From data to decisions: Applying an enterprise architecture to build learning health systems in hospitals globally (YEL 2025)

Authors: Abed Sounbol (Tanzania), Antti Markkola (Finland), Jaime Kristoffer Punzalan (Philippines), Liz Fox (Ireland) and Rebecca Hamilton (Australia)

Reviewers: Abel Tesfaye (YEL 2023) and Yosra Al Salami (YEL 2022)

Hospitals around the world are producing more data than ever before. Electronic medical records, diagnostics (Endoscopy, Radiology, Pathology results), staffing metrics, patient reported outcome measures (PROMS), activity data, and more are generated daily. Yet despite this abundance, many health services still feel starved of insight. Clinicians struggle to access real time, meaningful data at the point of care, Executives often spend more time chasing key performance indicators (KPIs) than using data to inform strategic decisions, and trends in patient outcome measures can be difficult to identify without significant time spent on data analysis. In short, our health systems are rich in data but poor in insight. This three-part article series will give an overview of the Learning Health System (LHS), the structure of Enterprise Architecture, and a framework for implementation.

Article 1: Big data, small impact? Rethinking hospital intelligence in the Learning Health System era

Introduction

This article is part of the YEL Learning Series ‘From Data to Decisions’, exploring how emerging leaders can drive continuous learning through data-driven hospital systems.

Patient safety and efficiency in a hospital are deeply interconnected, forming the core of Quality Improvement (QI) in healthcare. A safe environment reduces errors, which boosts efficiency by minimizing delays and wasted resources, while effective processes ensure patients receive the right care at the right time, improving outcomes and overall effectiveness.

According to the World Health Organization (WHO), the global rate of patient harm is about 1 in every 10 patients, and almost 50% of patient harm is preventable. One clear challenge in improving patient safety is the inability to disseminate knowledge in a timely and effect manner, to enable the implementation of best practices consistently across the entire healthcare system.

The problem is not a lack of information. The real issue is the inability to translate data into learning. Health services frequently lack the structures, systems, and capabilities to convert raw data into action. Instead of acting as a tool for continuous improvement, data often becomes something static, viewed retrospectively or used for compliance rather than meaningful change. This reality underscores the need to fundamentally rethink how we use data in healthcare.

In 2018, the Institute for Healthcare Improvement (IHI) convened the National Steering Committee for Patient Safety (NSC) in USA, an interdisciplinary collaboration of 27 organizations committed to achieving safer care and reducing harm to patients and caregivers.

This resulted in the development of the Safer Together: A National Action Plan to Advance Patient Safety, released by the Agency for Healthcare Research and Quality (AHRQ) in 2020, which specifies 17 recommendations centered on four priority areas:

• Culture, Leadership, and Governance;
• Patient and Family Engagement;
• Workforce safety;
• Learning.

The first article of our series will focus on one of these priority areas, Learning Health Systems.

Learning Health System

The Learning Health System (LHS) offers a compelling way forward. A LHS is a health system (i.e., a group of one or more healthcare organizations, such as physician practices, hospitals, insurers, and skilled nursing facilities) that systematically leverages internal data and experience together with external evidence to continuously learn, disseminate knowledge,

and facilitate high quality, safe patient care. Key components for creating and sustaining a successful LHS include a strong safety culture, support from leadership, and the infrastructure, systems, and resources that allow for data analysis to advance knowledge and practice.

Essentially, the LHS is about using what we already know to do better. It shifts organisations from reactive reporting to proactive learning, and from measuring performance to driving improvement (Friedman, Rubin and Sullivan, 2017). Becoming a LHS is an iterative journey characterized by strong leadership, effective use of data in the clinical and administrative settings, and both a culture and workforce committed to continuous learning and improvement.

