Clinical ops, health tech, care coordination

Hiring for healthcare? Here's how we evaluate.

We score how candidates handle protocol, ambiguity, and patient-facing pressure — not whether they've memorized a compliance manual. The model runs the scenario; the rubric is ours.

See the rubric

What we score

The dimensions, not the playbook.

We don't publish the exact criteria, weights, or sub-probes — that's how candidates would game the rubric. Here's what every healthcare candidate is scored against.

Protocol judgment
Whether they follow established protocol under pressure, and know exactly when a case calls for escalating past it — not blind rule-following, and not freelancing on judgment calls that aren't theirs to make.
Cross-functional communication
How clearly they hand off information to a clinician, a case manager, or a family member — each needs a different level of detail. We score whether they adjust, not just whether they're polite.
Documentation discipline
Whether their account of what happened would hold up as a record — complete, ordered in their own head, and free of the gaps that turn into liability later.
Composure under ambiguity
How they reason through a case that doesn't fit the textbook example — the ones where the right first move isn't obvious and the wrong one has real consequences.

Sample scenarios

What candidates actually face.

Two illustrative scenario types — the actual prompts vary per session and stay private to your tenant.

Scenario 1
A patient-facing update that doesn't go as planned.
The AI plays a patient or family member reacting to news they didn't expect. We score whether the candidate stays clear and honest under emotional pressure, or reaches for reassurance that isn't earned.
Scenario 2
An escalation call with an incomplete picture.
Mid-scenario, a key piece of information is missing or contradictory. We score whether the candidate flags the gap and escalates appropriately, or fills it in with an assumption they shouldn't have made.

Integrity signals

What we watch for — and what stays private.

We name the signals we capture, but not how we weight or threshold them. That's the part that breaks if we publish it.

  • Every session is recorded — audio, video, and full transcript — and retained per your tenant policy.
  • Every score ships with an ML confidence band. Low-confidence scores are flagged for human review before the candidate is decided on.
  • We evaluate communication and judgment signals only — SkillPlatform doesn't assess clinical competency, license status, or credentialing. Those stay with your credentialing process.
  • Admin labeling lets your team flag interviews where the AI's read of a scenario diverged from what a senior clinician or ops lead would catch.
  • We never train shared models on your candidate data.

What we measure

The outcome you can defend.

Protocol-judgment score, communication-clarity score, and end-to-end completion rate for every candidate — plus a confidence band on each. We measure how often our 'strong hire' candidates clear your clinical or ops panel, and we recalibrate when the gap widens. The metric that matters most: the rate at which our 'no hire' signal earns enough trust to skip a full second-round screen.

We frame these as what we measure, not as customer-attributed metrics.

Want to see how this rubric scores a real candidate?

An expert will walk you through a live healthcare interview transcript — including how the integrity signals played out — in 15 minutes.

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Other domains:EngineeringManufacturingEnergy
SOC 2 Type II — In progressGDPR-readyTenant-isolated infrastructureData residency: USOngoing rubric consistency reviewNo training on your candidate data