Real-World Evidence
Multicentre evidence in weeks, not after months of integration.
Latrikos turns routine clinical data into longitudinal, validated registries ready to answer questions of effectiveness, safety, resource use, disease progression and real clinical practice.
Designed once1 2
Common model
Identical across every site
For each wave of sites
Site A
Own HIS
Site B
Own templates
Site C
Different exports
Latrikos layer
- 3Site activation
- 4Privacy
- 5Longitudinal extraction
- 6Validation
Continuous7 Comparable longitudinal registryEvery data point keeps its date and its source fragment 8
Multicentre consolidation
Dashboards and analysis
Cohorts · KPIs · Export
A new site joins without redoing the study
The flow
Designed once, rolled out in waves.
Objective, population, endpoints and study period.
Variables, ontology, units and temporal rules.
Secure upload using the exports each site already produces.
De-identification or pseudonymisation according to governance.
Events, visits, tests and treatments by date.
Expert review, metrics and traceability to the source.
A harmonised model without requiring the same HIS.
Cohorts, KPIs, export and continuous monitoring.
The study is designed once and rolled out progressively. Each site contributes the documents or exports it can already produce. Latrikos applies a common ontology across heterogeneous sources, normalises the information and builds a comparable longitudinal registry. Adding a new site does not mean redoing the project or waiting for a complex integration.
Ten reasons to start.
Latrikos
Hover or tab through each node to see the detail
Common use cases.
- Multicentre retrospective studies.
- Disease registries and research networks.
- Effectiveness and safety in real clinical practice.
- Natural history and patient journey.
- Comparison of therapeutic patterns and treatment sequences.
- Post-authorisation follow-up and evidence for geographic expansion.
- Cohort identification and feasibility for future studies.
- Prospective registries with continuous intake of patients and visits.
Compared with a classic federated model.
Federated models and data warehouses are useful where homogeneous infrastructure and a long-term goal exist, but they tend to require integrations, prior normalisation and a catalogue limited to already-structured data. Latrikos complements that architecture: it reaches the richness of clinical text, can start with no integration, and lets you define variables specific to each clinical question.
Classic federated model
More integration dependency, predefined variables, less access to narrative detail and longer deployment times.
Latrikos
Variables designed for the question, access to unstructured text, a fast start and longitudinal reconstruction with traceability.