The Global Health Innovation Grants (GHIG) program focuses on fostering the growth and scale of health-focused innovations and social entrepreneurs in low- and middle-income countries (LMICs) through one-year grants. Since 2016, IiH has worked with grantees to identify metrics that align with their project objectives and activities. Analyzing this data, IiH provides an assessment of the portfolio in each cohort, as well as over time, to identify areas for improvement for the program. This case study is one of six from the GHIG9 grantee cohort.
Founded in 2019, Munai Health is a health technology company based in Curitiba, Brazil, that addresses the fragmentation of clinical data across hospital systems. Munai’s clinical intelligence platform, INTI, is designed to close that gap. It connects a hospital’s existing data sources and applies machine learning to detect clinical deterioration, including sepsis, acute kidney injury, and other in-hospital adverse events, before it becomes an emergency. The platform also supports antimicrobial stewardship, clinical documentation, and discharge decisions. Munai sells the platform to hospitals and health insurers rather than to individual patients; the platform reaches patients through the clinical teams caring for them. Together, these capabilities position INTI as clinical intelligence infrastructure that connects hospital data, AI-generated insights, and clinical workflows.
The platform now runs in more than 30 partner hospitals across Brazil and Peru and has processed some 18 million medical encounters. Its deterioration prediction system has been validated in a multicenter study spanning six Brazilian hospitals, and the company’s partners range from public health authorities and Becton Dickinson (BD) to international grant-makers including the Gates Foundation, the Inter-American Development Bank, and The Pfizer Foundation.
Munai increasingly operates as clinical intelligence and learning health system infrastructure, extending beyond its predictive models to connect clinical data, decision support, and hospital workflows.