Hospital leaders are making some of the largest capital decisions in the public and private sector with tools that were never built for them. Bed counts come from averages. Emergency department expansions are sized from last year’s volumes. Staffing plans live in spreadsheets that can’t show what happens on a Monday morning after a long weekend. Digital twins in healthcare give leadership a way to test those decisions against how the hospital actually behaves before money is committed.
MISIM is a North Vancouver simulation modeling and digital twin consulting firm. Our team of engineers, mathematicians and simulation specialists builds simulation-based digital twins for organizations where a wrong decision is expensive, and healthcare operations is one of the industries the firm serves.
This guide covers where digital twins in healthcare create value, why most attempts stall, and how MISIM approaches the work differently.
Why Are Hospitals Still Planning Capacity With Averages and Spreadsheets?
Hospitals are some of the most variable operating systems in any industry. Arrivals spike by hour and season. Patient acuity changes the length of every stay. A delayed discharge on one ward blocks an admission from the emergency department, which delays an ambulance offload, which pulls paramedics off the road. Every part of the system competes for the same beds, nurses, porters, imaging slots and operating rooms.
Planning tools rarely capture any of that. Most capacity plans rely on average length of stay, average occupancy and average arrival rates. Averages hide queues. A unit that looks 85% occupied on paper can be full for six hours a day and empty overnight, and the six full hours are when patients wait.
The consequences show up in the emergency department first. The Canadian Institute for Health Information reports that 1 in 10 emergency department patients spent more than 14 hours there in 2024–2025, a 28% increase from 2018–2019. In large urban hospitals, the 90th percentile wait for an inpatient bed after the admission decision rose from 27.6 hours to 40.8 hours over the same period. CIHI’s analysis points to pressures across the whole system, including bed availability, patient complexity and delays in discharging patients to other care settings.
That finding matters for anyone planning capital. If the constraint sits outside the emergency department, a larger emergency department will not fix it. A static study rarely catches this, because it looks at one department at a time. A simulation model looks at the whole flow and shows where the real bottleneck sits under real variability.
Three patterns make the planning gap worse:
The constraint moves. On a quiet day the limit might be inpatient beds. During a respiratory surge it might be isolation rooms or respiratory therapists. A plan built around one constraint misses the others.
Departments plan in isolation. Surgical services, emergency, medicine and diagnostics each build their own forecasts, and nobody tests how those plans interact.
Capital moves faster than evidence. Funding windows close, and leaders approve designs before anyone has tested how the building will run at full volume.
What Are Digital Twins in Healthcare, and How Does MISIM Define Them?
The term “digital twin” gets used loosely. Some vendors apply it to a 3D building model. Others use it for a live dashboard or a patient-level physiological model. For operations and capital planning, MISIM uses a more practical definition.
A digital twin in healthcare, as MISIM builds it, is a simulation model that recreates how patients, staff, beds, equipment and information move through a hospital or health system. It uses discrete event simulation to represent each patient as an individual moving through a sequence of steps: triage, assessment, diagnostics, treatment, admission, transfer and discharge. Each step has realistic variability, shared resources and operating rules. Run the model thousands of times and leadership sees a range of outcomes rather than a single average.
Digital twins in healthcare earn their value when they answer “what if” questions that can’t be tested on the real hospital. What happens to boarding hours if 20 beds open on a medicine ward? Does a second CT scanner shorten emergency stays, or does the delay simply move to the next step? How many surgical recovery spaces does a new ambulatory center actually need?
This operational focus also separates MISIM’s work from clinical digital twins in healthcare, which model an individual patient’s physiology to support diagnosis or treatment. Those tools serve clinicians at the bedside. MISIM’s models serve the executives, planners and operations directors who decide how many beds, rooms, staff and services the hospital needs, and how patients should move between them.
