A multiregional clinical database with longitudinal data from more than 40 selected hospitals across 5 regions of Brazil.
Combines structured and unstructured data — from admission to discharge — into a single federated, anonymized, and auditable data backbone.
The global pharmaceutical industry recognizes Brazil as a priority market. But when they need longitudinal clinical data on Brazilian patients for RWE studies, CONITEC dossiers, or market access analyses — the answer is almost always the same: it doesn't exist.
The data exists. It's in electronic health records at hospitals across Brazil. The problem is that it's fragmented, in incompatible formats, partly in unstructured free text, without regulatory traceability, and without the governance required for formal scientific research use.
iHealth's Collaborative Data Lake was built to solve this problem. Not as a passive data repository — but as an active clinical intelligence infrastructure that combines technical integration, NLP-based structuring, data governance, and regulatory compliance in a single platform.
The Data Lake integrates data from more than 40 selected partner hospitals — distributed across all 5 regions of Brazil. Each integration is established with a direct technical connection to the institution's electronic health record systems, with encrypted transmission and PII removal at the source. Partner selection follows rigorous criteria for data quality, epidemiological representativeness, and regulatory compliance.
The Data Lake combines two worlds that rarely interact in digital health: the structured data from medical record systems — admission fields, medications, exams, procedures, demographic data — and the unstructured data in free clinical text — progress notes, nursing notes, reports, and narrative prescriptions. Structuring free text via clinical NLP is what makes this data queryable and usable in research.
The data covers the patient's complete clinical journey — from admission to discharge, and across multiple hospitalizations and visits over time. This longitudinality is what enables analyses of disease progression, long-term treatment effectiveness, readmissions, and late outcomes — the kind of evidence cross-sectional studies simply cannot generate.
The Data Lake operates on a federated architecture: data from each institution is processed and structured while respecting the sovereignty of the originating institution. Data isn't simply copied into a central repository — it's integrated with granular access controls that ensure each partner sees only what it's authorized to see, and that their patients' data is used only for agreed purposes.
Every piece of data entering the Data Lake has gone through iHealth's anonymization process — with PII removal at the source, irreversible hashing of identifiers, and NLP-based anonymization of free text. The database is auditable at every layer: data origin, transformations applied, access performed, and the purpose of each query. Full data lineage, from capture to analytical consumption.
With data from 5 regions of Brazil and more than 5 million lives, it's possible to generate evidence that represents the epidemiological, demographic, and treatment-pattern diversity of the Brazilian population — not just one center or region.
With more than 10 years of historical depth, the database enables analyses of disease progression, treatment effectiveness over time, recurrence, late complications, and survival — evidence that changes a product's value argument.
The scale and quality of the database allow for building robust external control arms — with selection, matching, and documentation that meet FDA and EMA standards for regulatory submissions.
The indexed database allows locating specific clinical profiles in minutes — cross-referencing diagnosis, treatment, comorbidity criteria, and free-text variables — for clinical trial recruitment.
Talk to our team and understand how iHealth's technology pillars translate into real results — for clinical research, market access, hospital management, or data auditing.
