This two-year project was implemented by RAN in collaboration with Purdue University between 2017 and 2019. It explored innovative applications of digital technologies for improving maternal and child health (MCH) outcomes in Uganda. The study focused on developing and testing “demand sensing” systems that could predict healthcare needs based on various data sources, combined with “digital tracking” systems for monitoring supply availability and patient interactions.
The project used a design science approach, involving iterative development of prototypes in collaboration with healthcare providers, administrators, and community health workers in Mukono district, Uganda. Key technological components developed included: mobile applications for community health workers to register pregnancies, track expected delivery dates, and record service delivery; SMS-based reminder systems for antenatal care visits and immunization schedules; digital inventory management systems for essential medicines and supplies at health facilities; and data dashboards for health managers to monitor coverage gaps and resource needs.
The study conducted rigorous testing of these systems in real-world settings, examining not only technical functionality but also usability, acceptability, and integration with existing workflows. The project revealed the importance of human-centered design – systems that were theoretically sophisticated but required substantial changes to established practices faced much lower adoption rates than simpler systems that complemented existing workflows.
The study also documented the critical role of “infomediaries” – healthcare workers who helped bridge digital literacy gaps and interpret digital information for communities. From a supply chain perspective, the project developed algorithms for predicting commodity needs based on service utilization patterns, weather data (which affects disease incidence and accessibility), and demographic trends. These predictive models showed promising accuracy in reducing stock-outs while minimizing wastage from over-supply.
The final research outputs included both technological prototypes (open-source software for demand sensing and digital tracking) and implementation guides for scaling similar approaches in other contexts.