The difference between a digital twin and a one-off simulation study is mostly about lifespan and connection to data. A twin stays current with the operation it represents and is reused as new questions come up. MISIM has written about how to scope the right model for the decision in front of you, and that scoping question applies directly to digital twins in healthcare. Some health systems need a focused study on a single expansion. Others need a living model of patient flow that the planning team uses every quarter.
Where Do Digital Twins in Healthcare Deliver the Most Value? Applications MISIM Models
Healthcare decisions with high value at risk, high complexity and high variability are where MISIM’s work fits best. These are the applications where digital twins in healthcare tend to pay for themselves.
Emergency department flow and boarding. MISIM models arrivals by hour and acuity, triage, physician assessment, diagnostics, consults and the admission handoff. Leaders can test fast-track zones, staffing changes and new treatment spaces, and see how much boarding time each option actually removes.
Inpatient bed capacity and discharge timing. A model of inpatient units shows how discharge time of day, weekend discharges and alternate level of care patients affect available beds. Moving discharges earlier in the day often frees more capacity than adding beds, and a digital twin can quantify the difference. MISIM’s approach to capacity planning with simulation carries straight across to bed planning.
Surgical services and operating room scheduling. Operating room schedules interact with pre-op, recovery, ICU and ward beds. MISIM models block schedules, case mix, cancellations and downstream bed demand so surgical leaders can test schedule changes before they publish them.
New facility design and expansion. For a new tower, an ambulatory care center or a redevelopment, MISIM tests the design basis against forecast demand. Room counts, department adjacency, elevator capacity and support services can all be sized against realistic peaks rather than averages.
Workforce and resource planning. Nurses, physicians, porters, cleaners and technologists are shared across units. Digital twins in healthcare show where a staffing shortfall causes the most downstream delay, which helps leaders decide where an extra position buys the most flow.
Diagnostics, labs and support services. Imaging, laboratory turnaround, sterile processing and pharmacy often sit on the critical path for discharge. MISIM includes them where they drive the outcome.
Regional and system-wide planning. For health authorities, MISIM can link several sites, transfers and community care capacity in one model, which reflects CIHI’s finding that emergency pressures often originate elsewhere in the system.
How Does MISIM Build Digital Twins in Healthcare That Leadership Can Trust?
Many simulation models never influence a decision. They look convincing in a demo, but clinical leaders don’t recognize their hospital in the outputs, finance doesn’t trust the assumptions, and the model is shelved. MISIM builds what it calls decision-grade models: models framed around a real decision, tested against data and front-line knowledge, and able to withstand scrutiny from executives and clinicians alike.
MISIM’s engagement process for healthcare follows a consistent path.
MISIM starts with the decision. Before any modeling, the team works with the executive sponsor to define the decision, the options on the table and the value at risk. A scope such as “decide whether the new emergency department needs 48 or 60 treatment spaces” gives the model a clear job, where “model the hospital” leaves it without one.
MISIM builds the smallest credible model. The goal is the smallest model capable of answering the question with confidence. A focused model is faster to build, easier to validate and easier for stakeholders to understand.
MISIM works with the data the hospital already has. Health systems hold large volumes of time-stamped data in electronic health records, bed management systems, ED tracking boards, OR scheduling platforms and workforce systems. The data is rarely clean. MISIM starts with what exists, fills gaps with structured input from clinical and operational staff, and improves data quality as the model develops.
MISIM validates before anyone relies on the results. The model is run against historical periods and compared with actual length of stay, boarding hours, occupancy and throughput. Charge nurses, patient flow managers and physicians review the logic. Validation is where trust is built, and it’s the step most often cut short elsewhere. MISIM has published its view on what makes a simulation model trustworthy, and that standard applies fully in hospitals.
MISIM tests scenarios and translates results. Once validated, the twin is used to compare options, run surge and growth cases, and test sensitivity to assumptions. Outputs come back as probability ranges, bottleneck rankings and clear recommendations, presented in language a board or ministry can act on. Visual animation of patients moving through the hospital helps clinical teams see and challenge the logic.
