This is Part 3 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.
When a health system invests in an AI solution, the first question leadership asks is: "Is it working?"
The answer is rarely straightforward. Most health systems track basic adoption metrics—maybe alert volume, override rates, or user logins. Some measure clinical outcomes tied to the solution. A few track operational efficiency gains or cost savings.
But here's the uncomfortable reality: almost no health system has comprehensive visibility into whether their AI solutions are actually delivering value.
That gap isn't due to negligence. It's structural. Health systems are expected to monitor AI solutions using vendor-provided dashboards built to showcase the solution's best metrics. Internal analytics teams are stretched thin trying to build custom monitoring for each new solution. Meanwhile, leadership needs to make strategic decisions about renewals, expansions, and new investments with incomplete information.
The result? Health systems often can't answer fundamental questions:
Is the model performing worse now than when we launched it six months ago?
Are clinicians in different departments adopting the solution at vastly different rates, and if so, why?
Is the clinical outcome improvement we're seeing actually attributable to the AI solution, or would it have happened anyway?
Are we capturing the ROI we projected in the business case, or are hidden costs eroding the value?
Without answers to these questions, health systems can't optimize their AI investments. They can't distinguish between solutions that deliver real value and those that just look good in a vendor demo.
The Limitations of Today's Monitoring Approach
Most health systems cobble together monitoring in one of three ways:
1. Vendor-Provided Dashboards
Solution vendors offer monitoring tools focused on their own product's performance. These dashboards often highlight favorable metrics (alert volume, model accuracy) while obscuring concerning trends (alert fatigue, overrides, downstream outcomes). Vendors are incentivized to show their solutions in the best light—which means you're not getting an objective view.
2. Internal Analytics Team Custom Builds
Some health systems build their own monitoring infrastructure. This is expensive, time-consuming, and usually limited to a few high-priority solutions. Each new AI solution requires custom integration work. Metrics aren't standardized across solutions, making portfolio-level comparisons nearly impossible.
3. Ad Hoc Retrospective Analyses
Many health systems only investigate AI solution performance when something goes wrong—a spike in overrides, a vendor contract renewal, or a clinical leader raising concerns. By then, you've already lost months of potential optimization opportunities.
None of these approaches provide the comprehensive, objective, real-time monitoring that health systems need to manage AI as a strategic asset.
What Comprehensive Monitoring Actually Requires
To truly know if your AI solutions are working, you need visibility across four interconnected dimensions:
1. Technical Accuracy and Model Fidelity
Does the model perform as expected in your environment? Is it detecting the right patterns with acceptable precision and recall? Are predictions calibrated correctly? Is performance degrading over time due to data drift?
Why it matters: A model that performed well in a vendor's test environment might struggle with your patient population, data quality, or EHR integration. Without tracking technical accuracy in production, you're flying blind.
What to monitor:
Precision, recall, F1 score, AUC-ROC for classification tasks
Calibration curves for risk predictions
Data drift detection (feature distributions, prediction distributions)
Model version performance comparisons
EHR integration data quality (missing values, latency, format errors)
2. User Adoption and Clinical Integration
Are clinicians actually using the solution in their daily workflows? Are they following through on recommendations? What's the alert fatigue rate? How does adoption vary across departments, shifts, or provider types?
Why it matters: A technically accurate model is worthless if clinicians don't trust it, can't access it in their workflow, or are overwhelmed by false positives. Adoption metrics reveal whether your implementation strategy is working.
What to monitor:
Alert response rates and override patterns
Time-to-response for high-priority alerts
Adoption variation across departments, roles, and shifts
Workflow integration bottlenecks (e.g., alerts dismissed without viewing details)
User satisfaction and trust surveys
3. Clinical and Operational Outcomes
Is the solution improving patient care? Are you seeing measurable changes in clinical outcomes, length of stay, readmissions, or adverse events? Are operational workflows more efficient?
Why it matters: Technical accuracy and user adoption are necessary but not sufficient. The ultimate test is whether the solution improves patient outcomes or operational efficiency in a measurable way.
What to monitor:
Clinical outcomes (mortality, complications, readmissions, length of stay)
Operational efficiency (time-to-intervention, bed turnover, ED wait times)
Resource utilization (lab orders, imaging, consults)
Comparison to baseline workflows (before AI) and control groups (departments not using AI)
4. Return on Investment and Value Capture
What's the financial impact? Are you capturing the projected cost savings? How does performance compare to the business case? What's the total cost of ownership including maintenance, support, and hidden operational costs?
