An independent, multi-site network feeds proprietary drift analytics; the result becomes a clean component of your post-market evidence. We don't rebuild for each device — the engine is device-agnostic and takes a new input.
Performance is compared across an independent, multi-site network — a cross-site reference you can't assemble from your own deployments alone. That outside view is what separates a real signal from local noise.
Performance is measured against the device's registered validation — using confidence intervals, bias, or whatever natural range its key parameters call for. We flag true drift — consistent, sustained movement outside that range — and stay quiet on noise.
We deliver audit-ready performance evidence into your post-market reporting. You own the regulatory filing; we supply the independent data behind it.
A single accuracy figure hides what your quality and regulatory teams — and the reviewer on the other side — actually need to see. Every metric is reported against three thresholds set by the device's own validation data: the lower bound, the point estimate, and the upper bound of expected performance — whether that is a confidence interval, an expected bias, or an acceptable range. The uncertainty is explicit, and it tightens as real-world cases accumulate.
Drift is then defined the defensible way: consistent, deviated performance outside the expected range — not a one-off, and not judged against the point estimate alone. A statistically grounded trigger your reviewers can stand behind, not a judgement call.
Your own data managers can run quality and connectivity, but they can't be the neutral benchmark for your own device — the independence is structurally required. The value only holds if the acceptance bar is set objectively, by a party that isn't the one being measured.
And because the benchmark only strengthens as more independent data accrues, the assurance you can put in front of a regulator gets stronger over time, not weaker.
We monitor instrument and application performance against initial validation to detect drift — not adverse-event reporting, literature reviews, or full-PSUR authoring.
Performance data is anonymized at the source, so the central layer holds no personal data — clean by design, not by exception.
The first engagement runs on the manufacturer's own retrospective validation data — no hospital, no live integration. We structure site access once a pilot defines what's needed.
We can walk the same engine across whatever your device does, and show what independent monitoring would look like on it — against your own validation data.