This approach is how MISIM’s broader consulting work operates in every industry it serves. The vocabulary changes in a hospital setting, and the discipline stays the same.
What Makes MISIM Different From Healthcare Software Vendors?
Health systems evaluating digital twins in healthcare usually meet three kinds of providers: software vendors selling a platform, large consultancies offering transformation programs, and internal analytics teams with general modeling skills. MISIM occupies a different position.
MISIM is a consulting partner first. The firm doesn’t sell a license and leave the hospital to figure out the rest. Every model is custom-built around the client’s decision, layout, patient mix and operating rules. Generic templates rarely reflect how a specific hospital runs, and planners notice.
MISIM brings complex-system experience from outside healthcare. MISIM’s team has spent years modeling integrated value chains for large mining, energy, port and rail operators, where many interacting subsystems compete for shared resources under heavy variability. A hospital has the same structural problem. Patients replace trains and ore, and beds replace stockpiles, but the questions about queues, buffers and shifting constraints are familiar ones.
MISIM’s models are built to be challenged. Assumptions, logic and outputs are documented and tested so they hold up under questioning from physicians, finance teams, boards and funders. That matters in healthcare, where capital requests often go through several levels of approval.
MISIM complements internal teams. Many hospitals have strong decision support and analytics groups. MISIM works alongside them, bringing simulation structure, validation methods and scenario design, and leaves them with a model they can use.
MISIM treats the decision as if its own money were on the line. The aim is a better capital or operating decision for the hospital, and a technically impressive model is only useful if it gets there.
What Benefits Do Digital Twins in Healthcare Deliver When MISIM Leads the Work?
The benefits of digital twins in healthcare depend on whether the model is trusted enough to influence decisions. When it is, health systems typically see gains in several areas.
Better capital decisions. Leaders can size beds, treatment spaces and equipment against realistic peaks and avoid both underbuilding and overbuilding. Testing options before design is frozen is far cheaper than discovering a shortfall after opening day. MISIM covers this in more depth in its work on de-risking capital investment decisions.
Clearer view of the true bottleneck. The twin shows which constraint drives patient delay under different conditions, so effort goes where it changes outcomes.
More defensible business cases. Ministries, boards and donors want evidence. A validated model gives planning teams quantified ranges for wait times, occupancy and throughput under each option.
Shared understanding across departments. When emergency, surgery, medicine and diagnostics leaders look at the same model, conversations shift from competing forecasts to agreed trade-offs.
Measurable patient impact. Because digital twins in healthcare model each patient individually, MISIM can report results in terms clinical leaders care about: hours spent boarding, patients leaving without being seen, surgical cancellations caused by bed shortages, and time to inpatient bed. Those measures make it easier to connect a capital decision to patient experience.
Lower operational risk during change. New workflows, service relocations and phased openings can be tested on the twin first, which reduces the chance of a disruptive go-live.
What Future Trends Does MISIM See for Digital Twins in Healthcare?
Interest in digital twins in healthcare is growing quickly, and several trends will shape how health systems use them over the next few years.
Closer links to live data. As hospitals modernize their data platforms, twins can be refreshed more often and used for short-term planning, such as forecasting bed pressure over the next week, alongside long-range capital work.
System-level models. Health authorities increasingly recognize that emergency waits reflect pressures across acute, long-term and community care. MISIM expects more models that span multiple sites and care settings.
Pairing simulation with AI forecasting. Machine learning can improve demand forecasts, and simulation shows what those forecasts mean for beds, staff and waits. Used together, they give planners both a prediction and a way to test responses to it.
Resilience and surge planning. After recent respiratory seasons and staffing shortages, more leaders want to test how their hospital behaves under stress and how quickly it recovers.
Governance of models as assets. As twins become part of routine planning, health systems need someone to keep them accurate. MISIM’s Model Custodianship service exists for exactly this reason, keeping logic, data and assumptions current as the hospital changes.
