With a deep background in pioneering medical technologies for both diagnostics and treatment, Faisal Zain offers a unique perspective on the intersection of health care and innovation. Following a landmark J.P. Morgan Healthcare Conference, where over 150 health systems converged, he joins us to unpack the transformative trends shaping the future of patient care. Our conversation will explore the leap from administrative AI to life-saving clinical applications, the rise of autonomous “digital coworkers” to address workforce shortages, the strategic expansion of care into patients’ homes, and the complex decisions leaders face when choosing between mergers and non-traditional partnerships.
Health systems are using AI to detect conditions like pancreatic cancer years earlier and to predict treatment efficacy. What are the key steps for a hospital to move beyond administrative AI and deploy these advanced clinical tools? Could you share some metrics for measuring their success?
The most crucial first step is a fundamental shift in perspective. Leadership must stop viewing AI as merely a tool for optimizing billing or scheduling and start seeing it as a core clinical asset, a true partner in patient care. This means actively seeking out and investing in proven, specialized AI solutions. Look at what Mayo Clinic has accomplished; they developed a tool that can spot pancreatic cancer up to three years earlier than the human eye. That’s not an incremental improvement; it’s a paradigm shift. Success isn’t measured in saved administrative hours anymore. It’s measured in lives. For example, Tampa General Hospital deployed AI to flag early signs of infection, and they saw a 68% drop in their 48-hour sepsis mortality rate. That is an astonishing, undeniable metric of success. Similarly, Advocate Health is measuring success by tracking a reduction in patient falls after installing AI-enabled sensors in rooms, a direct improvement in patient safety.
With a projected global shortage of 11 million health professionals, some experts see autonomous “agentic AI” as digital coworkers. What specific, high-impact tasks could these systems realistically take over in the next few years? Please provide a detailed example of how this would change a clinician’s daily workflow.
This is one of the most exciting frontiers, and I agree with the sentiment that we’re on the cusp of a breakout year for agentic AI. These systems won’t be replacing clinicians; they will be augmenting them, acting as tireless digital partners to handle the overwhelming data and administrative load. Imagine a nurse on a busy ward. Today, they spend an enormous amount of time manually reviewing charts, cross-referencing lab results, and monitoring vital signs across a dozen patients. An agentic AI, integrated into the EMR, could autonomously monitor that data in real-time for an entire floor. It could identify a patient whose vitals are trending toward a septic event, cross-reference their genetic information to predict a poor reaction to a standard medication, and then synthesize this into a single, urgent alert for the nurse. The clinician’s workflow is transformed from reactive data-hunting to proactive, informed decision-making. Instead of searching for the problem, they are presented with a prioritized, data-backed call to action, freeing them to focus entirely on direct patient intervention and human connection.
The demand for home-based and virtual treatment is clearly growing, with expansions in both hospital-at-home programs and 24/7 virtual urgent care. What are the main financial and logistical hurdles for a health system when shifting services from hospital settings to patients’ homes?
The shift to home-based care is a powerful trend, but it presents significant challenges that go far beyond just technology. Financially, the biggest hurdle is the reimbursement model. Traditional fee-for-service models are built around the physical hospital infrastructure. Reconfiguring payment structures with insurers to adequately cover the complex logistics, staffing, and technology required for high-acuity home care is a massive undertaking. Logistically, you’re essentially creating hundreds of single-bed, remote hospital rooms. This requires a robust supply chain for medical equipment and medications, a mobile workforce of skilled nurses, and foolproof, secure telehealth platforms. It’s a fundamental reimagining of care delivery. Systems like ChristianaCare, which are committed to this shift, aren’t just launching an app; they are re-architecting their entire operational and financial frameworks to support this new, decentralized model of care.
We’re seeing major provider mergers achieve significant savings and improved safety ratings, alongside partnerships with non-traditional companies for outpatient services. How should a health system leader decide between merging with a peer versus partnering with a tech or retail company to expand care? What are the trade-offs?
That decision really comes down to a fundamental question of strategy: are you looking to achieve scale and internal efficiency, or are you seeking to rapidly expand your market footprint and access new patient populations? A merger, like the one that created Advocate Health, is about consolidation. The goal is to build a larger, more resilient non-profit system, integrate clinical services, and leverage that massive scale to achieve huge operating savings—they found $1.5 billion. The trade-off is a long, complex integration process. Partnering with a non-traditional player like Amazon One Medical, as Hackensack Meridian Health is doing, is about speed and market access. You gain an instant, trusted brand presence in the primary care space and can stand up 20 new clinics relatively quickly. The trade-off here is a potential loss of control and the need to manage a partnership with an organization that has a very different culture and business model. One path builds a fortress; the other builds a network of agile outposts.
What is your forecast for the adoption of AI in direct patient care over the next five years?
Over the next five years, I foresee the adoption of AI in direct patient care moving from the experimental phase in elite academic centers to standard practice in community hospitals across the country. We will see AI become an invisible, indispensable layer of the clinical workflow. It won’t be about a flashy new “AI tool” but about the diagnostic and predictive insights being seamlessly embedded within the EMR, the imaging software, and even handheld devices like AI-powered stethoscopes. The focus will shift from AI identifying anomalies to AI predicting them—forecasting a patient’s likely response to a specific cancer therapy or flagging a high-risk pregnancy before symptoms ever appear. This transition will be driven not just by the technology’s power, but by the urgent, undeniable need to address the health care workforce shortage, making AI an essential partner in delivering safer, more precise, and more proactive care to more people.
