We are facing a significant shortage of healthcare providers.
The total number of staffed hospital beds in the U.S. has decreased by roughly 16% over the last decade, while the population has increased by about 6.2% over the same period. At the same time, many digital health companies have made the problem worse, not by failing to innovate, but by fragmenting care into disconnected point solutions that ask patients to manage their own coordination. Somewhere in the middle of these two crises, a debate has taken hold: Should clinicians lead the future of care, or should AI and technology?
Nowhere is this debate sharper than in cardiometabolic care, where provider shortages collide with the fastest-growing, highest-cost category in the system.
But the real question isn’t who should deliver care; it’s whether the care model consistently delivers better outcomes. And right now, too much of the industry is arguing about inputs while ignoring outcomes.
The question underneath the question
Provider shortages and sporadic care are real and urgent problems, but seeking solutions by adding more clinicians, more tools, and more access points only puts Band-Aids over the wounds. They don’t reduce the demand for care. What’s more, cardiometabolic conditions, specifically, such as obesity, diabetes, and hypertension, are among the most prevalent and costly drivers of health decline, making them the most high-leverage point to address the root causes of chronic illness.
We need to dig deeper into not only why the way we provide care matters, but also ask ourselves: How do we lower the total cost of cardiometabolic care?
Medications like GLP-1s, but critical components like lifestyle and behavior change make all the difference.
Pharmaceutical interventions can break the inertia and create meaningful momentum, but lasting outcomes require sustained behavior change, clinical oversight, and ongoing support. A prescription can start the journey, but it rarely finishes it. Organizations that account for the total cost of care, not just access to care, are the ones that pair every prescription with the staffing and support that make it last, and ensure they’re giving the right care to the right patient at the right time.
Access without that support is not a strategy. It’s a bet. And nowhere is that bet more visible right now than with GLP-1s.
Access is not the same as outcomes
Access to GLP-1s has expanded faster than almost anything else in modern medicine. Alongside employer plans, we’re seeing benefits providers, including Medicare, grant access to certain medications s and GLP-1s to those who need them.
While there’s much good to be done in providing access to these medications, we’re also beginning to see the aftermath of treating a single medication as a silver bullet. Weight regain after discontinuation, nutrient deficiencies that go unmonitored, and patients who received a prescription but never received a plan.
This is the pattern that repeats across digital health more broadly. Fragmentation didn’t happen because the industry built too little in terms of technology. It happened because the industry provided access without building coordination.
More logins are not the same as more outcomes.
A point solution for diabetes, a separate one for mental health, and a separate one for weight management do not add up to a system. It amounts to a patient in many ways becoming the system integrator, coordinating medications and digital tools that haven’t been designed to work together, and doing the work the system was supposed to do for them.
It’s a lot like the same problem streaming created when it broke cable apart. Nobody quite misses the cable package, but somewhere along the way, ‘unbundled’ turned into ‘scattered.’ You end up with five apps, five subscriptions, and the job of remembering which one has what you’re looking for. Health tech is fragmented in the same way. While each solution is convenient on its own, they add up to the patient doing the work the system was supposed to do for them.
Nevertheless, technology isn’t the villain; it’s the path forward. It’s the reason access has expanded faster than ever, with more people reached and more gaps closed. But access to a streamlined solution isn’t the sole goal. It takes thoughtful implementation and the right people behind it for that access to become something that lasts.
The right patient, the right care, the right time
If access alone isn’t the answer, and technology alone isn’t the answer, and clinicians alone aren’t the answer, what is?
We believe the model has to be built around three deceptively simple filters:
The right patient. Cardiometabolic risk is not evenly distributed, and neither is need. A model built for the average patient is built for no one in particular. Risk stratification, not a one-size-fits-all intervention, has to be the starting point.
The right care. This is where the tech vs. medicine binary does the most damage. The choice is not between clinicians or technology. It’s clinician judgment, enabled by technology that extends its reach. Flagging risk earlier, closing gaps between visits, and making a limited supply of providers capable of supporting far more people than a purely human-delivered model ever could. Technology without clinical judgment is a tool without a hand to hold it. Clinicians without technology are a workforce stretched past its limits.
The right time. Sequencing matters as much as access. Prevention has to come before pharmacology, and pharmacology, when appropriate, has to come with sustained behavioral support, not instead of it. A model that intervenes only once a patient is in crisis has already lost the opportunity for more affordable, more human intervention.
Getting all three right also means being honest about how you’re measuring “right” in the first place.
Is success six months out, or three years out? Is it cost savings for the organization, or health gains for the individual, and are those the same thing on the timeline you’re measuring?
It’s worth it for employers to weigh the outcomes they’re seeking against the solutions they’re implementing before assuming the two are already aligned.
Prevention is not the absence of illness
The goal of a model like this is not only to make sick people better, but to prevent people from becoming sick in the first place. That distinction matters more than it might seem.
“Absence of illness” is a passive state. Prevention is an active one. It requires identifying risk before it becomes disease, intervening before a condition requires acute care, and sustaining behavior change long enough for it to become durable rather than temporary. This is also the differentiation that actually bends the total cost of care over time.
This is what a human-led model like Vida’s is designed to do. It starts with risk stratification, understanding who is trending toward a cardiometabolic condition, and how urgently, before that trajectory becomes a diagnosis. Or, in the case that a diagnosis exists, providing the care needed to manage and treat these conditions, including pharmacotherapy when necessary.
From there, a care team of dietitians, coaches, and providers, supported by technology that surfaces risk earlier and keeps them connected to a person between visits, works with that individual on the behavior change that actually shifts the trajectory: nutrition, movement, sleep, stress, and the daily habits that either compound risk or reduce it.
The technology isn’t the intervention. It’s what makes the right intervention possible at the scale a provider shortage otherwise wouldn’t allow.
The real choice
The provider shortage is real, and solution fragmentation is too, but the debate over whether it should be more clinicians or more technology to solve these issues is noise. Health technology isn’t a future consideration for care models to adopt; it’s already the infrastructure our current landscape runs on and the one the next several years of cardiometabolic care will be built around. The real work is deciding what that infrastructure should actually deliver.
The future of healthcare won’t be defined by choosing between clinicians and technology. It will be defined by how effectively we combine both to deliver measurable, durable outcomes.
The right clinical judgment and the right technology. The right access and the right accountability for outcomes. Short-term signals that can help predict long-term durability. This is what a model built for outcomes looks like.
