Harnessing Potential: A Systematic Investigation of How and Why Learning Happens in a Health System

ABSTRACT

Background

The ability to learn is central to innovation and performance across industries, and it is the hallmark feature of the rapidly growing movement toward Learning Health Systems. While learning drivers—such as experimentation, risk‐taking, participative decision‐making, dialogue, and interaction with the external environment—are well defined, they have received little attention in healthcare contexts.

Aim

This study explored the nature of organizational learning within Trillium Health Partners (THP), one of Canada's largest community‐based teaching hospital systems.

Methods

Formal leaders at THP completed the Organizational Learning Capability instrument. A latent profile analysis identified the number and nature of distinct learning profiles. Semi‐structured interviews were conducted to develop an empirically grounded explanation of how organizational learning drivers interact to enable—or constrain—learning within the organization.

Results

Of 334 eligible leaders, 231 responded to the survey, and 141 provided sufficient data for inclusion in the analyses. Latent Profile Analysis identified two distinct learning profiles: one with relatively higher perceived learning capability and another with relatively lower perceived capability. Interviews with leaders (n = 12) from both groups described how theoretical factors that influence organizational learning manifest to drive (or derail) learning in practice.

Conclusion

Our systematic investigation into how and why learning occurs within a large community hospital system may support other healthcare systems in developing programs that foster the learning necessary to develop and implement innovative delivery models at scale.

1. Introduction

The concept of the Learning Health System (LHS), first introduced by the Institute of Medicine in 2006, emphasizes continuous learning across organizational boundaries through the routine use of data to advance the quadruple aim and health equity [1, 2]. Nearly two decades later, the need for health systems to learn and translate learnings into performance improvement is no longer optional, but rather essential. The COVID‐19 pandemic exposed long‐standing weaknesses—emergency overcrowding, diagnostic and surgical backlogs, and workforce burnout—now deeply entrenched [3, 4, 5, 6]. With aging populations and widening inequities placing additional pressure on systems worldwide, health organizations face an urgent opportunity to reimagine care delivery models that meet the needs of patients, staff, and communities [7, 8]. Against this backdrop, health systems have an opportunity to reimagine care delivery—shifting toward models that meet the needs of patients, staff, and populations.

Learning and innovation are strongly associated with improved organizational performance [9, 10, 11]. In sectors such as banking, telecommunications, and healthcare, learning has been shown to drive innovation, which in turn enhances performance across customer satisfaction, cost management, productivity, and profitability. Organizations with advanced learning capabilities are more adept at managing complexity, identifying opportunities for improvement, and achieving sustained innovation [11, 12, 13]. In healthcare, hospitals with higher learning capability scores demonstrate stronger innovation and performance outcomes, reinforcing the potential of the LHS model to improve system performance [12, 14].

Despite the growing maturity of LHS theory, translating learning principles into routine organizational practices remains challenging [15]. While organizational learning capability has been widely conceptualized and measured [16, 17, 18], comparatively less attention has been paid to understanding how established learning drivers interact within complex healthcare environments to enable or constrain organizational learning in practice. Existing literature has identified important enabling conditions for learning, including leadership [19], culture [20], and experimentation [13, 21]; however, less is known about how these conditions operate together within the day‐to‐day realities of healthcare organizations. Developing empirically grounded explanations of these interactions is essential for designing LHS that move beyond aspiration toward sustained organizational learning and improvement [22].

Organizational learning is supported by systems that monitor, evaluate, and improve performance through continuous data cycles [23]. One instrument used to measure organizational learning is the Organizational Learning Capability (OLC) scale [24], which assesses five dimensions: experimentation, risk taking, interaction with the external environment, dialogue, and participative decision making. Prior research shows that organizations with higher OLC scores are significantly more likely to innovate and perform well than those with weaker learning capabilities [13]. Applying OLC measurement in healthcare can illuminate how learning emerges, is sustained, or stalls, and can inform targeted strategies to strengthen organizational capacity for learning. To address this gap, we combined quantitative assessment of organizational learning capability with qualitative exploration of leaders' experiences to develop an empirically grounded explanation of how organizational learning drivers interact within an LHS.

