Before You Deploy, Know What Breaks: VITAL Healthcare AI Sandbox
VITAL is an interactive sandbox where healthcare professionals explore 18 AI workflows across six clinical domains using 54 synthetic scenarios — before any system touches a live patient. Run the case, challenge the result, define what your next pilot needs to prove.
Most healthcare AI failures are not model failures. They are deployment failures — the AI worked in the demo, but nobody designed the workflow around it. The alert fired, but no clinician was assigned to act on it within a defined window. The prediction was accurate, but the override rate was 90% because the output did not fit the clinical decision point.
VITAL exists to surface these failures before they happen.
What VITAL is
VITAL is a browser-based sandbox — an interactive education and planning environment where healthcare professionals explore AI workflows using synthetic cases. No live patient data. No clinical connections. No AI model running against real records. The sandbox is the point: run the case, challenge the result, and define what your next pilot needs to prove.
The tagline is a constraint, not a slogan: before you deploy, know what breaks.
Six domains, eighteen workflows
VITAL organizes healthcare AI into six operational domains, each containing three guided workflows:
Documentation — Ambient Clinical Notes, Chart Summarization, and Coding Assistance. Each workflow produces a specific artifact: a source-linked clinical note with verification requirements, a dated topic summary with conflict and coverage flags, or a supported-tag list with withheld proposals and source evidence.
Prediction — workflows that model clinical deterioration, readmission risk, and resource demand. The sandbox forces users to define who acts on each prediction, within what window, and what the escalation path looks like when the model is wrong.
Diagnostics — AI-assisted image analysis, lab interpretation, and differential generation. Every diagnostic workflow includes an override mechanism and requires the user to inspect the reasoning chain before accepting a result.
Revenue Cycle — payment variance review, denial prediction, and charge capture. The sandbox demonstrates a payment variance review showing reference rates and calculated net variance, forcing the user to trace from output to source before approving an action.
Engagement — patient communication, appointment optimization, and care gap identification. These workflows test whether AI-generated outreach is clinically appropriate, correctly timed, and routed to the right human for approval.
Operations — staffing models, throughput prediction, and supply chain optimization. The sandbox measures whether operational predictions translate to actual schedule or resource changes — not just dashboards.
Fifty-four synthetic scenarios
Each of the eighteen workflows supports three condition configurations, producing 54 scenario variants. The conditions are not edge cases bolted on for completeness — they are the realistic variations that determine whether a workflow survives contact with clinical reality.
A documentation workflow that handles a straightforward visit note is not the same workflow when the patient has three active problems, conflicting medication lists, and a pending referral. The sandbox forces practitioners to test all three conditions before concluding that a workflow is ready for pilot.
The four-step case studio
Every scenario follows the same four-step structure:
Read the case. Understand the assignment, the ownership definition, and the success criteria. Before any AI output is generated, the user must know who is accountable, what "correct" looks like, and what the stop conditions are.
Run and inspect. Modify conditions, trace outputs to source evidence, and identify where the AI reasoning is transparent versus opaque. The sandbox highlights statement reversals and evidence gaps — places where the model's conclusion does not follow from its cited sources.
Make a decision. Test corrections, verify checks, and document the decision with named owners and independent evidence requirements. The decision is not "accept" or "reject" — it is a structured record of what was verified, what was overridden, and why.
Take it forward. Generate a printable case report and define the pilot actions. The output is not a grade — it is a deployment specification: what this workflow needs to prove in a real environment, what instrumentation is required, and what the governance committee should review before clearance.
What VITAL is not
VITAL is not a model evaluation tool. It does not benchmark accuracy, measure latency, or score model performance. Those are important — and they are the wrong starting point.
VITAL is a workflow evaluation tool. It tests whether the humans, processes, and decision points around the AI are designed to produce safe, accountable outcomes. A 99% accurate model deployed into a workflow where nobody acts on the output is still a failure. VITAL surfaces those failures in synthetic conditions before they become incident reports.
The relationship to Healthcare Playbook
VITAL is the companion to the Healthcare Playbook. The Playbook provides the strategic framework — 29 plays, readiness assessment, ROI modeler, governance toolkit. VITAL provides the operational sandbox — the place where teams test specific workflows against synthetic cases before committing to a pilot.
The Playbook tells you what to deploy and in what sequence. VITAL tells you whether your workflow design will survive deployment.
VITAL is live and free to use.
Launch the Healthcare Playbook → — the strategic companion.
Launch MedConsult AI → — the clinical workflow platform.
Richard Leclézio
Enterprise Transformation & AI Delivery Leader