Building new tech-enabled care models within a health system

AI and virtual care are the building blocks for the next generation of care models and health systems can lead in this transformation.

I’m often struck by how little the fundamental architecture of care has changed in over a century — we still ask patients to come see a doctor in a clinic when they have a problem, and in hospitals, we still round room to room much as we did a hundred years ago. The Covid pandemic accelerated digital modalities such as telehealth, but the underlying architecture of care delivery — how we organize work, teams, engagement, and follow-up — remains largely the same.

In an earlier post — What if we had to care for twice as many patients? — I posed what I believe is an existential question for healthcare in our country: what would it take to double our capacity for care, without doubling our workforce or costs?

When I look at new capabilities such as AI or virtual care, I always go back to this core question. We can deploy AI models into workflows or increase the number of telehealth encounters, but without re-architecting the underlying system, we risk perpetuating the same legacy inefficiencies, except with new technologies that may actually add to costs.

Nevertheless, these technologies are critical building blocks for the transformations that we need. If we are to upgrade the underlying chassis of care delivery, we have to leverage capabilities such as AI and virtual care as enabling functions for the care models of the future. These technologies are not solutions in themselves; they are materials from which new systems can be built.

Designing Systems for Better Care

Imagine the care models of the future: care would be informed by better, more accurate information; delivered through processes unconstrained by time and place; and experienced by patients who are informed, empowered, and connected.
It would be a learning system that captures experiences from each interaction to improve the next. It would also be scalable — able to expand access and capacity without a proportional rise in cost or burden.

How would we engineer such a system?

We might think of it as having two fundamental layers: an intelligence layer that makes sense of information and guides action, and a delivery layer that executes those actions efficiently across people, time, and space.

AI provides the foundation for the intelligence layer — organizing and interpreting data, surfacing the right information, and supporting decisions in context. Virtual care (coupled with AI) supports a lower cost, scalable delivery layer — the channels, workflows, and human touchpoints through which care can be delivered independent of physical proximity and constraints. Virtual care also provides a more seamless digital medium for AI to be integrated into the care delivery experience.

In person care will continue to be necessary, both as standalone models for higher acuity care delivery and complements to virtual first care. However, the future of care delivery will depend on how seamlessly we integrate in-person and virtual layers into a unified, intelligent system — one that delivers the right level of care, in the right place, at the right time.

We are already beginning to see early examples of this architecture take shape within health systems. At my institution, Stanford Health Care, two new digital health programs — eConsult 2.0 and Stanford Health Care at Home — illustrate how the combination of AI and virtualization can enable entirely new systems of care delivery. Each begins with the following question: how might intelligence and virtualization of care allow us to scale expertise and capacity without scaling cost?

eConsult 2.0 — An AI-Enabled Specialty Consult Service

At Stanford Health Care, our eConsult program has long improved access to specialty expertise. An eConsult is an asynchronous virtual care model where a provider — often a primary care clinician — consults a specialist digitally rather than referring their patient to a specialty clinic. This model enables patients to receive specialist input within days instead of waiting months for a traditional appointment. But the real opportunity lies in reimagining eConsult as a tech enabled specialty care service — one in which AI is built into the architecture of how expertise is delivered, not simply layered on top of it.

Yet scaling this model across a health system has proved challenging. Specialists face high cognitive burden, limited compensation, and outdated consultation templates that required constant manual upkeep. The program’s success has depended not only on clinical expertise but on labor-intensive processes — gathering data, reviewing charts, and drafting responses — that don’t scale linearly with demand.

Our first experiments with AI focused narrowly on chart review, using large language models to surface relevant clinical data and reduce time to completion. The early success of these efforts prompted a deeper question: what if AI could do more than streamline a task? What if it could become a core design element of the care model itself?

That question led to the creation of eConsult 2.0 — a re-architected, AI-enabled clinical service built from the ground up as a tech-enabled system of care, not a digital add-on. Rather than deploying AI just to help with chart review, we began designing around AI as an enabling function—the intelligence layer that supports a continuously learning and scalable specialty consultation service that consists of three layers:

1. Knowledge Base — continuously updated, AI-assisted consultation templates that serve as the shared memory of the service.

2. Application Layer — a “one-stop-shop” interface, integrated into Epic, that uses large language models to summarize relevant chart data for the specialist and draft responses while continuously learning and updating the knowledge base.

3. Workforce Layer — specialists whose expertise is enhanced by AI, allowing more consults in less time and with higher consistency.

The redesign of the eConsult program demonstrates how intelligence can be embedded into the fabric of a clinical service — turning what was once a high-friction workflow into a scalable system for distributing expertise.

