Get Guardian on GoogleIn densely populated Nigerian slums where access to healthcare is limited and discussions around mental and sexual health are often taboo, a data-driven approach is emerging through the work of Damilare Ogbon.
Ogbon leads the Data team at the Slum & Rural Health Initiative, a non-profit organisation working to improve healthcare delivery in underserved communities. His focus is on integrating Management Information Systems (MIS) and Artificial Intelligence (AI) to enhance mental and sexual health interventions.
Through a customised MIS platform, the organisation has introduced the Community Health Intelligence Platform, a system that captures real-time data on mental and sexual health trends. The platform records more than basic service delivery—it tracks community sentiment, monitors service uptake, and generates reports that inform decisions at both local and national levels.
“In a country where less than 10% of people with mental disorders receive any form of care, our MIS framework helps with early detection, follow-ups, referrals, and data-driven advocacy,” Ogbon said at a recent innovation forum in Lagos.
“If we’re not collecting the right data, we’re not solving the right problems,” he added. “Our MIS system turns whispers from the grassroots into a language policymakers and funders can finally understand.”
Beyond data collection, the organisation has developed AI tools trained on anonymised data from the MIS platform. These tools are used to predict mental health risks, identify gaps in sexual and reproductive health education, personalise outreach through low-literacy-friendly mobile apps, and detect misinformation trends that can inform campaign adjustments.
According to Ogbon, the integration of AI allows for preventive rather than reactive measures. “We’re able to intervene before crises escalate and better target education efforts,” he said.
The project has received support from the Bill & Melinda Gates Foundation, Grand Challenges Canada, and other partners who view the approach as a scalable model for community health systems.
Ogbon, who combines technical expertise with local knowledge, said the broader goal is not only to improve health outcomes but to reshape how health systems engage with underserved populations.
“We can’t rely on imported models,” he said. “We need solutions grounded in the lived experiences of the people we serve.”
