Hospital Digital Twin Maps Patients, Staff and Devices in Space and Time

Hospitals generate enormous quantities of data every day, yet most of it arrives fragmented, unstructured and scattered across disconnected systems. Administrative records, laboratory results, imaging archives and device logs rarely speak to one another, and conventional hospital information systems were simply never designed to show where a patient is, which staff members are attending to them, or which equipment is in use at a given moment. A team of engineers and surgeons at TUM University Hospital in Munich has now demonstrated a pragmatic way out of this impasse, using a digital twin framework called OMNI-SYS that models every relevant hospital agent as an object existing in a three-dimensional space of location, time and context.

The study, published in the International Journal of Computer Assisted Radiology and Surgery, is a feasibility experiment rather than a clinical trial, but its implications reach far beyond its modest size. The researchers asked a deceptively simple question: if a hospital information system recorded every interaction among patients, staff and devices as timestamped, spatially anchored, context-rich events, would the resulting data be directly usable for the modern analytical methods that healthcare research increasingly depends on, such as process mining and machine learning? Their answer, based on a simulated pre-operative workflow involving eighteen volunteers, is a qualified yes.

The conceptual foundation comes from earlier work by the same group, which proposed OMNI-SYS as a first step toward an object-oriented hospital information system, abbreviated oHIS. Unlike traditional systems that store data in disconnected relational tables, an oHIS represents patients, nurses, doctors, devices and rooms as objects with properties and relationships that evolve across three dimensions. The term pragmatic is deliberate: the framework captures only the data relevant to the process at hand, which keeps it flexible and scalable, whether information is entered manually, scanned via QR codes, or gathered through advanced techniques such as computer vision. This design choice allows the system to function as a living digital twin, dynamically mapping objects and updating their states in real time.

To test the concept under realistic conditions, the team conducted the study at TUM University Hospital in December 2025. For ethical reasons, they recruited eighteen healthy volunteers instead of real patients, a practice consistent with established healthcare simulation research in which standardized patients are used to analyze patient flow and system usability. The cohort size was chosen deliberately to approximate the maximum number of gastrointestinal surgery patients undergoing pre-operative testing at the local surgical department on a single clinical day. Each volunteer was assigned one of seven common gastrointestinal procedures and followed a predefined pre-operative pathway through real hospital departments, including administrative registration, premedication consultation, electrocardiography, lung function testing, endoscopy, computed tomography and magnetic resonance imaging.

The pathways were designed by an interdisciplinary group of surgeons, data scientists and engineers, following the Surgineering paradigm of close clinical-technical collaboration. Every pathway began and ended at the administrative registration office, which serves in the institution’s real workflow as both the entry and exit point where patients hand over collected documents. While the set of required stations was fixed for each procedure, the order of visits was intentionally varied so that two patients undergoing the same operation could move through the hospital in different sequences, loosely reflecting the variability of genuine clinical settings. Each participant received a personalized schedule, and upon arrival at each station their state was updated in the system.

Data collection relied on a web-based patient tracking interface integrated into OMNI-SYS, which allowed researchers to define pathways, assign patient-specific sequences and monitor progress in real time. Each station carried a QR code that participants scanned with smartphones upon arrival, generating timestamped events against a central server clock. As a redundancy measure, a researcher at each station also recorded arrival times manually, and all timestamps were standardized to one-minute resolution for analysis. The protocol specified that manually collected timestamps would be used whenever the two methods disagreed by more than sixty seconds, though no such discrepancy arose. Over the course of a thirty-minute session, the eighteen participants generated eighty-eight timestamped events, with most completing their pathways within about seven minutes and a median journey time of roughly five minutes.

The analytical core of the study examined whether this object-centric event data could feed directly into contemporary process mining tools. Using the PM4Py library, the researchers constructed a directly-follows graph in which activities corresponded to patient locations and arcs showed transitions with average travel times. The graph captured multiple pathway variants, showing for instance that patients could move from administrative registration to five different locations. Because participants did not undergo actual clinical procedures, activity durations registered as effectively zero, and the model represented prescribed routes rather than spontaneous behavior. Applied to real patients, the authors note, the same approach could capture work-as-done rather than work-as-imagined, a distinction that process mining researchers consider fundamental.

More revealing was the object-centric extension. When the event log was converted to the Object-Centric Event Log format, with patients, devices, nurses and doctors declared as object types, the resulting object-centric directly-follows graph could model the lifecycles of all agent types simultaneously. Patient edges traced movement between stations, while stationary resource objects produced edges and self-loops indicating continuous reuse, such as a device serving successive patients. The graph clearly separated two patterns: patients flowing through the hospital and resources being shared across concurrent cases. Because the pathways were designed so that all procedures passed through administrative registration twice while only one included lung function testing, the framework correctly displayed higher nurse engagement at the former, demonstrating that it captures resource utilization and sharing across overlapping patient journeys.

Network analysis added a further layer of insight. A transition graph built with Python’s NetworkX library revealed a hub-and-spoke structure centered on the triangular core of administrative registration, ECG and premedication stations, where the strongest patient flows occurred. The researchers also quantified patient co-occurrence, defined as multiple patients sharing the same location, time and context within one-minute windows. Administrative registration showed high-frequency co-occurrences concentrated early in the session, while specialized stations like MRI and lung function showed none. The authors are careful to stress that in this simulated setting such patterns illustrate what the data structure could reveal rather than proving congestion, since procedure and waiting times were not recorded. Even so, in a real deployment this kind of measurement could flag locations at risk of bottlenecks before they become visible to staff.

The study’s limitations are candidly acknowledged. The dataset is too small for meaningful machine learning, the pathways were predetermined rather than observed, and any process model fitted to them would risk overfitting, so the process mining results stand as proof of concept rather than validated models. QR-code scanning temporarily failed during parts of the session due to network issues, and manual backups kept collection going, underscoring the need for robust multi-modal tracking in future work. Yet the structural achievement is clear: the object-centric data can be replayed as a scenario within OMNI-SYS, supports conformance checking and resource engagement analysis, and offers the consistent feature definitions that machine learning depends on. The authors argue that following a few hundred real patients would likely suffice for predictive modeling, and that pairing spatiotemporal forecasting with optimization solvers or reinforcement learning could eventually improve staff scheduling, appointment routing and congestion management. As a blueprint for object-centric clinical research in surgery, the framework points toward hospital information systems that no longer fragment reality but mirror it, bridging the persistent gap between clinical routine and research.

Subject of Research: A pragmatic object-centric hospital digital twin that records patient, staff and device interactions across location, time and context to enable process mining and workflow optimization.

Article Title: Expanding research on clinical agent data to a virtual space of location, time, and context: exploration of data structures and limitations using a pragmatic digital twin

Article References: Rashid, S., Sliepkova, K., Bernhard, L., Stabenow, S., Spicker, E., Rinderle-Ma, S., Fottner, J., Wilhelm, D., & Berlet, M. (2026). Expanding research on clinical agent data to a virtual space of location, time, and context: exploration of data structures and limitations using a pragmatic digital twin. International Journal of Computer Assisted Radiology and Surgery. https://doi.org/10.1007/s11548-026-03788-9

Image Credits: AI Generated

DOI: 10.1007/s11548-026-03788-9

Keywords: digital twin, hospital information system, process mining, object-centric data, clinical workflow, pre-operative pathway, patient tracking, health informatics, machine learning, data integration, workflow optimization, OMNI-SYS