Monica Nolan of MU-JHU Care Limited in Uganda will adapt the existing open source Smart Register Platform, which digitally stores health records, for the real-time collection and transfer of immunization data, to improve vaccine coverage and other healthcare services for women and children in Uganda. In many low- to middle-income countries, records of childhood vaccinations are usually written by hand and can be poor quality. Digital records are of better quality and value, as they also enable the integration of different types of healthcare services, such as HIV services and vaccinations, to improve overall health. They will adapt existing technology and infrastructure, including the Smart Register Platform, which is already integrated into several national health systems and can produce automated SMS reminders of appointments. They will also design methods informed by mothers with young families, health workers and managers, to optimize data use and delivery of health services. They will use surveys and analyze health data to evaluate their approach for improving vaccine timeliness and coverage at selected clinics.

Chinedu Chugbo of Avigo Health L.L.C. in the U.S. will develop an approach to crowdsource reports of infant births and deaths from community members by health workers to better monitor vaccine coverage in low- to middle-income countries. In Nigeria, only 30% of births are registered, making it difficult to estimate numbers of vaccine-eligible children and ensure every child is properly vaccinated. Current methods for estimating population sizes include household surveys, which are costly, or records from health clinics, which suffer from limited coverage. Crowdsourcing is a proven method for efficient data collection, although data quality may be variable. They will develop electronic data-collection and storage tools and pilot test their crowdsourcing approach in a selected region in Nigeria. Health workers will be trained to administer brief interviews to community members visiting clinics and during outreach programs to document local births and deaths. They will evaluate the performance of their approach and particularly data accuracy by comparing it with data collected by household surveys in the same region.

The idea is to develop an artificial intelligence model capable of simultaneously analyzing data from the Laboratory Information System and from the Hospital Information System. This technology aims to enable the delivery to hospital physicians of a ranked list of antimicrobials that are more suitable to treat infection by multi-resistant microorganism with a focus on newborn and young children.

This project proposes the development of the One Health Brazilian Resistance (OneBR), a curated and integrated genomic database. OneBR will use algorithms based on artificial intelligence to conduct surveillance, diagnosis, management and treatment of antimicrobial resistance (AMR) in the human-animal-environment interface. The goal is for this platform to be used by Brazilian health professionals in diverse settings, particularly within the Unified Healthcare System (SUS).