This article is sponsored by Brightree. As HME providers face growing operational strain and rising expectations around patient access, many are looking to AI to strengthen calling operations without losing the human element. Brightree is helping providers rethink what that balance can look like when using AI to reduce friction, extend team capacity and support better patient experiences at scale. In this Voices interview, Justin Lewis, Senior Director of Voice Services at Brightree, shares how HME leaders can approach AI with more clarity, build trust with both patients and staff, and position their organizations to succeed in 2026.
HME Business: What core experience shaped the way you view the home medical equipment space today?
Justin Lewis: My perspective has really been shaped by years spent leading call center operations, especially around resupply programs. When you’re close to patient interactions every day, it becomes clear very quickly that the call center is not just a back-office function. For many patients, it is the HME experience.
That means operational friction has real consequences. Missed calls, long hold times and inconsistent answers are not just service issues. A delayed reorder can directly affect someone’s health. When you see that up close, it changes how you think about scale, reliability and accountability across the entire operation.
Working closely with frontline agents also made something else very clear. Asking people to manage high call volumes, repetitive work, and complex emotional situations every day is not sustainable in the long term. That’s when it became obvious to me that operational excellence is not just about efficiency metrics. It’s about building systems that work better for patients and for employees.
That experience has really shaped how I think about virtual agents and automation in the HME space. If it’s implemented the right way, technology does not replace human care. It protects it by removing friction and allowing people to focus on judgment, empathy and trust. In this industry, success is built in those everyday interactions, and you have to get that balance right every time.
What are the biggest misconceptions HME leaders have about AI in calling operations, especially the fear that it replaces staff, and how should executives reframe it as a way to extend the team while reducing operational strain?
One of the biggest misconceptions is that AI is about replacing staff. I think that framing is backwards. AI is not a headcount strategy. It’s a capacity and sustainability strategy.
Most calling operations are already under pressure from high inbound demand, repetitive resupply calls, after-hours volume and increasingly complex patient needs. AI does not remove the need for people. It changes how they spend their time. The best way to think about it is as a digital teammate or an extension of the team. Virtual agents can handle predictable, high-volume interactions like order status questions and other routine requests, especially during peak periods or outside business hours.
That gives live agents more space to focus on the conversations where human judgment, empathy and problem-solving really matter. The better question is not how many agents AI can replace. It’s how AI can reduce burnout and help teams deliver better outcomes. When you frame it that way, AI becomes an enabler, not a threat.
How should providers think about trust, both from patients and employees, when AI becomes a visible part of the patient experience?
Providers have to think about trust as something designed into the experience instead of something the technology earns on its own. From a patient perspective, trust starts with clarity. People need to understand what the virtual agent can help with, what it cannot do and when a live person is available. When that is clear, AI feels more like support than a barrier.
Trust also depends on reliability. Patients lose confidence quickly when they hit dead ends, get inconsistent answers or have to call back multiple times. But when AI delivers accurate information, completes routine tasks correctly and makes the process easier, it can actually build confidence in both the interaction and the organization.
The last piece is escalation. Patients do not expect AI to handle everything, especially when complexity or emotion enters the conversation. They do expect a smooth handoff when that moment comes. If they can move to a live person without repeating themselves, the experience feels coordinated and supportive. That is when AI stops feeling like technology and starts feeling like good service.
How can leaders involve frontline agents in AI design and rollout so adoption feels empowering rather than imposed, and what happens when they don’t?
Successful adoption starts with inclusion. Frontline agents need to be part of the conversation early. Leaders have to explain what is being implemented, why it matters and how workflows will change, including what AI will take off their plate and what will still require human judgment. That kind of transparency removes much of the uncertainty that creates resistance, because even small decisions can reinforce ownership. We made a point to involve our agents in naming the virtual agent, and that mattered. It showed this was not just technology being imposed on them, but something they were helping shape.
Leaders also need to listen during rollout. Agents will spot friction long before leadership sees it in dashboards or reports. That feedback is not noise. It helps improve workflows, strengthen adoption and creates a better experience for both staff and patients. When teams are excluded, resistance grows quickly. When they are included, AI becomes something they advocate for rather than tolerate.
What does effective collaboration between AI and live agents look like in practice, and how should leaders structure that partnership so AI absorbs friction while humans deliver trust and expertise?
Effective collaboration happens when AI and live agents are each used for what they do best. Virtual agents should handle predictable, high-volume work, while live agents focus on the conversations where judgment, empathy and experience really matter. That reduces friction and creates more operational stability. The goal is not for AI to solve everything. It is for AI to recognize when it should step aside and bring in a human with the right context already in place.
That handoff is key. When a live agent enters with verification complete and a clear summary of the issue, they can move straight to resolution instead of starting over. And it ultimately improves efficiency and strengthens trust because the experience feels coordinated. In turn, leaders need to measure success accordingly. Fewer routine calls reaching live agents does not mean less value. It often means they are handling more complex, higher-impact interactions.
As 24/7 virtual agents become part of the patient journey, how should HME providers measure success beyond call deflection, such as order completion, cycle time, items per order and patient satisfaction?
Call deflection is useful, but it’s really a capacity metric, not the full measure of success. The bigger question is whether the experience is producing the right outcomes. Patient satisfaction has to stay at the center. If patients leave confused, unresolved or forced to call back, deflection alone does not mean much.
Order completion is one of the most important measures. Did the interaction actually lead to a completed order, whether it was handled by AI or finished after escalation? High containment without completion just creates repeat volume. Cycle time matters too, especially after hours when virtual agents can solve problems in the moment instead of making patients wait until the next business day.
Items per order is also a significant metric in resupply because it ties directly to revenue performance and therapy continuity. A well-designed virtual agent should help educate patients, reinforce eligibility and support better ordering decisions—much more than processing volume. When that happens, patients get what they need faster, staff deal with less friction and the operation becomes more predictable overall.
Zooming out, what will separate HME providers that successfully integrate AI into the patient experience in 2026 from those that fall behind?
The providers that will lead in 2026 are the ones starting now. The biggest advantage early adopters gain is not just the technology itself. It’s the learning that comes with it. They get a much clearer view of where friction exists, where patients drop off and how the experience actually feels from the patient’s side. That matters because AI beyond automation is about building a more sustainable operating model while giving patients more flexibility in how they engage. The industry is moving toward a more multimodal experience where patients can interact how, when and where they want, not just through a single required channel.
The other separating factor will be scale. Patient expectations are not getting lower, and demand is not getting simpler. The providers that succeed will use AI now to understand friction, improve the experience and build capacity before the rest of the market is forced to catch up. The ones that wait will have a much harder time doing all of that at once.
Editor’s note: This interview has been edited for length and clarity.
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The Voices Series is a sponsored content program featuring leading executives discussing trends, topics and more shaping their industry in a question-and-answer format. For more information on Voices, please contact [email protected].