This is Part 2 of a four-part series on healthcare AI implementation. Part 1 explored why infrastructure matters. This piece examines how to access AI solutions that have demonstrated value in real clinical settings, and the first steps in assessing solution performance on your own data.
You’ve invested in the foundational AI infrastructure. Now what? How do you efficiently find and implement AI solutions that are most likely to benefit your patients and organization, without spending months sifting through vendor pitches? This is the question every health system leader asks after recognizing that infrastructure comes first. And it’s where many organizations get stuck.
The Choice Spectrum Problem
The way health systems are pushed into buying AI solutions today is backwards. You are expected to select an AI solution vendor before you actually know how well the product will perform in your environment. You have to navigate contracts, invest money, and allocate staff effort to support implementation of the AI solution.
By the time there’s sufficient data to understand how well the AI solution performs for you, it’s too late. Your vendor has closed the contract and wants to keep you locked in. Your requests for additional data and analysis to understand ROI go unanswered. You want to scale things that work and turn off things that don’t work, while your vendor wants to keep selling you more of their product. Incentives are not aligned.
This misalignment is pervasive because health systems face a constrained set of choices when it comes to AI solution selection. On one end of the spectrum, your EHR vendor offers a set of AI capabilities. These are often convenient, but development is constrained by what the EHR vendor has decided to prioritize. These capabilities often reflect what drives their business model, not what matters most to your organization’s priorities.
On the other end of the spectrum, there’s a flood of AI point solution vendors. Many promise transformational results based on impressive-sounding metrics. Most have limited experience implementing in clinical settings. Some are genuinely innovative but early-stage, which means you’re taking on significant risk. Others are overselling capabilities they haven’t proven.
The best-resourced health systems like Duke can build their own AI solutions internally. But for health systems that can’t build everything in-house, this creates an impossible situation. You have no way of de-risking your AI investments with reliable data on the anticipated ROI of AI solutions.
There’s a middle path – and it’s the better one. A path that gives you access to proven AI solutions and seamless local validation without locking you into a single vendor’s walled garden or forcing you to bet on unvalidated technology.
What Real Curation Means
Most vendors sell you a model and call it a solution. Conversations with Vega Health start differently: what can we solve together? Once we know your pain point or priority, we’ll help determine if the solution needs a model and if it does, which model or models are best suited for your operations.
Our goal is to solve your problem with the highest quality and lowest cost solution. At Vega Health, we built our marketplace on a straightforward premise: if Duke Health, or any health system has built and successfully implemented an AI model that solves a real clinical or operational problem, every health system should be able to access it.
These leading health systems have already done a great deal of hard work. They’ve invested in building models, tested them in real clinical environments, iterated based on what didn’t work, and proven what does (at least within their environment). That innovation shouldn’t stay locked within their walls.
But access to validated models alone isn’t enough. You need a partner who understands what separates marketing claims from operational reality. A team that can require a model to meet rigorous benchmarks before bringing it to you. A team that will conduct a local validation to ensure that the model performs well on your patients before recommending implementation. A team that can take a validated model and configure it into a complete solution that is best positioned to work in your environment, with your staff, and for your patients.
That’s what Vega Health does.
We license validated AI models from leading health systems and healthcare innovators. Before any model enters the Vega Health Marketplace, it must meet three criteria:
- First, it must have been implemented in a real-world clinical context and demonstrated results. Not just in-silico performance—actual clinical or operational deployment.
- Second, that evidence of success must be validated through publication in a peer-reviewed academic journal or as a pre-print publication undergoing peer review. This ensures independent verification of performance claims.
- Third, the developer must complete the Model Label Fact Sheet developed by the Health AI Partnership. This standardized documentation gives you complete transparency about the model’s development, validation, and intended use before you make any purchasing decision.
We don’t take payment from model developers to be in the marketplace. We don’t promote models based on relationships or revenue potential. Our job is to identify what actually works and make it available to you.
From Models to Solutions
Here’s where Vega Health’s approach differs from typical vendor relationships.
After we’ve aligned on a strategic priority to address, we don’t just hand you a model and wish you luck. We configure a complete solution designed specifically for your needs, and we lead local validation to evaluate its effectiveness with your patients and clinical environment.
This means taking the validated model from our marketplace and building around it: the clinical workflows that integrate with how your staff actually works, the user interface that makes it intuitive for your team to use, the communication protocols to make sure the right information reaches the right person at the right time, and the monitoring capabilities that track whether it’s delivering the value you expect.
We launched with a dozen solutions built at Duke Health. These are tools the Duke Institute for Health Innovation (DIHI) team implemented and monitored over a decade. They address critical use cases: emergency department triage, predictive models for post-operative complications, tools that streamline prior authorization processes.
But the marketplace isn’t just for Duke models. We’re actively adding solutions from other leading health systems and innovators who’ve built tools that solve real problems across clinical, operational, and administrative challenges.
How Access Enhances Your Strategy
With access to a curated marketplace of models, the capability to configure them into complete solutions, and the ability to locally validate, your AI strategy shifts from reactive to proactive.
Instead of responding to whichever new agentic AI vendor happens to pitch you this week, you work backwards from the ultimate goal. Need to reduce emergency department crowding? We can show you models that have demonstrated impact on that specific problem and configure a solution tailored to your workflows. Want to improve chronic disease management in your ACO population? There are validated models for that. Trying to reduce the number of denied claims for high-cost medications or procedures? Let’s look at what’s worked elsewhere and adapt it for your environment.
And because these solutions run on the Vega Health Platform, you can evaluate them in your own environment with your own data before making any long-term commitments. No vendor-provided metrics. No promises about how it might perform. You get actual performance data from your health system, all before you commit to clinical or operational use.
This is how AI procurement should work: start with your strategic needs, access proven models, validate them in your environment, and implement complete solutions that demonstrate value.
The Objectivity Commitment
Vega Health makes another commitment to our customers: helping you solve your problem is our priority. If the best approach for your use case comes from an EHR vendor or an existing AI vendor, we’ll tell you that. If the best approach for your use case does not require AI at all, we’ll tell you that too. Our value isn’t in steering you toward specific technologies. It’s in helping you find what actually works for your organization.
Sometimes that means the best solution for you is configured from a model in our marketplace. Sometimes it means we’ll help you evaluate external options using our platform’s standardized monitoring infrastructure. Sometimes it means we’ll work with your team to develop something new.
Our marketplace isn’t about limiting your choices. It’s about expanding them with options you can trust. It’s about giving you the infrastructure and expertise to evaluate any option objectively.
What’s Next
Having access to proven AI solutions configured for your specific use cases solves the selection problem. But selection is just the beginning.
In the next piece, we’ll talk about the implementation and monitoring gap: why so many AI pilots fail to scale, and how end-to-end support bridges that gap. We’ll also explore why objective monitoring is so critical, and why you can’t rely solely on vendors (including your EHR) to tell you whether their solutions are working.
Because finding the right solution matters only if it actually delivers value for your staff, your clinicians, and your patients.
Interested in learning more about how Vega Health’s curated marketplace can help advance your priorities? Contact us!
