From BIM to digital twins in public healthcare facility management: bridging the gap between technological potential and operational reality

1 Introduction

Public healthcare estates across developed economies face escalating operational pressures that demand more robust approaches to asset information management and decision support. Ageing building stock, rising energy costs, increasingly complex technical systems, and persistent fiscal constraints place significant strain on traditional facility management (FM) practices (House of Commons, 2023), which often struggle to maintain performance whilst controlling operational expenditure (Lu and Anumba, 2025; Schmitter et al., 2023; Shohet and Lavy, 2017). In response, digital technologies, most notably Building Information Modelling (BIM) and, more recently, Digital Twins (DTs) have been promoted as mechanisms for improving asset performance, maintenance efficiency, and lifecycle cost control.

BIM is now well established within the design and construction phases of healthcare capital projects, supported by public-sector mandates and evidence of benefits in coordination, clash detection, and delivery certainty (Lovell et al., 2024). However, its translation into operational FM contexts remains limited. Systematic reviews consistently demonstrate that BIM use in FM is fragmented and immature, with weak alignment to facility managers’ information needs and limited evidence of sustained operational value (Lovell et al., 2024; Chatsuwan et al., 2025). As a result, models frequently become static artefacts, detached from the evolving conditions of building operation.

Digital Twins have emerged as a proposed response to these limitations, extending BIM through the integration of real-time or near-real-time data streams from sensors, building management systems, and analytics platforms (Zhang et al., 2024). In principle, DTs enable continuous feedback between physical assets and digital representations, supporting predictive maintenance, performance optimisation, and informed operational decision-making (Deng et al., 2021; Hayat and Winkler, 2025; Boje et al., 2022). In practice, however, adoption within public healthcare FM remains sparse (Hakimi et al., 2024).

While Digital Twin technologies continue to advance, a number of studies caution against overly optimistic expectations regarding their transformative potential. Empirical evidence suggests that many implementations remain confined to pilot projects or demonstrators, with limited evidence of sustained operational integration or measurable performance improvement (Fuller et al., 2020; Boje et al., 2022). Some scholars further argue that Digital Twin discourse is characterised by a degree of techno-optimism, where anticipated benefits such as predictive maintenance and autonomous decision-making are often overstated relative to current organisational capabilities and data readiness (Lu et al., 2020; Sacks et al., 2020). Moreover, there is limited reporting on failed or discontinued implementations, which may further obscure the true maturity and scalability of Digital Twin adoption. These critiques highlight the need to move beyond technology-centric narratives and to critically examine the socio-technical conditions under which Digital Twins can deliver meaningful operational value.

Despite these technological advances, most Digital Twin initiatives in public healthcare remain confined to pilot projects or BIM-adjacent implementations (Fuller et al., 2020). This persistent gap suggests that barriers to adoption cannot be explained by technological capability alone. Instead, Digital Twin implementation in healthcare facility management must be understood as a socio-technical challenge, shaped by organisational structures, information governance, procurement practices, and workforce capability, rather than as a linear progression of digital tools.

This persistent gap between technological promise and operational reality raises critical questions for public-sector asset owners and FM professionals. What barriers inhibit the progression from BIM-enabled information management to operational Digital Twins? Which organisational, technical, and governance conditions enable successful implementation? And how can healthcare organisations avoid superficial adoption that delivers limited practical value?

This paper addresses these questions through a critical synthesis of the literature and practice-based evidence. It makes three interrelated contributions. First, it demonstrates that the persistent gap between BIM and operational Digital Twins in public healthcare is inadequately explained by technical immaturity alone, and instead reflects unresolved organisational, governance, and capability constraints that existing Digital Twin frameworks largely under-theorise. Second, it proposes a socio-technical, stage-based adoption framework that repositions BIM as a necessary but insufficient foundation for Digital Twin capability, explicitly integrating governance alignment, data stewardship, and organisational readiness as conditions for progression rather than parallel considerations. Third, it translates this framework into actionable implications for policy, procurement, and facility management practice, providing guidance on how public healthcare organisations can avoid superficial adoption and prioritise operational value over technological novelty. This study is positioned as a structured narrative review and conceptual contribution, combining a transparent synthesis with the development of a socio-technical adoption framework for BIM-enabled Digital Twin implementation in public healthcare facility management.

In doing so, the paper deliberately departs from technology-centric Digital Twin narratives prevalent in the smart buildings’ literature, which often assume that improved data integration or analytical sophistication will naturally translate into operational adoption. Figure 1 summarises this argument by illustrating the discontinuity between project-centric BIM adoption in capital delivery and the requirements of operational Digital Twins in public healthcare facility management.

FIGURE 1

2 Methodology

This study adopts a structured narrative literature synthesis to examine socio-technical barriers affecting the transition from Building Information Modelling (BIM) to Digital Twins in public healthcare facility management. While not a fully systematic review, the approach is designed to ensure transparency and analytical rigour in the identification, selection, and interpretation of relevant studies.

