improvement (CQI) strategies to optimize your practice. Health Information Technology Research Center. https://www.healthit.gov/sites/default/files/tools/nlc_continuousqualityimprovementprimer.pdf
O’Donnell, B., & Gupta, V. [Updated 2023 Apr 3]. Continuous quality improvement. In: StatPearls [Internet]. StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK559239/
Sensmeier, J. (2021). Realizing nurse-led innovation. Nursing Management, 52(6), 14–19. https://doi.org/10.1097/01.NUMA.0000752772.58567.9b
Taylor, M. J., McNicholas, C., Nicolay, C., Darzi, A., Bell, D., & Reed, J. E. (2014). Systematic review of the application of the plan-do-study-act method to improve quality in healthcare. BMJ Quality & Safety, 23(4), 290–298. https://doi.org/10.1136/bmjqs-2013-001862
Wang, C. J., Ng, C. Y., & Brook, R. H. (2020). Response to COVID-19 in Taiwan: Big data analytics, new technology, and proactive testing. JAMA, 323(14), 1341–1342. https://doi.org/10.1001/jama.2020.3151
Yip, I. (2022, Dec 8). What continuous quality improvement (CQI) means for nurses? Quality SIFU. https://qualitysifu.com/what-continuous-quality-improvement-cqi-means-for-nurses/