2. Objectives

In this organizational case study, we measured OLC and examined the conditions under which learning emerges—and stalls—within an acute care hospital system committed to the LHS model. Specifically, we sought to:

1. Characterize variation in organizational learning capability among formal leaders using latent profile analysis; and

2. Develop an empirically grounded explanation of how organizational learning drivers interact to enable and constrain organizational learning within an LHS.

3. Setting

This study was conducted at Trillium Health Partners (THP), Canada's largest community‐based academic teaching hospital. With nearly 1400 beds and over 17 000 staff, physicians, and volunteers, THP is a regional provider of specialized services to a diverse community, with over half the catchment population born outside of Canada. For over a decade, THP has been on an LHS journey, beginning with the establishment of the Institute for Better Health (IBH) in 2014, an embedded research and development arm tightly connected to the hospital. IBH is home to LHS experts and a team of LHS scientists that work closely with THP clinical and operational leaders, point of care staff, community organizations, and patients, striving to consistently improve care via a learning approach.

4. Methods

4.1. Study Design

The THP Research Ethics Board determined that this study did not constitute human subjects research. This organizational case study employed a multi‐method approach to characterize variation in organizational learning capability and develop an explanatory account of how organizational learning occurs within a large community hospital. The study was conducted in two phases: (1) a survey to assess leaders' perceptions of the hospital's OLC and (2) qualitative interviews to unpack and explore variations in organizational learning experiences.

4.2. Phase 1: Organizational Survey

4.2.1. Participant Recruitment

All individuals occupying formal leadership positions—including team leads, supervisors, managers, directors, medical leaders, and executives—were eligible to participate. Leaders were targeted given their influence on organizational learning processes [25]. The survey was distributed electronically to 334 leaders; 231 responded (69.2% response rate). Of the 231 survey respondents, 141 provided sufficient data for inclusion in the descriptive and latent profile analyses, representing 42.2% of the eligible sampling frame. Surveys with substantial missing questionnaire data were excluded from these analyses. During confirmatory factor analysis, Full Information Maximum Likelihood estimation was used to account for item‐level missing data in the measurement model.

4.2.2. Data Collection

Participants provided demographic and employment data (e.g., role, tenure, program area). Perceptions of organizational learning were measured using the 14‐item OLC scale, which assesses five subdomains: experimentation, risk taking, interaction with the external environment, dialogue, and participative decision making [24]. Sample constructs include the degree of support and encouragement for new ideas, encouragement of risk‐taking, communication effectiveness, and involvement of staff in organizational decision‐making. Responses were recorded on a 7‐point Likert scale (1 = completely disagree, 7 = completely agree). The OLC has demonstrated strong psychometric reliability (α = 0.92) and validity across multiple studies [12, 24].

4.2.3. Data Analysis

We conducted descriptive statistics to summarize the sample mean of the total OLC score, as well as the mean scores across five sub‐domains of OLC. We then conducted a Latent Profile Analysis (LPA) to identify distinct subgroups within the survey respondents using standardized factor scores for each OLC domain. We selected LPA as an analysis technique to advance our understanding of OLC in this context by identifying distinct participant profiles (i.e., configurations) that reflect the interrelationships among the five OLC sub‐factors [24]. We first evaluated the measurement model of the five OLC components using Confirmatory Factor Analysis. We used Full Information Maximum Likelihood to handle missing data and Robust Maximum Likelihood estimation to account for the potential deviations from the multivariate normality assumption due to Likert‐scale data. After confirming the satisfactory model fit (Robust Comparative Fit Index = 0.974, Robust Tucker‐Lewis Index = 0.964, Robust Root Mean Square Error of Approximation = 0.060, Standardized Root Mean Square Residual = 0.053), we extracted the standardized factor scores (mean = 0, standard deviation = 1) for each OLC component as the indicators for LPA to account for measurement error and facilitate interpretation [26]. We compared LPA models specifying 1–6 profiles across four model parameters [27]. Model selection was based on fit indices (AIC, BIC, Entropy, BLRT) and minimum subgroup size (≥ 5% of the sample) [28, 29]. After identifying the best‐fitting model (number of profiles = 2), we used multivariate logistic regression to explore whether sociodemographic factors (e.g., gender, role, site) predicted profile membership, assessing potential equity differences in learning opportunities.

