I’m thrilled to sit down with Faisal Zain, a leading healthcare expert with deep expertise in medical technology. With years of experience in the manufacturing of diagnostic and treatment devices, Faisal has been at the forefront of innovation, helping shape the future of healthcare delivery. Today, we’ll dive into how cutting-edge solutions like AI are transforming the industry, particularly in easing the overwhelming administrative load on care teams. Our conversation will explore the potential of autonomous AI systems, their impact on clinician burnout, practical applications in everyday workflows, and the delicate balance between automation and the human touch in patient care.
Can you explain what ‘agentic AI’ is and how it stands apart from other AI tools like chatbots in healthcare settings?
Agentic AI is a game-changer because it goes beyond just responding to prompts or summarizing data, which is what most chatbots or language models do. It’s designed to act independently, pursuing specific goals without needing constant human oversight. In healthcare, this means it can handle multi-step processes—like pulling patient data, cross-referencing insurance policies, and submitting forms—all on its own. Unlike a chatbot that might just answer a patient’s question, agentic AI can execute complex workflows, making decisions based on logic and context, which saves clinicians a tremendous amount of time on repetitive tasks.
What are some of the biggest administrative challenges healthcare workers face daily that contribute to burnout?
Burnout in healthcare often stems from the sheer volume of non-clinical tasks that pile up. Doctors and nurses spend hours on things like filling out intake forms, chasing down insurance pre-authorizations, and documenting post-visit follow-ups. These tasks are necessary but incredibly time-consuming, and they pull clinicians away from what they’re trained to do—care for patients. When you’re stuck on paperwork or navigating insurance red tape, you have less emotional and physical energy to connect with patients, which is often the most rewarding part of the job. That disconnect is a huge driver of exhaustion.
How does agentic AI help save significant time, like the 70 minutes per patient visit mentioned in some studies, and what does that mean for care teams?
The 70 minutes per visit figure comes from automating structured, repetitive processes that don’t require clinical judgment. Things like scheduling, insurance verification, and even drafting initial documentation can be handled by AI agents. For instance, an AI can pull relevant patient history, match it against insurance criteria, and prepare a pre-authorization request in minutes, tasks that might otherwise take a clinician or staff member much longer. This time savings translates directly to better patient care—doctors can focus on listening to patients, addressing their concerns, and building trust rather than being bogged down by clerical work.
Can you walk us through how AI streamlines something specific like insurance pre-authorizations, which seem to be a major pain point for clinicians?
Absolutely. Insurance pre-authorizations are a nightmare because they involve multiple steps and lots of back-and-forth. An AI agent can take over by first accessing secure patient records and insurance details through integrated systems. Then, it cross-checks the treatment plan against eligibility rules and submits the request directly to the insurer. It can even follow up if there’s a delay or missing information. This cuts down on the hours staff spend on hold with insurance companies or hunting down paperwork, reducing stress and letting them focus on supporting patients instead of navigating bureaucracy.
How does AI assist with follow-up care, such as post-procedure check-ins for routine surgeries, and what does that process look like?
For routine surgeries like cataract removal or endoscopies, AI can manage post-procedure check-ins by sending tailored reminders to patients about things like taking medication or following dietary restrictions. It uses the patient’s profile and procedure details to personalize messages. At the same time, it monitors responses for red flags—like reports of unusual pain or fever—and escalates those to a clinician for review. This setup ensures patients get consistent follow-up without overloading staff, while still keeping human oversight for anything outside the norm.
Why do you think AI falls short in situations that require deep empathy, such as supporting patients after a terminal diagnosis?
AI, no matter how advanced, can’t replicate the human connection that’s so vital in emotionally charged moments like delivering a terminal diagnosis. Patients need to feel seen and understood, and that requires a nurse or doctor who can read subtle cues, offer comfort, and navigate grief in a deeply personal way. AI lacks the emotional intelligence to handle such nuanced interactions. However, it can still play a supporting role by managing logistics—like scheduling counseling sessions or pulling relevant resources—so clinicians have more space to focus on the human side of care.
What steps do you believe are essential to build trust in AI systems among healthcare workers who might be skeptical about automation?
Trust starts with transparency. Clinicians need to know exactly what the AI is doing, how it’s making decisions, and what data it’s using. For example, showing a clear audit trail of every action—whether it’s submitting a form or flagging a patient response—helps staff feel confident that the system isn’t a black box. It’s also critical to give them control to step in and override decisions when necessary. When care teams see AI as a collaborative tool that works with them, not around them, they’re more likely to embrace it. Training and open dialogue about limitations are key to building that confidence.
What is your forecast for the role of AI in healthcare over the next decade, especially in balancing administrative efficiency with patient-centered care?
I’m optimistic that over the next ten years, AI will become an integral part of healthcare, particularly in offloading administrative burdens. We’ll see even smarter systems that can anticipate needs, streamline workflows, and free up clinicians to focus on patient relationships. But the challenge will be ensuring that this efficiency doesn’t come at the cost of the human touch. I believe we’ll find a sweet spot where AI handles the routine and repetitive, while empowering healthcare workers to prioritize empathy and complex decision-making. The key will be thoughtful implementation—designing systems that enhance, rather than replace, the personal connection at the heart of medicine.
