Global studies exclude Brazil due to a lack of structured, accessible clinical data. iHealth closes that gap with a proprietary longitudinal clinical database of +5 million patients — structured by NLP in medical Portuguese, covering 5 regions of the country, and methodologically aligned with CONITEC, ANVISA, FDA, and EMA standards.
We design and execute RWE studies end to end. From protocol to regulatory submission.
The global pharmaceutical industry recognizes Brazil as a strategic market — but has historically excluded it from real-world studies due to a lack of structured, auditable clinical data. The result: dossiers built with European or North American data that don't reflect the epidemiology, treatment patterns, or clinical outcomes of the Brazilian population.
This compromises the quality of dossiers submitted to CONITEC, weakens market access strategies, and delays the arrival of therapeutic innovations to Brazilian patients.
iHealth was built to eliminate this gap. With proprietary longitudinal data from real Brazilian patients, clinical NLP in medical Portuguese, and a regulatorily validated RWE methodology, we deliver the evidence the Brazilian market demands — and that global HTAs recognize.
Comparison of clinical outcomes across different treatment lines, medications, or interventions — based on real longitudinal data from Brazilian patients. Designed with methodological rigor compatible with CONITEC and EMA standards. Ideal for technology incorporation dossiers and value arguments to HTAs.
Characterization of the prevalence, incidence, clinical progression, and economic impact of a condition in the Brazilian population — with real data, not estimates. We use iHealth's longitudinal database to quantify disease burden by region, demographic profile, and current treatment line. Strategic support for product positioning and payer engagement.
Longitudinal mapping of the patient's clinical path: from diagnostic suspicion to outcome — including visits, exams, hospitalizations, medications, and comorbidities. Identification of breakpoints in care, diagnostic gaps, and intervention opportunities. With real data extracted via NLP from Brazilian electronic health records.
Detection and characterization of adverse events, side effects, and drug interactions in real populations — outside the controlled conditions of a clinical trial. Data extracted via clinical NLP from progress notes, nursing notes, and pharmacy records. Full traceability for use in pharmacovigilance and regulatory submissions.
Construction of external control arms from iHealth's longitudinal database — for single-arm trials, rare disease studies, and situations where classic RCTs are unfeasible. Data selected, matched by propensity score, and documented with full regulatory traceability for FDA, EMA, and CONITEC.
Creation and maintenance of prospective patient registries with continuous updates — integrating medical record, lab, and pharmacy data in real time. Ideal for post-incorporation effectiveness monitoring, long-term outcome tracking, and active pharmacovigilance studies.
iHealth owns its data infrastructure. We don't broker data from other sources — we generate, structure, and maintain our own longitudinal clinical database, with full control over quality, traceability, and regulatory compliance.
Our NLP engine isn't a generic tool adapted for Portuguese. It was built from the ground up for the vocabulary, abbreviations, and narrative structures of Brazilian medical Portuguese — with clinical accuracy validated by specialists.
40+ hospitals across all 5 regions of Brazil — not just São Paulo and Rio. This means real epidemiological representativeness of the Brazilian population, with geographic, demographic, and treatment-pattern diversity.
LGPD-first architecture with anonymization at the source, irreversible hashing, full lineage, and documented legal bases. Outputs ready for regulatory submission to CONITEC, ANVISA, FDA, and EMA.
We have the database. We have the methodology. And we have the hospital partners to support your project — from design to results.
