New research emerging from Rutgers University signals a transformative leap in the way oncology outpatient clinics manage patient flow and operational efficiency. Utilizing advanced computational simulation techniques, the Rutgers team has decoded the complex dynamics underlying prolonged wait times in a cancer treatment facility and pioneered a model for virtually redesigning clinical workflows. This groundbreaking approach not only slashes hours-long patient waits but also amplifies treatment capacity without necessitating costly expansions in staffing or infrastructure.
The project originated within the Rutgers Cancer Institute’s blood cancer clinic, where patients routinely faced waits stretching up to three hours between initial check-in and starting their infusion treatments. Recognizing the emotional and physical toll this imposed on patients undergoing strenuous cancer regimens, the research team sought a data-driven method to identify and alleviate such bottlenecks. By fusing operational research principles with detailed patient movement logs and time-stamped electronic health records, they constructed a digital twin of the clinic. This three-dimensional, animated simulation accurately recreated the clinic environment and patient journey, enabling exhaustive experimentation in a risk-free virtual arena.
What distinguished this digital twin model from traditional process analyses was its statistical validation against months of real patient data that the simulation itself had never encountered before. This rigorous validation established a high degree of confidence that simulated interventions could reliably forecast real-world impacts. Through this, the research dispensed with initial assumptions—such as adding more nursing staff—to pinpoint fundamental constraints in clinic throughput. For example, it became clear that increasing the number of nurses only marginally reduced visit times, shaving under a minute off average patient stay.
Instead, the study illuminated that the primary choke points were external to staff availability. Delays originated mainly from the lab processing of blood samples—a critical prerequisite to initiating infusions—which was conducted off-site and took approximately ninety minutes. Additionally, the clinic’s unified queue system led to inefficient patient sequencing, such as shorter-duration blood checks being delayed behind patients receiving eight-hour infusions. The simulation underscored that addressing these inefficiencies could lead to dramatic reductions in patient visit times—on the order of 75 to 90 minutes—even amid a 20% increase in patient volume.
In response to these insights, the clinic implemented significant operational changes. Lab processes were relocated on-site, accelerating blood work turnaround from roughly an hour and a half to under thirty minutes. Furthermore, they instituted a “fast track” separate from traditional cancer treatments, designed to streamline supportive care procedures such as transfusions and quick blood tests. Notably, this fast track feature had been available but underutilized in existing scheduling software prior to the study. The resultant workflow enhancements nearly doubled daily infusion patient throughput, scaling from around 50 to approximately 80 without compromising quality or safety.
The success story at Rutgers underscores the transformative power of computational modeling in healthcare settings, particularly those characterized by complex, multi-step patient pathways constrained by limited resources. The research highlights that while conventional wisdom might prompt institutions to augment human resources, a more fruitful approach lies in reengineering systemic bottlenecks through data-driven simulation. Each clinic’s unique layout, staffing, and patient population means that solutions must be tailored rather than transplanted wholesale, but the framework of virtual, simulation-based optimization offers a replicable blueprint.
Andrew Evens, deputy director for clinical services at Rutgers Cancer Institute and senior author of the study, emphasized the emotional and physical complexities cancer patients endure and the corresponding imperative to make their clinical experiences as efficient and compassionate as possible. Evens’ dual expertise—as a physician and holder of an executive MBA—fueled the partnership with Rutgers Business School. This interdisciplinary collaboration integrated clinical knowledge with advanced supply chain management techniques, leading to the project’s success.
Graduate students played a pivotal role, embedded in the clinic to meticulously observe and document patient movements and timing at each stage. These granular data points were combined with electronic health records in probabilistic models to generate detailed patient flow distributions. By analyzing patterns at every juncture—arrival, check-in, lab work, infusion, and check-out—the team constructed a highly granular and dynamic simulation reflective of real operations rather than theoretical approximations.
The validated digital twin empowered planners to test various hypothetical adjustments—such as rescheduling appointments, reallocating staff, or rerouting patient flows—without disrupting actual clinical operations. For instance, simulations revealed that equalizing appointment loads throughout the day rather than frontloading or clustering them reduced peak congestion, thereby smoothing patient experiences. These virtual trial runs eliminated guesswork and enabled strategic, evidence-based decision-making in optimizing clinic function.
With the transition of Rutgers Cancer Institute into the new Jack & Sheryl Morris Cancer Center—a state-of-the-art facility with distinct floors dedicated to blood draws, doctor visits, and infusion treatments—the previously solved workflow puzzles have evolved, presenting fresh operational challenges. Evens anticipates reengaging the business school team to create updated simulation models for this redesigned environment to maintain and improve efficiency gains.
The broader implications of this research resonate across the healthcare landscape, where patient throughput, wait times, and resource allocation remain persistent challenges. By harnessing computational simulations as virtual laboratories for operational experimentation, medical centers can make transformative improvements that enhance patient experience, increase treatment capacity, and potentially reduce costs. While the Rutgers model is site-specific, the underlying principle—that complex healthcare processes can be optimized through data-intensive, simulation-driven analysis—has universal application.
As healthcare systems worldwide grapple with increasing patient volumes and strained resources, this research charts a promising path. Digitally replicating clinical environments and iteratively testing process optimizations prior to implementation offers a proactive, precise, and patient-centric strategy. Rutgers’ success demonstrates that technological innovation combined with interdisciplinary collaboration can unravel even the most entrenched systemic inefficiencies in medical care delivery.
Subject of Research: Not available
Article Title: Enhancing efficiency and workflow in oncology outpatient services through simulation-based optimization
News Publication Date: 26-May-2026
Web References:
– https://link.springer.com/article/10.1007/s10479-026-07181-2
– http://dx.doi.org/10.1007/s10479-026-07181-2
– https://cinj.org/
– https://www.business.rutgers.edu/
– https://www.rwjbh.org/treatment-care/cancer/our-cancer-centers/jack-sheryl-morris-cancer-center/
References: Annals of Operations Research (2026). “Enhancing efficiency and workflow in oncology outpatient services through simulation-based optimization,” DOI: 10.1007/s10479-026-07181-2.
Keywords: Oncology outpatient services, computational simulation, digital twin, workflow optimization, cancer treatment efficiency, blood cancer clinic, operational research, patient flow management, healthcare analytics, infusion therapy throughput, laboratory turnaround time, healthcare process improvement.
