HDR UK Discovery

Core Functionality

Discovery is a Weave for cohort discovery against OMOP CDM databases. This Weave processes two main types of queries:

Availability: Answers the question “how many patients meet certain criteria?” without revealing individual-level data.
Distribution: Returns de-identified aggregate statistics (e.g. age, sex, diagnosis counts) for a cohort of interest.

These queries are designed to protect privacy through configurable obfuscation techniques such as rounding and low-count suppression.

Use in Federated Research

Discovery is built to support federated research networks where each site runs its own software locally.

This model allows institutions to collaborate on large-scale health research without sharing raw patient-level data, maintaining data sovereignty and privacy.

Reference Implementation

Bunny is open-source, lightweight, and security-conscious, enabling scalable health research without compromising data control at the institutional level. Bunny is an application developed by the University of Nottingham that enables privacy-preserving cohort discovery across federated health data networks. It is designed to work with OMOP Common Data Model (CDM) databases and is deployed at partner institutions as a secure, read-only query agent.

Weave details

Patterns

Component	Used in this Weave
Interface	Bunny, a query agent deployed at each site
Computational Pattern	Traditional Pattern
Egress Inspection Pattern	Fully automated: rounding and low-count suppression are applied at each site before results leave

What a researcher can do

Count the individuals meeting a set of criteria at each site, and across the network.
Get aggregate distributions (such as age and sex) for a cohort of interest.

This is mainly useful for assessing whether a study is feasible, and which sites hold suitable data, before applying for access.

Effect on results

Results are estimates, not exact counts. Rounded counts from several sites do not add up to the true total, and suppressed counts are missing, so a small cohort can appear as zero. The precision can also differ between sites, as the rounding and suppression settings are configured at each site.

What it can’t do

Run the researcher’s own code, or any analysis beyond the defined query types, such as statistical tests or modelling.
Link records or remove duplicates across sites, so individuals present at more than one site may be counted more than once.

Practical constraints

Results are returned without waiting for a manual check.
Each site must run Bunny against data in the OMOP Common Data Model, so query criteria are limited to what is captured in OMOP.