2.1 Literature search strategy

The literature search was conducted across major academic databases, including Web of Science, ScienceDirect, and Google Scholar, to capture peer-reviewed journal articles and relevant conference proceedings. The search strategy combined key concepts related to Digital Twins, BIM, facility management, and building operations. The primary search strings used to identify relevant literature are summarised in Table 1.

An initial pool of studies was identified through database searches, after which duplicates were removed, and records were screened for relevance based on titles and abstracts.

The search focused on literature published between 2015 and 2025, reflecting the rapid evolution of Digital Twin technologies and their application in the built environment.

2.2 Inclusion and exclusion criteria

To ensure relevance and analytical consistency, explicit inclusion and exclusion criteria were applied during the screening process. These criteria are summarised in Table 2.

Following the initial screening, full-text articles were assessed against the inclusion and exclusion criteria to ensure alignment with the study’s focus on BIM, Digital Twin adoption, and operational or facility management contexts.

The search and screening process resulted in a final sample of 47 studies, which were then analysed in detail to identify recurring themes, reported barriers, and implementation patterns. Although this was not a fully systematic review, the selected studies were appraised for relevance, scope, and contribution to understanding socio-technical barriers and enablers in BIM-enabled Digital Twin adoption.

2.3 Analytical approach

A thematic synthesis approach was adopted to identify recurring patterns, challenges, and relationships across the literature. Studies were iteratively reviewed and coded, leading to the identification of three interrelated barrier clusters: organisational and governance constraints, data and interoperability limitations, and skills and workflow integration challenges.

The selected studies were analysed using a thematic synthesis approach, focusing on identifying recurrent socio-technical barriers, implementation challenges, and reported outcomes. Particular attention was given to organisational, governance, and operational factors influencing the translation of technical capability into practice.

These clusters were not treated as independent categories but were analysed as mutually reinforcing elements within a broader socio-technical system. Insights from both academic literature and documented practice-based evidence informed the development of a conceptual framework that explains how these barriers collectively inhibit the operational adoption of Digital Twins.

2.4 Scope and limitations

The approach prioritises conceptual depth and cross-cutting insights over exhaustive coverage. As a structured narrative synthesis, it is subject to limitations associated with potential selection bias and the predominance of published success-oriented cases. However, efforts were made to include critical perspectives and to reflect inconsistencies and challenges reported in the literature.

Given the interpretive and conceptual nature of the approach, the findings should be understood as indicative rather than exhaustive, aimed at supporting theoretical insight rather than comprehensive empirical generalisation.

The resulting framework should therefore be interpreted as a propositional and diagnostic model, intended to guide understanding and future empirical investigation, rather than as a validated or predictive tool.

3 From BIM to digital twins: state of practice and research

The relationship between Building Information Modelling (BIM) and Digital Twins (DTs) is commonly framed in the literature as an evolutionary progression from static information models towards dynamic, data-driven representations of the built environment (Mousavi et al., 2024; Sepasgozar et al., 2023). In this framing, BIM provides the structured geometric and semantic foundation upon which DT capabilities are incrementally developed through real-time data integration and analytics. However, this evolutionary framing largely assumes continuity between design-phase information modelling and operational practice, offering limited explanation for why many organisations, particularly in public healthcare, fail to progress beyond early or BIM-adjacent Digital Twin implementations in live facility management contexts. As a result, the conceptual continuity often assumed between BIM and Digital Twins obscures the organisational, governance, and operational discontinuities that characterise their adoption in practice. Key distinctions between BIM’s role in capital delivery and the operational expectations associated with Digital Twins in healthcare facility management are summarised in Table 3.

Evidence from healthcare facility management (FM) indicates that BIM adoption remains largely confined to design and construction phases, supported by mandates and established delivery workflows, but weakly embedded in operational contexts (Lovell et al., 2024; Schmitter et al., 2023). Systematic reviews consistently report fragmented BIM-to-FM integration, characterised by inadequate information handover, non-standardised data structures, and limited articulation of facility managers’ information requirements during project delivery (Lovell et al., 2024; Chatsuwan et al., 2025). As a result, operational teams frequently inherit models that are geometrically detailed yet insufficiently populated with asset, system, and maintenance data necessary for effective FM (Godager et al., 2024).

These shortcomings are compounded by structural and technical constraints, the underlying implications of which are examined in detail inSection 3. Construction-phase BIM models are often simplified at handover, eroding the information richness required for operational use (Lovell et al., 2024). Moreover, open data standards such as Industry Foundation Classes (IFC) remain limited in their ability to represent complex system relationships, temporal asset states, and maintenance semantics at scale (Godager et al., 2024; Chatsuwan et al., 2025). Organisationally, the contractual and governance frameworks governing capital delivery rarely extend into the operational lifecycle, creating persistent ambiguities around data ownership, model updating, and accountability (Schmitter et al., 2023).