Impact on patient safety

Learning Health systems can positively impact patient safety and reduce patient harm by enabling healthcare systems to more efficiently identify risks, mitigate errors, and implement evidence-based best practices and interventions. Preventable errors in health care often have common root causes. Although each event is different, there are often similarities in risk factors and potential interventions that may be missed without effective reporting and analysis in combination with dissemination of the lessons learned. The role of a LHS is to foster this analysis and shared learning. (Savitz et al, 2025).

Impact on hospital operations

Learning Health systems can also be used to improve efficiency and streamline hospital operations. Santos Paiva et al 2024, examine the LHS in use in Brazil, when reducing length of stay (LOS) in an acute hospital. They reviewed the pre and post LOS in an acute hospital following the implementation of the Red2Green initiative. The Red2Green initiative, which is a visual management system of data captured to assist in the identification of wasted time in a patient’s journey. Applicable to in-patient wards in both acute and community settings, this approach is used to reduce internal and external delays as part of the SAFER patient flow bundle. The study showed a reduction from 19day LOS to 14.2day LOS.

Impact on IT technology

Information technology (IT) is integral to building a LHS, and more importable, ensuring long term success, especially for collecting and analysing data. Electronic health records (EHR) are critical in allowing LHS to record, compare, and present information in real time to support patient safety goals. However, due to the fragmented nature of healthcare systems, the complexities involved, the various siloes and varying data sources and access policies, it can be difficult to efficiently identify, and leverage much needed data for analysis, to facilitate shared learning and continuous improvement across the health systems.

An example of using the EHR as part of a LHS is the “Learn from Every Patient Program,” which used a tool within the EHR to capture data and review outcomes for patients who received annual x-ray screenings for hip displacement. As a result of this analysis, an evidence-based change to limit hip X-rays for mildly affected patients was agreed, reducing costs to the patient and the healthcare system (Savitz et al, 2025).

Conclusion

The vision of a LHS cannot be achieved through technology alone. A LHS must be intentionally designed. Data governance, systems integration, organisational culture, and digital infrastructure all need to work in harmony. Enterprise Architecture (EA) provides the blueprint to do so. Originating in industries such as manufacturing and government to manage complexity and large-scale transformation, EA is a strategic discipline that aligns an organisation’s business goals with its data, systems, and infrastructure (Sessions, 2007; Herculano da Luz Júnior et al., 2020). Its cross-sector origins make it especially adaptable to hospitals, which face similar challenges of scale, fragmentation, and rapid change.

Our next article in this series will explore EA and the frameworks which can be used to develop EA.

Article 2: The structure of enterprise architecture behind the Learning Health System

Introduction

This article is part of the YEL Learning Series ‘From Data to Decisions’, exploring how emerging leaders can drive continuous learning through data-driven hospital systems.

Enterprise Architecture (EA) was first developed in the 1960’s in relation to information system planning processes. EA is not a single document or function, but an approach and a framework that looks at different organizational activities, IT-processes and infrastructure requirements necessary to facilitate those functions. A commonly used approach for EA involves breaking down the organisation into key domains that must work together for effective service delivery. One such model is BDAT (Business, Data, Applications, and Technology):

• Business refers to the clinical and strategic goals that shape service
• Data encompasses the information captured, used to support those
• Applications are the digital tools and platforms that help staff and patients engage with data.
• Technology includes the infrastructure that enables systems to function reliably, securely, and at scale (Viccars, 2024).

Each domain represents a critical dimension of how modern organisations operate and are deeply interdependent. If a hospital [business] seeks to improve discharge efficiency as a business priority, this must be reflected in the data collected on length of stay, readmissions, and care coordination. Applications need to enable clinicians to visualise and act on this data through real time dashboards or alerts. The underlying technology must be interoperable and scalable to support data exchange across departments.

When any one of these layers is weak or disconnected from the others, the hospital’s ability to learn and adapt is compromised. This layered model helps leaders align strategy, information, tools, and infrastructure into a cohesive system that supports continuous learning.

