This is Part 1 of a four-part series exploring how health systems can successfully implement, scale, monitor, and realize value from AI. Each piece examines one of Vega Health's four core capabilities to address the most pressing challenges in healthcare AI adoption.
Every day, our team meets with health system leaders to discuss how AI can advance organizational strategies tackle AI integration challenges. Almost every conversation starts from a similar place: healthcare leaders feel trapped by their available options to expand system use of AI. On one side, their existing EHR vendors project roadmaps filled with AI functionality. On the other, AI point solution vendors flood their inbox. In either case, these leaders are overwhelmed by overpromises of transformation and are struggling to figure out how to turn executive vision of AI integration into tangible, real-world results.
Each of the options above has its respective drawbacks. But they share a lack of appreciation for the robust and necessary infrastructure to curate and maintain high-quality data, host individual AI solutions, and effectively monitor their performance. Without this foundation, health system leaders are either evaluating individual AI solutions – a prediction model here, a documentation tool there – or ceding far too much control to their EHR vendor in curating and maintaining their AI portfolio.
Right now, health system leaders need to rapidly build AI capabilities. They can make significant, multi-million-dollar investments to build a foundation from scratch. This will require teams of engineers and data scientists who weave together different AI solutions and the evaluation and monitoring tooling associated with each. Or they can rely entirely on their EHR vendor, accepting convenience over best-fit AI solutions, yielding control of their own intellectual property placed in the EHR, and struggle to objectively evaluate the performance of any individual solution.
With the rapidly emerging capabilities of AI and the unique needs of diverse patient populations, health systems deserve a better option. Health systems need a partner that will keep pace with AI innovation and has the incentives to always find the AI solutions that work best for each use case.
The Infrastructure and Impact Gap
At the Duke Institute for Health Innovation (DIHI), we built and integrated more than 60 AI solutions into real-world clinical conditions. We quickly learned that managing a portfolio of AI solutions required novel infrastructure. The infrastructure had to facilitate the rapid curation of high-quality data to develop new AI solutions and streamline retrospective validations, prospective silent trials, and post-implementation monitoring. We also learned that infrastructure must have active data cleaning and standardization capabilities that ensure real-world data is fit for purpose to be fed into AI solutions.
Most health systems do not have this infrastructure. They are implementing AI solutions one at a time, each with its own technical requirements, its own integration approach, and (maybe) its own monitoring setup. Controls or guarantees on the quality of data used for AI solution development or performance are largely nonexistent.
The current approach is not sustainable. It leaves health system leaders in the dark as to whether strategic investments are yielding benefits or not. When organizations expand AI portfolios without unifying infrastructure, data science teams spend months on integration work for each new solution. IT teams juggle competing technical requirements across vendors. Clinical and operational leaders cannot get a comprehensive view of AI performance across the enterprise. Front-line clinical workers roll their eyes at yet another "game-changing" AI solution they must change their workflows to use. And when something goes wrong, health systems may be completely unaware or in the rare case where they have visibility into AI solution performance they troubleshoot in isolation, solution by solution.
We've seen organizations invest millions in AI programs that never show results because they neglected to build the foundation first.
What Real Infrastructure Looks Like
At Vega Health, we've built our platform to solve these exact problems. The Vega Health Platform is installed in a health system's local environment, behind its firewall. The health system retains control over its data and intellectual property while accessing the capabilities it needs to successfully manage a rapidly expanding portfolio of AI solutions.
The platform handles three critical functions that every health system needs but few have built internally:
First, we curate and prepare your internal data so AI solutions can access it to provide reliable results. Healthcare data is disorganized, unstructured, and messy. Ever had to normalize serum creatinine values reported in mg/dL and mg/mL? We have, and we know it's critical to creating a Sepsis prediction model that actually works.
But today, most vendors expect health systems to figure out data preparation on their own. Vega Health handles it at the platform level, building data pipelines that only curate the data needed, maintain data mappings to preserve elements over time, and facilitate the rapid implementation and monitoring of new AI solutions.
Second, our platform provides a standardized runtime environment for AI models. This means you can evaluate and install solutions from different developers without rebuilding your technical stack each time. Want to compare multiple solutions to evaluate which works best with your patient population? The platform makes that possible. Need to run both internally developed tools and external solutions? The same infrastructure supports both.
Third, it enables enterprise-wide monitoring and governance. You get visibility into how every AI solution is performing in silent trials and in the real world. Importantly, these aren't simply technical metrics, or vendor-provided measurements that give a partial picture. We track technical performance, user adoption rates, and patient outcome impact, measured consistently across models. We are committed to surfacing real performance data from your environment, measured consistently across all models. We'll talk more about the range of our monitoring capabilities in a subsequent post.
Why This Matters Now
The AI market in healthcare is exploding. Teams are going to be evaluating more solutions, not fewer. Your organization needs to move faster to realize value from these investments.
You can't do any of that without infrastructure.
When our team at Duke began working on sepsis, we spent two years building the model and infrastructure required to run Sepsis Watch. With the right platform foundation, that same work now can be done in days. We've seen organizations struggle to evaluate competing vendors because they couldn't run comparative analyses in their own environment. With standardized infrastructure, those evaluations become straightforward.
The difference isn't technical sophistication. It's having the right foundation in place to always select the best product for your patients.
What Happens Next
Getting the infrastructure right is the great enabler for scaling AI capabilities. It determines how quickly you can evaluate, implement, and demonstrate the value of new solutions. It shapes how effectively you can scale what works. It defines whether you're a cost-center or creating value.
At Vega Health, we believe effectively scaling AI should not require the levels of resourcing and expertise available only to the largest academic medical centers. Health centers of all sizes should be able to leverage AI effectively for clinicians and patients.
Our goal is to give healthcare delivery organizations independence from the limitations of their current systems. Our platform runs in your local environment with your data, under your control. We will never lock you into a specific AI solution. We equip health systems to evaluate any solution, scale the ones that work, and move on from the ones that don't.
That's what real AI infrastructure enables: strategic agility, backed by technical expertise.
In the next piece, we'll talk about what you can do once that foundation is in place: accessing a curated marketplace of proven AI solutions without getting lost in the noise of vendor pitches and marketing hype.
But infrastructure comes first. Because without it, nothing else scales.
Ready to build the foundation your AI initiatives deserve? Let's talk about how the Vega Health Platform can help your organization move from evaluation to impact.