Within this context, Digital Twins are positioned as a means of overcoming the static nature of BIM by enabling continuous alignment between physical assets and digital representations. In healthcare settings, this promise is most commonly articulated around energy management, maintenance responsiveness, and environmental performance, yet remains realised primarily through isolated pilot deployments rather than routine operational systems. Deng et al. (2021) conceptualise this progression through a five-level maturity taxonomy, ranging from static BIM models to fully synchronised, predictive systems with closed-loop feedback. Empirical evidence suggests, however, that most healthcare-related DT implementations remain concentrated at the lower end of the maturity levels, with limited real-time integration or decision-support functionality (Hakimi et al., 2024; Mousavi et al., 2024). Conceptual frameworks further emphasise the importance of data governance, interoperability, and user-centred design, yet these dimensions are rarely operationalised in healthcare FM contexts (Hayat and Winkler, 2025; Boje et al., 2022).

Documented examples of successful DT deployment in healthcare settings remain limited but instructive. Tan et al. (2021) and Harode et al. (2023) report hospital implementation integrating BIM with live building systems to support real-time monitoring and AI-enabled fault diagnosis, achieving measurable improvements in energy performance and maintenance responsiveness. Similarly, Zhairy et al. (2025) demonstrate how linking BIM spatial data with IoT-based air quality monitoring can support actionable FM decision-making. These cases illustrate that where technical integration is coupled with clearly defined operational use cases, Digital Twins can deliver tangible value.

Nevertheless, such implementations remain exceptions rather than normative practice. Recent healthcare-focused studies similarly report limited operational embedding beyond pilot applications (Ebiloma et al., 2025). Reviews consistently highlight that DT initiatives are typically pilot-scale, short-term, and biased towards new-build assets with favourable data and system conditions (Hakimi et al., 2024; Mousavi et al., 2024). Evidence from existing and ageing healthcare estates is sparse, and definitional ambiguity persists, with the term ‘Digital Twin’ applied to a wide spectrum of systems with markedly different capabilities (Miraj et al., 2025). This conceptual looseness complicates comparative evaluation and obscures the distinction between enhanced BIM and genuinely operational Digital Twins.

Three interrelated explanatory gaps emerge from this body of research. First, there is limited longitudinal evidence on the operational impacts of BIM-to-DT transitions within live healthcare environments. Second, validated methods for transforming construction BIM deliverables into FM- and DT-ready asset information models remain underdeveloped. Third, and most critically, the organisational, contractual, and governance transformations required to sustain Digital Twin capability over time remain under-theorised and weakly operationalised, receiving substantially less attention than technical architectures and data integration (Godager et al., 2024; Schmitter et al., 2023). Addressing these gaps is essential if Digital Twins are to move beyond experimental deployments towards routine use in public healthcare facility management.

Collectively, this body of research demonstrates that the limited operational uptake of Digital Twins in public healthcare cannot be adequately explained by technological immaturity or data integration challenges alone; instead, it points to deeper organisational, governance, and capability constraints that structure how BIM and Digital Twin technologies are specified, procured, and used in practice; constraints that are examined systematically in the following section.

4 Barriers to operational adoption

Evidence from public healthcare facility management indicates that stalled Digital Twin adoption is not primarily a consequence of technological immaturity, but a structurally produced outcome shaped by organisational, governance, and capability constraints embedded within capital delivery and operational practices. While technical challenges related to interoperability, data quality, and real-time integration remain significant, they do not fully explain the limited operational uptake observed in practice. The literature consistently shows that these issues are mediated and often intensified by procurement arrangements, information governance practices, and limited organisational readiness (Schmitter et al., 2023; Godager et al., 2024).

While Digital Twins are frequently presented as transformative technologies for building operations, the supporting evidence remains uneven and, in some cases, inconclusive. Many reported implementations are confined to controlled pilots, experimental testbeds, or narrowly scoped applications, raising persistent questions about scalability and long-term value realisation. The literature also tends to privilege technically successful demonstrations while offering limited insight into failed or abandoned initiatives, contributing to a form of publication bias that may overstate the maturity of Digital Twin adoption. Evidence further suggests that many initiatives remain undocumented beyond early-stage pilots, obscuring the true extent of unsuccessful or discontinued implementations (Fuller et al., 2020; Boje et al., 2022). In addition, there is limited systematic reporting of unsuccessful or discontinued implementations in healthcare settings, which further obscures common failure modes and may lead to an overestimation of Digital Twin readiness in practice.

In practice, organisational resistance, misaligned incentives, and limited integration with existing workflows frequently constrain the translation of technical capability into meaningful operational impact. Furthermore, organisational resistance to change, competing operational priorities, and risk-averse decision-making cultures in public healthcare environments often limit the adoption of Digital Twin systems beyond experimental or pilot stages. This indicates that Digital Twin adoption is not simply a technological progression, but a socio-technical transformation requiring alignment across governance, data, and organisational practices. This reinforces the concern that current Digital Twin discourse may overstate readiness and scalability, particularly in complex public-sector environments where organisational constraints remain unresolved.