4.3. Phase 2: Qualitative Interviews

4.3.1. Participant Selection and Sample

Survey respondents who indicated willingness to participate in a follow‐up interview were eligible for the qualitative phase. Twelve respondents expressed interest in participating, and all were invited to complete an interview. Collectively, participants represented a range of leadership roles, functional areas (e.g., clinical, quality improvement, human resources, informatics), and both latent OLC profiles.

4.3.2. Data Collection: Realist Teacher‐Learner Cycles

Drawing on established literature from organizational learning [13, 30, 31] and LHS [9, 32, 33], we formulated an initial program theory to hypothesize how learning happens in healthcare contexts (Figure 1). We theorized that the antecedent domains of organizational structure, context, and leadership would affect the way that learning was operationalized within the organization [30]. Aligned with Raisch and Birkinshaw [30], we conceptualized structural antecedents as those that allow organizations to cope with the demands of learning and innovation via organizational design elements; contextual antecedents as the systems, processes, organizational culture, and beliefs that shape individual‐level learning and innovation behaviors in an organization; and leadership antecedents as all internal leadership processes with the potential to foster learning and innovation. Our initial program theory was used to guide the development of an interview schedule (provided as a Supporting Information).

FIGURE 1.

Initial program theory.

Virtual interviews were conducted using a realist‐informed approach, drawing on teacher‐learner cycles [34] to iteratively refine our initial program theory. During teacher‐learner cycles, a member of the research team with training in realist research (MB, LD) presented the initial program theory and associated evidence‐based statements about the nature of learning drivers to participants. Participants were asked to reflect on whether the program theory and learning statements resonated with their own learning experiences by building on the statements using examples. This approach allowed participants to articulate their experiences of when organizational learning occurred (and when it did not), deepening understanding of the mechanisms underpinning learning within the hospital. Interviewers and analysts were blinded to participants' OLC group assignment to minimize bias.

4.3.3. Data Analysis: Iterative Deductive Thematic Analysis

Audio‐taped interviews were transcribed verbatim, and transcripts were proofread for accuracy before being imported into MAXQDA qualitative analysis software. All transcripts were first analyzed individually such that relevant theories of organizational learning could first be proposed in context and later compared across individuals to detect patterns and differences [35]. Interview transcripts were analyzed using deductive thematic analysis [36, 37]. Initial coding was guided by structural, contextual, and leadership antecedents from the literature, then refined inductively through open coding and pattern synthesis [38]. Data analysis was iterative, with emerging insights informing refinements to the program theory that encompassed our evolving understanding of organizational learning. Data collection and analysis were conducted iteratively. Following completion of the 12 interviews, sufficient depth and consistency of themes had been achieved to support refinement of the program theory. This approach is consistent with principles of data sufficiency in qualitative research, prioritizing explanatory depth and conceptual richness over numeric thresholds [39]. Finally, we developed, compared, and contrasted our understanding of the nature of organizational learning from individual participant perspectives. Final results were formulated into a theory of the nature of organizational learning at THP and related themes.