MISIM has written more broadly about digital twin technology and how it supports business decisions, and the same principles apply as healthcare adoption matures.
Which Industries and Solutions Does MISIM Support Beyond Healthcare?
Healthcare operations sits alongside the other industries MISIM serves: mining, oil and gas, ports and terminals, rail, and warehousing and logistics. Each involves large, variable systems where shared resources and shifting constraints make static planning unreliable. MISIM’s case studies show how that experience applies across value chains, terminals and processing facilities.
MISIM works directly with owners, operators and engineering teams on major capital and operating decisions globally, from its base in North Vancouver, British Columbia. Its client base includes multinational operators such as BHP, Vale, TotalEnergies and De Beers, and the same decision-grade standard applies to hospital and health authority work.
Beyond building new twins, MISIM offers Model Audits for health systems that already have a simulation model and want an independent review of its assumptions, logic and accuracy before relying on it for a funding request. Model Custodianship keeps an existing model current, and MISIM’s consulting team supports broader planning questions where simulation is one input among several.
Digital Twins in Healthcare with MISIM: FAQs
What does MISIM mean by digital twins in healthcare?
MISIM uses the term to describe a simulation model that recreates how patients, staff, beds and equipment move through a hospital or health system. Each patient is modeled individually with realistic variability, so leaders can test capacity, flow and staffing options and see a range of outcomes before making changes in the real hospital.
Which healthcare decisions does MISIM help hospitals test with a digital twin?
MISIM focuses on decisions with meaningful value at risk: emergency department expansions, inpatient bed capacity, discharge process changes, operating room scheduling, new towers and ambulatory centers, staffing plans, and regional transfer and flow questions across several sites.
What data does MISIM need to build a digital twin for a hospital?
MISIM typically uses time-stamped data from electronic health records, ED tracking systems, bed management tools, OR scheduling platforms and workforce systems. Perfect data is not required. MISIM starts with what the hospital has, fills gaps with clinical and operational input, and improves data quality as the model develops.
How does MISIM validate digital twins in healthcare before leaders rely on them?
MISIM runs the model against historical periods and compares results with actual length of stay, occupancy, boarding hours and throughput. Clinical and patient flow staff review the logic. Only once the model reproduces known performance does MISIM use it to test future options.
How long does a MISIM digital twin project for a hospital take?
Timing depends on the decision and the scope. MISIM builds the smallest credible model capable of answering the question, which keeps focused studies relatively short. Larger system-wide models take longer. MISIM scopes the timeline after an initial conversation about the decision and the available data.
Can MISIM work with our hospital’s internal analytics team?
Yes. MISIM regularly works alongside internal decision support and analytics teams. MISIM brings simulation structure, validation methods and scenario design, while the internal team brings local data knowledge. The hospital keeps a model its own staff understand and can continue to use.
Who owns the digital twin once MISIM delivers it?
Ownership terms are agreed during scoping. Many clients keep the model as an ongoing planning asset, and MISIM’s Model Custodianship service can maintain it so the logic and data stay current as services, layouts and patient volumes change.
Why would a hospital choose MISIM for digital twins in healthcare over a software vendor?
MISIM is a consulting partner focused on decision-grade models built around the hospital’s specific decision. MISIM delivers a validated model, scenario analysis and clear recommendations, backed by experience modeling large, highly variable industrial systems where errors are costly.
Final Thoughts on Digital Twins in Healthcare
Hospitals face the same problem as every complex operation MISIM works with: large decisions, shared resources and heavy variability, planned with tools that rely on averages. Digital twins in healthcare give leaders a way to test beds, departments, schedules and staffing against how the hospital actually behaves, before capital is committed or care is disrupted.
MISIM brings consulting-led, decision-grade simulation to that problem. The firm starts with the decision, validates the model with the people who know the hospital best, and translates the results into evidence a board or ministry can act on.