Building on this perspective, this section synthesises these constraints into three interrelated barrier clusters: organisational and governance constraints, data and interoperability limitations, and skills and workflow integration challenges, and highlights their mutually reinforcing nature. These relationships are illustrated in Figure 2.

FIGURE 2

To provide an auditable synthesis of the specific barriers reported across the literature and their operational implications, Table 4 summarises each cluster and its consequences for routine facility management practice.

Organisational and governance barriers arise from the structural separation between capital project delivery and operational facility management. Public healthcare procurement frameworks prioritise construction delivery, with facility management considerations marginalised or deferred until post-handover stages (RICS, 2022; House of Commons, 2023). This separation produces governance voids in which ownership of digital asset information, responsibility for model maintenance, and accountability for data quality remain unclear. Empirical studies show that even where technical capability exists, the absence of explicit governance arrangements can render BIM-based assets operationally irrelevant (Godager et al., 2024). In practice, this results in Digital Twin initiatives that are technically delivered but operationally orphaned. As a result, Digital Twin initiatives are frequently delivered as technically complete systems without an organisational home, leaving no clear mandate for maintenance, evolution, or operational use. These challenges are further compounded by institutional inertia and risk-averse decision-making cultures in public-sector organisations, where innovation is often constrained by accountability structures, regulatory pressures, and competing operational priorities (Schmitter et al., 2023; Godager et al., 2024).

This fragmentation is further reinforced by procurement models that fragment responsibility across design, construction, and operation, limiting incentives for lifecycle information continuity (Schmitter et al., 2023) and discouraging investment in long-term Digital Twin stewardship. Facility management teams are frequently excluded from early project stages, restricting their ability to define operational information requirements and resulting in deliverables misaligned with everyday FM needs (Lovell et al., 2024). Many organisations also lack the internal capability to specify and govern Digital Twin solutions, increasing reliance on vendors and reducing strategic control over long-term system evolution (David et al., 2024). Change management deficits further compound these issues, as Digital Twins are often treated as discrete technology acquisitions rather than organisation-wide transformation initiatives (Heaton et al., 2019; Sacks et al., 2020).

Data and interoperability challenges constitute the second barrier cluster. Although widely documented, their operational implications in public healthcare contexts are often underestimated because responsibility for resolving them rarely sits within operational teams. Industry Foundation Classes (IFC), while central to BIM data exchange, were developed primarily for design and construction coordination and remain poorly suited to representing maintenance relationships, asset states, and system hierarchies required for FM and Digital Twin applications (Chatsuwan et al., 2025; Bolpagni, 2021; Harode et al., 2023). Semantic extensions and alternative modelling approaches have been proposed, but these remain fragmented and largely experimental in live healthcare estates.

These interoperability challenges are amplified by heterogeneous and legacy FM system landscapes, including building management, maintenance, and asset tracking platforms with proprietary data structures (Li et al., 2024). Integrating these systems with BIM geometry and real-time sensor data requires complex middleware and tool-based architectures (Harode et al., 2023; David et al., 2024; Lu et al., 2020), sustained technical expertise, and ongoing data management (Eneyew et al., 2022; Dlesk et al., 2023). Persistent data quality problems, stemming from incomplete as-built BIM models and unreliable operational data streams, further undermine trust in Digital Twin outputs and discourage their use in decision-making (Lovell et al., 2024; Zhairy et al., 2025).

Skills and workflow misalignment forms the third cluster. Digital Twin implementation demands competencies extending beyond traditional facility management expertise, including data analytics, information modelling, and IoT systems management (Schmitter et al., 2023). Reviews consistently identify significant skills gaps within healthcare FM organisations, exacerbated by limited training pathways, resource constraints, and staff turnover (Lovell et al., 2024; Godager et al., 2024). Workflow misalignment further constrains adoption: where Digital Twin outputs do not align with established decision points and operational priorities, they are frequently bypassed in favour of familiar practices (Godager et al., 2024). Evidence from successful cases highlights the importance of co-design with end users to ensure outputs are timely, actionable, and operationally relevant (Tan et al., 2021).

Beyond these challenges, a further limitation in current Digital Twin adoption is the insufficient integration of end-user perspectives, particularly those of facility managers and clinical staff. While technical and governance challenges are widely documented, less attention has been given to how Digital Twin systems align with everyday operational workflows, decision-making practices, and user expectations. Where systems are not co-designed with end users or fail to integrate with existing routines, they are often bypassed in favour of established practices, limiting their operational impact. This highlights the importance of user-centred design and workflow integration as critical, yet underdeveloped, dimensions of Digital Twin implementation in healthcare contexts, and reinforced their role as essential conditions within the proposed socio-technical adoption framework.

Taken together, these barriers are mutually reinforcing. Weak governance exacerbates data and interoperability problems; technical shortcomings erode organisational confidence; and skills deficits limit the ability to specify, govern, and sustain Digital Twin solutions. This interdependence explains why many initiatives stall at pilot or BIM-adjacent stages and underscores the need for a coordinated, staged approach to adoption, developed in the following section.