Adapting BDAT for healthcare settings

EA is not limited to any one field of business, whilst its roots are in the IT-sector, it can be used in very different organizations and on different organizational levels. A systematic review of EA in healthcare found that BDAT is associated with improved governance, clearer organisational design, and better alignment between strategy and technology (Herculano da Luz Júnior et al., 2020). In Jordan, a national diabetes centre implemented a BDAT model to improve coordination between IT services and clinical operations, resulting in more agile and responsive service models (Al Omari et al., 2024). In Malaysia, public hospitals applying BDAT principles reported stronger digital alignment and more coherent service design (Yatya et al., 2023).

Australian researchers have identified a need to adapt general purpose architecture frameworks to the unique demands of health. In particular, issues such as delayed discharges, bed access blocks, and poor care transitions have been traced to underlying architectural weaknesses. Frameworks such as ZiRA, the Hospital Reference Architecture used in the Netherlands, have emerged as tailored models that have been built on a specific EA framework, (The Open Group Architecture Framework) while embedding hospital specific logic and process patterns (EAPJ, 2024). The Open Group’s Reference Architecture for Healthcare initiative has also advanced this thinking, proposing models that combine TOGAF, ArchiMate, and ISO standards into a common approach for healthcare organisations globally (The Open Group, 2020).

Healthcare specific architectural models must also integrate clinical knowledge. Afonina et al. (2023) argue for combining technical architecture diagrams with medical ontologies to ensure systems reflect the semantics of care delivery. In parallel, the ISO 12967 Health Informatics Service Architecture standard provides modular building blocks for integrating health information systems in vendor neutral ways. These approaches reinforce the principle that architecture should not only reflect what organisations do, but how they think and learn.

Challenges in implementing enterprise architecture

Despite its value, implementing EA in hospitals is not without challenges. Organisational culture can present significant barriers. In many hospitals, BDAT domains are unintentionally siloed. Business strategy documents articulate improvement goals without reference to how they will be supported by data. Data is collected inconsistently or stored in ways that limit its accessibility. Applications are implemented with little regard for how they integrate into existing workflows. Technology decisions are often reactive rather than strategically aligned. The BDAT model allows leaders to break this cycle. It provides a shared language and structure for ensuring that goals, data, tools, and infrastructure work in collaboration.

EA requires cross departmental collaboration, long term planning, and shared accountability, all of which can be difficult to sustain in environments that are clinically driven and resource constrained (McLachlan, 2020). Staff may resist change if the rationale and benefits are not clearly communicated. Successful implementation often depends on the presence of strong leadership, governance structures, and capability building efforts that extend beyond the technical domain.

Building maturity and capability

Maturity models have emerged to help guide organisations on their EA journey. For example, the Open Group’s Architecture Maturity Model provides criteria for assessing progress in areas such as stakeholder engagement, documentation, governance, and integration. In healthcare, adapting such models can help health services set realistic goals, measure progress, and maintain momentum over time. Embedding Enterprise architecture into strategic planning cycles and clinical governance frameworks helps ensure it is not seen as an IT only initiative, but as a whole of organisation capability.

Capacity building is another critical enabler. Health services require staff who understand both clinical context and system architecture. Developing hybrid roles, such as clinical informaticians or health data architects, can bridge the divide between users and designers. Partnerships with academic institutions and participation in global learning networks, such as the International Hospital Federation’s digital transformation programs, can also accelerate capability development. These initiatives strengthen the human foundation required to sustain a Learning Health System.

Sustaining long term transformation

Sustaining EA efforts in healthcare requires more than a technical roadmap. It demands organisational commitment, adaptive leadership, and continuous evaluation. One of the most common reasons architecture initiatives fail is the perception that they are purely project based, or IT owned. In reality, successful EA must be embedded into the governance structures of the organisation, integrated into strategic and operational planning cycles, and supported by executive sponsorship. Senior leadership must signal that architecture is not a one-time exercise but a long-term capability essential for transformation.

