Brazilian hospitals lose revenue every day due to undercoding of diagnoses and procedures. Not out of bad faith — but due to overload, process gaps, and systems that can't read the medical record. The correct DRG is written in the clinical notes. iHealth extracts that data, codes it automatically, and flags discrepancies before submission to the payer.
More billing accuracy. Fewer denials. Clinical compliance at scale — without relying on line-by-line manual review.
In a mid-sized hospital, a difference of 10 to 15% in DRG coding accuracy can represent millions in unrecognized revenue per year. And most of that loss shows up in no report — because the data that would justify a more complex code is written in free text in the medical record, invisible to the billing system.
The current process depends on auditing teams that review claims manually — slow, expensive, and scalable only up to a point. At the same time, payers and health plans have their own auditing teams, trained to spot noncompliance and deny claims. The hospital enters that dispute at a disadvantage when its coding doesn't reflect the real complexity of the case.
iHealth levels that playing field. With clinical NLP that reads the entire medical record — not just structured fields — and AI models trained to identify the correct DRG and any noncompliance before submission.
Our AI reads physician progress notes, nursing notes, reports, and procedure records — and automatically identifies the diagnoses and procedures relevant to correct DRG coding. Comorbidities, complications, and secondary procedures that are typically left out of manual coding are now systematically captured. The result: a more accurate DRG that reflects the case's real complexity and maximizes recognized revenue.
Before a claim is submitted to the payer, our platform automatically identifies the most common issues that lead to denials — mismatches between diagnosis and procedure, missing clinical documentation to support a given code, inconsistencies between progress note dates and procedure dates, and incompatible code combinations. Automatic alerts for the billing team, with supporting documentation already located in the medical record.
Automated review of hospital claims before submission — with denial-risk scoring by claim and by type of noncompliance. Smart prioritization of human review: the auditing team focuses on the cases with the highest financial impact and highest likelihood of dispute, while low-risk cases follow the normal workflow. Operational efficiency without giving up control.
For claims that have already been denied, iHealth automatically locates the clinical documentation in the medical record that supports the appeal — progress notes, prescriptions, reports, and notes that prove the relevance of the disputed code. Structured documentation package for the appeal process, with full traceability. Higher denial-reversal rates without expanding the auditing team.
Control panel with real-time visibility into billing performance: claim volume by status, denial rate by payer and by type of noncompliance, cumulative financial impact, and benchmarking against iHealth's network of partner hospitals. Identification of recurring denial patterns and alerts to adjust internal processes.
Beyond billing, iHealth's auditing identifies clinical inconsistencies that may represent care-quality or regulatory risks — prescriptions without a corresponding progress note, procedures without documented indication, and incomplete records that could jeopardize hospital accreditation. Clinical and financial governance integrated into a single platform.
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 to 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 genuine 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.
