Rural healthcare in Bangladesh faces significant challenges — limited access to doctors, lack of diagnostic tools, delayed treatment, and constrained infrastructure. To bridge this gap, Primacy Infotech Limited developed an AI-powered mobile diagnostic assistant. This tool enables community health workers and paramedics to perform accurate preliminary diagnoses even in areas with no internet connectivity.
Step by step approach to deliver excellence
Our team collaborated with healthcare professionals and NGOs to identify the core challenges faced by rural health workers. Through detailed surveys and interviews, we mapped out the diagnostic needs, workflow patterns, and offline operational requirements, ensuring that our AI solution would be practical, relevant, and easy to use in real-life conditions.
Our team designed a lightweight AI engine optimized for low-end smartphones while maintaining high diagnostic precision. The architecture balances computational efficiency with offline capability, ensuring the application delivers fast and accurate predictions even without internet connectivity.
We gathered and curated a combination of open-source medical datasets and locally sourced clinical data. This included preprocessing, labeling, and augmenting images and records to train robust machine learning models that are contextually relevant to the healthcare environment in Bangladesh.
The mobile interface was crafted to be intuitive and multilingual, enabling health workers with varying literacy levels to navigate the app with ease. Key features include minimal data entry, clear visual cues, and actionable diagnostic guidance to facilitate accurate assessments in the field.
We integrated the AI models into a secure and responsive mobile framework. Features include offline inference, local patient data storage, real-time recommendations, and encrypted synchronization with cloud services whenever internet access is available.
Rigorous testing was conducted both in simulated conditions and real healthcare environments. Accuracy, speed, and usability were measured, and feedback from frontline health workers was incorporated to refine both the AI predictions and the overall user experience.
The application was deployed in selected rural regions with hands-on training sessions for community health workers. Training focused on efficient app operation, interpretation of diagnostic results, and best practices to ensure confident adoption and consistent usage.
Post-deployment, we implemented a monitoring and feedback loop to track AI performance and app usage. Insights from real-world interactions are continuously fed back into the system to enhance diagnostic accuracy, expand capabilities, and address emerging healthcare needs.
Real-world applications and solutions we delivered
Developed an intelligent AI engine capable of performing accurate preliminary medical diagnoses without requiring internet connectivity. This offline functionality ensures that health workers in remote rural areas can provide timely assessments, bridging gaps caused by limited infrastructure and network coverage.
Designed the system to incorporate contextual medical data relevant to the Bangladeshi population. By leveraging localized datasets, the app provides highly accurate disease predictions, tailored recommendations, and ensures clinical relevance for frontline healthcare workers.
Created a clean, multilingual interface optimized for non-technical users. Features include simplified navigation, intuitive input forms, and clear visual diagnostic suggestions, enabling health workers to operate the system efficiently and confidently in field conditions.
Integrated a secure framework to store patient data locally with optional cloud synchronization. This ensures that sensitive medical records are protected while allowing seamless data transfer when connectivity is available, supporting both privacy and operational efficiency.
Implemented real-time decision support by providing instant diagnostic insights and actionable recommendations. This feature empowers health workers to make informed decisions quickly, improving patient care and reducing the likelihood of misdiagnoses.
Developed comprehensive training modules for community health workers, including tutorials and hands-on workshops. This ensures smooth adoption of the technology, enhances user confidence, and promotes proper use of the diagnostic tool in daily operations.
Built a monitoring system that captures real-world usage and feedback. Insights from daily operations are used to continuously refine the AI model, improve predictive accuracy, and adapt the tool to emerging healthcare challenges in rural communities.
The solution significantly enhances healthcare accessibility and quality in rural areas. By equipping frontline health workers with AI-powered diagnostics, patients receive faster assessments and interventions, reducing delays in treatment and contributing to better health outcomes for underserved populations.
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