A culture of learning and reflection is critical. Hospitals that are serious about becoming Learning Health Systems must build in mechanisms to review the performance of their architecture on a regular basis. This could include maturity assessments, system audits, or internal benchmarking. Incorporating real time metrics that link architectural decisions to clinical or operational outcomes can also increase transparency and reinforce the value of Enterprise architecture. For example, improvements in patient flow, staff satisfaction, or avoidable admission rates may serve as early indicators that architecture led transformation is working.

Sustainability also depends on decentralised ownership. While a central architecture function can provide governance and technical consistency, each department or service line must see itself as a stakeholder in the system’s design. Business unit leaders, clinicians, and allied health professionals should be involved in shaping requirements, validating changes, and co-owning the delivery of outcomes. EA works best when it is participatory and iterative, rather than imposed.

Where to start: Practical steps

For hospitals just beginning their Enterprise Architecture journey, the most important step is to start small but strategically. Rather than attempting a full enterprise-wide redesign, it is often more effective to select a high priority challenge such as improving patient transfers, modernising outpatient scheduling, or reducing unplanned returns to the emergency department and apply the BDAT lens to that issue. This allows the organisation to test the approach, demonstrate value, and build momentum.

Establishing a clear governance structure is another foundational move. Defining roles and responsibilities across executive, clinical, and technical domains will support consistency in decision making and ensure that EA is not siloed. This might include forming an architecture review board, designating an EA lead or team, and embedding EA checkpoints into investment or change approval processes.

Training and capability building must occur in parallel. Staff cannot be expected to support architectural thinking if they do not understand its relevance. Introductory sessions on BDAT, strategic alignment, and systems integration can help cultivate a shared language across disciplines. Engaging early adopters particularly those who straddle clinical and operational roles can foster champions who advocate for and guide implementation. A critical consideration is that EA is a means to and end and should be treated as such. Without clearly defined and monitored goals Enterprise Architecture process that can consume all the resources allocated to it with very few tangible benefits. Drawing multi-tier maps of key systems and their interactions with various stakeholders is moot if the said stakeholders are not invested in the results.

Ultimately, EA is not about perfection or compliance. It is about intentionality. It is about designing systems that are capable of supporting healthcare organisations in a world of increasing complexity, constrained resources, and rising expectations. In an era where health services must rapidly adapt to demographic shifts, emerging technologies, and volatile funding environments, the ability to learn faster than we change is not a luxury, it is a necessity.

Enterprise Architecture gives us the tools to do just that. But only if we commit to using them.

The next article in this series will explore an implementation framework for the BDAT and demonstrate how they work together in real world hospital settings to support continuous learning.

Article 3: Making Big Data Work – Implementing BDAT to enable Learning Health Systems

Introduction: From potential to performance

This article is part of the YEL Learning Series ‘From Data to Decisions’, exploring how emerging leaders can drive continuous learning through data-driven hospital systems.

Hospitals today possess an unprecedented amount of data. Electronic health records, diagnostic imaging, laboratory systems, IoT-enabled devices, and patient-generated information all contribute to streams of data that, when properly utilized, can revolutionize the way care is provided. Big Data and Analytics (BDA) have the technical ability to turn these streams into actionable insights, but in most hospitals, their impact remains scattered. Dashboards may exist, predictive models may be tested, and reporting may be routine, but without a clear architecture and change strategy, these tools rarely lead to lasting improvements in outcomes or operations.

To bridge this gap, hospitals need both structure and strategy. The BDAT framework— Business, Data, Applications, Technology—provides the architectural backbone that ensures analytics are not isolated experiments but are integrated into core hospital functions. However, architecture alone cannot drive transformation; it must be energized by frameworks that focus on people, processes, and progress.