5 Enablers and preconditions

Despite substantial barriers, the literature identifies a set of enabling conditions that support the effective transition from BIM to operational Digital Twins in public healthcare facility management (Whyte et al., 2024). These enablers directly mirror the organisational, technical, and human constraints identified earlier, but shift the focus from remediation to anticipatory design, governance alignment, and staged capability development Importantly, these conditions also emphasise the role of end users, particularly facility managers and operational staff, whose requirements and workflows must be embedded from the outset to ensure meaningful adoption.

Data readiness and information completeness emerge as foundational preconditions. Successful Digital Twin adoption depends on information management frameworks that extend beyond geometric BIM models to include semantically rich, complete, and quality-assured asset data suitable for operational use (Godager et al., 2024; Al-Saeed et al., 2019). Lifecycle information standards, such as the ISO 19650 series, provide mechanisms for defining information requirements, data structures, and validation processes across both project delivery and operational phases (Abanda et al., 2025; Chatsuwan et al., 2025). Enterprise-level BIM approaches that align information production with organisational asset management strategies, particularly during the transition from design to handover (RICS, 2022), rather than treating BIM as a project-specific output, are consistently associated with more sustainable outcomes (David et al., 2024).

Semantic enrichment and planned operational data integration constitute key technical enablers. Given the limitations of IFC for FM and analytics applications, effective implementations supplement BIM geometry with semantic layers that capture asset relationships, maintenance attributes, and system hierarchies in machine-readable forms (Chatsuwan et al., 2025). Equally important is the early integration of sensor strategies to avoid costly retrospective integration (Sun et al., 2022), spatial referencing, and data pipelines during design and construction, enabling continuity of data linkage from handover into operation and avoiding costly retrospective integration (Tan et al., 2021; Dlesk et al., 2023; Zhairy et al., 2025).

Clear governance alignment and information ownership constitute critical organisational enablers. Studies consistently emphasise the need for explicit ownership of asset information models, with clear responsibilities assigned for validation, updating, and quality assurance embedded within formal organisational structures rather than informal practices (Godager et al., 2024). Early and sustained involvement of facility management teams in capital projects is essential to ensure that information deliverables reflect operational priorities rather than construction-centric assumptions (Lovell et al., 2024). This early engagement also ensures that Digital Twin development is aligned with real operational workflows and decision-making requirements, rather than abstract technical specifications. At a system level, procurement models that support lifecycle information continuity, whether through integrated delivery approaches or strengthened contractual requirements, are more conducive to Digital Twin sustainability than traditional phase-segmented arrangements (Schmitter et al., 2023).

Organisational capability and change readiness underpin both technical and governance measures. Healthcare-specific DT studies emphasise the importance of FM capability building and clinical risk awareness in sustaining adoption (Ebiloma et al., 2025). Successful adopters invest in internal capacity to specify requirements, govern vendor relationships, and manage organisational change, rather than relying entirely on external expertise (Godager et al., 2024). Structured change management programmes that address workflows, roles, and professional development are repeatedly identified as necessary complements to technical deployment, particularly in complex healthcare environments (Schmitter et al., 2023). Crucially, this includes ensuring that Digital Twin outputs are interpretable, trusted, and usable by end users within their existing operational contexts.

Incremental, use-case-driven implementation emerges as a pragmatic strategy for managing risk and building confidence. Rather than pursuing comprehensive Digital Twins from the outset, organisations that focus on high-value operational applications, such as energy management, preventative maintenance, or indoor environmental quality monitoring, are better positioned to demonstrate value, refine data architectures, and develop organisational capability progressively (Tan et al., 2021; Hayat and Winkler, 2025; Zhairy et al., 2025). This approach responds directly to evidence that many Digital Twin initiatives prioritise platform capabilities and visualisation features over clearly articulated operational questions, limiting scalability and long-term integration into routine facility management practice (Shahzad et al., 2025). Pilot projects therefore play a critical role in this process, provided they are designed with scalability and long-term integration in mind rather than as isolated demonstrations (Desogus et al., 2023; Hakimi et al., 2024). Where pilots are not aligned with user needs and workflows, they risk remaining disconnected from routine practice and failing to scale.

Taken together, these enablers indicate that Digital Twin adoption in public healthcare facility management depends less on technological novelty than on coordinated attention to data readiness, governance reform, and organisational capability development, and explicit integration of user requirements and workflows Their interdependence reinforces the need for a staged, socio-technical approach in which technical, organisational, and governance capacities co-evolve; an approach operationalised in the adoption framework presented in the following section.

