New
28 Mar 2026
AI & Surveillance
AI-Assisted Health Surveillance Framework — Policy Brief Published
Rohitah International has published a comprehensive policy brief on the application of AI-assisted
methodologies in national health surveillance systems. The brief outlines a framework for integrating
machine-learning anomaly detection into routine epidemiological reporting workflows — enabling
faster outbreak identification, more reliable signal triangulation, and reduced burden on human reviewers.
The framework has been field-validated across public health programmes in Sub-Saharan Africa and South
Asia,
and is now available for institutional adoption by Ministries of Health and donor-funded programmes.
Open
15 Mar 2026
Scholars Program
Research Scholars Cohort 2026 — Applications Now Open
The Rohitah Research Scholar Program is now accepting applications for the 2026 cohort. The program
provides structured mentorship, hands-on analytical support, and co-authorship pathways for early-career
researchers and doctoral candidates from low- and middle-income countries working on global health data
systems, AI applications in public health, or MEL frameworks. Successful applicants receive support
valued between USD 1,000 – 5,000 depending on engagement scope.
Applications are reviewed on a rolling basis — early submissions are encouraged.
Report
05 Mar 2026
Tool Release & Methodology
Data Report Cards Methodology — Field-Tested in 12 Countries
Rohitah International's Data Report Card methodology — a standardised quality scorecard system for
health information systems — has now been successfully field-tested and deployed across programmes in
12 countries spanning West Africa, East Africa, South Asia, and Southeast Asia. The methodology
tracks four core data quality dimensions: completeness, timeliness, accuracy, and
internal consistency. It integrates with DHIS2, ODK Collect, and national HIS
platforms, providing facility-level, district-level, and national-level quality dashboards that
give health system managers a real-time structured view of their data pipeline performance.
