PrevIQ Docs
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Welcome to PrevIQ

OSPRI's pathogen prevalence intelligence platform — what it does and who it's for.

PrevIQ turns your laboratory's molecular testing data into defensible, syndrome-specific prevalence intelligence — the kind of evidence you need for panel design, payer reimbursement, and clinical conversations.

Instead of digging through spreadsheets, you get a single place to answer questions like:

  • How often is each organism actually detected in my UTI specimens?

  • Which targets justify inclusion on a respiratory panel — and which are too rare to defend?

  • How does my detection rate compare across syndromes and against other labs?

  • Quickstart — Sign in, find your lab, and read your first prevalence number in five minutes.

  • Key concepts — The five ideas that make every number in PrevIQ make sense.

  • Using the app — A guided tour of the Dashboard, Explore, Reports, Vendors, and Geo pages.

  • API reference — Pull prevalence rollups and reports straight into your own systems.

What PrevIQ is

PrevIQ ingests raw result exports from your laboratory information system, normalizes organism names and panel labels into a consistent vocabulary, and computes prevalence — the rate at which each organism is detected — within each syndrome and panel.

Every rate ships with a sample-size tier and a 95% confidence interval, so you always know how much weight a number can bear.

Who it's for

  • Lab & clinical leaders — Understand the true detection landscape across your menu.
  • Panel designers — Decide which targets to INCLUDE, WATCH, or treat as RARE.
  • Sales & reimbursement — Generate client-ready reports backed by a transparent denominator.

The one rule that matters most

Important

Prevalence in PrevIQ is always computed within a syndrome and panel — never blended across panels for the same organism. Streptococcus in a respiratory context is a different signal than Streptococcus in a vaginosis context, and PrevIQ keeps them separate by design. See Prevalence methodology.

How the platform fits together

  1. Ingest

    Raw LIS/Ignite exports (CSV or Excel) are loaded and de-duplicated by specimen.

  2. Normalize

    Organism names and panel labels are mapped to a canonical vocabulary, and each panel is bucketed into a syndrome.

  3. Compute

    Prevalence is calculated per (syndrome, panel, organism), with a sample-size tier and Wilson confidence interval.

  4. Explore & report

    You read the results in the web app, or pull them through the API and exports.

Note

This documentation is client-facing usage guidance. It explains how to use PrevIQ and how to interpret what you see — not how the engine is built internally.