6 BIM-enabled digital twin adoption framework

Existing Digital Twin frameworks in the built environment literature have predominantly conceptualised adoption as a progression in technical maturity, typically expressed through levels of data integration, synchronisation, and analytical sophistication (e.g., Deng et al., 2021). While these models are valuable for classifying technological capability, they provide limited guidance on why most organisations, particularly in public healthcare, fail to progress beyond early, BIM-adjacent stages of implementation (Whyte et al., 2024). In practice, stagnation is rarely caused by technical infeasibility alone, but by unresolved organisational, governance, and capability constraints that are insufficiently captured in technology-centric maturity models (Sacks et al., 2020).

While existing Digital Twin maturity models typically conceptualise progression in terms of increasing levels of data integration, automation, and analytical capability (e.g., Deng et al., 2021), they tend to assume a linear and predominantly technological trajectory of development. In contrast, the framework proposed in this study adopts a socio-technical perspective, positioning Digital Twin implementation as contingent on the alignment of organisational governance, data infrastructures, and operational practices. Rather than functioning as a purely prescriptive maturity model, the framework is intended as an analytical and diagnostic tool to explain stalled adoption and to guide staged progression towards operational Digital Twin capability in public healthcare facility management contexts. The framework is intentionally conceptual and has not been empirically validated; it is proposed as a diagnostic and explanatory model to guide future empirical investigation rather than a predictive or prescriptive tool.

Building on this conceptual positioning, this paper proposes a BIM-enabled Digital Twin adoption framework that explicitly repositions progression as a socio-technical process, in which advancement is conditional upon the alignment of data readiness, governance structures, and organisational capability at each stage. Rather than treating governance and human factors as parallel considerations or contextual modifiers, the framework integrates them as necessary preconditions for technical progression. As a result, failure to advance is understood not as underperformance against a maturity benchmark, but as an expected outcome where organisational conditions are misaligned, explaining the empirical prevalence of pilot projects, stalled implementations, and regression to static BIM use (Parn and Edwards, 2019).

The framework positions BIM as the canonical spatial and asset reference layer throughout all stages, while rejecting the assumption that richer geometry or increased data volume alone leads to operational Digital Twin capability. Instead, progression is structured as a staged pathway in which technical functionality emerges only where information governance, lifecycle alignment, and operational embedding are sufficiently developed. This distinguishes the framework from existing taxonomies by focusing on how organisations transition in practice, rather than how Digital Twins are theoretically defined.

Building on insights from Digital Twin maturity taxonomies (Deng et al., 2021), DT-enabled FM frameworks (Hayat and Winkler, 2025), and Enterprise BIM approaches (David et al., 2024), the proposed model synthesises these strands into an operationally oriented structure tailored to public healthcare facility management. It is intended not as a prescriptive roadmap, but as a diagnostic and planning tool that supports maturity assessment, prioritisation of interventions, and incremental capability development under real-world constraints.

6.1 Stage 1: operational information grounding

Operational information grounding establishes facility management requirements as the primary driver of digital development. Facility management needs are articulated early and translated into structured information requirements embedded within capital delivery processes, supported by lifecycle information standards such as ISO 19650 (Lovell et al., 2024; Chatsuwan et al., 2025). The key enabler at this stage is data readiness: BIM is used not as a design coordination tool, but as a mechanism for capturing quality-assured asset information aligned with operational priorities and governance responsibilities.

6.2 Stage 2: governed asset information consolidation

Construction BIM outputs are transformed into an Asset Information Model (AIM) that functions as the authoritative organisational reference for asset data. Semantic enrichment addresses the limitations of geometry-centric BIM by incorporating maintenance attributes, system relationships, and links to FM systems (Demirdöğen et al., 2023; Chatsuwan et al., 2025). Governance alignment is critical at this stage, with explicit ownership, validation processes, and accountability for ongoing model stewardship embedded within organisational structures.

6.3 Stage 3: contextualised operational data integration

This stage introduces dynamic operational data through the integration of sensors, building management systems, and other live data sources. BIM-based spatial and asset identifiers provide the contextual framework that links data streams to physical systems, enabling meaningful interpretation rather than abstract monitoring (Eneyew et al., 2022; Dlesk et al., 2023). Incremental, use-case-driven deployment acts as the primary enabler, ensuring that integration effort is focused on operationally valuable systems and manageable data pipelines.

6.4 Stage 4: analytics-enabled operational intelligence

Integrated static and dynamic data are leveraged to support monitoring, diagnostics, and optimisation through analytics and visualisation embedded within BIM-based interfaces (Tan et al., 2021; Hayat and Winkler, 2025). Organisational capability building becomes central at this stage, as analytical outputs must align with existing FM workflows and decision points to influence practice. Progression depends less on analytical sophistication than on trust in data quality and usability within operational contexts.

6.5 Stage 5: embedded use and continuous improvement

Digital Twin capabilities become embedded within routine facility management processes, with DT outputs actively informing maintenance planning, performance optimisation, and strategic decision-making. Feedback loops between operational use, data quality improvement, and model refinement support continuous learning and system evolution (Godager et al., 2024). Sustained governance, workforce development, and change management are the critical enablers, ensuring that Digital Twins function as durable organisational capabilities rather than time-limited technical projects.

