Impact of Change
AI, pharmaceutical innovation, and consumerism are rewriting healthcare utilization. Here’s what health systems must do before the market moves without them.
A patient managing Type 2 diabetes today may delay—or never need—dialysis thanks to new therapies preventing progression to chronic kidney disease. Another receives an early-stage cancer diagnosis after a routine colonoscopy because an AI assistant detected what otherwise might have gone unnoticed. A third pays $100 out of pocket for a wellness app and hasn’t needed to call their health system in six months.
These aren’t edge cases. They are new patient journeys, and they represent one of the most significant structural shifts in healthcare utilization in a generation.
Our 2026 Impact of Change forecast is built on a clear premise: anticipating the future of healthcare relies on a mix of data and expert insight. Monitoring innovation trends isn’t enough; effective strategic planning requires a compounding perspective that intersects demand with care redesign, consumer behavior, and technological advancements in ways that make singular trend-watching an inadequate planning approach.
To lead, don’t ask what’s changing?
Ask how do these changes interact, and what does that mean for the decisions we make today?
Consider what a patient's path into the healthcare system looks like in 2026. Before they ever schedule a visit, many have already consulted an AI tool for a preliminary read on their symptoms. They've reviewed data from their wearables, compared providers through consumer review platforms rather than insurance directories, and, increasingly, established relationships with concierge or direct-to-consumer primary care providers that operate outside the traditional health system entirely.
We used to worry about Dr. Google. Now we have Dr. ChatGPT. Patients aren't just arriving with more information—they're arriving with new expectations. They expect clinicians to engage with AI-generated insights, discuss trends from wearable devices, and help interpret an expanding stream of health data. For health systems, that shift is likely to create near-term throughput pressure as visits become more complex and diagnostic demand increases. As AI tools improve in accuracy and reliability, that pressure may ease. But managing the transition will require deliberate planning.
Consumerism data makes the scale of this shift difficult to ignore. According to Vizient Strategy Intelligence research, 25% of adults now report having a concierge primary care provider. Nearly nine in 10 say they’re more likely to use a wellness coach. Consumers are 233% more likely to engage in DIY wellness and self-management than they were previously. And among people enrolled in high-deductible health plans, 77% say they would pay more than $100 per month out of pocket for wellness services they believe provide value.
That last number deserves a pause. For less than an average annual deductible, many patients can subscribe to apps that help them monitor their wellness. The direct-to-consumer model may not be winning on clinical quality, but it is winning on experience, convenience, and continuous engagement.
You’ve likely spent decades building primary care networks on the assumption that patients come to them first, but that assumption is quickly breaking down. According to the 2025 Strategy Intelligence Consumer Survey, one in four adults now has a concierge PCP. They’re not waiting for the health system to change—many of them have already left. For health systems, that raises an uncomfortable question: If someone else owns the first interaction, who owns the long-term relationship?
One of the most persistent misconceptions in healthcare strategy is that innovation reduces demand. In practice, it almost never does. What it does, consistently, is redistribute demand across service lines, care settings, and time horizons. That’s why planning for redistribution rather than simply replacement is a far better strategy than hunting for procedural growth alone.
Recent innovations in cardio-metabolic condition treatment serve as a clear current illustration. SGLT2 inhibitors slow the progression to end-stage renal disease, which over time will reduce the population requiring dialysis. But it simultaneously expands the population requiring years of medication management, specialty visits, and intervention. The demand doesn’t disappear; it moves upstream and shifts who owns the relationship.
The bariatric surgery data already has started to suggest this dynamic in real time. The Vizient Clinical Data Base shows a 37% decline in overall inpatient bariatric surgery volumes from 2022-2025. Women saw a decline of 52%, 18-64-year-olds saw a decline of 39%, and commercially insured patients saw a decline of 43% in the same timeframe (all percentages rounded to the nearest percentage point, 0-18 is excluded). Whether a meaningful volume rebound occurs is a legitimate debate, but what’s not debatable is that the shift from surgical management to pharmaceutical management is underway, and the downstream implications for service line volume, margin, and capital planning deserve explicit strategic attention now, not after the data has settled.
AI is creating a parallel redistribution dynamic, but with a different mechanism. Clinical AI tools are not reducing the need for care so much as they are changing which care happens, when, and where. In the inpatient setting, some AI tools for sepsis detection claim they can flag the condition in as little as six minutes using patient biomarkers, with meaningful potential to shorten length of stay and improve resource utilization. In the outpatient setting, AI-assisted colonoscopy tools are increasing the detection of polyps during procedures, driving higher rates of diagnostic colonoscopies (the 2026 Impact of Change forecast anticipates diagnostic colonoscopies to grow by 27% over the next 10 years while colonoscopy screenings are expected to decline -6%, driven by at-home DNA-FIT tests and blood-based tests). These tools don’t eliminate clinical volume so much as reshape it.
On the administrative side, the majority of health systems already are deploying some form of ambient scribing. The benefits are real: reduced physician burnout, faster documentation, and improved reimbursement accuracy in an environment where payers and health systems are increasingly engaged in a documentation-intensive negotiation over prior authorization and retrospective reimbursement justification.
It’s tempting to approach innovations such as AI, advanced therapies, and GLP-1s, and consumer behavior shifts as discrete trends to evaluate sequentially, but that’s not how they affect strategy. They interact with each other, with care pathways, and with the consumer expectations described above, which creates effects that are substantially larger than any single technology or trend. The Impact of Change forecast is built specifically to capture those interactions, examining how innovation intersects with policy, economics, and shifting models of care rather than treating each force in isolation.
That’s why scenario modeling has become the most important planning capability for health systems navigating this environment. The is to understand which decisions remain smart across multiple plausible futures. An ambulatory infusion center that wasn’t viable two years ago may be the right investment today, not because the clinical case changed, but because capacity constraints, payer pressure, and the pharmaceutical pipeline caught up to the strategy. An AI investment shouldn’t only be evaluated on documentation efficiency but also on its potential impact on throughput, access, and downstream demand.
Innovation, payer mix, and consumer behavior are all shifting simultaneously. Strategic plans that haven’t revisited their core utilization assumptions—who the patient is, where they enter the system, what they demand from the encounter—are planning for a healthcare market that no longer exists. The window to get ahead of these shifts is narrowing. Scenario modeling is the only responsible way to make capital and operational decisions in an environment where any single forecast is almost certainly wrong, and the goal is to be right across several of them.
If you treat this moment as a reason to wait for more data, your organization will make reactive investments in a market that has already moved. But if you act now—with the right analytical foundation and the discipline to plan across futures rather than for one—you’ll be more prepared to confront the major shifts that lie ahead.
The Vizient Impact of Change forecast provides health system leaders with the analytics, scenario modeling tools, and expert guidance needed to navigate utilization shifts across service lines, sites of care, and planning horizons. Learn more about Impact of Change and the rest of our comprehensive data and insights portfolio.
Principal, Vizient Strategy Intelligence
Karyl Kopaskie, PhD, is a healthcare strategy expert and leader at Vizient. In her role as principal, she partners with members to navigate the complexities of a rapidly evolving healthcare landscape. Her work focuses on strategic prioritization, forecasting future trends in care delivery, and analyzing inpatient and outpatient utilization data to generate actionable insights. Through her leadership, she helps health...
Senior Associate, Strategy Intelligence
As a senior associate on the Strategy Intelligence team, Emily Fitt provides support and expertise to all things medicine service line development, which represents 12 individual subspecialties that range from allergy and immunology to rheumatology. Her favorite topics to discuss with clients relate to digestive health programs, including bariatric surgery, and weight management.