Throughout the analytic process, the research team actively considered alternative interpretations and sought evidence that challenged emerging themes. Although participants emphasized different aspects of organizational learning based on their roles and experiences, no fundamentally disconfirming cases were identified. Variation across interviews reflected differences in emphasis and context rather than contradictory explanatory mechanisms. Dependability and confirmability were supported through the maintenance of an audit trail throughout the analysis [40]. The research team met bi‐weekly to review emerging findings and refine interpretation using a shared analytic document that captured detailed meeting notes, reflexive memos, diagrams of emerging analytic relationships, coding and interpretive decisions, high‐level thematic summaries, and planned next steps. This evolving record provided transparency in the analytic process and supported consistency in the development and refinement of the program theory. To enhance credibility, all 12 interview participants were invited to participate in a member checking exercise following preliminary analysis. Participants received a summary of the emerging findings, including key themes, interpretive summary statements, working definitions, and illustrative quotations, and were invited to provide feedback on the accuracy, completeness, and resonance of the findings. Three participants responded. Feedback was confirmatory overall and further enriched interpretation by contextualizing findings within broader organizational experiences, elaborating on mechanisms underlying competing priorities and burnout, and providing additional insight into how organizational accountability structures influence leaders' capacity for learning. This feedback was incorporated into refinement of the thematic interpretation and program theory.

5. Results

5.1. Survey Results

Of 334 eligible leaders, 231 responded to the survey (69.2% response rate); after excluding incomplete responses, 141 were included in the analysis. Most respondents were women (67%) and had been with the organization for more than 3 years (75%). Slightly over half (52%) held frontline management roles (e.g., managers or team leads), while 40% held program or strategic oversight positions (e.g., directors or senior leaders).

The mean total OLC score was 4.74 (SD = 0.99), suggesting moderate overall perceptions of learning. Dialogue had the highest subdomain mean (5.72, SD = 0.97), while interaction with the external environment (3.82, SD = 1.30) and risk taking (4.02, SD = 1.33) were lowest.

The LPA revealed two distinct profiles of perceived organizational learning within the hospital across two balanced subgroups with similar sizes (Figure 2). One group (n = 71) demonstrated a relatively higher perception of OLC than the sample mean across all five sub‐domains. In the other group (n = 70), participants perceived a relatively lower degree of OLC than the sample mean across all five sub‐domains. The clear segmentation of distinct subgroups lends credence to the hypothesis that although people perceive things differently, they often do so in recognizable patterns, validating the use of LPA in this study.

FIGURE 2.

Organizational learning capability profiles. NB: Value represents the factor score (mean = 0, standard deviation = 1) used in LPA.

Leaders in program accountability roles were more likely to belong to the higher OLC group than frontline managers (OR = 2.04, 95% CI: 0.94–4.76, p = 0.09). No other demographic predictors were significantly associated with profile membership.

The latent profile analysis demonstrated meaningful variation in perceived organizational learning capability across the organization. Rather than serving as predefined comparison groups for the qualitative phase, these profiles were used to ensure variation in interview participants and to provide context for interpreting how organizational learning mechanisms may operate across differing organizational experiences.

5.2. Interview Findings

Interviews included leaders representing both latent OLC profiles. The purpose of the qualitative phase, however, was not to compare experiences between higher and lower OLC groups, but rather to develop an explanatory understanding of the organizational mechanisms through which learning is enabled or constrained. Consequently, themes were developed across the full interview dataset and interpreted in light of the quantitative findings, with the latent profiles providing contextual insight into variation in perceived organizational learning capability rather than serving as the basis for comparative qualitative analysis.

5.2.1. The Nature of Organizational Learning

Across interviews, participants consistently described organizational learning as an ongoing, dynamic process shaped by communication, leadership, and structural context. Although participants represented both higher and lower OLC profiles, common explanatory mechanisms emerged across interviews. Dialogue consistently emerged as the foundational driver of learning, enabling participative decision making, external engagement, and experimentation. However, true risk taking was uncommon and typically confined to teams where leaders explicitly modeled and supported it. Participants expressed a strong desire for continuous learning but cited time constraints and operational pressures as persistent barriers. Many noted that healthcare funding models reward predictability rather than innovation, limiting opportunities for experimentation.

