Transforming healthcare delivery is a complex and multifaceted endeavor that demands collaboration among diverse stakeholders, including clinicians, patients, families, and policymakers. Achieving meaningful improvements requires not only identifying gaps in care but also implementing and rigorously testing interventions to ensure they deliver better outcomes, enhanced safety, and greater value. With a multitude of methodologies available—such as lean, six sigma, the model for improvement, healthcare delivery science, and implementation science—healthcare professionals often utilize these approaches independently. However, recognizing and leveraging the overlaps between these strategies, particularly between quality improvement (QI) and implementation science (IS), can lead to more effective and sustainable change. Integrating insights from both fields enhances the ability to scale successful innovations across different settings and populations, ultimately advancing the quality of care.
Interdisciplinary Tensions
Over recent years, a noticeable tension has emerged between the disciplines of quality improvement and implementation science. Both aim to enhance healthcare quality, safety, and value, but they approach these goals from different philosophical and methodological angles. QI traditionally concentrates on localized, context-specific enhancements, employing iterative cycles to test changes within particular microsystems. Meanwhile, IS emphasizes understanding and modifying the broader factors that influence the adoption and spread of evidence-based practices across diverse settings. This divergence has fostered a perception of competition rather than collaboration, which is counterproductive given the pressing need to address persistent gaps in healthcare quality.
The COVID-19 pandemic exemplifies this dynamic. During the crisis, local healthcare systems relied heavily on QI techniques to rapidly address urgent issues such as PPE shortages. While this localized approach was essential, the opportunity to apply IS methods—focused on understanding systemic barriers and facilitators—could have accelerated the dissemination of effective solutions. Both fields possess distinct yet complementary knowledge bases; their combined application could have amplified the impact of pandemic responses. Despite their different origins—QI rooted in systems operations and IS in behavioral science—both recognize that change is heavily influenced by context. Embracing their shared principles can foster more cohesive efforts to improve healthcare delivery.
Both QI and IS aim to facilitate evidence-based practice, but they differ in their focus areas. QI emphasizes safety, timeliness, effectiveness, equity, efficiency, and patient-centeredness—collectively known as the six domains of quality—within specific microsystems. In contrast, IS concentrates on the adoption, implementation, and sustainability of evidence-based interventions, considering factors like acceptability, cost, and feasibility. Both fields prioritize dissemination of findings through peer-reviewed publications and share the overarching goal of translating evidence into practice—an ongoing challenge often described as moving knowledge into real-world application.
Different Approaches to Change
Implementing and documenting healthcare modifications is inherently challenging. Variability in reporting quality improvement and implementation efforts hampers the ability to learn from past experiences and replicate successful strategies. To address this, the Standards for Quality Improvement Reporting Excellence (SQUIRE) guidelines were established to improve the transparency and consistency of reporting in both fields.
One core challenge involves capturing how interventions evolve in response to local context. Initially, interventions have a planned structure, but they frequently undergo modifications to better suit specific environments. Both QI and IS employ methods to document these changes, each offering valuable insights. In QI, changes are often tested through “tests of change”—small, deliberate experiments such as plan-do-study-act (PDSA) cycles—that focus on understanding the local microsystem. These cycles promote iterative learning, enabling teams to refine interventions based on real-time feedback.
In IS, the concept of “adaptation” pertains to tailoring interventions to new settings while maintaining core components. This approach recognizes that interventions must often be modified to fit different contexts, and systematically documenting these adaptations helps generate knowledge that can be shared broadly. The strength of IS lies in its structured assessment and reporting of such modifications, which could inform the more localized work of QI, especially when scaling interventions across multiple sites.
PDSA Cycles versus Adaptation
PDSA cycles are a fundamental tool in QI for testing small changes within a system. These cycles should be focused, deliberate, and data-driven, aiming to learn about the system’s response to specific interventions. Success in PDSA is gauged by collecting relevant data and analyzing each cycle to inform subsequent tests. Although some teams may worry that small tests won’t lead to significant change, the primary purpose of PDSA is to understand the system deeply, whether a change succeeds or fails. This iterative learning process helps to build a nuanced understanding of what works, for whom, and under what circumstances.
In contrast, IS employs detailed planning at the outset, considering contextual factors that could influence implementation success. This involves identifying potential facilitators and barriers before launching an intervention. Adaptation plays a central role in IS, allowing strategies to evolve in response to changing circumstances—a process that shares similarities with PDSA cycles. Balancing fidelity to the original plan with the need for flexibility is a common tension, but both approaches acknowledge that real-world settings require ongoing adjustments for effective implementation.
Fidelity in QI and IS
Maintaining fidelity—the degree to which an intervention is delivered as intended—is critical for understanding what works and why. In IS, fidelity refers to adherence to the planned protocol, with adaptations made only to facilitate better fit without compromising core elements. In QI, fidelity encompasses both strict adherence to the planned procedures within each PDSA cycle and the disciplined use of data to guide subsequent tests. Ensuring fidelity helps teams learn from each iteration, fostering continuous improvement and increasing the likelihood of sustaining positive changes.
Context and Change
Healthcare systems are dynamic, with their environments constantly evolving due to policy shifts, staffing, patient populations, and other factors. Recognizing and understanding the influence of context is crucial for designing interventions that are sustainable over time. In QI, context includes the physical environment, organizational culture, and stakeholder interpretations—all of which are fluid and subject to change. Tools such as process flow diagrams or cause-and-effect analyses facilitate the assessment of context, enabling teams to tailor interventions effectively.
IS frameworks, like the consolidated framework for implementation research (CFIR), delineate domains of context—such as adaptability and trialability—that influence implementation success. These models support formative evaluations, guiding the adaptation process to optimize intervention fit. Both fields recognize the bidirectional relationship between intervention and context: each influences the other over time.
Learning from One Another
Despite their different origins—systems operations for QI and behavioral science for IS—both disciplines share the common goal of advancing healthcare quality through collaborative efforts. Each can learn from the other’s strengths: QI can incorporate framework-driven planning and evaluation methods from IS to enhance generalizability, while IS can adopt data-driven flexibility typical of QI to demonstrate intervention success across diverse settings.
Figure 1 illustrates key characteristics and overlaps in the modification of interventions within QI and IS, highlighting the potential for mutual enrichment. Organizations dedicated to quality improvement should consider integrating IS strategies, particularly to identify what measures facilitate the scaling of successful practices. Conversely, IS can deepen its impact by embedding iterative cycles like PDSA into sustainability efforts, fostering continuous learning.
Funding agencies and academic journals have traditionally valued IS because of its research-oriented approach. Expanding support for combined use of QI and IS methods can accelerate the development of robust, scalable change strategies. Recognizing that neither approach alone has fully transformed healthcare as hoped, fostering collaboration between these fields promises the emergence of innovative, durable solutions. By uniting their knowledge and methodologies, QI and IS can forge a comprehensive, effective framework for sustainable healthcare improvements.
Key Messages
- Both implementation science and quality improvement utilize iterative testing to tailor interventions to specific healthcare environments.
- The success of change efforts is heavily influenced by the contextual factors present during implementation.
- Fidelity encompasses both adherence to the planned intervention and the disciplined use of data to guide iterative improvements.
- Recognizing the shared features of these approaches can enhance the quality and transparency of reporting changes in healthcare practices.
Further reading on the role of emerging technologies like artificial intelligence in healthcare transformation can be found at how AI assists healthcare. Additionally, understanding the importance of demographic details in health data can improve intervention targeting; explore what demographic data entails in healthcare. For insights into leveraging data effectively, see how data informs healthcare decision-making.