Build trust in AI with rigorous, independent model evaluation on your own data - giving clinicians, patients, and regulators the evidence they need to adopt AI.
As health systems embrace AI solutions to improve patient outcomes and lower the cost of care, earning the trust of clinicians and patients is critical to driving use case adoption and sustained value.
Vega Health's model evaluations build trust and improve outcomes from health AI implementation. Evidence from evaluations enables developers to build products responsibly, health systems deploy AI confidently, and regulators to prioritize AI solutions best positioned to benefit the public.
Model evaluation is not one-size-fits-all. To achieve meaningful outcomes from AI implementation, evaluation methodology must flexibly incorporate the insights most relevant to each solution's workflow, model dimensions, and intended end-users.
| Dimension | Evaluation Implications | |
|---|---|---|
| Workflow | ClinicalOperational | Determines applicable workflows, end-users, and actions |
| Modality | PredictiveGenerative | Determines which evaluation metrics best apply |
| Output | ContinuousThresholdFree Text | Determines how outputs are presented and evaluated |
| Decision | InformAdviseDirectAutomate | Determines how end-users act on model outputs |
| Risk & Harm | False PositiveFalse Negative | Determines where harm can occur from model outputs |
| Feedback Loop | YesNo | Determines if model use changes the ability to evaluate outputs |
Effective AI monitoring in healthcare requires assessing performance across four interconnected dimensions. Missing any one gives you an incomplete picture. Vega Health goes further than most vendors, measuring what actually matters: outcomes and ROI.
During retrospective evaluation, AI solutions are tested on a dataset of historical patient encounters from within the target population across a selected period (e.g., 12-24 months).
These evaluations are particularly helpful for generating reliable evidence of output quality, clinical equivalence, potential harms, model bias, and accuracy statistics.
Silent evaluations generate evidence of model performance on real-time clinical encounters or operational cases without exposing outputs to end-users.
The key objectives of a silent evaluation are to verify that real-time model performance is comparable to retrospective performance, establish baselines that live model monitoring will be compared against, and inform workflow integration.
Pilots are the final step in evaluating the safety, reliability, and impact of AI tools prior to a full rollout. A pilot is the first opportunity to observe and evaluate the actual impact of an AI solution on workflows and outcomes.
Pilots can generate critical end-user insights and support risk mitigation and AI governance priorities.
We'll walk through what an evaluation looks like and how it can help your organization successfully harness AI.
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