While Chiva et al. [24] conceptualize the dimensions of OLC as related and coexisting constructs, our qualitative findings suggest a possible explanatory sequence through which these learning drivers unfold in practice (Figure 3). We emphasize that this proposed ordering represents an empirically grounded interpretation arising from this organizational case study rather than a universally established model of organizational learning.

FIGURE 3.

Ordered nature of learning drivers.

Dialogue serves as the foundation, creating psychological safety and collective sense‐making. While open dialogue may be theoretically valued within an organization, individuals must also have sufficient time and opportunity to engage in meaningful dialogue with colleagues for these benefits to occur. This enables participative decision making and external interaction, which in turn foster experimentation. Notably, differences observed between higher and lower OLC profiles—particularly in participatory decision making and risk‐taking—provided important context for interpreting qualitative findings, suggesting that variation in these domains may shape how learning mechanisms are experienced and enacted in practice. Dialogue also creates pathways for interaction with individuals and groups external to the organization by fostering curiosity and openness to perspectives beyond the organization's boundaries. This external engagement, in turn, feeds back into internal conversations and decision‐making, forming a dynamic loop of internal‐external learning. Building on these processes, when dialogue is present, the organization becomes more capable of experimentation, whereby new ideas are encouraged and tested. Experimentation, as our experience confirms, tends to flourish only when it is grounded in shared understanding (via dialogue), organizational alignment (via decision‐making), and awareness of external opportunities and challenges (via interaction with the external environment). Finally, risk‐taking—defined as a willingness to accept uncertainty and learn from failure—both enables and is enabled by experimentation. In our study, risk taking was another sub‐construct with particularly pronounced differences between those with higher versus lower total OLC scores, suggesting heterogeneity of experiences across the organization. In Figure 3, risk‐taking and experimentation are shown with a bidirectional arrow to reflect their interdependence: experimentation can expose the organization to failure, while a culture of risk tolerance legitimizes such failure as a source of learning and, ultimately, improvement. This figure not only interprets Chiva et al.'s [24] subconstructs through a temporal lens, but also reflects how learning efforts in our organization are scaffolded over time. Our results suggest that fostering dialogue may be a critical leverage point for activating other learning capacities—an insight with practical implications for designing learning‐oriented interventions. Together, these findings suggest that variation in perceived organizational learning capability may reflect differences in how consistently these mechanisms are supported across teams, rather than fundamentally different learning processes operating within the organization.

From our interview data, we also developed four interrelated themes, reflecting how the drivers of organizational learning unfold within the healthcare environment.

5.2.1.1. Theme 1: Dialogue Drives Learning, but Only When Paired With Transparency and Integrated Into Decision‐Making

Participants emphasized that open dialogue underpins organizational learning but must be accompanied by transparency and inclusion in decision‐making to be effective. Dialogue opens opportunities for collaboration, yet inconsistencies remain in how data, research, and lived experience inform decisions.

I can think of an example very recently where we were just trying to solve a business problem… We really did bring the professional staff groups in to say we actually don't, as leaders, have the answer to this problem, and we need your help. Because you're the ones that see the most critically ill patients who need the care, and we need to figure out a model that will be able to keep those patients safe.

However, when communication lacks openness, dialogue can erode trust:

I have no idea as to what's going on. I know that people are having conversations up there. But I have no idea. So I think that impacts trust, etc. … We just know that there's going to be a change, but nobody knows what that change is.

These insights highlight the critical nature of transparency and information sharing and use of insights generated through dialogue in decision‐making as foundational to creating a culture of learning.

5.2.1.2. Theme 2: Structural Constraints Have the Potential to Undermine Learning, Despite Strong Individual Motivations

Leaders described strong motivation to learn but limited structural capacity to act. Funding and accountability mechanisms favor proven approaches over experimentation, making innovation financially risky.

There is so much greatness that we do. I think we're limited also by the fact that healthcare is run by the government, and we are taxpayer provided. We can't experiment with money. We can't afford to experiment. That's why we can't take risks. We have to know that where we're going to put our money is actually going to work.