• Kotter’s change management focuses on leadership, vision, and organizational culture, which are key to ensuring that analytics-driven change is welcomed rather than
• Implementation science brings methods to adapt solutions to local contexts and ensure that innovations endure beyond pilot
• Systems thinking frames hospitals as interconnected ecosystems, highlighting feedback loops and ripple effects across clinical, operational, and financial
• Maturity assessment models provide a staged progression to benchmark current capabilities and plan realistic advancement toward more sophisticated analytics

Together, these approaches turn BDAT from a fixed structure into a flexible guide for developing Learning Health Systems (LHS), hospitals that constantly adapt, improve, and evolve through evidence produced within their own walls.

This article focuses on how hospitals can move from potential to performance by implementing BDAT in practice, guided by these complementary frameworks. We outline five practical steps, from readiness assessment to governance for sustainability, that can help hospitals across diverse global contexts leverage big data to make better decisions, build stronger systems, and achieve improved patient outcomes.

Step 1: Establish readiness and shared urgency

Every hospital’s journey with BDA must begin with a clear-eyed look at where it stands today. A readiness assessment sets the baseline, highlighting which capabilities are already in place and where gaps must be addressed before scaling. Without this step, hospitals risk chasing advanced analytics projects that collapse under weak foundations.

The BDAT framework offers a practical structure for this assessment. Leaders first clarify the business priorities: the outcomes the hospital aims to improve, and the metrics that will define success. They then evaluate the data environment: What information is being collected, how reliable is it, and can it move seamlessly across systems? The next focus is on applications, reviewing the tools currently in use and whether they are meaningfully integrated into workflows rather than sitting as isolated pilots. Finally, the technology infrastructure is examined for its security, scalability, and ability to support future needs.

The result is a readiness map, a visual summary of strengths, weaknesses, and opportunities. This not only guides immediate priorities but also generates urgency and alignment among stakeholders. In practice, hospitals that engage clinical, administrative, and IT leaders in the process build stronger coalitions for change. Readiness assessments also surface interdependencies: a weakness in laboratory data quality, for example, may be the hidden reason a predictive model in emergency care underperforms.

Key point: “Urgency + baseline first. Use Kotter (Steps 1–2) to build urgency and coalitions. Map gaps with BDAT, benchmark with maturity assessment model.”

Hospitals can then use this map to benchmark themselves against maturity levels, helping them set realistic expectations for what comes next. The output of Step 1 is a baseline scorecard that becomes the foundation for prioritizing analytics use cases in Step 2.

Step 2: Co-Design the vision and prioritize analytics use cases

Once readiness is established, the next move is to set a clear vision and decide where to focus analytics first. Hospitals that succeed in this step take time to involve both leadership and frontline staff in co-designing what “learning through data” should look like. This shared vision serves as a compass for the entire program, ensuring that analytics are not viewed as abstract dashboards but as tools directly linked to solving pressing problems.

With the vision in place, the hospital must then choose use cases carefully. The best starting points are those that promise measurable impact, can be realistically supported by current data and technology, and connect directly to existing institutional goals. Each chosen use case should be specific enough to generate visible wins but broad enough to demonstrate the potential of analytics as a system-wide enabler.

Key point: “Vision drives action. Apply Kotter (Steps 3–4) to co-create the vision. Use

Implementation science to ensure context fit. Prioritize use cases with BDAT lens.”

The result of this step is a prioritized roadmap of analytics projects that everyone can rally behind. By aligning selection with strategic objectives and making sure frontline voices are included, hospitals create both clarity and buy-in before moving into technical execution.

Step 3: Build learning loops and deliver early wins

With a shared vision and clear priorities in place, the next task is to put analytics into motion. This means moving beyond planning into real-world pilots that show how data can directly improve care and operations. The key is to design learning loops: data is collected, analyzed, used for action, monitored for results, and refined in response to those results. These loops make analytics part of daily work, not a side project.