Why it matters: AI solutions are expensive. Between licensing fees, implementation costs, ongoing support, and internal resources, you need to know if the financial return justifies the investment. Leadership needs this data to make strategic decisions about scaling, renewing, or discontinuing solutions.
What to monitor:
Direct cost savings (reduced length of stay, fewer readmissions, optimized resource use)
Revenue impact (improved throughput, better coding accuracy, reimbursement optimization)
Total cost of ownership (licensing, support, internal FTE time, infrastructure costs)
Break-even analysis and payback period
ROI comparison to original business case projections
The Objectivity Problem
Even when health systems build comprehensive monitoring, there's a structural challenge: vendor-provided monitoring tools have inherent conflicts of interest.
Solution vendors are incentivized to show their products in the best light. Dashboards may highlight favorable metrics while obscuring concerning trends. Alert override rates might be normalized against arbitrary benchmarks. Clinical outcomes might be measured in ways that favor the solution's design.
This isn't necessarily malicious—it's structural. Vendors need to demonstrate value to retain contracts and win new business. A vendor dashboard showing declining performance doesn't help them renew your contract.
Health systems need monitoring infrastructure that's independent from solution vendors. You need tools built to answer your strategic questions, not to validate a vendor's claims.
This is especially critical as health systems scale beyond one or two AI pilots to managing portfolios of dozens or hundreds of solutions. You can't optimize a portfolio when every solution is monitored differently, with different metrics, different definitions, and different levels of transparency.
How Vega Health Enables Comprehensive Monitoring
Vega Health's monitoring capabilities are designed to give health systems objective, comprehensive visibility across their entire AI portfolio.
Our approach includes:
Unified Monitoring Framework
Track technical performance, user adoption, clinical outcomes, and financial impact across all AI solutions in one place. Compare apples to apples across vendors and use cases. See which solutions are delivering value and which aren't—regardless of what vendor dashboards claim.
Independent Data Collection
We integrate directly with your EHR, operational systems, and AI solution APIs to collect data independently—not filtered through vendor dashboards. This gives you an objective view of what's actually happening in production.
Configurable Metrics and Alerts
Define the KPIs that matter to your organization. Set thresholds that trigger alerts when performance degrades, adoption drops, or unexpected patterns emerge. Customize monitoring to your strategic priorities, not a vendor's preferred metrics.
Longitudinal Performance Tracking
Monitor how solutions perform over time as your environment changes. Detect data drift, seasonal variations, and long-term trends. See if a solution that worked well at launch is still performing six months later—or if it's quietly degrading.
Comparative Analytics
Benchmark solutions against each other and against baseline workflows. Identify your best-performing solutions and understand why they succeed. See which vendors consistently deliver on their promises and which consistently overpromise.
Portfolio-Level Insights
Move beyond monitoring individual solutions one at a time. See which solutions are delivering on their promises, which need optimization, and which should be reconsidered or replaced. Make data-driven decisions about renewals, expansions, or discontinuations.
From Individual Solutions to Portfolio Strategy
Most health systems manage AI solutions one at a time. Monitoring becomes a collection of disconnected dashboards, each answering different questions in different ways. Each solution is evaluated in isolation, without context or comparison.
Vega Health's monitoring infrastructure turns your AI investments into a managed portfolio. You can:
Identify your highest-ROI solutions and expand their deployment. Don't guess which solutions are worth scaling—prove it with data.
Catch underperforming solutions before they waste significant resources. If a solution isn't working after six months, you need to know before you renew a three-year contract.
Understand patterns across solutions that inform future procurement decisions. If solutions from a particular vendor consistently underperform, that should influence your next RFP.
Make data-driven decisions about renewals, expansions, or discontinuations. Stop relying on vendor anecdotes and pilot success stories. Use real production data.
Communicate AI value to leadership, clinicians, and boards with clear evidence. Show what's working, what's not, and what you're doing about it.
This portfolio view is essential as health systems scale beyond a few pilot projects to dozens or hundreds of AI solutions. You can't manage what you can't measure—and you can't optimize a portfolio you can't compare.
What's Next
Monitoring is the feedback loop that turns AI pilots into sustainable programs. It's how you know what's working, what's not, and what needs to change.
But monitoring is only valuable if it leads to action. In Part 4, we'll explore how health systems can capture and communicate the value their AI investments generate—not just clinically and operationally, but financially and strategically.
Because ultimately, scaling AI isn't about having more solutions. It's about having solutions that provably improve care, reduce costs, and strengthen your organization's strategic position.
Chris Provan is co-founder of Vega Health. Before Vega, he led AI implementation and evaluation work at health systems and academic medical centers.