Entrenched ways of working further restricted learning, even when staff recognized the need for change:

We're constantly battled with legacy thought. And this is just how we've done it in the past and it worked in the past. But because it worked in the past doesn't mean that it will work in the future. And just because it worked in the past also doesn't mean that it's equitable, fair, inclusive, and creates access to growth.

These findings highlight how legacy structures and risk‐averse funding models constrain learning capacity and how dismantling such norms is essential to unlocking innovation.

5.2.1.3. Theme 3: Leadership and Targeted Investment Create Learning Microclimates

Learning occurred unevenly across the organization, with “pockets” of success linked to leaders who empowered staff and encouraged problem‐solving.

This year we have been working really hard to support what—I don't want to call it – but empowerment, but also that our team is really bringing—they know so much. They are experts. In some ways, experts more than the clinical business themselves… So we said come to us with your recommendations on what we should do, as opposed to [name] and I dictating what we think you should do.

When leaders tied training to real‐time needs, they observed higher curiosity and confidence:

Building capacity in your team for growth and development is such a huge component to then get people curious and get the staff realizing there's other opportunities out there and that they have the ability and trust from their managers to speak up.

These learning “microclimates” exemplify how leadership behaviors and targeted investments in capacity can be scaled across teams to strengthen organizational learning.

5.2.1.4. Theme 4: Over‐Prioritization Dilutes the Efforts of Individuals and Teams to Create New Knowledge and Sustain Learning

Leaders described competing initiatives and constant operational pressures that restricted meaningful learning. The resulting fragmentation often forced staff to prioritize execution over reflection.

I really want to, let's say, engage with [the committee] on their ideas on how we can tackle a certain problem, but finding the time to do that and even just finding a space where [the committee] all fits is challenging. So in an ideal world, yes, I would love to be really creative and allow people to give ideas and test things out, take a risk, and if it doesn't work out then shift gears, but in our tight operational environment I just find it very constricting.

Despite awareness of this challenge, leaders found it difficult to align on fewer priorities:

But it's still too much, so it's like we're getting better at it and we're learning in that way of like, how do we, like focus in on a core set of things and do those things really well opposed to boiling the ocean and not doing anything well, cause we found that we're often revisiting the same initiatives year after year because we fix something, but we actually haven't sustained it.

These reflections highlight the need to continuously pursue data‐driven paring back, deprioritizing nonurgent items, and aligning on true priorities.

6. Discussion

This study examined how and why learning occurs—or is impeded—in a large community hospital within an LHS framework. Two distinct learning profiles were identified through survey data, and interviews revealed that learning is a dynamic, socially mediated process influenced by structural, cultural, and leadership conditions. The identification of two distinct learning profiles highlights meaningful variation in perceived learning capability across the organization. While this study was not designed to directly compare experiences between groups, these profiles provide important context for interpreting the qualitative findings, suggesting that variation in domains such as participatory decision making and risk‐taking may influence how learning mechanisms are activated in practice. Four insights emerged: dialogue is foundational but insufficient alone; structural barriers undermine learning; leadership microcultures enable or constrain learning; and over‐prioritization fragments learning efforts.

6.1. Dialogue Drives Learning, but Is Not Enough on Its Own

Dialogue fosters shared understanding and trust but must be embedded in transparent systems and linked to decision‐making authority to produce change. This aligns with research emphasizing the need for structural and cultural alignment to transform insights into action [41, 42, 43]. Without mechanisms for translation, dialogue risks becoming performative rather than transformative. Our findings extend prior work by illustrating that transparency and feedback loops are critical mediators between communication and organizational learning.