Staff must be prepared and empowered to act on the insights. Training sessions, accessible dashboards, and simplified workflows ensure that analytics tools are actually used rather than left on the shelf. Barriers, whether technical, organizational, or cultural, should be identified and addressed quickly. Each early success should be celebrated and shared widely, reinforcing momentum and demonstrating value across the organization.

Key point: “Make data actionable. Build feedback loops with systems thinking. Empower staff with Kotter (Steps 5–6). Show wins, adapt using implementation science evaluation.

The goal is to produce visible wins that matter: reductions in avoidable readmissions, shorter wait times in the emergency department, or improved supply chain reliability. These results not only validate the analytics effort but also help expand trust and commitment for the next phase of scaling.

Step 4: Scale and contextualize implementation

Once pilots have proven value, the challenge shifts to scaling analytics across the hospital in ways that strengthen learning without creating new silos. This step is about consolidating gains and expanding successful use cases to additional departments, services, or functions. Scaling should be deliberate, guided by a clear strategy that ensures new projects align with overall business goals, use shared data flows, and connect seamlessly with existing applications.

Context matters. In high-income hospitals, scaling often involves integrating advanced analytics, such as machine learning models, into established systems and ensuring governance for their safe and accountable use. In low- and middle-income hospitals, the priority may be building modular, ofline-ready tools, gradually expanding data quality efforts, and ensuring the infrastructure can grow as resources allow. What matters most is that scaling matches the maturity of the institution and strengthens the hospital’s capacity to learn continuously.

Key point: “Scale smart. Consolidate gains with Kotter (Step 7). Adapt with implementation science sustainability. Use systems thinking to align across silos. Advance along the maturity assessment model.

Sustainability must be built in from the start. Scaling efforts should include plans for staff training, budget allocation, system maintenance, and ongoing adaptation to evolving clinical and operational needs. By applying a systems lens, leaders can avoid fragmented growth and instead build a cohesive environment where analytics reinforce each other across functions.

Step 5: Institutionalize governance and a culture of learning

The final step is to move from projects to permanence. For analytics to truly transform hospitals into learning systems, they must be integrated into governance structures and the hospital’s culture. This begins with establishing data governance committees that define accountability for data quality, security, and use. These bodies conduct regular reviews of analytic models, checking for bias, accuracy, and relevance, and ensure that systems remain trustworthy over time.

Equally important is building reliable feedback loops. Clinicians and administrators need simple ways to flag when analytics outputs do not align with practice, so that adjustments can be made quickly. Policies and workflows should evolve to integrate analytics into everyday decision-making, from bedside care to boardroom strategy.

Key point: “Anchor change. Apply Kotter (Step 8) to embed culture. Establish BDAT governance. Use Systems Thinking to link governance → policy → outcomes. Track maturity with Maturity assessment model.”

Institutionalizing analytics means integrating it into the hospital’s daily operations, not just a one-time initiative. This involves embedding data-driven responsibilities into job roles, aligning performance metrics with analytics-informed outcomes, and ensuring oversight by hospital leadership. A strong culture of learning develops when staff recognize that analytics are not a passing trend, but a permanent capability that enhances care and operations.

Hospitals that learn are hospitals that improve

The promise of BDA in healthcare is vast, but its impact depends entirely on how it is implemented. Without structure, tools remain underutilized; with structure, they become engines of transformation. The BDAT framework provides the architectural backbone for aligning business priorities, data systems, applications, and technology. When combined with principles from change management, implementation science, and systems thinking, hospitals gain a practical pathway to adoption, contextualization, and sustainability.

Progress must also be measurable. Using maturity assessment models such as the Maturity Assessment Model, hospitals can benchmark their current level, monitor growth, and track the shift from early pilots to embedded, system-wide learning capabilities.

The call to action is clear: hospitals must treat analytics not as a temporary project or isolated innovation, but as a permanent system function woven into governance, culture, and daily operations. From data to decisions, BDAT makes big data work for patients, providers, and the health system as a whole.

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