NHIRD — Taiwan
Strengths: long longitudinal follow-up, near-complete population coverage.
Data Partners
The platform is built around collaborating institutions — not merely a list of databases. Each partner brings its own coverage, strengths and readiness.
Public view
Participating databases
Partner data environments include national claims, hospital EHRs, regional databases, registries and sampled or defined populations. Diverse sources should not be compared by size alone — each is profiled for what it can contribute to a given question.
CGRD — Taiwan
Strengths: rich clinical depth — laboratory results and in-hospital detail that claims alone cannot provide.
NHIS — South Korea
Strengths: national coverage, linked health-screening data, established research use.
HIRA — South Korea
Strengths: nationwide claims supporting large disease cohorts — millions of patients in established CDM cohorts.
CDARS — Hong Kong
Strengths: linked clinical & laboratory data across the public hospital system.
DeSC — Japan
Strengths: claims linked with annual health-checkup data across age groups.
CPRD — United Kingdom
Strengths: deep primary-care records — a cross-regional reference point for transportability beyond Asia.
Across these partners, around 15 CDM disease cohorts have been established — spanning hospitalized ASCVD, atopic dermatitis and prurigo nodularis — from tens of thousands to millions of patients each.
The platform's initial focus is Asian data and research needs — but collaborators are not restricted to Asia. Researchers, data holders and methodological teams from Europe, the United States and other regions are welcome, particularly for cross-regional transportability studies.
Public profiles describe data types and research suitability. Detailed data dictionaries, variable-availability tables and feasibility dashboards are available to members and data partners.
Visualization preview
Four Dimensions of Readiness
Whether a multinational study can run here is measured across four readiness dimensions — the same four that make IMPRESIVE more than a single study. (Preview with sample values.)
Data readiness82%
Method readiness74%
Collaboration readiness88%
Interpretation readiness69%
Data Readiness
Whether the target population, exposures, outcomes, variables and data formats are adequate for the proposed study.
Methodological Readiness
Whether the common protocol, analytical specifications and methods can be executed reproducibly — variable definitions, code lists and QA programs in place.
Collaborative Readiness
Whether the responsibilities of the coordinating center and each data partner — local PIs, analysts, IRB / governance, result sharing — are defined and workable.
Interpretive Readiness
Whether result integration, visualization standards and the interpretation of cross-country heterogeneity are in place before findings are communicated.
Thinking of joining?
A readiness checklist for prospective partners
You do not need everything on this list to start — many items can be completed together during the collaboration, with conversion and validation support from the network.
- Data type (claims, EHR, registry, sampled or defined population)
- Population coverage and size
- Observation period and data latency
- Temporal resolution (exact dates vs monthly records)
- Available research domains (Dx, Rx, procedures, labs, outcomes)
- Coding systems in use (ICD, ATC, local codes)
- Local data-governance and IRB pathway
- Ability to run analyses locally (SAS / R environment)
- A local PI or data custodian
- Willingness to share aggregate results
For members & data partners
Beyond the public view
Members and data partners gain access to detailed, internal database information, including: data dictionary, variable-availability table, coding systems, drug-coding structure, outcome-validation status, data latency, missingness profile, study-feasibility dashboard, local IRB / governance requirements, contact person and analysis environment.
Become a data partner