6.2. Structural Constraints Undermine Learning Despite Motivation

Participants' motivation to learn was strong, but structural factors—time scarcity, limited resources, and risk‐averse funding—constrained follow‐through. These barriers mirror literature describing how health systems, oriented toward operational stability, often deprioritize learning and innovation [41, 44]. Addressing these barriers requires organizational slack—protected time, space, and flexible funding—and governance structures that reward learning outcomes alongside efficiency. Measurement tools such as the Alberta Context Tool [45], and frameworks from implementation science [46, 47, 48] and de‐implementation initiatives [49, 50] can guide organizations in creating supportive contexts for change.

6.3. Leadership Is a Decisive Upstream Influence That Enables or Constrains Learning

Leaders were the clearest differentiators of local learning environments. Departments led by inclusive, inquiry‐driven managers showed greater engagement and confidence. These findings echo prior studies identifying leadership as both a cultural and structural lever for learning [42, 43]. In organizational learning theory, these leadership dynamics are captured in both the leadership aspects (i.e., management approach and shared organizational identity) [12, 30] and their interplay with contextual norms such as autonomy, trust, and collective leadership [51, 52]. The dual role of leadership as both a cultural influencer and a structural decision‐maker positions it as a central lever for advancing organizational learning within an LHS [16, 53].

6.4. Over‐Prioritization Has a Dilutional Effect on Learning Efforts

Competing initiatives diffuse attention and inhibit sustainability. Participants' experiences illustrate the challenge of balancing exploration and optimization—known as organizational ambidexterity [30, 54]. Ambidextrous organizations balance innovation with stability, achieving superior performance across industries [13]. Governance mechanisms that support deliberate de‐prioritization—such as sunset frameworks or learning‐aligned scorecards—can preserve cognitive and operational bandwidth for learning.

Together, these findings demonstrate that learning is both cultural and structural: it depends on trust and communication but thrives only when organizations intentionally align leadership, priorities, and resources to support it.

7. Limitations

This study has several limitations. First, although the survey achieved a strong initial response rate, a proportion of responses were incomplete and excluded from analysis. As limited data were available on noncompleters, we were unable to assess potential response bias. Second, the qualitative findings are based on interviews with 12 leaders from a single hospital system. While participants represented a range of leadership roles and functional areas, the sample size and single‐site design limit the extent to which findings can be generalized. Consistent with theory‐generating qualitative approaches, the aim of this work was to develop in‐depth, explanatory insight into contextually situated organizational learning processes rather than achieve generalizability. The findings are therefore intended to be transferable to contexts with similar organizational features. Future research could extend this work through larger, more diverse samples and multi‐site or mixed‐methods designs to further test and refine the proposed insights.

8. Conclusion

This study offers insights into how learning unfolds in a large community hospital pursuing an LHS model. Dialogue emerged as essential but insufficient without structural reinforcement; leadership behaviors shaped localized cultures of learning; and excessive priorities diluted focus. These interrelated factors reveal the multilevel complexity of enabling learning in practice. Our findings suggest several considerations for health systems seeking to strengthen organizational learning:

1. Link dialogue explicitly to decision‐making and action, ensuring that insights generated through communication are translated into organizational change;

2. Create structural conditions (e.g., time, resources, and flexible funding) that enable experimentation and the application of learning;

3. Support leadership practices that actively model and reinforce risk‐tolerant learning behaviors, fostering localized “learning microclimates” and

4. Establish prioritization and de‐prioritization mechanisms that preserve capacity for learning and prevent fragmentation of effort.

At system and policy levels, funding and accountability models should reward adaptive capacity—not just efficiency—by embedding learning as a performance indicator. Taken together, these findings move beyond identifying enabling factors to explicating how they interact dynamically, offering a more actionable foundation for designing learning‐oriented health systems.

Building a Learning Health System requires more than data infrastructure; it requires an understanding of how learning is enacted through the interaction of structural conditions, leadership practices, and organizational culture. By identifying the mechanisms through which learning is enabled or constrained in practice, this study provides insight into how health systems can move beyond aspiration toward operationalizing learning within everyday organizational routines.

Funding

This project was supported by an internal “LHS Accelerator Grant” from Trillium Health Partners.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Data S1: Supporting Information.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.