The study, published in Global Surgical Education, the Journal of the Association for Surgical Education, emerged from a problem that will feel familiar to clinicians everywhere. Secure messaging embedded within the electronic medical record has become the standard channel for continuous communication among multidisciplinary teams, replacing pagers and phone calls with a searchable, auditable, and legally compliant record. Yet the same features that make these platforms attractive, their immediacy and their low barrier to sending a message, also make them prone to overuse. At the trauma center in question, investigators identified high secure chat volume and fragmented communication as concrete barriers to efficiency, resident education, and interprofessional collaboration between surgeons and nurses.

The consequences of message overload are not merely anecdotal. A growing body of research has linked electronic health record stress to clinician burnout, and studies of clinical decision support systems have documented how repeated alerts produce a phenomenon known as alert fatigue, in which users become desensitized to notifications and may begin ignoring them. Direct observation studies in emergency departments have shown that interruptions and multitasking are associated with task errors among physicians, while prospective observational research has tied workflow interruptions to increased workload for hospital doctors. For surgical residents, whose training depends on sustained engagement in the operating room and at the bedside, the stakes are particularly high: every ping during an operation or a teaching moment represents a potential disruption of intraoperative learning.

To understand the scale of the problem, the research team, led by Jackson A. Fos of the University of Tennessee Health Science Center College of Medicine in Chattanooga, together with colleagues at Erlanger Health, began with measurement rather than assumption. Baseline data were extracted from the electronic medical record’s secure chat logs over two separate 72-hour audit periods, providing a quantitative portrait of who was messaging whom, and how often, across the trauma surgery service. This audit-driven approach is a hallmark of rigorous quality improvement methodology: rather than relying on impressions of being overwhelmed, the team established an objective numerical baseline against which any intervention could be judged.

The intervention itself was designed by a multidisciplinary quality improvement team that included resident surgeons, advanced practice providers, and nursing leadership, ensuring that the people affected by the workflow shaped its redesign. The core insight was one of triage and routing: not every message directed at a resident needed to reach a resident. During daytime hours, defined as 06:00 to 16:00, nursing communications were rerouted to advanced practice providers rather than to the resident surgeons. The APPs, who are experienced clinicians embedded in the trauma service, would address messages falling within their scope of practice directly, and escalate only unresolved or genuinely resident-level concerns to the appropriate provider. This created a filtering layer that preserved the clinical content of communication while shielding trainees from routine traffic.

Rerouting messages was only one strand of a broader communication restructuring. The intervention package also included structured handoffs, which standardize the transfer of patient information between shifts and reduce the need for clarifying messages later in the day. Multidisciplinary rounds were enhanced, bringing nurses, APPs, and residents into the same physical conversation at the bedside so that questions could be answered synchronously rather than through asynchronous chat threads. Finally, the team delivered education on communication expectations, establishing shared norms about what belongs in a secure message, who should receive it, and when an alternative channel is more appropriate. A three-month implementation period was deliberately established before post-intervention audits, allowing the new workflow to bed in and avoiding the trap of measuring a system mid-transition.

The results, measured against the baseline audits, were substantial. Residents demonstrated a 27.7 percent reduction in the average number of messages received per 24-hour period. Nursing-related communications to residents fell by 35.9 percent, and during the targeted daytime window of 06:00 to 16:00, the nursing communications received by residents dropped by 31.3 percent. The authors attribute this daytime improvement to the APP management of routine concerns, enhanced communication during rounds, and improved utilization of the secure messaging platform itself. In other words, the intervention did not simply shift burden silently onto someone else’s shoulders; it changed how the whole team communicated, making the messaging system more purposeful for everyone involved.

What makes these findings resonate beyond a single trauma service is the way they reframe the problem of communication fatigue. Much of the discourse around electronic health records treats message volume as a byproduct of software design, something to be solved by vendors building better interfaces. This study suggests that a significant portion of the burden is organizational rather than technological. The same platform, with the same features, produced dramatically different message loads once the team clarified roles and routing. The intervention required no new software, no capital expenditure, and no change to the medical record itself, only a deliberate redistribution of communication responsibilities that leveraged the existing expertise of advanced practice providers.

The educational implications deserve particular attention. Surgical residency is built on apprenticeship, and the operating room is its most protected classroom. When residents are tethered to a buzzing device, answering routine questions about laboratory values or discharge logistics, the threads of intraoperative teaching unravel. By reducing message volume by more than a quarter, the workflow change returned a measurable slice of cognitive bandwidth to trainees during precisely the hours when most elective and trauma operations take place. The authors emphasize that these findings highlight the impact of communication restructuring on the resident workflow and the educational environment within academic surgical services, positioning message management as a matter of curriculum protection, not just administrative convenience.

There are also lessons here for interprofessional collaboration, an area where poorly designed communication systems can quietly breed friction. Nurses need timely responses to patient concerns; residents need uninterrupted focus; APPs occupy a clinical middle ground that is often underused as a communication hub. By formally recognizing APPs as the first point of contact for daytime nursing communications, the trauma center did not merely offload work, it clarified a professional role and, according to the study’s acknowledgements, the APPs’ willingness to assume an expanded role in frontline communication management was essential to the project’s success. The result was a system in which each message reached the person best positioned to act on it, the definition of communication efficiency in a complex clinical environment.

For hospital leaders watching message volumes climb on their own services, the study offers a replicable template: audit the actual traffic, convene a multidisciplinary team to identify workflow inefficiencies, reroute routine communications to appropriate intermediaries, standardize handoffs and rounds, set explicit communication expectations, and then wait through a genuine implementation period before measuring again. The 27.7 percent overall reduction and the 35.9 percent drop in nursing-related messages achieved in Chattanooga demonstrate that meaningful relief from message fatigue is achievable with modest, well-targeted changes. As secure messaging continues its spread through healthcare, the question is shifting from whether to use these platforms to how to use them well, and this study provides one of the clearest answers yet: less ping, more purpose.

Subject of Research: Reducing secure messaging burden and communication fatigue among surgical residents through workflow restructuring

Article Title: Less ping, more purpose: reducing message fatigue and boosting collaboration through secure chat optimization

Article References: Fos, J. A., Jones, J. M., Mezick, H. M., Rippy, M. G., Nunez, N., Buerster, K. B., Zeringue, A., Holder, L., Pairitz, M., Urevick, A., Cox, E. S., Spain, S., Holladay, J., Giles, W. H., & Bhattacharya, S. D. (2026). Less ping, more purpose: reducing message fatigue and boosting collaboration through secure chat optimization. Global Surgical Education – Journal of the Association for Surgical Education, 5(1), Article 187. https://doi.org/10.1007/s44186-026-00603-6

Image Credits: AI Generated

DOI: 10.1007/s44186-026-00603-6

Keywords: secure messaging, communication fatigue, surgical residency, electronic medical record, advanced practice providers, quality improvement, trauma surgery, interprofessional communication, resident education, workflow optimization, alert fatigue, multidisciplinary rounds

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