The framework can be visualised as a staged pathway underpinned by BIM as a continuous reference layer, with feedback loops emphasising learning and adaptation rather than linear progression. Its purpose is not to prescribe a uniform endpoint, but to support maturity assessment, prioritisation, and incremental capability development. While not all organisations will progress to full Stage 5 adoption (Figure 3), the framework provides a structured basis for achieving meaningful operational value at intermediate stages, particularly in resource-constrained public healthcare environments.

FIGURE 3

To illustrate how the framework may be applied in practice, consider a typical public healthcare organisation seeking to improve maintenance efficiency in an ageing hospital estate. At Stage 1, facility management requirements such as asset condition monitoring and maintenance scheduling are defined and translated into structured information requirements during capital project delivery. In Stage 2, these outputs are consolidated into a governed Asset Information Model (AIM), ensuring data completeness and accountability. Stage 3 introduces targeted integration of building management system data, for example, linking HVAC performance data to spatially referenced assets within the BIM environment. At Stage 4, analytics are applied to support fault detection and maintenance prioritisation, while ensuring outputs align with existing FM workflows. Finally, Stage 5 embeds these capabilities into routine operations, with continuous feedback loops improving data quality and supporting long-term performance optimisation. This example is illustrative and demonstrates how progression depends not only on technical integration, but on the alignment of governance, data, and organisational capability at each stage.

This reframing is particularly important in public healthcare contexts, where organisational and governance constraints often determine whether technical capability can be translated into sustained operational value.

7 Implications for policy and practice

The analysis demonstrates that effective Digital Twin adoption in public healthcare facility management depends less on technological sophistication than on sustained organisational alignment, information governance, and capability development across the asset lifecycle. Effective implementation therefore requires coordinated action across policy, procurement, professional practice, and research, with emphasis on information governance, capability building, and staged value creation aligned to operational priorities. While several barriers identified in this study, such as data interoperability and governance fragmentation, are common across sectors, they are often amplified in public healthcare environments. In these contexts, stringent regulatory requirements, the need to maintain continuous service delivery, and low tolerance for operational disruption create additional constraints that make Digital Twin implementation more complex and risk-sensitive than in many other sectors.

For policymakers and public healthcare asset owners, the central implication is that BIM and Digital Twin initiatives should be framed as long-term organisational transformation programmes, not technology mandates. Requirements to adopt BIM or DTs without addressing governance, data readiness, and workforce capability risk producing compliance-driven implementations with limited operational value (Godager et al., 2024; Schmitter et al., 2023). Evidence from Digital Twin implementations in building operation further indicates that technology-led initiatives introduced without parallel investment in organisational processes and user capability often result in superficial adoption, where systems are used primarily for monitoring rather than embedded in routine facility management decision-making (Shahzad et al., 2025). This highlights the risk of policy approaches that prioritise digital adoption targets over demonstrable operational outcomes. More effective approaches therefore prioritise lifecycle information standards, procurement reform that aligns capital and operational incentives, and targeted support for pilots designed to generate transferable organisational learning. Policy frameworks should explicitly allow for staged progression, reflecting variation in organisational maturity and asset condition. This aligns with parliamentary findings highlighting fragmented digital governance and uneven capability across NHS estates (House of Commons, 2023).

Evidence highlights the importance of sector-specific guidance on FM information requirements and BIM-to-AIM transitions, alongside formal mechanisms to ensure early and sustained FM engagement in capital projects (Lovell et al., 2024; Godager et al., 2024). Public clients can also shape market behaviour by mandating open standards and interoperability, reducing dependence on proprietary platforms and vendor lock-in.

For facility management professionals, the implications centre on proactive engagement and capability development. Facility managers must move beyond passive receipt of BIM deliverables to define operational information requirements, validate handover outputs, and shape Digital Twin use cases from project inception (Lovell et al., 2024). Incremental, use-case-driven initiatives, such as energy management, preventative maintenance, or indoor environmental quality monitoring, offer pragmatic pathways to demonstrate value and build organisational confidence (Tan et al., 2021; Zhairy et al., 2025). However, without alignment to existing workflows and decision-making routines, such initiatives risk remaining isolated pilots rather than scaling into sustained operational practices.

For technology vendors, the findings underscore the importance of prioritising interoperability, transparency, and workflow-aligned design over analytical sophistication. Long-term adoption depends on co-design with end users and realistic articulation of implementation effort and organisational change implications, particularly in complex, resource-constrained healthcare environments (Godager et al., 2024). Overstating system capabilities without addressing integration and usability challenges may further contribute to unmet expectations and stalled adoption.

For researchers, priorities include shifting attention towards longitudinal studies of sustained operational outcomes beyond pilot phases (Deng et al., 2021; Hakimi et al., 2024), alongside comparative evaluation of semantic modelling and data integration approaches (Chatsuwan et al., 2025), and analysis of procurement and governance models that support information continuity across capital and operational phases (Ebiloma et al., 2025; Schmitter et al., 2024). Greater emphasis is also needed on documenting unsuccessful or discontinued implementations to provide a more balanced evidence based and improve understanding of adoption barriers in practice.