Stanford Health Care at Home — Expanding Hospital Capacity Through Virtual Care

If eConsult 2.0 reimagines how expertise is delivered, Stanford Health Care at Home reimagines where hospital care is delivered. The program was born from a pressing organizational need to expand hospital bed capacity, leading to the following question: what if patients who no longer require hospital-level intensity care could continue recovery safely at home, supported virtually by their care team?

In today’s system, inpatient providers often face a binary choice — keep the patient in the hospital, or discharge them into a full ambulatory environment that may not be sufficient to support their immediate post discharge care needs. Patients and providers often describe hospital discharge as “walking off a cliff” — a sudden drop in support that can lead to unnecessarily prolonged hospitalizations or, conversely, premature discharges that result in readmissions and other adverse outcomes.

Stanford Health Care at Home creates a third option: a virtual-first, home-based model that bridges the transition between inpatient and ambulatory care. Patients who are clinically stable but still need close monitoring — for example, those resolving sepsis, restarting medications, or awaiting culture results — can be discharged earlier and managed at home through a hybrid model of virtual physician visits, nursing support, and coordinated follow-up after hospitalization.

This design expands hospital capacity without building new beds. By virtualizing the “tail end” of hospitalization — the final days often defined by low-acuity, high-cost care — it converts fixed inpatient infrastructure into flexible, distributed capacity. Over 350 patients have been enrolled since the program’s launch six months ago, with overwhelmingly positive feedback from patients and families who describe it as a safe, high quality, and patient centered care experience.

A virtual care chassis for deploying AI

After demonstrating the feasibility of this virtual care model in providing high acuity care at home, the next step is to layer intelligence onto its virtual chassis. As the model grows in size and reach, it must find ways to scale care safely without inflating cost or staff burden — a challenge well suited to AI as the intelligence layer that learns from operations, anticipates patient needs, and orchestrates care across settings.

Because the program already operates through digital workflows and virtual touchpoints, it provides a natural substrate for integrating AI — data flows are continuous, interfaces are digital by default, and feedback loops can be instrumented directly into the care process.

In practice, this intelligence layer could take many forms. AI models could continuously synthesize remote monitoring data and patient messages to identify early signs of deterioration or unmet needs, prompting timely virtual interventions. AI agents can support care coordination tasks based on these needs. Generative models could summarize longitudinal data and encounters for the care team, reducing documentation burden while improving situational awareness. Predictive algorithms could help determine which patients are suitable for home-based care and dynamically adjust visit frequency based on evolving risk.

Together, these capabilities furthers Stanford Health Care at Home’s ability to become a re-engineered virtual extension of inpatient care at scale.

Why Health Systems Are Poised to Lead

I have always thought of health systems as the builders, rather than just consumers and implementers, of the tech enabled care delivery products of the future.

Both eConsult 2.0 and Stanford Health Care at Home reveal a deeper truth: the most transformative applications of AI in healthcare are not just technology deployments, but system designs. They require not just the technology, but the infrastructure, workflows, and broader care delivery systems of a health system to make them real. This is why health systems are uniquely positioned to lead. They already possess three key elements — the delivery environment, the clinical expertise, and the trust of patients and providers that enable AI to be developed, tested, and refined to redesign the ecosystems where care happens.

To build these systems, health systems must begin to see themselves not only as providers of care, but as engineers of care models. The next era of innovation will not be defined by who has the most sophisticated AI model, but by who can integrate intelligence into new architectures of care delivery.

Engineering the Care Models of the Future

We might imagine a new discipline: “Care Model Engineering” that fuses delivery science, clinical informatics, and product design into a unified practice. It treats care models the way technology organizations treat products: something that can be designed, tested, and iterated, with clear hypotheses about how structure and process drive outcomes. In this framework, AI becomes part of the design language of healthcare — a material for building systems that are more adaptive, equitable, and scalable.

For health systems, this represents an opportunity to truly bend the cost curve for healthcare by leaning into what they do well. With the right infrastructure — cross-functional product teams and a culture of iterative design that elevate the expertise inherent in all the frontline clinicians and operators who know where care breaks down — health systems can transform from slow adopters into the architects of new models of care delivery.

AI and virtual care are the tools that make reinvention possible. What we build with them — the new systems, the new architectures of care — will determine whether we finally make healthcare scalable, sustainable, and capable of caring for the people who most need it.

More than a century after the birth of modern hospitals and clinics, we find ourselves at a moment to define what the next phase of care delivery should be.