James Maitland has spent his career at the intersection of robotics, IoT, and healthcare operations, witnessing firsthand how fragmented technology can hinder even the most skilled medical teams. He understands that the “operational backbone” of a hospital is just as critical as the clinical tools used at the bedside. In this conversation, we explore the transition from manual, siloed systems to integrated platforms like EpicOps and its Teamwork module. Maitland provides deep insights into how unifying financial, clinical, and operational data can alleviate clinician burnout, optimize physical space, and ultimately provide a smoother journey for the patient.
The discussion covers the shift from traditional tools like Excel and paper to rule-based scheduling, the impact of real-time data on nursing and on-call assignments, and the strategic advantages of a healthcare-specific ERP. We also dive into the future of supply chain management and how generative AI is being woven into these operational workflows.
Transitioning from manual methods like Excel spreadsheets to integrated, rule-based scheduling systems can be a massive cultural and operational shift for a health system. How do you see these automated patterns changing the daily lives of the people who manage these complex rotations?
Moving away from “plain old paper” and disorganized Excel spreadsheets is like finally clearing a thick fog that has hung over hospital administration for decades. When a system like Teamwork is implemented, the sheer friction of managing a workforce evaporates because you are no longer fighting with disparate methods across different departments. I’ve seen administrators spend endless hours cross-referencing emails just to fill a single shift, but with rule-based templates, that administrative burden is slashed by as much as 75%. It changes the “source of truth” from a static file on someone’s desktop to a living, breathing system that everyone can see and trust. This doesn’t just save time; it reduces the soul-crushing burnout that comes from constant scheduling conflicts and manual errors. Clinicians feel a sense of relief when they can simply look at one system for their day-to-day work and their schedule, knowing that the “shift marketplace” allows them to sign up for work electronically rather than playing phone tag.
One of the most significant pain points in a hospital is the “lag time” between a provider becoming available and that change reflecting on the on-call schedule. How does a natively integrated system bridge this gap to ensure patients receive care faster?
In the high-stakes environment of a hospital, every minute of lag time is a minute where a patient might be waiting in discomfort or even danger. By having the scheduling module live within the same system that runs patient care, the update happens in true real-time, which is a game-changer for care coordinators and nurses. Imagine a critical test result hitting the system; instead of a nurse scrambling to find out who is actually on call, they can see the assignment immediately and use Secure Chat to reach the right person. This integration extends to patient appointments as well through tools like Cadence, where a provider picking up a clinic shift automatically triggers the opening of new appointment slots. The “Fast Pass” feature then leaps into action, offering those fresh slots to patients on a waiting list who are desperate to be seen sooner. It turns a clunky, manual process into a fluid, automated cycle that prioritizes the patient’s time and the clinician’s availability.
Nursing departments often struggle with predicting staffing needs based on patient complexity rather than just headcounts. How does the ability to look two weeks into the future change the way a unit handles potential shortages?
The beauty of a sophisticated system is that it doesn’t just look at how many beds are filled; it looks at the actual complexity of the care those patients require over a two-week horizon. This foresight allows a unit manager to see a looming crisis before it happens, flagging exactly when a specific floor will need more hands on deck to maintain safety standards. If a caregiver calls in sick at the last minute, the real-time nature of the platform allows for immediate reassignment, ensuring that no patient is left without a primary contact. We saw this implemented successfully with Dubai Health, the first system outside the U.S. to adopt this tech, proving that this need for streamlined, sophisticated nursing logic is a global challenge. It moves the conversation from reactive “firefighting” to proactive resource management, which creates a much calmer and more predictable environment for the nursing staff.
With five organizations currently live and eleven more in the process of installing this technology, what have we learned about the actual timeline and effort required to roll out such a massive operational change?
A full-scale rollout across an entire health system is a marathon, typically taking between six to nine months, but the “wave” approach makes it manageable. We’ve seen that the first staffing group can be up and running in as little as three to four months, which provides an early win and helps build momentum and trust among the staff. Epic works with small sets of early adopters to refine the process, and now they can scale up to 50 staffing groups of providers in that same short three-to-four-month window. It’s not just about installing software; it’s about a cultural transition where the staff learns to rely on automated coverage views and templates. The organizations that have gone live, like Parkview Health and University of Iowa Health Care, show us that while the initial setup requires focus, the long-term cost savings and efficiency gains make the 270-day journey well worth the effort.
Beyond just people, a hospital’s physical space is one of its most expensive assets. How can room tracking and capacity data help a system grow without the need for new construction?
Many health systems are sitting on “hidden” capacity simply because they don’t have a clear window into how their exam rooms are being used hour by hour. By activating room tracking functionality, a busy specialty clinic can see exactly where the unused gaps are and allocate those rooms to the providers who have the highest patient demand. This allows a hospital to onboard new doctors and increase patient access using the square footage they already pay for, which is a massive win for the bottom line. It effectively allows leadership to offload the staggering capital expenses associated with building or renting more space, which can run into the millions. When you match patient throughput with actual room availability in real-time, you aren’t just saving money—you’re ensuring that a patient isn’t sitting in a waiting room while an exam room stands empty three doors down.
The roadmap for this technology includes a healthcare-specific ERP that tackles supply chains and financial operations. How will connecting surgical schedules to supply predictions prevent the waste we see in traditional systems?
The traditional ERP model is often a “one size fits all” tool where only a tiny fraction of the focus is on healthcare, but a dedicated system flips that ratio entirely. By natively integrating with upcoming surgery schedules, the system doesn’t just guess what supplies will be needed based on last month’s trends; it knows exactly what is required for the procedures on the books for next week. This prevents the heart-sinking moment when a surgery is delayed because a specific kit or implant isn’t in stock, while simultaneously reducing the waste of overstocking items that might expire. In the future, this “EpicOps” vision will allow leaders to look at a procedure and see the full picture—not just the cost of the gauze and the surgeon’s time, but the clinical outcome. They will be able to see if spending a bit more on a specific supply leads to a patient going home a day earlier, which is the ultimate goal of value-based care.
What is your forecast for the role of Generative AI in hospital operations over the next five years?
I expect Generative AI to move from a “experimental” phase to being the invisible engine that powers every operational decision within the hospital ecosystem. We are already seeing systems like Parkview Health moving “fast and furious” to deploy these features, using AI to pull insights from a single, unified data source rather than trying to bridge information from disconnected silos. Within five years, AI won’t just be suggesting schedules; it will be predicting patient census with uncanny accuracy and automatically adjusting staffing levels before a human even realizes there’s a trend. We will see AI-driven “cost accounting” that can surface deep financial insights in seconds, helping thin operating margins stay healthy without compromising care quality. Ultimately, the “source of truth” will become an intelligent partner that anticipates shortages and bottlenecks, allowing the medical staff to return their full attention to the human being lying in the hospital bed.