Across stakeholders, a consistent conclusion emerges: Digital Twins are socio-technical systems whose value depends on governance, data stewardship, and human capability. Incremental deployment, realistic expectations, and sustained organisational commitment are therefore essential to avoid superficial adoption and realise durable operational benefits in public healthcare facility management.

8 Limitations and future research

This review has several limitations that shape the scope of its findings and identify clear priorities for future research. First, the evidence base is dominated by academic literature, pilot projects, and illustrative case studies, with limited insight into routine, long-term Digital Twin use in operational public healthcare estates. A publication bias towards successful or innovative implementations is evident, while stalled or abandoned initiatives remain underreported. As a result, feasibility may be overstated and organisational failure modes underexplored. Future research should explicitly examine unsuccessful implementations to better understand why BIM-to-Digital Twin transitions frequently stall in practice.

Second, the focus on public healthcare facility management limits direct transferability to other sectors. Healthcare estates are characterised by continuous operation, high regulatory burden, and critical system dependencies that may intensify governance and capability challenges. Comparative studies across public and private sectors would help distinguish context-specific barriers from more generalisable socio-technical conditions of Digital Twin adoption.

Third, the geographic concentration of existing empirical evidence constrains understanding of how institutional and regulatory contexts shape adoption trajectories. Much of the detailed literature originates from Northern Europe and East Asia, while robust studies grounded in UK National Health Service contexts remain scarce despite the NHS’s scale and policy significance. Comparative international research examining procurement regimes, information governance, and professional cultures would therefore add substantial value.

Fourth, the staged adoption framework proposed in this paper has not been empirically validated. Although grounded in synthesis of existing theory and evidence, its applicability across different organisational scales, asset types, and resource conditions remains untested. Longitudinal case studies and action research applying the framework in live healthcare estates are needed to assess its practical utility, identify limitations, and refine its stages.

Finally, this review has focused primarily on supply-side factors; technical architectures, governance arrangements, and organisational capability while giving less attention to demand-side dynamics such as user acceptance, perceived usefulness, and alignment with everyday decision-making. Future research employing user-centred methods, including ethnographic and longitudinal studies, would provide deeper insight into why some Digital Twins become embedded in practice while others remain marginal.

Across these areas, a critical gap concerns long-term sustainability. Most existing studies focus on initial deployment, with limited evidence on how Digital Twin capabilities are maintained, governed, and adapted over time. Research examining data stewardship, technology obsolescence, and organisational continuity is essential to understanding the true costs and commitments required for sustained operational value.

9 Conclusion

This paper has examined why the transition from Building Information Modelling to Digital Twins in public healthcare facility management has not translated into routine operational practice despite sustained technological optimism. The analysis shows that stalled adoption is not primarily a consequence of technical immaturity, but a structurally produced outcome shaped by organisational, governance, and capability constraints. While challenges related to interoperability, semantic modelling, and real-time data integration remain significant, they are more accurately understood as manifestations of deeper issues, including procurement misalignment, weak information governance, skills deficits, and limited engagement with facility management requirements during capital project delivery.

The paper makes three contributions. First, it consolidates fragmented evidence into a coherent socio-technical explanation of why BIM-enabled Digital Twin initiatives frequently stall in public healthcare contexts. Second, it introduces a staged adoption framework that repositions BIM as a necessary but insufficient foundation, explicitly linking progression to data readiness, governance alignment, and organisational capability rather than technical maturity alone. Third, it translates this framework into targeted implications for policy, professional practice, and research, challenging technology-centric narratives that continue to dominate Digital Twin discourse.

A central argument is that Digital Twins should be conceptualised as organisational capabilities rather than discrete technological artefacts. Their value depends on sustained data stewardship, clearly defined governance arrangements, and alignment with everyday operational workflows as much as on analytical sophistication. Framing Digital Twin adoption in this way shifts attention from technology procurement towards organisational transformation and long-term capability building.

For public healthcare organisations, the implications are straightforward: begin with operational priorities rather than technological ambition, invest early in information management and governance foundations, involve facility management stakeholders from project inception, and pursue staged, use-case-driven implementation that demonstrates value while building confidence and competence. Digital Twins offer genuine potential to improve operational performance and decision-making, but realising this potential requires realistic expectations and sustained organisational commitment.

Future progress in the field depends on moving beyond proof-of-concept demonstrations towards rigorous evaluation of operational outcomes in routine practice. Researchers, policymakers, and practitioners must focus less on what Digital Twin technologies can theoretically enable and more on what is organisationally feasible, economically justified, and operationally sustainable. Only through such coordinated, evidence-informed action can the persistent gap between technological potential and operational reality in public healthcare facility management be effectively addressed.

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