Before Hospitals Hit a Bottleneck, Digital Twins Could Help Them See It Coming

Hospitals rarely get to rehearse a surge in admissions, an equipment breakdown or a change in staffing before dealing with the consequences. Digital twins (DTs) offer hospitals a way to examine those connections before problems escalate. These continuously updated virtual representations can combine operational information with simulation and artificial intelligence (AI) to explore how changes in one service might affect another. Their promise is to give managers more room to anticipate pressure and compare responses before committing scarce resources.

The research paper "Digital Twins for Hospital and Healthcare Operations: A Systematic Review of Resource Allocation, Infection Control, and Workflow Optimization", published in Bioengineering, examines this emerging approach. Authors Nesma Abd El-Mawla, Mohamed Shehata and Mostafa A. Elhosseini review applications spanning resource allocation, infection control and workflow management, alongside the barriers that could prevent promising systems from becoming dependable hospital infrastructure.

They primarily argue that digital twins could help hospitals move from reacting to disruption towards planning for it. However, the review also describes fragmented implementations, difficult data integration and unresolved governance concerns.

A Hospital's Hidden Capacity Lies in Its Connections

The review defines a healthcare DT as a dynamic virtual representation of a patient, device or facility, maintained through continuing data exchange with its physical counterpart. Unlike a static model, it is intended to remain aligned with changing conditions, supporting monitoring, prediction and operational simulation.

At hospital level, the authors describe an architecture connecting individual resources, departments and wider healthcare networks. Data about patients, personnel and equipment feed models of healthcare units, while a broader coordinating layer can link facilities. The ambition is to make interdependencies visible rather than leaving each department to optimise its own activities in isolation.

The difference directly affects how hospitals operate. For instance, a scheduling decision that improves equipment utilisation may also affect waiting areas, staffing needs and the availability of beds. Models capable of examining these relationships could help managers compare competing priorities and identify where an apparently local improvement might create pressure elsewhere.

The authors identify fragmentation as a major weakness in existing implementations. Many applications concern a single task, such as MRI scheduling or patient monitoring, rather than a connected representation of the whole facility. They also describe a need to progress beyond displaying conditions towards evaluating possible actions and their consequences.

The review reports including 70 publications from 2021–2026 following a Scopus search conducted on 15 May 2026. Its evidence combines different research designs and publication types, including simulations, engineering implementations and reviews. This breadth helps identify applications and recurring obstacles, but does not establish a single model that hospitals can adopt with predictable results.

Smarter Scheduling Could Turn Information Into Usable Capacity

Workflow optimisation is among the clearest applications discussed. The review describes research combining digital twins with reinforcement learning, an artificial-intelligence approach that learns decision strategies through repeated interaction with a modelled environment. In MRI scheduling, such systems are intended to improve resource utilisation while reducing waiting times.

The broader opportunity is to make allocation responsive to changing conditions. Fixed schedules can struggle when demand fluctuates or equipment becomes unavailable. A model that incorporates updated operational information could help explore alternative arrangements, allowing managers to assess choices before introducing them into a functioning service.

Emergency departments present another coordination challenge. The authors discuss cloud-based digital twins and connected devices as tools for monitoring resources and supporting patient allocation. They also examine proposals for networks that share information between hospitals, potentially improving the distribution of demand during pandemics or other periods of exceptional pressure.

The review cites reported reductions of 20–40% in emergency-department waiting times and improvements of 10–20% in patient throughput. These are illustrative performance claims drawn from cited literature, not pooled estimates across the 70 publications. They should therefore be understood as indications of potential within particular applications, rather than expected gains for any hospital adopting the technology.

For hospital management, the consequential question is whether better information produces action that staff and infrastructure can support. Predicting demand can help expose a resource mismatch, but addressing it still requires operational decisions. The review's emphasis on interoperability and organisational integration recognises that analytical capability must connect with the people and processes responsible for delivery.

Infection Control Makes Efficiency a More Demanding Goal

The paper places infection control alongside scheduling and resource allocation, highlighting how hospital performance extends beyond the number of patients moving through services. Room assignments, patient movement, ventilation and preventive practices all influence the conditions under which care is delivered. A useful operational model must account for these relationships.

The authors discuss computer vision for monitoring hygiene and protective-equipment practices, simulation of pathogen spread, and approaches to matching patients with rooms while considering infection risks. These applications could give hospitals additional ways to identify problems and evaluate preventive arrangements before making changes.

The analytical distinction is between identifying a risk and demonstrating that an intervention reduces harm. The review presents a range of technical approaches, but does not provide a pooled estimate of reductions in healthcare-associated infections. Its infection-control discussion consequently offers an agenda for implementation and evaluation, rather than a uniform prediction of clinical benefit.

Building management adds another dimension. The review examines the integration of digital twins with building information modelling and connected sensors to support ventilation, heating and energy management. These systems could help facilities consider environmental conditions alongside operational demand, bringing infrastructure decisions into the same planning framework as patient services.

The resulting challenge is to define efficiency carefully. Lower energy use, faster throughput and appropriate infection-control conditions cannot simply be treated as interchangeable achievements. The authors' framework connects service benefits with human concerns, encouraging assessment of whether operational improvements remain consistent with patient safety, rights and responsible management.

The Real Test Is Whether Hospitals Can Trust the Replica

A digital twin depends on information collected through systems that may not communicate easily. The review repeatedly identifies barriers integrating electronic health records, medical devices and building-management platforms. Reliable synchronization is essential because decisions lose value when the virtual representation no longer reflects the facility's actual condition.

Privacy and cybersecurity become equally important as more information is connected. The authors discuss blockchain-based approaches and federated learning as possible directions for secure collaboration. However, they also identify continuing integration and network-security challenges, making these technologies subjects for further development rather than complete answers to institutional risk.

Organisational accountability must develop alongside technical capability. The review draws attention to patient choice, societal concerns and responsibility for increasingly autonomous systems. For hospital leaders, this implies a need to establish how recommendations are assessed, who authorises operational changes and how professional judgement remains part of decision-making.

The research has limitations that temper its broad conclusions. Its search was restricted to Scopus and English-language publications, while initial selection, extraction and appraisal were performed by one reviewer before team verification. The authors used a structured assessment informed by established appraisal principles, but did not undertake a quantitative meta-analysis or prospectively register the review.

For resource-constrained health systems, the findings suggest a practical priority: connect investment to a defined operational need and the capacity to maintain the supporting data. The paper's concerns about interoperability, sustainability and inclusivity imply that financing software without addressing organisational readiness could leave hospitals with additional complexity rather than lasting improvements.

Future progress will depend on evaluations that connect technical performance with hospital outcomes. The authors highlight waiting times, bed utilisation, length of stay, infection-control indicators and resource efficiency as relevant measures. These provide a stronger basis for judging progress than the sophistication of a visual model alone.